system

Abstract

The system according to this embodiment aims to efficiently grasp students' learning progress and provide appropriate learning content and advice. [Solution] The system according to the embodiment comprises a reception unit, an information collection unit, an analysis unit, a provision unit, and a progress management unit. The reception unit receives input from students. The information collection unit analyzes the information received by the reception unit and collects relevant information. The analysis unit analyzes the information collected by the information collection unit and extracts important points. The provision unit provides students with learning content and advice based on the information extracted by the analysis unit. The progress management unit grasps the students' learning progress based on the information provided by the provision unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of making it difficult to efficiently grasp students' learning progress and provide appropriate learning content and advice.

[0005] The system according to this embodiment aims to efficiently grasp students' learning progress and provide appropriate learning content and advice. [Means for solving the problem]

[0006] The system according to this embodiment comprises a reception unit, an information collection unit, an analysis unit, a provision unit, and a progress management unit. The reception unit receives input from students. The information collection unit analyzes the information received by the reception unit and collects relevant information. The analysis unit analyzes the information collected by the information collection unit and extracts important points. The provision unit provides students with learning content and advice based on the information extracted by the analysis unit. The progress management unit monitors students' learning progress based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp the learning progress of students and provide appropriate learning content and advice. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The inquiry-based learning support system according to an embodiment of the present invention is a system that collects and analyzes information necessary for students' inquiry-based learning and provides appropriate learning content and advice. This system uses a generative AI to automatically collect and analyze information related to questions and themes entered by students. For example, if a student enters "I want to research environmental problems," the generative AI collects relevant information from the internet, extracts important points, and provides them. Next, based on the collected and analyzed information, it provides students with appropriate learning content and advice. For example, based on the collected information, it presents students with the task of "creating a report on the current state and solutions to environmental problems" and provides necessary materials and references. It also organizes and presents the information in a way that is easy for students to understand. Furthermore, it provides teachers with a tool that allows them to grasp students' learning progress and level of understanding in real time. For example, it visualizes how much progress students have made and where they are struggling, enabling teachers to provide appropriate support. In addition, to improve the efficiency of teachers' work, it also provides functions for automatic evaluation of reports and automatic generation of feedback. Through this mechanism, students' inquiry-based learning can be advanced efficiently, and the workload of teachers is reduced. For example, students can focus on deeper learning because they are saved the trouble of gathering and analyzing information themselves. Also, teachers can monitor students' progress in real time, allowing them to provide support at the appropriate time. This improves student learning effectiveness and enhances teacher work efficiency. Thus, inquiry-based learning support systems can efficiently support students' inquiry-based learning and improve teacher work efficiency.

[0029] The inquiry-based learning support system according to this embodiment comprises a reception unit, an information gathering unit, an analysis unit, a provision unit, and a progress management unit. The reception unit receives input from students. Student input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also use a microphone and voice recognition technology for receiving voice input. Furthermore, the reception unit may use a camera and image recognition technology for receiving image input. For example, the reception unit provides a text input interface, allowing students to input questions or themes. The reception unit also includes a microphone for receiving voice input and can convert what students dictate into text using voice recognition technology. Furthermore, the reception unit can use a camera to input what students handwrite as an image and convert it into text using image recognition technology. The information gathering unit analyzes the information received by the reception unit and collects related information. The information gathering unit collects information from the internet using, for example, web scraping technology. The information gathering unit can also obtain information from a specific database using an API. Furthermore, the information gathering unit can automatically collect relevant information using generative AI. For example, the information gathering unit can collect relevant information from the internet using web scraping technology. It can also obtain relevant information from academic paper databases using APIs. Additionally, the information gathering unit can automatically collect relevant information in response to questions and themes entered by students using generative AI. The analysis unit analyzes the information collected by the information gathering unit and extracts key points. For example, the analysis unit analyzes text data using natural language processing technology. It can also classify and cluster data using machine learning algorithms. Furthermore, the analysis unit can extract key points using generative AI. For example, the analysis unit analyzes collected text data using natural language processing technology and extracts important keywords and phrases. It can also classify data using machine learning algorithms and extract highly relevant information.Furthermore, the analysis unit can automatically extract important points from the collected information using generative AI. The provision unit provides learning content and advice to students based on the information extracted by the analysis unit. The provision unit provides, for example, video materials and text materials. The provision unit can also provide interactive learning content. Furthermore, the provision unit can provide advice to students using generative AI. For example, the provision unit provides video materials that students can watch. The provision unit also provides text materials that students can read. Furthermore, the provision unit provides interactive learning content that students can learn by actually interacting with. The progress management unit monitors students' learning progress based on the information provided by the provision unit. For example, the progress management unit records students' test results and assignment submission status. Furthermore, the progress management unit can analyze students' learning logs and visualize their learning progress. Furthermore, the progress management unit can monitor students' learning progress in real time using generative AI. For example, the progress management unit records students' test results and evaluates their learning progress. Furthermore, the progress management unit can record the submission status of students' assignments and manage their progress. In addition, the progress management unit can analyze students' learning logs and visualize their learning progress. As a result, the inquiry-based learning support system according to this embodiment can efficiently support students' inquiry-based learning and improve the efficiency of teachers' work.

[0030] The progress management unit can track students' learning progress and provide this information to teachers. For example, the progress management unit can provide a dashboard for real-time monitoring of students' learning progress. For example, it can display students' test results and assignment submission status in graphs and charts. The progress management unit can also analyze students' learning logs and visualize their learning progress. For example, it can analyze students' learning logs chronologically and display their learning progress in graphs. The progress management unit can also automatically generate reports to provide teachers with information on students' learning progress. For example, it can generate a report summarizing students' learning progress in PDF format and provide it to teachers. This allows teachers to track students' learning progress in real time and provide appropriate support. Some or all of the above processes in the progress management unit may be performed using, for example, a generation AI, or not. For example, the progress management unit can input students' learning logs into a generation AI and have the generation AI perform the analysis of learning progress.

[0031] The service provider can provide students with learning content and advice based on the collected information. For example, the service provider can provide students with video learning materials based on the collected information. For example, the service provider can also provide students with text learning materials based on the collected information. Furthermore, the service provider can also provide students with interactive learning content based on the collected information. For example, the service provider can provide students with video learning materials based on the collected information, which they can watch. The service provider can also provide students with text learning materials based on the collected information, which they can read. Furthermore, the service provider can provide students with interactive learning content based on the collected information, which they can learn by actually interacting with it. This improves learning effectiveness by providing students with appropriate learning content and advice. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the collected information into a generative AI and have the generative AI generate learning content and advice.

[0032] The information collection unit can automatically collect information related to the questions or topics entered by the students. The information collection unit can collect information on the Internet using, for example, web scraping technology. For example, the information collection unit can obtain information from a specific database using an API. Furthermore, the information collection unit can automatically collect related information using a generation AI. For example, the information collection unit can collect related information on the Internet using web scraping technology. Furthermore, the information collection unit can obtain related information from an academic paper database using an API. Furthermore, the information collection unit can automatically collect information related to the questions or topics entered by the students using the generation AI. This enables efficient information collection by automatically collecting information related to the questions or topics entered by the students. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the information collection unit can input the questions or topics entered by the students into the generation AI and have the generation AI collect related information.

[0033] The analysis unit can analyze the collected information and extract key points. For example, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit can also classify and cluster data using machine learning algorithms. Furthermore, the analysis unit can extract key points using generative AI. For example, the analysis unit can analyze collected text data using natural language processing technology and extract important keywords and phrases. The analysis unit can also classify data using machine learning algorithms and extract highly relevant information. Furthermore, the analysis unit can automatically extract key points from the collected information using generative AI. This deepens the understanding of the information by analyzing the collected information and extracting key points. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not be performed using generative AI. For example, the analysis unit can input the collected information into a generative AI and have the generative AI perform the extraction of key points.

[0034] The service provider can provide functions for automatic report evaluation and automatic feedback generation. For example, the service provider can use a generation AI to automatically evaluate reports submitted by students. For example, the service provider can use a generation AI to analyze the content of the report and assign a score based on evaluation criteria. The service provider can also use a generation AI to automatically generate feedback. For example, the service provider can use a generation AI to analyze the content of the report and generate feedback including areas for improvement and advice. Furthermore, the service provider can use a generation AI to provide the evaluation results of the report to teachers. For example, the service provider can use a generation AI to generate the evaluation results of the report in PDF format and provide them to teachers. By providing functions for automatic report evaluation and automatic feedback generation, the service provider can improve the efficiency of teachers' work. Some or all of the above processes in the service provider may be performed using a generation AI, or they may not be performed using a generation AI. For example, the service provider can input reports submitted by students into a generation AI and have the generation AI perform the evaluation and generate feedback.

[0035] The reception desk can analyze a student's past input history and select a reception method. For example, the reception desk can use generative AI to analyze a student's past input history. For instance, it can analyze questions and topics previously entered by the student to identify frequently used input methods. It can also select the most suitable input method for a specific time slot based on the student's past input history. Furthermore, the reception desk can suggest relevant input methods based on the student's past input history. For example, it can prioritize suggesting input methods (voice, text, etc.) that the student has frequently used in the past. It can also suggest relevant input methods based on topics and questions previously entered by the student. Furthermore, it can suggest the most suitable input method for a specific time slot based on the student's past input history. This allows the reception desk to select the optimal reception method by analyzing the student's past input history. Some or all of the above processing in the reception desk may be performed using generative AI, or without it. For example, the reception desk can input the student's past input history into a generative AI and have the generative AI select the optimal reception method.

[0036] The reception desk can filter input content based on the student's current learning status and areas of interest. For example, the reception desk can use generative AI to analyze the student's current learning status and areas of interest. For example, the reception desk can analyze the student's test results and assignment submission status to understand their current learning status. The reception desk can also analyze the student's past learning history and survey results to identify areas of interest. Furthermore, the reception desk can filter input content using generative AI. For example, the reception desk prioritizes receiving questions and input content related to the topic the student is currently studying. The reception desk can also filter relevant information based on the student's areas of interest and accept input accordingly. Furthermore, the reception desk can filter input content to an appropriate difficulty level according to the student's learning progress. This allows the reception desk to receive more appropriate information by filtering input content based on the student's current learning status and areas of interest. Some or all of the above processing in the reception desk may be performed using generative AI, for example, or without using generative AI. For example, the reception desk can input the student's current learning status and areas of interest into a generating AI, and then have the generating AI filter the input content.

[0037] The reception unit can prioritize receiving highly relevant inputs based on the student's geographical location information. The reception unit, for example, uses a generation AI to analyze the student's geographical location information. For example, the reception unit can identify the student's current location using GPS data. The reception unit can also estimate the student's geographical location using an IP address. The reception unit can also prioritize receiving highly relevant inputs using a generation AI. For example, if the student is in a specific area, the reception unit can prioritize receiving information related to that area. If the student is traveling, the reception unit can prioritize receiving information related to the travel destination. If the student is at school, the reception unit can prioritize receiving information related to the school curriculum. In this way, by taking the student's geographical location information into consideration, highly relevant information can be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the student's geographical location information to the generation AI and cause the generation AI to select highly relevant inputs.

[0038] The reception unit can analyze the student's social media activity and receive related input. The reception unit can analyze the student's social media activity using, for example, a generation AI. For example, the reception unit can analyze topics the student is interested in on social media. The reception unit can also analyze information on accounts the student follows. Furthermore, the reception unit can analyze information shared by the student on social media. For example, the reception unit can preferentially receive input related to topics the student is interested in on social media. The reception unit can also accept related input based on information on accounts the student follows. Furthermore, the reception unit can accept related input based on information shared by the student on social media. In this way, by analyzing the student's social media activity, related information can be preferentially received. Some or all of the above-described processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the student's social media activity to the generation AI and cause the generation AI to select related input.

[0039] The information gathering unit can collect information by referring to the student's past learning history. For example, the information gathering unit can analyze the student's past learning history using a generative AI. For example, the information gathering unit can analyze themes the student has studied in the past and prioritize collecting related information. The information gathering unit can also analyze the student's past test results and submitted assignments and collect information according to their level of understanding. Furthermore, the information gathering unit can also collect information related to themes the student has shown interest in in the past. For example, the information gathering unit can prioritize collecting information related to themes the student has studied in the past. Furthermore, the information gathering unit can analyze the student's past test results and submitted assignments and collect information according to their level of understanding. Furthermore, the information gathering unit can collect information related to themes the student has shown interest in in the past. This allows the information gathering unit to collect the most relevant information by referring to the student's past learning history. Some or all of the above processing in the information gathering unit may be performed using a generative AI, for example, or without a generative AI. For example, the information gathering unit can input the student's past learning history into a generative AI and have the generative AI collect the most relevant information.

[0040] When collecting information, the information collection unit can filter information based on the student's current study topic. The information collection unit, for example, uses a generation AI to analyze the student's current study topic. For example, the information collection unit analyzes the topic the student is currently studying and prioritizes collecting related information. The information collection unit can also filter unnecessary information based on the student's current study topic. Furthermore, the information collection unit can also collect the latest information related to the student's current study topic. For example, the information collection unit prioritizes collecting information related to the topic the student is currently studying. The information collection unit can also filter unnecessary information based on the student's current study topic. Furthermore, the information collection unit can collect the latest information related to the student's current study topic. In this way, by filtering information based on the student's current study topic, more appropriate information can be collected. Some or all of the above-described processing in the information collection unit may be performed using, or without, the generation AI. For example, the information collection unit can input the student's current study topic to the generation AI and cause the generation AI to filter the information.

[0041] The information gathering unit can prioritize the collection of highly relevant information by considering the student's geographical location during information gathering. For example, the information gathering unit can analyze the student's geographical location using generative AI. For example, the information gathering unit can determine the student's current location using GPS data. The information gathering unit can also estimate the student's geographical location using an IP address. Furthermore, the information gathering unit can prioritize the collection of highly relevant information using generative AI. For example, if the student is in a specific region, the information gathering unit can prioritize the collection of information related to that region. Also, if the student is traveling, the information gathering unit can prioritize the collection of information related to the travel destination. Furthermore, if the student is at school, the information gathering unit can prioritize the collection of information related to the school curriculum. In this way, by considering the student's geographical location, the information gathering unit can prioritize the collection of highly relevant information. Some or all of the above processing in the information gathering unit may be performed using generative AI, for example, or without using generative AI. For example, the information gathering unit can input the student's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant information.

[0042] The information gathering department can analyze students' social media activities and collect relevant information during the information gathering process. For example, the information gathering department can use generative AI to analyze students' social media activities. For example, the information gathering department can analyze the themes that students show interest in on social media. The information gathering department can also analyze information about accounts that students follow. Furthermore, the information gathering department can analyze information that students have shared on social media. For example, the information gathering department can prioritize collecting information related to the themes that students show interest in on social media. Furthermore, the information gathering department can collect relevant information based on information about accounts that students follow. Furthermore, the information gathering department can collect relevant information based on information that students have shared on social media. In this way, by analyzing students' social media activities, relevant information can be collected preferentially. Some or all of the above processing in the information gathering department may be performed using generative AI, for example, or without generative AI. For example, the information gathering department can input students' social media activities into generative AI and have the generative AI collect relevant information.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. The analysis unit, for example, uses a generation AI to analyze the category of information. For example, the analysis unit identifies the category of information and applies an analysis algorithm according to the category. The analysis unit can also select an optimal analysis algorithm based on the category of information. Furthermore, the analysis unit can use a generation AI to perform analysis according to the category of information. For example, the analysis unit can have the generation AI apply a scientific analysis algorithm to scientific information. The analysis unit can also have the generation AI apply a historical analysis algorithm to historical information. The analysis unit can also have the generation AI apply an economic analysis algorithm to economic information. This makes it possible to apply an optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the category of information to the generation AI and have the generation AI apply the optimal analysis algorithm.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. The analysis unit, for example, uses a generation AI to analyze the time of information submission. For example, the analysis unit records the date and time of information submission and determines the priority of analysis based on that. The analysis unit can also prioritize analysis of information with an upcoming submission deadline. Furthermore, the analysis unit can perform analysis based on the time of information submission using a generation AI. For example, the analysis unit can have the generation AI prioritize analysis of the most recent information. The analysis unit can also have the generation AI prioritize analysis of information with an upcoming submission deadline. Furthermore, the analysis unit can have the generation AI determine the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of information submission, analysis can be performed at a more appropriate time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the time of information submission to the generation AI and have the generation AI determine the priority of analysis.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, uses a generation AI to analyze the relevance of the information. For example, the analysis unit analyzes the degree of similarity between topics and keywords in the information and evaluates the relevance. The analysis unit can also adjust the order of analysis based on the relevance of the information. Furthermore, the analysis unit can use a generation AI to perform analysis according to the relevance of the information. For example, the analysis unit can have the generation AI prioritize analysis of information with high relevance. Furthermore, the analysis unit can have the generation AI analyze information with low relevance later. Furthermore, the analysis unit can have the generation AI determine the order of analysis based on the relevance. This enables more efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance of the information to the generation AI and have the generation AI adjust the order of analysis.

[0046] The content delivery unit can adjust the level of detail provided based on the importance of the learning content at the time of delivery. For example, the content delivery unit can analyze the importance of the learning content using generative AI. For example, the content delivery unit can evaluate the importance to the learning objectives and adjust the level of detail provided based on that. The content delivery unit can also add detailed explanations based on the importance of the learning content. Furthermore, the content delivery unit can use generative AI to deliver content according to its importance. For example, the content delivery unit can have the generative AI provide detailed explanations for high-importance content. The content delivery unit can also have the generative AI provide concise explanations for low-importance content. Furthermore, the content delivery unit can have the generative AI determine the priority of delivery according to importance. This allows for the delivery of more appropriate content by adjusting the level of detail provided based on the importance of the learning content. Some or all of the above processing in the content delivery unit may be performed using generative AI, for example, or without using generative AI. For example, the content delivery unit can input the importance of the learning content into the generative AI and have the generative AI perform the adjustment of the level of detail provided.

[0047] The provision unit can apply different provision algorithms depending on the category of the learning content when providing the learning content. The provision unit, for example, uses a generation AI to analyze the category of the learning content. For example, the provision unit identifies the category of the learning content and applies a corresponding provision algorithm. The provision unit can also select an optimal provision algorithm based on the category of the learning content. Furthermore, the provision unit can provide the learning content according to the category using a generation AI. For example, the provision unit can apply a scientific provision algorithm to scientific content. For example, the provision unit can apply a historical provision algorithm to historical content. For example, the provision unit can apply an economic provision algorithm to economic content. This allows the optimal provision algorithm to be applied depending on the category of the learning content. Some or all of the above-mentioned processing in the provision unit can be performed using, or without, the generation AI. For example, the provision unit can input the category of the learning content into the generation AI and cause the generation AI to apply the optimal provision algorithm.

[0048] The provision unit can determine the provision priority based on the submission date of the learning content when the learning content is provided. The provision unit, for example, uses a generation AI to analyze the submission date of the learning content. For example, the provision unit records the submission date and time of the learning content and determines the provision priority based on that. The provision unit can also prioritize providing content with an upcoming submission deadline. Furthermore, the provision unit can also provide the learning content based on the submission date and time using the generation AI. For example, the provision unit can have the generation AI prioritize providing content with an upcoming submission deadline. Furthermore, the provision unit can have the generation AI prioritize providing content with a more distant submission deadline. Furthermore, the provision unit can have the generation AI determine the provision priority based on the submission date and time. In this way, by determining the provision priority based on the submission date and time of the learning content, content can be provided at a more appropriate time. Some or all of the above-described processing in the provision unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the provision unit can input the submission date of the learning content to the generation AI and have the generation AI determine the provision priority.

[0049] The provision unit can adjust the order of provision based on the relevance of the learning content when providing the learning content. The provision unit, for example, uses a generation AI to analyze the relevance of the learning content. For example, the provision unit analyzes the degree of agreement between topics and keywords in the learning content and evaluates the relevance. The provision unit can also adjust the order of provision based on the relevance of the learning content. Furthermore, the provision unit can use a generation AI to provide learning content according to the relevance of the learning content. For example, the provision unit can have the generation AI prioritize providing content with high relevance. Furthermore, the provision unit can have the generation AI provide content with low relevance later. Furthermore, the provision unit can have the generation AI determine the order of provision based on the relevance. This enables more efficient content provision by adjusting the order of provision based on the relevance of the learning content. Some or all of the above-described processing in the provision unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the provision unit can input the relevance of the learning content into the generation AI and have the generation AI adjust the order of provision.

[0050] When managing progress, the progress management unit can select a progress management method by referring to the student's past learning history. The progress management unit, for example, uses a generation AI to analyze the student's past learning history. For example, the progress management unit analyzes the student's past test results and submitted assignments to select an optimal progress management method. The progress management unit can also prioritize and suggest progress management methods that the student has used in the past. Furthermore, the progress management unit can also suggest an optimal progress management method for a specific time period based on the student's past learning history. For example, the progress management unit analyzes the student's past test results and submitted assignments to suggest an optimal progress management method. The progress management unit can also prioritize and suggest progress management methods that the student has used in the past. Furthermore, the progress management unit can suggest an optimal progress management method for a specific time period based on the student's past learning history. In this way, the optimal progress management method can be selected by referring to the student's past learning history. Some or all of the above-mentioned processing in the progress management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the progress management department can input students' past learning history into a generating AI and have the AI ​​select the optimal progress management method.

[0051] The progress management unit can customize its progress management methods based on the student's current learning status. For example, the progress management unit can analyze the student's current learning status using generative AI. For instance, it can analyze the student's test results and assignment progress to understand their current learning status. The progress management unit can also analyze the student's learning log to visualize their learning progress. Furthermore, the progress management unit can perform progress management based on the student's current learning status using generative AI. For example, it can analyze the student's test results and assignment progress to customize its progress management methods. Furthermore, it can analyze the student's learning log to visualize their learning progress. In addition, the progress management unit can perform progress management based on the student's current learning status using generative AI. This allows for more appropriate progress management by customizing the progress management methods based on the student's current learning status. Some or all of the above-described processes in the progress management unit may be performed using, for example, generative AI, or without using generative AI. For example, the progress management department can input the students' current learning status into a generating AI and have the AI ​​customize the methods of progress management.

[0052] The progress management unit can select a progress management method based on the student's geographical location information during progress management. The progress management unit, for example, uses a generating AI to analyze the student's geographical location information. For example, the progress management unit can identify the student's current location using GPS data. The progress management unit can also estimate the student's geographical location using an IP address. Furthermore, the progress management unit can perform progress management based on the geographical location information using a generating AI. For example, if the student is in a specific area, the progress management unit can suggest a progress management method related to that area. If the student is traveling, the progress management unit can suggest a progress management method related to the travel destination. Furthermore, if the student is at school, the progress management unit can suggest a progress management method related to the school curriculum. This makes it possible to select an optimal progress management method by taking the student's geographical location information into consideration. Some or all of the above-described processing in the progress management unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the progress management unit can input the student's geographical location information into the generating AI and have the generating AI select an optimal progress management method.

[0053] During progress management, the progress management unit can analyze the student's social media activity and suggest progress management methods. The progress management unit analyzes the student's social media activity using, for example, a generation AI. For example, the progress management unit analyzes topics the student is interested in on social media. The progress management unit can also analyze information on accounts the student follows. Furthermore, the progress management unit can analyze information shared by the student on social media. For example, the progress management unit suggests a progress management method related to a topic the student is interested in on social media. The progress management unit can also suggest a related progress management method based on information on accounts the student follows. Furthermore, the progress management unit can suggest a related progress management method based on information shared by the student on social media. In this way, by analyzing the student's social media activity, a related progress management method can be suggested. Some or all of the above-described processing in the progress management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the progress management unit can input the student's social media activity into the generation AI and have the generation AI suggest progress management methods.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The inquiry-based learning support system can also analyze students' learning styles and provide individually optimized learning plans. For example, a student who prefers visual learning can be provided with learning materials that make heavy use of visual content. A student who prefers auditory learning can be provided with learning materials in audio or podcast format. Furthermore, a student who prefers hands-on learning can be provided with learning materials that include interactive simulations and experiments. This maximizes learning effectiveness by providing an optimal learning plan tailored to each student's learning style.

[0056] In addition to providing learning content, the provider can also offer advice to optimize students' learning environments. For example, it can suggest environmental settings to improve concentration and effective study schedules. It can also offer advice on the timing and methods of taking breaks. It can also introduce tools and applications that are useful for studying. This helps create an environment in which students can study more effectively.

[0057] The information gathering section can gather not only relevant information for questions or themes entered by students, but also information that includes different perspectives and opposing opinions. For example, when gathering information on environmental issues, it can gather not only pro-opinions but also opposing opinions and different approaches. It can also gather information from different cultural and regional perspectives. It can also gather information on the latest research and technology. This helps students develop the ability to think about problems from multiple perspectives.

[0058] When analyzing collected information, the analysis unit can also evaluate the reliability and source of the information. For example, it can evaluate whether the information comes from a reliable academic paper or official institution. It can also evaluate reliability by taking into account the date and time the information was updated and the author's expertise. It can also check whether the content of the information is consistent with other reliable information. This can improve the quality of the information provided to students and enable them to learn based on reliable information.

[0059] The reception unit not only analyzes the student's past input history, but also learns input patterns and can suggest predictive input. For example, it can predict and suggest what the student will input next based on phrases and keywords that the student has frequently used in the past. It can also automatically suggest related questions or topics when a student asks a question about a specific topic. Furthermore, it can display predicted input before the student begins to type, reducing the effort required for input. This can make the student's input process more efficient and support smoother learning.

[0060] The reception unit not only filters input content based on the student's current learning situation and areas of interest, but also adjusts the difficulty level of the input content according to the student's learning progress. For example, basic questions and topics can be prioritized in the early stages of learning, and more advanced questions and topics can be accepted as the student progresses. Furthermore, it can suggest questions and topics of an appropriate level of difficulty according to the student's level of understanding. This makes it possible to support effective learning by providing appropriate input content according to the student's learning progress.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit accepts student input. Student input includes text input, voice input, and image input. For example, the reception unit provides a text input interface, allowing students to enter questions or topics. It is also possible to use a microphone and voice recognition technology to accept voice input, and a camera and image recognition technology to accept image input. Step 2: The information collection unit analyzes the information received by the reception unit and collects related information. The information collection unit can use web scraping technology, APIs, or generation AI to collect information from the Internet or specific databases. For example, it can obtain related information from a database of academic papers. Step 3: The analysis unit analyzes the information collected by the information collection unit and extracts key points. The analysis unit can analyze text data using natural language processing technology, machine learning algorithms, and generative AI to extract important keywords and phrases. Step 4: The provision unit provides learning content and advice to students based on the information extracted by the analysis unit. The provision unit can provide video learning materials, text learning materials, and interactive learning content. It can also provide advice to students using generative AI. Step 5: The progress management unit grasps the student's learning progress based on the information provided by the provision unit. The progress management unit can record the student's test results and assignment submission status, and analyze the learning log to visualize the learning progress. Furthermore, it can also grasp the student's learning progress in real time using the generation AI.

[0063] (Example 2) An inquiry-based learning support system according to an embodiment of the present invention collects and analyzes information necessary for students' inquiry-based learning and provides appropriate learning content and advice. This system uses a generation AI to automatically collect and analyze information related to questions and topics entered by students. For example, if a student enters, "I want to research environmental issues," the generation AI collects related information from the Internet, extracts key points, and provides them. Next, based on the collected and analyzed information, the system provides appropriate learning content and advice to students. For example, based on the collected information, the system presents students with the assignment, "Write a report on the current state of environmental issues and solutions," and provides the necessary materials and references. The system also organizes and presents information in a way that is easy for students to understand. Furthermore, the system provides teachers with tools that enable them to monitor students' learning progress and comprehension in real time. For example, the system visualizes students' progress and where they are struggling, allowing teachers to provide appropriate support. To improve teacher efficiency, the system also provides functions for automatic report evaluation and feedback generation. This system promotes efficient inquiry-based learning for students and reduces the workload of teachers. For example, students can focus on deeper learning by eliminating the need to collect and analyze information themselves. In addition, teachers can grasp students' progress in real time and provide support at the appropriate time. This improves students' learning effectiveness and teachers' work efficiency. As a result, the inquiry-based learning support system can efficiently support students' inquiry-based learning and improve teachers' work efficiency.

[0064] The inquiry-based learning support system according to this embodiment comprises a reception unit, an information gathering unit, an analysis unit, a provision unit, and a progress management unit. The reception unit receives input from students. Student input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, an interface for receiving text input. The reception unit may also use a microphone and voice recognition technology for receiving voice input. Furthermore, the reception unit may use a camera and image recognition technology for receiving image input. For example, the reception unit provides a text input interface, allowing students to input questions or themes. The reception unit also includes a microphone for receiving voice input and can convert what students dictate into text using voice recognition technology. Furthermore, the reception unit can use a camera to input what students handwrite as an image and convert it into text using image recognition technology. The information gathering unit analyzes the information received by the reception unit and collects related information. The information gathering unit collects information from the internet using, for example, web scraping technology. The information gathering unit can also obtain information from a specific database using an API. Furthermore, the information gathering unit can automatically collect relevant information using generative AI. For example, the information gathering unit can collect relevant information from the internet using web scraping technology. It can also obtain relevant information from academic paper databases using APIs. Additionally, the information gathering unit can automatically collect relevant information in response to questions and themes entered by students using generative AI. The analysis unit analyzes the information collected by the information gathering unit and extracts key points. For example, the analysis unit analyzes text data using natural language processing technology. It can also classify and cluster data using machine learning algorithms. Furthermore, the analysis unit can extract key points using generative AI. For example, the analysis unit analyzes collected text data using natural language processing technology and extracts important keywords and phrases. It can also classify data using machine learning algorithms and extract highly relevant information.Furthermore, the analysis unit can automatically extract important points from the collected information using the generation AI. The provision unit provides learning content and advice to students based on the information extracted by the analysis unit. The provision unit provides, for example, video learning materials and text learning materials. The provision unit can also provide interactive learning content. The provision unit can also provide advice to students using the generation AI. For example, the provision unit provides video learning materials, which students can watch. The provision unit also provides text learning materials, which students can read. The provision unit also provides interactive learning content, which students can learn by actually operating the content. The progress management unit monitors the student's learning progress based on the information provided by the provision unit. For example, the progress management unit records the student's test results and assignment submission status. The progress management unit can also analyze the student's learning log and visualize the learning progress. The progress management unit can also monitor the student's learning progress in real time using the generation AI. For example, the progress management unit records the student's test results and evaluates the learning progress. The progress management unit can also record the status of students' assignment submissions and manage their progress. Furthermore, the progress management unit can analyze students' learning logs and visualize their learning progress. This allows the inquiry-based learning support system according to the embodiment to efficiently support students' inquiry-based learning and improve the work efficiency of teachers.

[0065] The progress management unit can track students' learning progress and provide this information to teachers. For example, the progress management unit can provide a dashboard for real-time monitoring of students' learning progress. For example, it can display students' test results and assignment submission status in graphs and charts. The progress management unit can also analyze students' learning logs and visualize their learning progress. For example, it can analyze students' learning logs chronologically and display their learning progress in graphs. The progress management unit can also automatically generate reports to provide teachers with information on students' learning progress. For example, it can generate a report summarizing students' learning progress in PDF format and provide it to teachers. This allows teachers to track students' learning progress in real time and provide appropriate support. Some or all of the above processes in the progress management unit may be performed using, for example, a generation AI, or not. For example, the progress management unit can input students' learning logs into a generation AI and have the generation AI perform the analysis of learning progress.

[0066] The service provider can provide students with learning content and advice based on the collected information. For example, the service provider can provide students with video learning materials based on the collected information. For example, the service provider can also provide students with text learning materials based on the collected information. Furthermore, the service provider can also provide students with interactive learning content based on the collected information. For example, the service provider can provide students with video learning materials based on the collected information, which they can watch. The service provider can also provide students with text learning materials based on the collected information, which they can read. Furthermore, the service provider can provide students with interactive learning content based on the collected information, which they can learn by actually interacting with it. This improves learning effectiveness by providing students with appropriate learning content and advice. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the collected information into a generative AI and have the generative AI generate learning content and advice.

[0067] The information collection unit can automatically collect information related to the questions or topics entered by the students. The information collection unit can collect information on the Internet using, for example, web scraping technology. For example, the information collection unit can obtain information from a specific database using an API. Furthermore, the information collection unit can automatically collect related information using a generation AI. For example, the information collection unit can collect related information on the Internet using web scraping technology. Furthermore, the information collection unit can obtain related information from an academic paper database using an API. Furthermore, the information collection unit can automatically collect information related to the questions or topics entered by the students using the generation AI. This enables efficient information collection by automatically collecting information related to the questions or topics entered by the students. Some or all of the above-mentioned processing in the information collection unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the information collection unit can input the questions or topics entered by the students into the generation AI and have the generation AI collect related information.

[0068] The analysis unit can analyze the collected information and extract important points. The analysis unit can analyze text data using, for example, natural language processing technology. For example, the analysis unit can classify and cluster the data using a machine learning algorithm. Furthermore, the analysis unit can extract important points using a generative AI. For example, the analysis unit can analyze the collected text data using natural language processing technology and extract important keywords and phrases. The analysis unit can also classify the data using a machine learning algorithm and extract highly relevant information. Furthermore, the analysis unit can automatically extract important points from the collected information using a generative AI. This allows for a deeper understanding of the information by analyzing the collected information and extracting important points. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the analysis unit can input the collected information into a generative AI and have the generative AI extract important points.

[0069] The providing unit can provide a function for automatic report evaluation and automatic feedback generation. The providing unit, for example, automatically evaluates a report submitted by a student using a generation AI. For example, the providing unit can analyze the content of the report using the generation AI and assign a score based on evaluation criteria. The providing unit can also automatically generate feedback using the generation AI. For example, the providing unit can analyze the content of the report using the generation AI and generate feedback including areas for improvement and advice. The providing unit can also provide the report evaluation results to teachers using the generation AI. For example, the providing unit can generate the report evaluation results in PDF format using the generation AI and provide them to teachers. This provides the automatic report evaluation and feedback generation functions, thereby improving the work efficiency of teachers. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the providing unit can input a report submitted by a student into the generation AI and have the generation AI perform evaluation and feedback generation.

[0070] The reception unit can estimate the student's emotions and adjust the timing of input reception based on the estimated emotions. For example, the reception unit can estimate the student's emotions using generative AI. For example, the reception unit can analyze the student's facial expressions using facial recognition technology and estimate their emotions. The reception unit can also analyze the tone and speed of the student's voice using voice analysis technology and estimate their emotions. Furthermore, the reception unit can analyze the content of the student's input using text analysis technology and estimate their emotions. For example, the reception unit can analyze the student's facial expressions using facial recognition technology and estimate their stress and relaxation levels. The reception unit can also analyze the tone and speed of the student's voice using voice analysis technology and estimate changes in their emotions. Furthermore, the reception unit can analyze the content of the student's input using text analysis technology and estimate the nuances of their emotions. This allows the reception unit to receive input at a more appropriate time by adjusting the timing of input reception according to the student's emotions. Some or all of the above processing in the reception unit may be performed using generative AI, for example, or without using generative AI. For example, the reception desk can input student facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0071] The reception unit can analyze the student's past input history and select a reception method. The reception unit can analyze the student's past input history using, for example, a generation AI. For example, the reception unit can analyze questions and topics entered by the student in the past and identify frequently used input methods. The reception unit can also select the optimal input method for a specific time period from the student's past input history. The reception unit can also suggest a related input method based on the student's past input history. For example, the reception unit can prioritize and suggest input methods (voice, text, etc.) that the student has frequently used in the past. The reception unit can also suggest a related input method based on the topics and questions entered by the student in the past. The reception unit can also suggest the optimal input method for a specific time period from the student's past input history. In this way, the optimal reception method can be selected by analyzing the student's past input history. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input the student's past input history into the generation AI and have the generation AI select the optimal reception method.

[0072] The reception unit can filter the input content based on the student's current learning situation and areas of interest. The reception unit, for example, uses a generation AI to analyze the student's current learning situation and areas of interest. For example, the reception unit analyzes the student's test results and assignment submission status to grasp the student's current learning situation. The reception unit can also analyze the student's past learning history and survey results to identify the student's areas of interest. The reception unit can also filter the input content using a generation AI. For example, the reception unit prioritizes accepting questions and input content related to the topic the student is currently studying. The reception unit can also accept input by filtering related information based on the student's areas of interest. Furthermore, the reception unit can filter input content of an appropriate level of difficulty according to the student's learning progress. As a result, more appropriate information can be accepted by filtering the input content based on the student's current learning situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the student's current learning situation and areas of interest into the generation AI and have the generation AI filter the input content.

[0073] The reception unit can estimate the student's emotions and determine the priority of inputs to be received based on the estimated student's emotions. The reception unit estimates the student's emotions using, for example, a generation AI. For example, the reception unit can analyze the student's facial expression using facial expression recognition technology to estimate the emotion. The reception unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate the emotion. The reception unit can also analyze the student's input content using text analysis technology to estimate the emotion. For example, the reception unit can analyze the student's facial expression using facial expression recognition technology to estimate the student's state of excitement or relaxation. The reception unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate changes in emotion. The reception unit can also analyze the student's input content using text analysis technology to estimate the nuances of emotion. By thus determining the priority of inputs to be received based on the student's emotions, more appropriate information can be received preferentially. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception desk can input student facial expression data into a generating AI and have the AI ​​perform emotion estimation.

[0074] The reception desk can prioritize receiving inputs that are highly relevant based on the student's geographical location information. For example, the reception desk can analyze the student's geographical location information using generative AI. For example, the reception desk can determine the student's current location using GPS data. The reception desk can also estimate the student's geographical location using an IP address. Furthermore, the reception desk can prioritize receiving inputs that are highly relevant using generative AI. For example, if the student is in a specific region, the reception desk can prioritize receiving information related to that region. Also, if the student is traveling, the reception desk can prioritize receiving information related to the travel destination. Furthermore, if the student is at school, the reception desk can prioritize receiving information related to the school curriculum. In this way, by considering the student's geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using generative AI, for example, or without generative AI. For example, the reception desk can input the student's geographical location information into a generative AI and have the generative AI select highly relevant inputs.

[0075] The reception unit can analyze students' social media activities and receive relevant inputs. For example, the reception unit can use generative AI to analyze students' social media activities. For example, the reception unit can analyze themes students are interested in on social media. The reception unit can also analyze information about accounts students follow. Furthermore, the reception unit can analyze information students share on social media. For example, the reception unit prioritizes receiving inputs related to themes students are interested in on social media. The reception unit can also receive relevant inputs based on information about accounts students follow. Furthermore, the reception unit can receive relevant inputs based on information students share on social media. This allows the reception unit to prioritize receiving relevant information by analyzing students' social media activities. Some or all of the above processing in the reception unit may be performed using generative AI, or without generative AI. For example, the reception unit can input students' social media activities into a generative AI and have the generative AI select relevant inputs.

[0076] The information collection unit can estimate the student's emotions and adjust the information collection method based on the estimated student's emotions. The information collection unit estimates the student's emotions using, for example, a generative AI. For example, the information collection unit can analyze the student's facial expressions using facial expression recognition technology to estimate the student's emotions. The information collection unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate the student's emotions. Furthermore, the information collection unit can analyze the student's input content and estimate the student's emotions using text analysis technology. For example, the information collection unit can analyze the student's facial expressions using facial expression recognition technology to estimate the student's state of relaxation or stress. The information collection unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate changes in emotion. Furthermore, the information collection unit can analyze the student's input content and estimate emotional nuances using text analysis technology. This allows the information collection method to be adjusted according to the student's emotions, thereby collecting more appropriate information. Some or all of the above-described processing in the information collection unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the information collection unit can input the student's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0077] When collecting information, the information collection unit can collect information by referring to the student's past learning history. The information collection unit, for example, uses a generation AI to analyze the student's past learning history. For example, the information collection unit analyzes the topics the student has studied in the past and prioritizes collecting related information. The information collection unit can also analyze the student's past test results and submitted assignments and collect information according to the student's level of understanding. The information collection unit can also collect information related to topics the student has previously shown interest in. For example, the information collection unit prioritizes collecting information related to topics the student has previously studied. The information collection unit can analyze the student's past test results and submitted assignments and collect information according to the student's level of understanding. The information collection unit can also collect information related to topics the student has previously shown interest in. This allows optimal information to be collected by referring to the student's past learning history. Some or all of the above-described processing in the information collection unit may be performed using, or without, the generation AI. For example, the information collection unit can input the student's past learning history into the generation AI and cause the generation AI to collect optimal information.

[0078] When collecting information, the information collection unit can filter information based on the student's current study topic. The information collection unit, for example, uses a generation AI to analyze the student's current study topic. For example, the information collection unit analyzes the topic the student is currently studying and prioritizes collecting related information. The information collection unit can also filter unnecessary information based on the student's current study topic. Furthermore, the information collection unit can also collect the latest information related to the student's current study topic. For example, the information collection unit prioritizes collecting information related to the topic the student is currently studying. The information collection unit can also filter unnecessary information based on the student's current study topic. Furthermore, the information collection unit can collect the latest information related to the student's current study topic. In this way, by filtering information based on the student's current study topic, more appropriate information can be collected. Some or all of the above-described processing in the information collection unit may be performed using, or without, the generation AI. For example, the information collection unit can input the student's current study topic to the generation AI and cause the generation AI to filter the information.

[0079] The information collection unit can estimate the student's emotions and determine the priority of information to be collected based on the estimated student's emotions. The information collection unit estimates the student's emotions using, for example, a generation AI. For example, the information collection unit can analyze the student's facial expression using facial expression recognition technology to estimate the emotion. The information collection unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate the emotion. Furthermore, the information collection unit can analyze the student's input content using text analysis technology to estimate the emotion. For example, the information collection unit can analyze the student's facial expression using facial expression recognition technology to estimate the student's state of excitement or relaxation. The information collection unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate changes in emotion. Furthermore, the information collection unit can analyze the student's input content using text analysis technology to estimate the nuances of emotion. By thus determining the priority of information to be collected according to the student's emotions, more appropriate information can be collected preferentially. Some or all of the above-described processing in the information collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the information collection unit can input the student's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0080] When collecting information, the information collection unit can prioritize collecting highly relevant information by taking into account the student's geographical location information. The information collection unit, for example, uses a generation AI to analyze the student's geographical location information. For example, the information collection unit can identify the student's current location using GPS data. The information collection unit can also estimate the student's geographical location using an IP address. Furthermore, the information collection unit can prioritize collecting highly relevant information by using a generation AI. For example, if the student is in a specific area, the information collection unit can prioritize collecting information related to that area. Furthermore, if the student is traveling, the information collection unit can prioritize collecting information related to the travel destination. Furthermore, if the student is at school, the information collection unit can prioritize collecting information related to the school curriculum. In this way, by taking the student's geographical location information into account, highly relevant information can be prioritized. Some or all of the above-described processing in the information collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the information collection unit can input the student's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0081] When collecting information, the information collection unit can analyze the student's social media activities and collect related information. The information collection unit can, for example, use a generation AI to analyze the student's social media activities. For example, the information collection unit can analyze topics in which the student is interested on social media. The information collection unit can also analyze information on accounts the student follows. Furthermore, the information collection unit can analyze information shared by the student on social media. For example, the information collection unit preferentially collects information related to topics in which the student is interested on social media. The information collection unit can also collect related information based on information on accounts the student follows. Furthermore, the information collection unit can collect related information based on information shared by the student on social media. In this way, by analyzing the student's social media activities, related information can be preferentially collected. Some or all of the above-mentioned processing in the information collection unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the information collection unit can input the student's social media activities into the generation AI and cause the generation AI to collect related information.

[0082] The analysis unit can estimate the student's emotions and adjust the information analysis method based on the estimated student's emotions. The analysis unit estimates the student's emotions using, for example, a generative AI. For example, the analysis unit can analyze the student's facial expression using facial expression recognition technology to estimate the student's emotions. The analysis unit can also use voice analysis technology to analyze the student's tone and speed of voice to estimate the student's emotions. The analysis unit can also analyze the student's input content using text analysis technology to estimate the student's emotions. For example, the analysis unit can analyze the student's facial expression using facial expression recognition technology to estimate the student's state of relaxation or stress. The analysis unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate changes in emotion. The analysis unit can also analyze the student's input content using text analysis technology to estimate emotional nuances. This allows the information analysis method to be adjusted according to the student's emotions, enabling more appropriate analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can input a student's facial expression data into the generation AI and have the generation AI estimate their emotions.

[0083] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. The analysis unit, for example, uses a generation AI to analyze the category of information. For example, the analysis unit identifies the category of information and applies an analysis algorithm according to the category. The analysis unit can also select an optimal analysis algorithm based on the category of information. Furthermore, the analysis unit can use a generation AI to perform analysis according to the category of information. For example, the analysis unit can have the generation AI apply a scientific analysis algorithm to scientific information. The analysis unit can also have the generation AI apply a historical analysis algorithm to historical information. The analysis unit can also have the generation AI apply an economic analysis algorithm to economic information. This makes it possible to apply an optimal analysis algorithm depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the category of information to the generation AI and have the generation AI apply the optimal analysis algorithm.

[0084] The analysis unit can estimate the student's emotions and adjust the display method of the analysis results based on the estimated student's emotions. The analysis unit estimates the student's emotions using, for example, a generative AI. For example, the analysis unit can analyze the student's facial expression using facial expression recognition technology to estimate the student's emotions. The analysis unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate the student's emotions. The analysis unit can also analyze the student's input content using text analysis technology to estimate the student's emotions. For example, the analysis unit can analyze the student's facial expression using facial expression recognition technology to estimate the student's state of tension or relaxation. The analysis unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate changes in emotion. The analysis unit can also analyze the student's input content using text analysis technology to estimate emotional nuances. This allows the display method of the analysis results to be adjusted according to the student's emotions, enabling more appropriate display. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the analysis unit can input a student's facial expression data into the generation AI and have the generation AI estimate their emotions.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. The analysis unit, for example, uses a generation AI to analyze the time of information submission. For example, the analysis unit records the date and time of information submission and determines the priority of analysis based on that. The analysis unit can also prioritize analysis of information with an upcoming submission deadline. Furthermore, the analysis unit can perform analysis based on the time of information submission using a generation AI. For example, the analysis unit can have the generation AI prioritize analysis of the most recent information. The analysis unit can also have the generation AI prioritize analysis of information with an upcoming submission deadline. Furthermore, the analysis unit can have the generation AI determine the priority of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of information submission, analysis can be performed at a more appropriate time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the time of information submission to the generation AI and have the generation AI determine the priority of analysis.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, uses a generation AI to analyze the relevance of the information. For example, the analysis unit analyzes the degree of similarity between topics and keywords in the information and evaluates the relevance. The analysis unit can also adjust the order of analysis based on the relevance of the information. Furthermore, the analysis unit can use a generation AI to perform analysis according to the relevance of the information. For example, the analysis unit can have the generation AI prioritize analysis of information with high relevance. Furthermore, the analysis unit can have the generation AI analyze information with low relevance later. Furthermore, the analysis unit can have the generation AI determine the order of analysis based on the relevance. This enables more efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the relevance of the information to the generation AI and have the generation AI adjust the order of analysis.

[0087] The providing unit can estimate the student's emotions and adjust the expression method of the content to be provided based on the estimated student's emotions. The providing unit, for example, uses a generation AI to estimate the student's emotions. For example, the providing unit can analyze the student's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also use voice analysis technology to analyze the tone and speed of the student's voice to estimate the emotion. The providing unit can also analyze the student's input content using text analysis technology to estimate the emotion. For example, the providing unit can analyze the student's facial expression using facial expression recognition technology to estimate the student's state of relaxation or stress. The providing unit can also analyze the student's voice tone and speed using voice analysis technology to estimate changes in emotion. The providing unit can also analyze the student's input content using text analysis technology to estimate emotional nuances. This allows the content to be provided more appropriately by adjusting the expression method of the content to be provided based on the student's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input facial expression data of a student into the generating AI and have the generating AI estimate the emotion.

[0088] The providing unit can adjust the level of detail of the learning content based on its importance during provision. The providing unit, for example, uses a generation AI to analyze the importance of the learning content. For example, the providing unit evaluates its importance relative to the learning objective and adjusts the level of detail of the learning content based on the evaluation. The providing unit can also add detailed explanations based on the importance of the learning content. Furthermore, the providing unit can use a generation AI to provide learning content according to its importance. For example, the providing unit can have the generation AI provide detailed explanations for content with high importance. The providing unit can also have the generation AI provide concise explanations for content with low importance. Furthermore, the providing unit can have the generation AI determine the priority of provision based on its importance. This allows more appropriate content to be provided by adjusting the level of detail of the learning content based on its importance. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the importance of the learning content into the generation AI and have the generation AI adjust the level of detail of the content.

[0089] The provision unit can apply different provision algorithms depending on the category of the learning content when providing the learning content. The provision unit, for example, uses a generation AI to analyze the category of the learning content. For example, the provision unit identifies the category of the learning content and applies a corresponding provision algorithm. The provision unit can also select an optimal provision algorithm based on the category of the learning content. Furthermore, the provision unit can provide the learning content according to the category using a generation AI. For example, the provision unit can apply a scientific provision algorithm to scientific content. For example, the provision unit can apply a historical provision algorithm to historical content. For example, the provision unit can apply an economic provision algorithm to economic content. This allows the optimal provision algorithm to be applied depending on the category of the learning content. Some or all of the above-mentioned processing in the provision unit can be performed using, or without, the generation AI. For example, the provision unit can input the category of the learning content into the generation AI and cause the generation AI to apply the optimal provision algorithm.

[0090] The service provider can estimate a student's emotions and adjust the length of the content provided based on the estimated emotions. For example, the service provider can estimate a student's emotions using generative AI. For example, the service provider can analyze a student's facial expressions using facial recognition technology and estimate their emotions. The service provider can also analyze the tone and speed of a student's voice using speech analysis technology and estimate their emotions. Furthermore, the service provider can analyze the content of a student's input using text analysis technology and estimate their emotions. For example, the service provider can analyze a student's facial expressions using facial recognition technology and estimate their state of relaxation or stress. Furthermore, the service provider can analyze the tone and speed of a student's voice using speech analysis technology and estimate changes in their emotions. Furthermore, the service provider can analyze the content of a student's input using text analysis technology and estimate the nuances of their emotions. This allows the service provider to provide more appropriate content by adjusting the length of the content provided according to the student's emotions. Some or all of the above processing in the service provider may be performed using generative AI, for example, or without using generative AI. For example, the providing unit can input facial expression data of a student into the generating AI and have the generating AI estimate the emotion.

[0091] The provision unit can determine the provision priority based on the submission date of the learning content when the learning content is provided. The provision unit, for example, uses a generation AI to analyze the submission date of the learning content. For example, the provision unit records the submission date and time of the learning content and determines the provision priority based on that. The provision unit can also prioritize providing content with an upcoming submission deadline. Furthermore, the provision unit can also provide the learning content based on the submission date and time using the generation AI. For example, the provision unit can have the generation AI prioritize providing content with an upcoming submission deadline. Furthermore, the provision unit can have the generation AI prioritize providing content with a more distant submission deadline. Furthermore, the provision unit can have the generation AI determine the provision priority based on the submission date and time. In this way, by determining the provision priority based on the submission date and time of the learning content, content can be provided at a more appropriate time. Some or all of the above-described processing in the provision unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the provision unit can input the submission date of the learning content to the generation AI and have the generation AI determine the provision priority.

[0092] The provision unit can adjust the order of provision based on the relevance of the learning content when providing the learning content. The provision unit, for example, uses a generation AI to analyze the relevance of the learning content. For example, the provision unit analyzes the degree of agreement between topics and keywords in the learning content and evaluates the relevance. The provision unit can also adjust the order of provision based on the relevance of the learning content. Furthermore, the provision unit can use a generation AI to provide learning content according to the relevance of the learning content. For example, the provision unit can have the generation AI prioritize providing content with high relevance. Furthermore, the provision unit can have the generation AI provide content with low relevance later. Furthermore, the provision unit can have the generation AI determine the order of provision based on the relevance. This enables more efficient content provision by adjusting the order of provision based on the relevance of the learning content. Some or all of the above-described processing in the provision unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the provision unit can input the relevance of the learning content into the generation AI and have the generation AI adjust the order of provision.

[0093] The progress management unit can estimate students' emotions and adjust the progress management method based on the estimated emotions. For example, the progress management unit can estimate students' emotions using generative AI. For example, the progress management unit can analyze students' facial expressions using facial recognition technology and estimate their emotions. The progress management unit can also analyze the tone and speed of students' voices using voice analysis technology and estimate their emotions. Furthermore, the progress management unit can analyze students' input using text analysis technology and estimate their emotions. For example, the progress management unit can analyze students' facial expressions using facial recognition technology and estimate their state of relaxation or stress. Furthermore, the progress management unit can analyze the tone and speed of students' voices using voice analysis technology and estimate changes in their emotions. Furthermore, the progress management unit can analyze students' input using text analysis technology and estimate the nuances of their emotions. This allows for more appropriate progress management by adjusting the progress management method according to students' emotions. Some or all of the above-described processes in the progress management unit may be performed using generative AI, for example, or without using generative AI. For example, the progress management department can input a student's facial expression data into the generation AI and have the generation AI estimate their emotions.

[0094] When managing progress, the progress management unit can select a progress management method by referring to the student's past learning history. The progress management unit, for example, uses a generation AI to analyze the student's past learning history. For example, the progress management unit analyzes the student's past test results and submitted assignments to select an optimal progress management method. The progress management unit can also prioritize and suggest progress management methods that the student has used in the past. Furthermore, the progress management unit can also suggest an optimal progress management method for a specific time period based on the student's past learning history. For example, the progress management unit analyzes the student's past test results and submitted assignments to suggest an optimal progress management method. The progress management unit can also prioritize and suggest progress management methods that the student has used in the past. Furthermore, the progress management unit can suggest an optimal progress management method for a specific time period based on the student's past learning history. In this way, the optimal progress management method can be selected by referring to the student's past learning history. Some or all of the above-mentioned processing in the progress management unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the progress management department can input students' past learning history into a generating AI and have the AI ​​select the optimal progress management method.

[0095] The progress management unit can customize its progress management methods based on the student's current learning status. For example, the progress management unit can analyze the student's current learning status using generative AI. For instance, it can analyze the student's test results and assignment progress to understand their current learning status. The progress management unit can also analyze the student's learning log to visualize their learning progress. Furthermore, the progress management unit can perform progress management based on the student's current learning status using generative AI. For example, it can analyze the student's test results and assignment progress to customize its progress management methods. Furthermore, it can analyze the student's learning log to visualize their learning progress. In addition, the progress management unit can perform progress management based on the student's current learning status using generative AI. This allows for more appropriate progress management by customizing the progress management methods based on the student's current learning status. Some or all of the above-described processes in the progress management unit may be performed using, for example, generative AI, or without using generative AI. For example, the progress management department can input the students' current learning status into a generating AI and have the AI ​​customize the methods of progress management.

[0096] The progress management unit can estimate the student's emotions and determine progress management priorities based on the estimated student's emotions. The progress management unit estimates the student's emotions using, for example, a generative AI. For example, the progress management unit can analyze the student's facial expressions using facial expression recognition technology to estimate emotions. The progress management unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate emotions. The progress management unit can also analyze the student's input content using text analysis technology to estimate emotions. For example, the progress management unit can analyze the student's facial expressions using facial expression recognition technology to estimate their state of excitement or relaxation. The progress management unit can also analyze the student's tone and speed of voice using voice analysis technology to estimate changes in emotions. The progress management unit can also analyze the student's input content using text analysis technology to estimate emotional nuances. This enables more appropriate progress management by determining progress management priorities based on the student's emotions. Some or all of the above-described processing in the progress management unit may be performed using, for example, a generative AI, or may be performed without using a generative AI. For example, the progress management department can input a student's facial expression data into the generation AI and have the generation AI estimate their emotions.

[0097] The progress management unit can select a progress management method based on the student's geographical location information during progress management. The progress management unit, for example, uses a generating AI to analyze the student's geographical location information. For example, the progress management unit can identify the student's current location using GPS data. The progress management unit can also estimate the student's geographical location using an IP address. Furthermore, the progress management unit can perform progress management based on the geographical location information using a generating AI. For example, if the student is in a specific area, the progress management unit can suggest a progress management method related to that area. If the student is traveling, the progress management unit can suggest a progress management method related to the travel destination. Furthermore, if the student is at school, the progress management unit can suggest a progress management method related to the school curriculum. This makes it possible to select an optimal progress management method by taking the student's geographical location information into consideration. Some or all of the above-described processing in the progress management unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the progress management unit can input the student's geographical location information into the generating AI and have the generating AI select an optimal progress management method.

[0098] During progress management, the progress management unit can analyze the student's social media activity and suggest progress management methods. The progress management unit analyzes the student's social media activity using, for example, a generation AI. For example, the progress management unit analyzes topics the student is interested in on social media. The progress management unit can also analyze information on accounts the student follows. Furthermore, the progress management unit can analyze information shared by the student on social media. For example, the progress management unit suggests a progress management method related to a topic the student is interested in on social media. The progress management unit can also suggest a related progress management method based on information on accounts the student follows. Furthermore, the progress management unit can suggest a related progress management method based on information shared by the student on social media. In this way, by analyzing the student's social media activity, a related progress management method can be suggested. Some or all of the above-described processing in the progress management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the progress management unit can input the student's social media activity into the generation AI and have the generation AI suggest progress management methods. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, information collection unit, analysis unit, provision unit, and progress management unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit accepts input from the student using the touch panel 38A or microphone 38B of the smart device 14. The information collection unit collects information on the Internet using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The provision unit provides learning content and advice to the student using the display 40A or speaker 40B of the smart device 14. The progress management unit keeps track of the student's learning progress using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, information collection unit, analysis unit, provision unit, and progress management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input from the student using the microphone 238 of the smart glasses 214. The information collection unit collects information on the Internet by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The provision unit provides learning content and advice to the student using the speaker 240 of the smart glasses 214. The progress management unit keeps track of the student's learning progress by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, information collection unit, analysis unit, provision unit, and progress management unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives voice input from the student using the microphone 238 of the headset-type terminal 314. The information collection unit collects information on the Internet using the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12. The provision unit provides learning content and advice to the student using the display 343 and speaker 240 of the headset-type terminal 314. The progress management unit keeps track of the student's learning progress using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, information gathering unit, analysis unit, provision unit, and progress management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives voice input from students using the microphone 238 of the robot 414. The information gathering unit collects information from the internet using the specific processing unit 290 of the data processing unit 12. The analysis unit analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The provision unit provides students with learning content and advice using the speaker 240 of the robot 414. The progress management unit monitors students' learning progress using the specific processing unit 290 of the data processing unit 12.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The inquiry-based learning support system can also analyze students' learning styles and provide individually optimized learning plans. For example, a student who prefers visual learning can be provided with learning materials that make heavy use of visual content. A student who prefers auditory learning can be provided with learning materials in audio or podcast format. Furthermore, a student who prefers hands-on learning can be provided with learning materials that include interactive simulations and experiments. This maximizes learning effectiveness by providing an optimal learning plan tailored to each student's learning style.

[0101] The progress management department not only tracks students' learning progress but also provides feedback to maintain their motivation. For example, it can display messages of praise and encouragement when students achieve certain milestones. Visually demonstrating progress towards goals makes it easier for students to see their own progress. Furthermore, if progress is behind schedule, it can provide specific improvement measures and advice. This helps to increase students' motivation and support sustained learning.

[0102] In addition to providing learning content, the provider can also offer advice to optimize students' learning environments. For example, it can suggest environmental settings to improve concentration and effective study schedules. It can also offer advice on the timing and methods of taking breaks. It can also introduce tools and applications that are useful for studying. This helps create an environment in which students can study more effectively.

[0103] The information gathering section can gather not only relevant information for questions or themes entered by students, but also information that includes different perspectives and opposing opinions. For example, when gathering information on environmental issues, it can gather not only pro-opinions but also opposing opinions and different approaches. It can also gather information from different cultural and regional perspectives. It can also gather information on the latest research and technology. This helps students develop the ability to think about problems from multiple perspectives.

[0104] When analyzing collected information, the analysis unit can also evaluate the reliability and source of the information. For example, it can evaluate whether the information comes from a reliable academic paper or official institution. It can also evaluate reliability by taking into account the date and time the information was updated and the author's expertise. It can also check whether the content of the information is consistent with other reliable information. This can improve the quality of the information provided to students and enable them to learn based on reliable information.

[0105] In addition to automatic report evaluation and feedback generation, the system can estimate students' emotions and provide emotionally appropriate feedback. For example, if a student is feeling stressed, it can offer encouraging messages and suggestions for relaxation. If a student is confident, it can provide feedback that encourages further challenges. Furthermore, if a student has questions, it can provide detailed explanations and additional reference materials. By providing appropriate feedback tailored to students' emotions, the system can enhance their motivation to learn.

[0106] The reception desk can not only estimate students' emotions but also monitor emotional changes in real time, enabling interaction at the appropriate time. For example, if it is estimated that a student is losing focus, it can suggest a short break. If a student is excited, it can present a more challenging task. Furthermore, if a student is relaxed, it can offer advice to help them maintain that relaxation. By providing timely interaction tailored to each student's emotions, the learning effect can be enhanced.

[0107] The reception unit not only analyzes the student's past input history, but also learns input patterns and can suggest predictive input. For example, it can predict and suggest what the student will input next based on phrases and keywords that the student has frequently used in the past. It can also automatically suggest related questions or topics when a student asks a question about a specific topic. Furthermore, it can display predicted input before the student begins to type, reducing the effort required for input. This can make the student's input process more efficient and support smoother learning.

[0108] The reception unit not only filters input content based on the student's current learning situation and areas of interest, but also adjusts the difficulty level of the input content according to the student's learning progress. For example, basic questions and topics can be prioritized in the early stages of learning, and more advanced questions and topics can be accepted as the student progresses. Furthermore, it can suggest questions and topics of an appropriate level of difficulty according to the student's level of understanding. This makes it possible to support effective learning by providing appropriate input content according to the student's learning progress.

[0109] The reception unit not only estimates the student's emotions and determines the priority of inputs to be received based on the estimated student's emotions, but also customizes the interface according to the emotions. For example, if the student is feeling stressed, a simple and intuitive interface can be provided. If the student is relaxed, detailed information and options can be provided. Furthermore, if the student is excited, an interface with many interactive elements can be provided. This makes it possible to improve the learning experience by providing an optimal interface according to the student's emotions.

[0110] The processing flow of the second embodiment will be briefly explained below.

[0111] Step 1: The reception unit accepts student input. Student input includes text input, voice input, and image input. For example, the reception unit provides a text input interface, allowing students to enter questions or topics. It is also possible to use a microphone and voice recognition technology to accept voice input, and a camera and image recognition technology to accept image input. Step 2: The information collection unit analyzes the information received by the reception unit and collects related information. The information collection unit can use web scraping technology, APIs, or generation AI to collect information from the Internet or specific databases. For example, it can obtain related information from a database of academic papers. Step 3: The analysis unit analyzes the information collected by the information collection unit and extracts key points. The analysis unit can analyze text data using natural language processing technology, machine learning algorithms, and generative AI to extract important keywords and phrases. Step 4: The provision unit provides learning content and advice to students based on the information extracted by the analysis unit. The provision unit can provide video learning materials, text learning materials, and interactive learning content. It can also provide advice to students using generative AI. Step 5: The progress management unit grasps the student's learning progress based on the information provided by the provision unit. The progress management unit can record the student's test results and assignment submission status, and analyze the learning log to visualize the learning progress. Furthermore, it can also grasp the student's learning progress in real time using the generation AI.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0114] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0142] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0154] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0156] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0168] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0173] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0176] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0177] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0178] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0181] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0183] [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from students; An information gathering unit analyzes the information received by the aforementioned reception unit and collects related information, An analysis unit analyzes the information collected by the aforementioned information collection unit and extracts important points, Based on the information extracted by the aforementioned analysis unit, a provision unit provides students with learning content and advice. The system includes a progress management unit that grasps the learning progress of students based on the information provided by the aforementioned provisioning unit. A system characterized by:

2. The progress management unit To track students' learning progress and provide this information to teachers. The system of claim 1 .

3. The providing unit Based on the collected information, we provide students with learning content and advice. The system of claim 1 .

4. The information collecting unit The system automatically collects relevant information in response to questions and topics entered by students. The system of claim 1 .

5. The analysis unit Analyze the collected information and extract the key points. The system of claim 1 .

6. The providing unit It provides features for automated report evaluation and automated feedback generation. The system of claim 1 .

7. The reception unit The system estimates the students' emotions and adjusts the timing of input acceptance based on the estimated emotions. The system of claim 1 .

8. The reception unit Analyze students' past input history to select the appropriate registration method. The system of claim 1 .

Images