system

Abstract

The system according to the embodiment aims to allow students to easily ask questions and identify what they do not understand. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives a question. The analyzing unit analyzes the question received by the receiving unit. The generating unit generates a test based on the results of the analysis by the analyzing unit. The providing unit provides feedback based on the results of the test generated by the generating 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] With conventional technology, it was difficult to identify what students did not understand, and they could not easily ask questions.

[0005] The system according to the embodiment aims to allow students to easily ask questions and identify what they do not understand. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit receives a question. The analyzing unit analyzes the question received by the receiving unit. The generating unit generates a test based on the result of the analysis by the analyzing unit. The providing unit provides feedback based on the result of the test generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment allows students to easily ask questions and identify what they do not understand. [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) A learning support system according to an embodiment of the present invention identifies where a student is struggling and provides effective learning support. The learning support system allows students to easily identify their lack of understanding by posting multiple questions to an AI or taking simple tests. For example, in the learning support system, a student posts a question to an AI. For example, if a student does not understand a math problem, the student posts the problem to the AI. The AI ​​then analyzes the posted question and identifies where the student is struggling. For example, if the student does not understand part of the solution to the problem, the AI ​​identifies that part. Furthermore, the learning support system uses the AI ​​to administer a simple test to the student. This test is designed to assess the student's level of understanding and is automatically generated by the AI. For example, the AI ​​generates test questions to assess the student's understanding of a math problem. Finally, the learning support system uses the AI ​​to provide feedback to the student. Specifically, the AI ​​clarifies where the student is struggling and advises the student to focus on studying that part. For example, if the student does not understand a particular solution, the AI ​​provides additional learning materials related to that solution. This allows the learning support system to allow students to progress through their studies at their own pace and efficiently make up for any areas where they lack understanding. Also, since all students need to do is post a question to the AI, students can easily receive learning support. This allows the learning support system to identify where students are struggling and provide effective learning support. For example, it allows students to progress through their studies at their own pace and efficiently make up for any areas where they lack understanding. Also, since all students need to do is post a question to the AI, students can easily receive learning support.

[0029] A learning support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions submitted by students to an AI. For example, the reception unit can receive questions in text, audio, or image format. The reception unit can also receive multiple questions submitted by students. The analysis unit analyzes the questions received by the reception unit. For example, the analysis unit can analyze the content of the questions using natural language processing technology. The analysis unit can also analyze the questions using statistical analysis or machine learning algorithms. The analysis unit can identify where the student is struggling based on the analysis results of the questions. For example, if the student does not understand part of the solution to a problem, the analysis unit can identify that part. The generation unit generates a simple test based on the analysis results of the analysis unit. For example, the generation unit generates a simple test in the form of multiple-choice questions, essay questions, practical tests, or the like. The generation unit can also automatically generate test questions using AI. The generation unit can provide feedback based on the results of the generated simple test. The providing unit provides feedback based on the results of the simple test generated by the generating unit. For example, the providing unit clarifies where the student is struggling and provides advice to focus on studying that part. Furthermore, if a student does not understand a particular solution, the providing unit can provide additional study materials related to that solution. This allows the learning support system according to the embodiment to identify where the student is struggling and provide effective learning support. For example, students can progress through their studies at their own pace and efficiently supplement areas of insufficient understanding. Furthermore, students can easily receive learning support by simply posting a question to the AI.

[0030] The reception unit can analyze the student's past question history and select the optimal reception method. The reception unit analyzes the student's past question history and selects the optimal reception method. For example, the reception unit prioritizes receiving topics that the student has frequently asked about in the past. The reception unit can also prioritize suggesting question formats (audio, text, etc.) that the student has used in the past. The reception unit can also adjust the reception of questions to be accepted during specific time periods based on the student's past question history. In this way, the optimal reception method can be selected by analyzing the student's past question history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's past question history data into the generation AI and have the generation AI select the optimal reception method.

[0031] The reception unit can filter questions based on the student's current learning situation and areas of interest when receiving the questions. The reception unit filters questions based on the student's current learning situation and areas of interest when receiving the questions. For example, the reception unit only accepts questions related to the topic the student is currently studying. The reception unit can also preferentially accept questions that are highly relevant based on the student's areas of interest. The reception unit can also filter and accept questions of an appropriate level of difficulty according to the student's learning progress. In this way, by filtering questions based on the student's current learning situation and areas of interest, highly relevant questions can be accepted. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's learning situation data to a generation AI and cause the generation AI to filter the questions.

[0032] When receiving a question, the reception unit can select an appropriate reception means depending on the student's input method. When receiving a question, the reception unit selects the optimal reception means depending on the student's input method (voice, text, image, etc.). For example, when a student inputs a question by voice, the reception unit can use voice recognition technology to receive the question. When a student inputs a question by text, the reception unit can also use text analysis technology to receive the question. When a student inputs a question by image, the reception unit can also use image recognition technology to receive the question. This allows questions to be received smoothly by selecting the optimal reception means depending on the student's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's input data into a generation AI and have the generation AI select the optimal reception means.

[0033] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the student's geographical location information. When receiving a question, the reception unit prioritizes receiving highly relevant questions by taking into account the student's geographical location information. For example, if the student is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the student is at school, the reception unit can prioritize receiving questions related to the school curriculum. Furthermore, if the student is at home, the reception unit can prioritize receiving questions related to home learning. In this way, by taking the student's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the student's geographical location information data into the generation AI and cause the generation AI to select highly relevant questions.

[0034] The reception unit can analyze the student's social media activity when receiving a question and receive related questions. The reception unit analyzes the student's social media activity when receiving a question and receive related questions. For example, the reception unit prioritizes receiving questions related to learning content shared by the student on social media. The reception unit can also analyze the student's social media posts and suggest related questions. The reception unit can also accept related questions based on the activity of the student's friends on social media. In this way, by analyzing the student's social media activity, related questions can be preferentially accepted. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's social media data into a generation AI and cause the generation AI to select related questions.

[0035] When receiving a question, the reception unit can adjust the reception method by reflecting the student's past feedback. When receiving a question, the reception unit customizes the reception method by reflecting the student's past feedback. For example, the reception unit preferentially suggests question formats that the student has previously preferred. The reception unit can also customize the optimal question reception method based on the student's past feedback. The reception unit can also adjust the method to avoid specific question formats based on the student's past feedback. In this way, the optimal question reception method can be provided by reflecting the student's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's past feedback data into the generation AI and have the generation AI customize the reception method.

[0036] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the question during analysis. For example, the analysis unit performs a detailed analysis for questions with high importance. The analysis unit can also perform a concise analysis for questions with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the question. In this way, by adjusting the level of detail of the analysis based on the importance of the question, it is possible to provide a detailed analysis for important questions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms depending on the question category during analysis. The analysis unit can apply different analysis algorithms depending on the question category during analysis. For example, the analysis unit can apply a mathematical expression analysis algorithm to a mathematics question. The analysis unit can also apply a text analysis algorithm to a literature question. The analysis unit can also apply a data analysis algorithm to a science question. In this way, by applying different analysis algorithms depending on the question category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input question category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results during analysis. For example, the analysis unit improves the analysis accuracy of the current question based on the student's past analysis results. The analysis unit can also extract specific patterns from the student's past analysis results and reflect them in the analysis. The analysis unit can also adjust the analysis algorithm by referring to the student's past analysis results. In this way, the analysis accuracy of the current question can be improved by referring to the student's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0039] The analysis unit can determine the analysis priority based on the time when the question was submitted during analysis. The analysis unit determines the analysis priority based on the time when the question was submitted during analysis. For example, the analysis unit determines the analysis priority based on the time of day when the question was submitted. The analysis unit can also adjust the order of analysis depending on the time when the question was submitted. The analysis unit can also adjust the analysis schedule taking into account the time when the question was submitted. In this way, by determining the analysis priority based on the time when the question was submitted, it is possible to perform the analysis in an appropriate order. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question submission time data to the generation AI and have the generation AI determine the analysis priority.

[0040] The analysis unit can adjust the order of analysis based on the relevance of questions during analysis. The analysis unit adjusts the order of analysis based on the relevance of questions during analysis. For example, the analysis unit determines the order of analysis based on the relevance of questions. The analysis unit can also adjust the priority of analysis taking into account the relevance of questions. The analysis unit can also adjust the analysis schedule according to the relevance of questions. In this way, by adjusting the order of analysis based on the relevance of questions, highly relevant questions can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] The analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise during analysis. The analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terminology according to the student's level of expertise. Furthermore, if the student is a beginner, the analysis unit can avoid technical terminology when performing the analysis. Furthermore, if the student is an advanced learner, the analysis unit can use a lot of technical terminology when performing the analysis. In this way, by adjusting the use of technical terminology in the analysis according to the student's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the student's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0042] The generation unit can adjust the level of detail of the test based on the importance of the question when generating the test. The generation unit adjusts the level of detail of the test based on the importance of the question when generating the test. For example, the generation unit generates a detailed simple test for a question of high importance. The generation unit can also generate a concise simple test for a question of low importance. The generation unit can also adjust the depth of the simple test according to the importance of the question. In this way, by adjusting the level of detail of the test based on the importance of the question, it is possible to provide a detailed test for an important question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the test.

[0043] The generation unit can apply different test generation algorithms depending on the question category when generating a test. The generation unit applies different test generation algorithms depending on the question category when generating a test. For example, the generation unit applies a mathematical expression analysis algorithm to generate a simple test for a mathematics question. The generation unit can also apply a text analysis algorithm to generate a simple test for a literature question. The generation unit can also apply a data analysis algorithm to generate a simple test for a science question. In this way, by applying different test generation algorithms depending on the question category, more appropriate tests can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input question category data to the generation AI and cause the generation AI to apply a test generation algorithm.

[0044] The generation unit can improve the accuracy of a test by referring to a student's past test results when generating the test. The generation unit can improve the accuracy of a test by referring to a student's past test results when generating the test. For example, the generation unit improves the accuracy of a current simple test based on a student's past test results. The generation unit can also extract specific patterns from a student's past test results and reflect them in the simple test. The generation unit can also adjust the test generation algorithm by referring to a student's past test results. In this way, the accuracy of a current simple test can be improved by referring to a student's past test results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a student's past test result data into the generation AI and cause the generation AI to improve the accuracy of the test.

[0045] The generation unit can determine the priority of tests based on the time when questions are submitted when generating tests. The generation unit determines the priority of tests based on the time when questions are submitted when generating tests. For example, the generation unit determines the priority of quick tests based on the time of day when questions are submitted. The generation unit can also adjust the order of quick tests depending on the time when questions are submitted. The generation unit can also adjust the schedule of quick tests taking into account the time when questions are submitted. In this way, by determining the priority of tests based on the time when questions are submitted, it is possible to provide tests in an appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question submission time data into a generation AI and have the generation AI determine the priority of tests.

[0046] The generation unit can adjust the order of tests based on the relevance of questions when generating tests. The generation unit adjusts the order of tests based on the relevance of questions when generating tests. For example, the generation unit determines the order of quick tests based on the relevance of questions. The generation unit can also adjust the priority of quick tests taking into account the relevance of questions. The generation unit can also adjust the schedule of quick tests according to the relevance of questions. In this way, by adjusting the order of tests based on the relevance of questions, highly relevant questions can be tested preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of tests.

[0047] The generation unit can adjust the use of technical terminology in the test according to the student's level of expertise when generating the test. The generation unit can adjust the use of technical terminology in the test according to the student's level of expertise when generating the test. For example, the generation unit adjusts the use of technical terminology according to the student's level of expertise. The generation unit can also generate a simple test that avoids technical terminology if the student is a beginner. The generation unit can also generate a simple test that uses a lot of technical terminology if the student is an advanced learner. In this way, by adjusting the use of technical terminology in the test according to the student's level of expertise, it is possible to provide a test that is easier to understand. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the student's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0048] The providing unit can adjust the level of detail of the feedback based on the importance of the question when providing feedback. The providing unit adjusts the level of detail of the feedback based on the importance of the question when providing feedback. For example, the providing unit provides detailed feedback for questions with high importance. The providing unit can also provide brief feedback for questions with low importance. The providing unit can also adjust the depth of the feedback according to the importance of the question. In this way, by adjusting the level of detail of the feedback based on the importance of the question, detailed feedback can be provided for important questions. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input question importance data to the generating AI and cause the generating AI to adjust the level of detail of the feedback.

[0049] The providing unit can apply different feedback algorithms depending on the category of the question when providing feedback. The providing unit can apply different feedback algorithms depending on the category of the question when providing feedback. For example, the providing unit can provide feedback by applying a mathematical expression analysis algorithm to a mathematics question. The providing unit can also provide feedback by applying a text analysis algorithm to a literature question. The providing unit can also provide feedback by applying a data analysis algorithm to a science question. In this way, by applying different feedback algorithms depending on the category of the question, more appropriate feedback can be provided. Some or all of the above-mentioned processing by the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input question category data to a generation AI and cause the generation AI to apply a feedback algorithm.

[0050] When providing feedback, the providing unit can improve the accuracy of the feedback by referring to the student's past feedback results. When providing feedback, the providing unit can improve the accuracy of the feedback by referring to the student's past feedback results. For example, the providing unit improves the accuracy of the current feedback based on the student's past feedback results. The providing unit can also extract specific patterns from the student's past feedback results and reflect them in the feedback. The providing unit can also adjust the feedback algorithm by referring to the student's past feedback results. In this way, the accuracy of the current feedback can be improved by referring to the student's past feedback results. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the student's past feedback result data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0051] The providing unit, when providing feedback, can determine the priority of the feedback based on the time when the question was submitted. The providing unit, when providing feedback, determines the priority of the feedback based on the time when the question was submitted. For example, the providing unit determines the priority of the feedback based on the time period when the question was submitted. The providing unit can also adjust the order of the feedback depending on the time when the question was submitted. The providing unit can also adjust the feedback schedule taking into account the time when the question was submitted. In this way, by determining the priority of the feedback based on the time when the question was submitted, it is possible to provide feedback in an appropriate order. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input question submission time data to the generating AI and cause the generating AI to determine the priority of the feedback.

[0052] The providing unit can adjust the order of feedback based on the relevance of questions when providing feedback. The providing unit adjusts the order of feedback based on the relevance of questions when providing feedback. For example, the providing unit determines the order of feedback based on the relevance of questions. The providing unit can also adjust the priority of feedback taking into account the relevance of questions. The providing unit can also adjust the feedback schedule according to the relevance of questions. In this way, by adjusting the order of feedback based on the relevance of questions, highly relevant questions can be given priority as feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input question relevance data to a generating AI and cause the generating AI to adjust the order of feedback.

[0053] When providing feedback, the providing unit can adjust the use of technical terminology in the feedback according to the student's level of expertise. When providing feedback, the providing unit adjusts the use of technical terminology in the feedback according to the student's level of expertise. For example, the providing unit adjusts the use of technical terminology according to the student's level of expertise. Furthermore, if the student is a beginner, the providing unit can provide feedback that avoids technical terminology. Furthermore, if the student is an advanced learner, the providing unit can provide feedback that uses a lot of technical terminology. In this way, by adjusting the use of technical terminology in the feedback according to the student's level of expertise, it is possible to provide feedback that is easier to understand. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the student's level of expertise data into a generation AI and cause the generation AI to use technical terminology.

[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 reception unit can propose an individual study plan based on the student's study history. For example, the reception unit can suggest that the student prioritize studying topics that the student found difficult in the past. The reception unit can also suggest the next topic to study based on the student's study progress. Furthermore, the reception unit can customize a study plan to suit the student's learning style. This allows students to receive the study plan that is best suited to them and progress with their studies efficiently.

[0056] The generation unit can set individual learning goals based on the student's learning history. For example, the generation unit can set the next goal to be achieved by the student based on goals that the student has achieved in the past. The generation unit can also adjust the difficulty of the goals according to the student's learning progress. Furthermore, the generation unit can set goals that match the student's learning style. This allows students to have learning goals that are optimal for them and allows them to continue learning while maintaining their motivation.

[0057] The reception unit can suggest the optimal question format based on the student's learning style. For example, if the student is a visual learner, the reception unit can suggest a question format using images or diagrams. If the student is an auditory learner, the reception unit can also suggest a question format using audio. Furthermore, if the student is a tactile learner, the reception unit can also suggest a question format that involves practical skills. In this way, by suggesting a question format that suits the student's learning style, more effective learning support can be provided.

[0058] The generation unit can provide individual learning resources based on the student's learning history. For example, the generation unit can suggest the next resource that the student should use based on resources that the student has used in the past. The generation unit can also adjust the difficulty level of the resources according to the student's learning progress. Furthermore, the generation unit can provide resources that match the student's learning style. This allows students to use learning resources that are best suited to them and progress with their studies efficiently.

[0059] The reception unit can suggest individual learning activities based on the student's learning history. For example, the reception unit can suggest activities related to topics that the student struggled with in the past. The reception unit can also suggest the next activity the student should do based on the student's learning progress. Furthermore, the reception unit can suggest activities that match the student's learning style. This allows students to do learning activities that are best suited to them and progress with their studies efficiently.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit accepts questions submitted by students to the AI. For example, the reception unit can accept questions in text format, audio format, image format, etc. The reception unit can also accept multiple questions submitted by students. Step 2: The analysis unit analyzes the question received by the reception unit. For example, the analysis unit analyzes the content of the question using natural language processing technology. The analysis unit can also analyze the question using statistical analysis or machine learning algorithms. Furthermore, the analysis unit identifies where the student is struggling based on the results of the question analysis. For example, if the student does not understand part of the solution to a problem, the analysis unit identifies that part. Step 3: The generation unit generates a simple test based on the results analyzed by the analysis unit. For example, the generation unit generates a simple test in the form of multiple choice questions, essay questions, practical tests, etc. The generation unit can also automatically generate test questions using AI. Furthermore, the generation unit provides feedback based on the results of the generated simple test. Step 4: The provider provides feedback based on the results of the simple test generated by the generator. For example, the provider can clarify where the student is struggling and provide advice to focus on that area. The provider can also provide additional learning materials for a specific solution if the student does not understand it.

[0062] (Example 2) A learning support system according to an embodiment of the present invention identifies where a student is struggling and provides effective learning support. The learning support system allows students to easily identify their lack of understanding by posting multiple questions to an AI or taking simple tests. For example, in the learning support system, a student posts a question to an AI. For example, if a student does not understand a math problem, the student posts the problem to the AI. The AI ​​then analyzes the posted question and identifies where the student is struggling. For example, if the student does not understand part of the solution to the problem, the AI ​​identifies that part. Furthermore, the learning support system uses the AI ​​to administer a simple test to the student. This test is designed to assess the student's level of understanding and is automatically generated by the AI. For example, the AI ​​generates test questions to assess the student's understanding of a math problem. Finally, the learning support system uses the AI ​​to provide feedback to the student. Specifically, the AI ​​clarifies where the student is struggling and advises the student to focus on studying that part. For example, if the student does not understand a particular solution, the AI ​​provides additional learning materials related to that solution. This allows the learning support system to allow students to progress through their studies at their own pace and efficiently make up for any areas where they lack understanding. Also, since all students need to do is post a question to the AI, students can easily receive learning support. This allows the learning support system to identify where students are struggling and provide effective learning support. For example, it allows students to progress through their studies at their own pace and efficiently make up for any areas where they lack understanding. Also, since all students need to do is post a question to the AI, students can easily receive learning support.

[0063] A learning support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions submitted by students to an AI. For example, the reception unit can receive questions in text, audio, or image format. The reception unit can also receive multiple questions submitted by students. The analysis unit analyzes the questions received by the reception unit. For example, the analysis unit can analyze the content of the questions using natural language processing technology. The analysis unit can also analyze the questions using statistical analysis or machine learning algorithms. The analysis unit can identify where the student is struggling based on the analysis results of the questions. For example, if the student does not understand part of the solution to a problem, the analysis unit can identify that part. The generation unit generates a simple test based on the analysis results of the analysis unit. For example, the generation unit generates a simple test in the form of multiple-choice questions, essay questions, practical tests, or the like. The generation unit can also automatically generate test questions using AI. The generation unit can provide feedback based on the results of the generated simple test. The providing unit provides feedback based on the results of the simple test generated by the generating unit. For example, the providing unit clarifies where the student is struggling and provides advice to focus on studying that part. Furthermore, if a student does not understand a particular solution, the providing unit can provide additional study materials related to that solution. This allows the learning support system according to the embodiment to identify where the student is struggling and provide effective learning support. For example, students can progress through their studies at their own pace and efficiently supplement areas of insufficient understanding. Furthermore, students can easily receive learning support by simply posting a question to the AI.

[0064] The learning support system further includes a reception unit that estimates a student's emotions and determines the timing of accepting questions based on the estimated student emotions. The reception unit estimates a student's emotions and adjusts the timing of accepting questions based on the estimated student emotions. For example, if a student is feeling stressed, the reception unit temporarily delays accepting questions to provide time for the student to relax. If a student is concentrating, the reception unit can immediately accept questions to prevent interruption to the flow of learning. If a student is tired, the reception unit can temporarily stop accepting questions and display a message encouraging the student to take a break. By adjusting the timing of accepting questions based on the student's emotions, questions can be accepted at a more appropriate time. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or without an AI. For example, the reception unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0065] The reception unit can analyze the student's past question history and select the optimal reception method. The reception unit analyzes the student's past question history and selects the optimal reception method. For example, the reception unit prioritizes receiving topics that the student has frequently asked about in the past. The reception unit can also prioritize suggesting question formats (audio, text, etc.) that the student has used in the past. The reception unit can also adjust the reception of questions to be accepted during specific time periods based on the student's past question history. In this way, the optimal reception method can be selected by analyzing the student's past question history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's past question history data into the generation AI and have the generation AI select the optimal reception method.

[0066] The reception unit can filter questions based on the student's current learning situation and areas of interest when receiving the questions. The reception unit filters questions based on the student's current learning situation and areas of interest when receiving the questions. For example, the reception unit only accepts questions related to the topic the student is currently studying. The reception unit can also preferentially accept questions that are highly relevant based on the student's areas of interest. The reception unit can also filter and accept questions of an appropriate level of difficulty according to the student's learning progress. In this way, by filtering questions based on the student's current learning situation and areas of interest, highly relevant questions can be accepted. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's learning situation data to a generation AI and cause the generation AI to filter the questions.

[0067] When receiving a question, the reception unit can select an appropriate reception means depending on the student's input method. When receiving a question, the reception unit selects the optimal reception means depending on the student's input method (voice, text, image, etc.). For example, when a student inputs a question by voice, the reception unit can use voice recognition technology to receive the question. When a student inputs a question by text, the reception unit can also use text analysis technology to receive the question. When a student inputs a question by image, the reception unit can also use image recognition technology to receive the question. This allows questions to be received smoothly by selecting the optimal reception means depending on the student's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's input data into a generation AI and have the generation AI select the optimal reception means.

[0068] The reception unit can estimate the student's emotions and determine the priority of questions to be received based on the estimated student's emotions. The reception unit can estimate the student's emotions and determine the priority of questions to be received based on the estimated student's emotions. For example, if a student is feeling anxious, the reception unit can prioritize urgent questions. If a student is relaxed, the reception unit can also prioritize general questions. If a student is excited, the reception unit can also prioritize questions that pique the student's interest. In this way, by determining the priority of questions according to the student's emotions, urgent questions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input student facial expression data into the generation AI and cause the generation AI to estimate the student's emotions.

[0069] When receiving a question, the reception unit can prioritize receiving highly relevant questions by taking into account the student's geographical location information. When receiving a question, the reception unit prioritizes receiving highly relevant questions by taking into account the student's geographical location information. For example, if the student is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the student is at school, the reception unit can prioritize receiving questions related to the school curriculum. Furthermore, if the student is at home, the reception unit can prioritize receiving questions related to home learning. In this way, by taking the student's geographical location information into account, highly relevant questions can be prioritized. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the student's geographical location information data into the generation AI and cause the generation AI to select highly relevant questions.

[0070] The reception unit can analyze the student's social media activity when receiving a question and receive related questions. The reception unit analyzes the student's social media activity when receiving a question and receive related questions. For example, the reception unit prioritizes receiving questions related to learning content shared by the student on social media. The reception unit can also analyze the student's social media posts and suggest related questions. The reception unit can also accept related questions based on the activity of the student's friends on social media. In this way, by analyzing the student's social media activity, related questions can be preferentially accepted. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's social media data into a generation AI and cause the generation AI to select related questions.

[0071] When receiving a question, the reception unit can adjust the reception method by reflecting the student's past feedback. When receiving a question, the reception unit customizes the reception method by reflecting the student's past feedback. For example, the reception unit preferentially suggests question formats that the student has previously preferred. The reception unit can also customize the optimal question reception method based on the student's past feedback. The reception unit can also adjust the method to avoid specific question formats based on the student's past feedback. In this way, the optimal question reception method can be provided by reflecting the student's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the student's past feedback data into the generation AI and have the generation AI customize the reception method.

[0072] The analysis unit can estimate the student's emotions and adjust the analysis presentation method based on the estimated student's emotions. The analysis unit can estimate the student's emotions and adjust the analysis presentation method based on the estimated student's emotions. For example, if the student is nervous, the analysis unit can provide a simple, highly visible analysis result. If the student is relaxed, the analysis unit can provide a detailed analysis result. If the student is in a hurry, the analysis unit can provide a concise analysis result. This allows for more appropriate analysis results to be provided by adjusting the analysis presentation method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the student's facial expression data into the generation AI and have the generation AI estimate the student's emotions.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the question during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the question during analysis. For example, the analysis unit performs a detailed analysis for questions with high importance. The analysis unit can also perform a concise analysis for questions with low importance. The analysis unit can also adjust the depth of the analysis according to the importance of the question. In this way, by adjusting the level of detail of the analysis based on the importance of the question, it is possible to provide a detailed analysis for important questions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the question category during analysis. The analysis unit can apply different analysis algorithms depending on the question category during analysis. For example, the analysis unit can apply a mathematical expression analysis algorithm to a mathematics question. The analysis unit can also apply a text analysis algorithm to a literature question. The analysis unit can also apply a data analysis algorithm to a science question. In this way, by applying different analysis algorithms depending on the question category, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input question category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0075] The analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the student's past analysis results during analysis. For example, the analysis unit improves the analysis accuracy of the current question based on the student's past analysis results. The analysis unit can also extract specific patterns from the student's past analysis results and reflect them in the analysis. The analysis unit can also adjust the analysis algorithm by referring to the student's past analysis results. In this way, the analysis accuracy of the current question can be improved by referring to the student's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the student's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0076] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated student's emotions. The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated student's emotions. For example, if the student is in a hurry, the analysis unit can provide a short and to-the-point analysis. If the student is relaxed, the analysis unit can provide a detailed analysis. If the student is excited, the analysis unit can provide an analysis with visually stimulating effects. This allows for adjusting the length of the analysis according to the student's emotions to provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input student facial expression data into the generative AI and cause the generative AI to estimate the student's emotions.

[0077] The analysis unit can determine the analysis priority based on the time when the question was submitted during analysis. The analysis unit determines the analysis priority based on the time when the question was submitted during analysis. For example, the analysis unit determines the analysis priority based on the time of day when the question was submitted. The analysis unit can also adjust the order of analysis depending on the time when the question was submitted. The analysis unit can also adjust the analysis schedule taking into account the time when the question was submitted. In this way, by determining the analysis priority based on the time when the question was submitted, it is possible to perform the analysis in an appropriate order. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question submission time data to the generation AI and have the generation AI determine the analysis priority.

[0078] The analysis unit can adjust the order of analysis based on the relevance of questions during analysis. The analysis unit adjusts the order of analysis based on the relevance of questions during analysis. For example, the analysis unit determines the order of analysis based on the relevance of questions. The analysis unit can also adjust the priority of analysis taking into account the relevance of questions. The analysis unit can also adjust the analysis schedule according to the relevance of questions. In this way, by adjusting the order of analysis based on the relevance of questions, highly relevant questions can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input question relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0079] The analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise during analysis. The analysis unit can adjust the use of technical terminology in the analysis according to the student's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terminology according to the student's level of expertise. Furthermore, if the student is a beginner, the analysis unit can avoid technical terminology when performing the analysis. Furthermore, if the student is an advanced learner, the analysis unit can use a lot of technical terminology when performing the analysis. In this way, by adjusting the use of technical terminology in the analysis according to the student's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the student's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0080] The generation unit can estimate the student's emotions and adjust the method for generating a quick test based on the estimated student's emotions. The generation unit can estimate the student's emotions and adjust the method for generating a quick test based on the estimated student's emotions. For example, if the student is relaxed, the generation unit can generate a quick test that proceeds at a leisurely pace. If the student is in a hurry, the generation unit can generate a quick test that can be completed in the shortest time. If the student is excited, the generation unit can generate a quick test with visually stimulating effects. This allows for adjusting the method for generating a quick test based on the student's emotions, thereby providing a more appropriate test. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input student facial expression data into the generation AI and cause the generation AI to estimate the student's emotions.

[0081] The generation unit can adjust the level of detail of the test based on the importance of the question when generating the test. The generation unit adjusts the level of detail of the test based on the importance of the question when generating the test. For example, the generation unit generates a detailed simple test for a question of high importance. The generation unit can also generate a concise simple test for a question of low importance. The generation unit can also adjust the depth of the simple test according to the importance of the question. In this way, by adjusting the level of detail of the test based on the importance of the question, it is possible to provide a detailed test for an important question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question importance data to the generation AI and cause the generation AI to adjust the level of detail of the test.

[0082] The generation unit can apply different test generation algorithms depending on the question category when generating a test. The generation unit applies different test generation algorithms depending on the question category when generating a test. For example, the generation unit applies a mathematical expression analysis algorithm to generate a simple test for a mathematics question. The generation unit can also apply a text analysis algorithm to generate a simple test for a literature question. The generation unit can also apply a data analysis algorithm to generate a simple test for a science question. In this way, by applying different test generation algorithms depending on the question category, more appropriate tests can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input question category data to the generation AI and cause the generation AI to apply a test generation algorithm.

[0083] The generation unit can improve the accuracy of a test by referring to a student's past test results when generating the test. The generation unit can improve the accuracy of a test by referring to a student's past test results when generating the test. For example, the generation unit improves the accuracy of a current simple test based on a student's past test results. The generation unit can also extract specific patterns from a student's past test results and reflect them in the simple test. The generation unit can also adjust the test generation algorithm by referring to a student's past test results. In this way, the accuracy of a current simple test can be improved by referring to a student's past test results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input a student's past test result data into the generation AI and cause the generation AI to improve the accuracy of the test.

[0084] The generation unit can estimate the student's emotions and adjust the length of the quick test based on the estimated student's emotions. The generation unit can estimate the student's emotions and adjust the length of the quick test based on the estimated student's emotions. For example, if the student is in a hurry, the generation unit can generate a short, concise quick test. If the student is relaxed, the generation unit can generate a longer quick test with detailed explanations. If the student is excited, the generation unit can generate a quick test with visually stimulating effects. This allows for adjusting the length of the quick test according to the student's emotions, thereby providing a more appropriate test. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input student facial expression data into the generation AI and cause the generation AI to estimate the student's emotions.

[0085] The generation unit can determine the priority of tests based on the time when questions are submitted when generating tests. The generation unit determines the priority of tests based on the time when questions are submitted when generating tests. For example, the generation unit determines the priority of quick tests based on the time of day when questions are submitted. The generation unit can also adjust the order of quick tests depending on the time when questions are submitted. The generation unit can also adjust the schedule of quick tests taking into account the time when questions are submitted. In this way, by determining the priority of tests based on the time when questions are submitted, it is possible to provide tests in an appropriate order. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question submission time data into a generation AI and have the generation AI determine the priority of tests.

[0086] The generation unit can adjust the order of tests based on the relevance of questions when generating tests. The generation unit adjusts the order of tests based on the relevance of questions when generating tests. For example, the generation unit determines the order of quick tests based on the relevance of questions. The generation unit can also adjust the priority of quick tests taking into account the relevance of questions. The generation unit can also adjust the schedule of quick tests according to the relevance of questions. In this way, by adjusting the order of tests based on the relevance of questions, highly relevant questions can be tested preferentially. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of tests.

[0087] The generation unit can adjust the use of technical terminology in the test according to the student's level of expertise when generating the test. The generation unit can adjust the use of technical terminology in the test according to the student's level of expertise when generating the test. For example, the generation unit adjusts the use of technical terminology according to the student's level of expertise. The generation unit can also generate a simple test that avoids technical terminology if the student is a beginner. The generation unit can also generate a simple test that uses a lot of technical terminology if the student is an advanced learner. In this way, by adjusting the use of technical terminology in the test according to the student's level of expertise, it is possible to provide a test that is easier to understand. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the student's level of expertise data into the generation AI and cause the generation AI to use technical terminology.

[0088] The providing unit can estimate the student's emotions and adjust the way the feedback is expressed based on the estimated student's emotions. The providing unit can estimate the student's emotions and adjust the way the feedback is expressed based on the estimated student's emotions. For example, if the student is nervous, the providing unit can provide simple, highly visible feedback. If the student is relaxed, the providing unit can provide detailed feedback. If the student is in a hurry, the providing unit can provide feedback that focuses on the main points. This allows for more appropriate feedback to be provided by adjusting the way the feedback is expressed based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the student's facial expression data into the generation AI and cause the generation AI to estimate the student's emotions.

[0089] The providing unit can adjust the level of detail of the feedback based on the importance of the question when providing feedback. The providing unit adjusts the level of detail of the feedback based on the importance of the question when providing feedback. For example, the providing unit provides detailed feedback for questions with high importance. The providing unit can also provide brief feedback for questions with low importance. The providing unit can also adjust the depth of the feedback according to the importance of the question. In this way, by adjusting the level of detail of the feedback based on the importance of the question, detailed feedback can be provided for important questions. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input question importance data to the generating AI and cause the generating AI to adjust the level of detail of the feedback.

[0090] The providing unit can apply different feedback algorithms depending on the category of the question when providing feedback. The providing unit can apply different feedback algorithms depending on the category of the question when providing feedback. For example, the providing unit can provide feedback by applying a mathematical expression analysis algorithm to a mathematics question. The providing unit can also provide feedback by applying a text analysis algorithm to a literature question. The providing unit can also provide feedback by applying a data analysis algorithm to a science question. In this way, by applying different feedback algorithms depending on the category of the question, more appropriate feedback can be provided. Some or all of the above-mentioned processing by the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input question category data to a generation AI and cause the generation AI to apply a feedback algorithm.

[0091] When providing feedback, the providing unit can improve the accuracy of the feedback by referring to the student's past feedback results. When providing feedback, the providing unit can improve the accuracy of the feedback by referring to the student's past feedback results. For example, the providing unit improves the accuracy of the current feedback based on the student's past feedback results. The providing unit can also extract specific patterns from the student's past feedback results and reflect them in the feedback. The providing unit can also adjust the feedback algorithm by referring to the student's past feedback results. In this way, the accuracy of the current feedback can be improved by referring to the student's past feedback results. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the student's past feedback result data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0092] The providing unit can estimate the student's emotions and adjust the length of the feedback based on the estimated student's emotions. The providing unit can estimate the student's emotions and adjust the length of the feedback based on the estimated student's emotions. For example, if the student is in a hurry, the providing unit can provide short, to-the-point feedback. If the student is relaxed, the providing unit can provide longer feedback with detailed explanations. If the student is excited, the providing unit can provide feedback with visually stimulating effects. This allows for adjusting the length of the feedback according to the student's emotions, thereby providing more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the student's facial expression data into the generation AI and cause the generation AI to estimate the student's emotions.

[0093] The providing unit, when providing feedback, can determine the priority of the feedback based on the time when the question was submitted. The providing unit, when providing feedback, determines the priority of the feedback based on the time when the question was submitted. For example, the providing unit determines the priority of the feedback based on the time period when the question was submitted. The providing unit can also adjust the order of the feedback depending on the time when the question was submitted. The providing unit can also adjust the feedback schedule taking into account the time when the question was submitted. In this way, by determining the priority of the feedback based on the time when the question was submitted, it is possible to provide feedback in an appropriate order. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit may input question submission time data to the generating AI and cause the generating AI to determine the priority of the feedback.

[0094] The providing unit can adjust the order of feedback based on the relevance of questions when providing feedback. The providing unit adjusts the order of feedback based on the relevance of questions when providing feedback. For example, the providing unit determines the order of feedback based on the relevance of questions. The providing unit can also adjust the priority of feedback taking into account the relevance of questions. The providing unit can also adjust the feedback schedule according to the relevance of questions. In this way, by adjusting the order of feedback based on the relevance of questions, highly relevant questions can be given priority as feedback. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input question relevance data to a generating AI and cause the generating AI to adjust the order of feedback.

[0095] When providing feedback, the providing unit can adjust the use of technical terminology in the feedback according to the student's level of expertise. When providing feedback, the providing unit adjusts the use of technical terminology in the feedback according to the student's level of expertise. For example, the providing unit adjusts the use of technical terminology according to the student's level of expertise. Furthermore, if the student is a beginner, the providing unit can provide feedback that avoids technical terminology. Furthermore, if the student is an advanced learner, the providing unit can provide feedback that uses a lot of technical terminology. In this way, by adjusting the use of technical terminology in the feedback according to the student's level of expertise, it is possible to provide feedback that is easier to understand. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the student's level of expertise data into a generation AI and cause the generation AI to use technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 is realized by the control unit 46A of the smart device 14 and receives questions submitted by students to the AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions received by the reception unit. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a simple test based on the analyzed results. The provision unit is realized by the control unit 46A of the smart device 14 and provides feedback based on the results of the generated simple test. Furthermore, the emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and adjusts the timing of receiving questions based on the student's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 is realized by the control unit 46A of the smart glasses 214 and receives questions submitted by students to the AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions received by the reception unit. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a simple test based on the analyzed results. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides feedback based on the results of the generated simple test. Furthermore, the emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and adjusts the timing of receiving questions based on the student's emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 is realized by the control unit 46A of the headset-type terminal 314 and receives questions submitted by students to the AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions received by the reception unit. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a simple test based on the analysis results. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides feedback based on the results of the generated simple test. Furthermore, the emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and adjusts the timing of receiving questions based on the student's emotions. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives questions submitted by students to the AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions received by the reception unit. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a simple test based on the analyzed results. The provision unit is realized by the control unit 46A of the robot 414 and provides feedback based on the results of the generated simple test. Furthermore, the emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and adjusts the timing of receiving questions based on the emotions of students.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can propose an individual study plan based on the student's study history. For example, the reception unit can suggest that the student prioritize studying topics that the student found difficult in the past. The reception unit can also suggest the next topic to study based on the student's study progress. Furthermore, the reception unit can customize a study plan to suit the student's learning style. This allows students to receive the study plan that is best suited to them and progress with their studies efficiently.

[0098] The analysis unit can estimate the student's emotions and adjust the way in which the analysis results are presented based on the estimated student's emotions. For example, if the student is feeling anxious, the analysis unit can provide a simple and easy-to-understand analysis result. If the student is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the student is excited, the analysis unit can also provide a visually appealing analysis result. In this way, by adjusting the way in which the analysis results are presented according to the student's emotions, more effective learning support can be provided.

[0099] The generation unit can set individual learning goals based on the student's learning history. For example, the generation unit can set the next goal to be achieved by the student based on goals that the student has achieved in the past. The generation unit can also adjust the difficulty of the goals according to the student's learning progress. Furthermore, the generation unit can set goals that match the student's learning style. This allows students to have learning goals that are optimal for them and allows them to continue learning while maintaining their motivation.

[0100] The providing unit can estimate the student's emotions and adjust the content of the feedback based on the estimated student's emotions. For example, if the student is feeling stressed, the providing unit can provide feedback including an encouraging message. If the student is feeling relaxed, the providing unit can also provide detailed feedback. Furthermore, if the student is excited, the providing unit can also provide visually appealing feedback. In this way, by adjusting the content of the feedback according to the student's emotions, more effective learning support can be provided.

[0101] The reception unit can suggest the optimal question format based on the student's learning style. For example, if the student is a visual learner, the reception unit can suggest a question format using images or diagrams. If the student is an auditory learner, the reception unit can also suggest a question format using audio. Furthermore, if the student is a tactile learner, the reception unit can also suggest a question format that involves practical skills. In this way, by suggesting a question format that suits the student's learning style, more effective learning support can be provided.

[0102] The analysis unit can estimate the student's emotions and adjust the timing of analysis based on the estimated student's emotions. For example, the analysis unit can provide analysis results immediately if the student is concentrating. The analysis unit can also temporarily delay providing analysis results if the student is tired. Furthermore, the analysis unit can provide detailed analysis results if the student is relaxed. This makes it possible to provide more effective learning support by adjusting the timing of analysis according to the student's emotions.

[0103] The generation unit can provide individual learning resources based on the student's learning history. For example, the generation unit can suggest the next resource that the student should use based on resources that the student has used in the past. The generation unit can also adjust the difficulty level of the resources according to the student's learning progress. Furthermore, the generation unit can provide resources that match the student's learning style. This allows students to use learning resources that are best suited to them and progress with their studies efficiently.

[0104] The providing unit can estimate the student's emotions and adjust the timing of the feedback based on the estimated student's emotions. For example, the providing unit can provide immediate feedback when the student is concentrating. The providing unit can also temporarily delay providing feedback when the student is tired. Furthermore, the providing unit can provide detailed feedback when the student is relaxed. In this way, more effective learning support can be provided by adjusting the timing of feedback according to the student's emotions.

[0105] The reception unit can suggest individual learning activities based on the student's learning history. For example, the reception unit can suggest activities related to topics that the student struggled with in the past. The reception unit can also suggest the next activity the student should do based on the student's learning progress. Furthermore, the reception unit can suggest activities that match the student's learning style. This allows students to do learning activities that are best suited to them and progress with their studies efficiently.

[0106] The analysis unit can estimate the student's emotions and adjust the depth of the analysis based on the estimated student's emotions. For example, if the student is feeling anxious, the analysis unit can provide a simple and to-the-point analysis. If the student is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the student is excited, the analysis unit can also provide a visually appealing analysis. This makes it possible to provide more effective learning support by adjusting the depth of the analysis according to the student's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit accepts questions submitted by students to the AI. For example, the reception unit can accept questions in text format, audio format, image format, etc. The reception unit can also accept multiple questions submitted by students. Step 2: The analysis unit analyzes the question received by the reception unit. For example, the analysis unit analyzes the content of the question using natural language processing technology. The analysis unit can also analyze the question using statistical analysis or machine learning algorithms. Furthermore, the analysis unit identifies where the student is struggling based on the results of the question analysis. For example, if the student does not understand part of the solution to a problem, the analysis unit identifies that part. Step 3: The generation unit generates a simple test based on the results analyzed by the analysis unit. For example, the generation unit generates a simple test in the form of multiple choice questions, essay questions, practical tests, etc. The generation unit can also automatically generate test questions using AI. Furthermore, the generation unit provides feedback based on the results of the generated simple test. Step 4: The provider provides feedback based on the results of the simple test generated by the generator. For example, the provider can clarify where the student is struggling and provide advice to focus on that area. The provider can also provide additional learning materials for a specific solution if the student does not understand it.

[0109] 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.

[0110] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 section for accepting questions; an analysis unit that analyzes the question received by the reception unit; a generator that generates a test based on the results of the analysis by the analyzer; a providing unit that provides feedback based on the results of the test generated by the generating unit; Equipped with A system characterized by:

2. The reception unit Estimate the student's emotions and decide when to accept questions based on the estimated student emotions 2. The system of claim 1.

3. The reception unit Analyze students' past question history and select the appropriate reception method 2. The system of claim 1.

4. The reception unit Filter questions based on students' current learning status and interests 2. The system of claim 1.

5. The reception unit When accepting questions, select the appropriate method depending on the student's input method.

2. The system of claim 1.

6. The reception unit Estimate the student's emotions and prioritize the questions to be accepted based on the estimated student emotions.

2. The system of claim 1.

7. The reception unit When accepting questions, the system takes into account the student's geographic location information to prioritize relevant questions.

2. The system of claim 1.

8. The reception unit When accepting questions, analyze students' social media activity and accept relevant questions.

2. The system of claim 1.

Images