system

Abstract

The system according to this embodiment aims to reduce the burden on teachers and to efficiently manage and analyze students' understanding and grades. [Solution] The system according to this embodiment comprises a distribution unit, a teaching unit, and a platform unit. The distribution unit distributes tablet devices. The teaching unit conducts lessons using AI. The platform unit manages and analyzes comprehension levels and performance.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the burden on teachers is large, and the management and analysis of understanding and grades are not efficiently carried out.

[0005] The system according to the embodiment aims to reduce the burden on teachers and efficiently manage and analyze understanding and grades.

Means for Solving the Problems

[0006] The system according to the embodiment includes a distribution unit, a class unit, and a platform unit. The distribution unit distributes tablet terminals. The class unit conducts classes by AI. The platform unit manages and analyzes understanding and grades.

Effects of the Invention

[0007] The system according to this embodiment reduces the burden on teachers and enables efficient management and analysis of comprehension levels and grades. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An embodiment of the present invention provides an educational support system that reduces the burden on teachers by distributing tablet devices and conducting lessons using AI. The educational support system reduces the burden on teachers by distributing tablet devices and conducting lessons using AI. For example, the educational support system distributes tablet devices to each school. Next, the educational support system conducts lessons using AI. AI is used in parts of each subject, allowing teachers to perform other tasks during lessons. For example, teachers can answer questions or monitor the progress of the lesson. Furthermore, the educational support system builds a platform to manage and analyze students' understanding and performance. This platform allows for understanding individual students' levels of comprehension and providing appropriate guidance. For example, the AI ​​can analyze students' performance data, identify areas of low comprehension, and suggest supplementary lessons for those areas. This system reduces the burden on teachers and improves the quality of education. For example, teachers can reduce the time spent on lesson preparation and dedicate that time to individual instruction. Also, having AI conduct lessons allows teachers to maintain mental and physical well-being. Prior explanation and understanding from parents and students are crucial before introducing this system. Furthermore, the costs associated with developing tablet devices and platforms, as well as ensuring security, are also challenges. By resolving these issues, the system's effectiveness can be maximized. This will allow the educational support system to reduce the burden on teachers and improve the quality of education.

[0029] The educational support system according to this embodiment comprises a distribution unit, a lesson unit, and a platform unit. The distribution unit distributes tablet devices. For example, the distribution unit distributes tablet devices to each school. When distributing tablet devices, the distribution unit can consider the timing of distribution and the selection criteria for recipients. The lesson unit conducts lessons using AI. For example, the lesson unit has the AI ​​ask questions to students, and students answer using tablet devices. For example, the lesson unit has the AI ​​conduct the lesson, allowing teachers to perform other tasks during the lesson. When the AI ​​conducts the lesson, the lesson unit can consider the AI ​​technology to be used and the method of conducting the lesson. The platform unit manages and analyzes comprehension and grades. For example, the platform unit collects student grade data, and the AI ​​analyzes that data to identify areas of low comprehension. For example, the platform unit suggests supplementary lessons for the areas of low comprehension identified by the AI. When managing and analyzing comprehension and grades, the platform unit can consider the data collection method and analysis algorithm. As a result, the educational support system according to this embodiment will be able to distribute tablet devices, conduct AI-driven lessons, and manage and analyze students' comprehension levels and grades.

[0030] The distribution department will distribute tablet devices. Specifically, when distributing tablet devices to each school, the distribution department can consider the timing of distribution and the selection criteria for recipients. For example, tablet devices could be distributed at the start of the new school year or when students advance to a specific grade level. The selection criteria for recipients may include grade level, academic performance, and progress in specific subjects. Furthermore, the distribution department will perform the initial setup of the tablet devices and install necessary applications so that each student can use them immediately. This includes pre-installing educational materials and learning apps tailored to each school's curriculum. Even after the distribution of the tablet devices, the distribution department is required to perform regular maintenance and software updates to keep them up-to-date. In this way, the distribution department can distribute tablet devices efficiently and effectively, creating an environment where students can smoothly begin learning.

[0031] The teaching department uses AI to conduct lessons. Specifically, the AI ​​asks students questions, and students answer using tablet devices. The AI ​​analyzes students' answers in real time and provides immediate feedback. For example, if the answer is correct, it moves on to the next question; if incorrect, it displays an explanation. The AI ​​also adjusts the difficulty of the questions according to the students' level of understanding, providing a individually optimized learning experience. Furthermore, the AI ​​is responsible for managing the lesson, allowing teachers to perform other tasks during the lesson. This allows teachers to focus on individual instruction and student support. The teaching department can consider the AI ​​technology to use and the method of conducting the lesson. For example, it can use natural language processing technology to generate answers to students' questions, or use machine learning algorithms to analyze students' learning patterns and propose the optimal learning plan. In this way, the teaching department can leverage AI to provide efficient and effective lessons and maximize student learning effectiveness.

[0032] The platform unit manages and analyzes students' comprehension and academic performance. Specifically, it collects student performance data and uses AI to analyze that data and identify areas of low comprehension. For example, it stores each student's test results and assignment submission status in a database, and the AI ​​analyzes this data to detect low comprehension in specific subjects or units. The AI ​​suggests supplementary lessons for areas of weakness and supports students in learning efficiently. The platform unit can consider data collection methods and analysis algorithms. For example, it can collect data through regular tests and quizzes and analyze students' learning patterns using machine learning algorithms. The platform unit can also provide teachers and parents with the results of comprehension and academic performance management and analysis, sharing students' learning progress. This allows teachers and parents to understand students' learning progress and provide appropriate support. Furthermore, the platform unit can accumulate long-term learning data and use it to improve future learning plans and revise educational policies. In this way, the platform unit can effectively manage and analyze students' comprehension and academic performance and provide individually optimized learning support.

[0033] The teaching department can use AI to ask students questions, and students can answer them using tablet devices. For example, the teaching department can consider the format, timing, and content of questions when the AI ​​asks students questions. For example, the teaching department can ask students multiple-choice questions, and students can select an answer using their tablet device. For example, the teaching department can ask students open-ended questions, and students can answer them freely using their tablet device. For example, the teaching department can have the AI ​​provide students with real-time feedback and grasp their level of understanding in real time. This means that by using AI, students' level of understanding can be grasped in real time.

[0034] The platform unit can collect student performance data, and AI can analyze that data to identify areas of low understanding. For example, when collecting student performance data, the platform unit can consider the type of data to collect and the timing of collection. For example, when the AI ​​analyzes performance data, the platform unit can use specific algorithms and evaluation criteria. For example, when the AI ​​analyzes performance data to identify areas of low understanding, the platform unit can perform data preprocessing and feature extraction. For example, when the AI ​​analyzes performance data to identify areas of low understanding, the platform unit can refer to teacher feedback. This allows for a detailed understanding of students' comprehension levels and enables appropriate instruction.

[0035] The platform unit can propose supplementary lessons for areas identified by the AI ​​as having low levels of understanding. For example, when proposing supplementary lessons for areas identified by the AI ​​as having low levels of understanding, the platform unit can consider the timing, content, and format of the proposal. For example, the platform unit can propose individualized instruction for areas identified by the AI ​​as having low levels of understanding. For example, the platform unit can propose supplementary lessons using online learning materials for areas identified by the AI ​​as having low levels of understanding. For example, the platform unit can propose group learning supplementary lessons for areas identified by the AI ​​as having low levels of understanding. This improves student learning effectiveness by proposing supplementary lessons for areas of low understanding.

[0036] The distribution department can distribute tablet devices to each school. For example, when distributing tablet devices to each school, the distribution department can consider the timing of distribution and the selection criteria for recipients. For example, when distributing tablet devices, the distribution department can consider the facilities and student population of each school. For example, when distributing tablet devices, the distribution department can consider the grade level and class composition of the students to be distributed. For example, when distributing tablet devices, the distribution department can consider the special needs of the students to be distributed. This makes AI-driven lessons possible by distributing tablet devices to each school.

[0037] The teaching department allows AI to conduct lessons, enabling teachers to perform other tasks during class. For example, the teaching department can consider the teaching methods and AI technologies used when the AI ​​is conducting the lesson. For example, the teaching department can adjust the pace and content of the lesson when the AI ​​is conducting it. For example, the teaching department can monitor students' understanding in real time and adjust the lesson accordingly when the AI ​​is conducting it. For example, the teaching department can provide support to teachers so they can perform other tasks during the lesson when the AI ​​is conducting it. This reduces the burden on teachers by allowing AI to conduct lessons.

[0038] The distribution department can analyze the equipment status of each school and select the optimal distribution method. For example, when analyzing the equipment status of each school, the distribution department can consider the criteria for analysis and the data to be used. For example, the distribution department can analyze the internet connectivity status of each school and select tablet devices suitable for online classes. For example, the distribution department can consider the size of each school's classrooms and distribute an appropriate number of tablet devices. For example, the distribution department can check the power supply equipment of each school and determine the location of charging stations. For example, when selecting the optimal distribution method, the distribution department can consider the criteria and methods for selection. This enables optimal distribution according to the equipment status of each school. Some or all of the above processes in the distribution department may be performed using AI, for example, or not using AI. For example, the distribution department can input equipment status data for each school into a generating AI and have the generating AI select the optimal distribution method.

[0039] The distribution unit can adjust the number of tablet devices distributed at each school based on the number of students and class composition. For example, the distribution unit can calculate the number of tablet devices needed based on the number of students in each class. For example, the distribution unit can distribute additional tablet devices considering the number of students in special education classes. For example, the distribution unit can create a distribution plan for tablet devices based on the class composition of each grade level. For example, the distribution unit can consider the criteria and methods for adjustment when adjusting the quantity. This allows for the distribution of an appropriate number of tablet devices according to the number of students and class composition. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the number of students and class composition data for each school into a generating AI and have the generating AI perform the adjustment of the number of tablet devices.

[0040] The distribution department can optimize distribution routes by considering the geographical location information of each school during distribution. For example, the distribution department can calculate the shortest route based on the location of each school and create a distribution plan. For example, the distribution department can select a route that avoids congestion by considering traffic conditions. For example, the distribution department can create an efficient distribution schedule by considering the opening hours of the schools to be distributed to. For example, the distribution department can consider optimization criteria and methods when optimizing distribution routes. This enables efficient distribution by selecting the optimal distribution route based on geographical location information. Some or all of the above processes in the distribution department may be performed using AI, for example, or without AI. For example, the distribution department can input geographical location data of each school into a generating AI and have the generating AI perform the optimization of the distribution route.

[0041] The distribution department can analyze each school's past distribution history and create an optimal distribution schedule. For example, the distribution department can determine the optimal distribution time based on past distribution history. For example, the distribution department can predict the time required for distribution based on past distribution history and adjust the schedule. For example, the distribution department can analyze past distribution history and select an efficient distribution route. For example, the distribution department can consider the criteria and methods for creating the distribution schedule. This enables efficient distribution by creating an optimal distribution schedule based on past distribution history. Some or all of the above processes in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input past distribution history data for each school into a generating AI and have the generating AI create the distribution schedule.

[0042] The teaching department can adjust the pace of the lesson based on the students' level of understanding. For example, if the students' level of understanding is high, the teaching department will speed up the lesson. For example, if the students' level of understanding is low, the teaching department will slow down the lesson. For example, the teaching department will add supplementary explanations according to the students' level of understanding. For example, when adjusting the pace of the lesson, the teaching department can consider the criteria and methods for adjustment. This makes it possible to conduct effective lessons by adjusting the pace of the lesson according to the students' level of understanding. Some or all of the above processes in the teaching department may be performed using AI, for example, or without AI. For example, the teaching department can input student understanding data into a generating AI and have the generating AI adjust the pace of the lesson.

[0043] The teaching department can apply different teaching algorithms to each subject during the course of a lesson, depending on its characteristics. For example, the teaching department might apply a problem-solving algorithm to a mathematics lesson, a storytelling algorithm to a history lesson, or an experiment / observation algorithm to a science lesson. The teaching department can also consider criteria and methods for applying teaching algorithms. This allows for effective lessons by applying teaching algorithms tailored to the characteristics of each subject. Some or all of the above-described processes in the teaching department may be performed using AI, or not. For example, the teaching department can input characteristic data for each subject into a generating AI and have the generating AI execute the application of teaching algorithms.

[0044] The teaching department can determine lesson priorities based on students' past performance data during lesson progression. For example, the teaching department can prioritize lessons in subjects where students have a low level of understanding, based on their past performance data. For example, the teaching department can analyze students' past performance data and focus on specific units in lessons. For example, the teaching department can allocate time for individual tutoring based on students' past performance data. For example, the teaching department can consider criteria and methods for determining priorities. This enables effective lessons by determining lesson priorities based on students' past performance data. Some or all of the above processes in the teaching department may be performed using AI, for example, or not. For example, the teaching department can input students' past performance data into a generating AI and have the generating AI determine lesson priorities.

[0045] The teaching department can customize lesson content based on students' interests and concerns during the lesson. For example, the teaching department can create lesson content that incorporates topics that will interest students. For example, the teaching department can introduce relevant examples and case studies based on students' interests. For example, the teaching department can adjust the way the lesson is conducted according to students' interests. For example, the teaching department can consider criteria and methods for customization when customizing lesson content. This makes it possible to conduct effective lessons by customizing lesson content based on students' interests and concerns. Some or all of the above processes in the teaching department may be performed using AI, for example, or not using AI. For example, the teaching department can input student interest data into a generating AI and have the generating AI perform the customization of lesson content.

[0046] The platform unit can predict current grades by referring to past data when analyzing grade data. For example, the platform unit predicts current grades based on a student's past grade data. For example, the platform unit analyzes a student's past grade data to understand grade trends. For example, the platform unit predicts future grades by referring to a student's past grade data. For example, the platform unit can consider prediction criteria and methods when predicting grades. This makes it possible to predict current grades based on past data. Some or all of the above processes in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input a student's past grade data into a generating AI and have the generating AI perform a current grade prediction.

[0047] The platform unit can apply different analysis methods to each subject when analyzing performance data. For example, the platform unit can apply statistical analysis methods to mathematics performance data. For example, the platform unit can apply linguistic analysis methods to English performance data. For example, the platform unit can apply experimental results analysis methods to science performance data. For example, the platform unit can consider criteria and methods for application when applying analysis methods. This improves the accuracy of performance data analysis by applying the most appropriate analysis method for each subject. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input performance data for each subject into a generating AI and have the generating AI perform the application of analysis methods.

[0048] The platform unit can weight the performance data based on the student's submission timing when analyzing performance data. For example, the platform unit can assign higher weight to performance data for assignments submitted early. For example, the platform unit can assign lower weight to performance data for assignments submitted late. For example, the platform unit can adjust the weighting of the performance data based on the student's submission timing. For example, the platform unit can consider the criteria and methods for weighting the data when performing the weighting. This improves the accuracy of the performance data analysis by weighting the data based on the submission timing. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input student submission timing data into a generating AI and have the generating AI perform the data weighting.

[0049] The platform unit can improve the accuracy of its analysis of academic performance data by referencing relevant student activity data. For example, the platform unit can reference student club activity data and reflect it in the analysis of academic performance data. For example, the platform unit can reference student extracurricular activity data and reflect it in the analysis of academic performance data. For example, the platform unit can reference student volunteer activity data and reflect it in the analysis of academic performance data. For example, when improving the accuracy of the analysis, the platform unit can consider the type of data to reference and the analysis method. As a result, referencing relevant activity data improves the accuracy of the analysis of academic performance data. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input student relevant activity data into a generating AI and have the generating AI perform the analysis of academic performance data.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The educational support system can further analyze students' learning styles and provide individually optimized learning plans. For example, students who prefer visual learning can be provided with materials that heavily utilize videos and diagrams. Students who prefer auditory learning can be provided with audio explanations and podcast-style materials. Students who prefer tactile learning can be provided with materials that include interactive simulations and experiments. This maximizes learning effectiveness by providing the optimal learning plan tailored to each student's learning style.

[0052] The educational support system can further monitor students' learning progress in real time and automatically adjust learning plans as needed. For example, if a student struggles with a particular unit, it can immediately provide supplementary materials related to that unit. Once a student achieves their goals, it presents new challenges to help them move on to the next step. It also adjusts the pace of the learning plan to match the student's learning pace. This allows for improved learning efficiency by providing flexible learning plans tailored to students' progress.

[0053] The educational support system can further predict future learning content based on students' learning history and prepare appropriate materials in advance. For example, when students review a unit they previously struggled with, it provides more detailed explanations and additional practice problems. For areas where students excel, it provides more advanced content and application problems. By analyzing students' learning history and predicting what they should learn next, the system can prepare materials accordingly. This predictive learning support based on students' learning history can enhance the effectiveness of their learning.

[0054] The educational support system can further optimize the student learning environment by adjusting ambient sounds and lighting during study. For example, it can play nature sounds or classical music to provide ambient sounds that help students concentrate. It can also adjust the color temperature and brightness of the lighting to help students relax while studying. By monitoring the student's learning environment and making adjustments to provide the optimal environment, the system can improve learning efficiency by providing an environment in which students can concentrate on their studies.

[0055] Educational support systems can incorporate gamification elements to further enhance students' learning motivation. For example, a system could be implemented where students earn points or badges based on their learning progress. Virtual rewards or titles could be offered when students achieve their goals. Learning content could be presented in a game format, making learning enjoyable. This can increase students' motivation, thereby improving the continuity and effectiveness of their learning.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The distribution department distributes the tablet devices. For example, when distributing tablet devices to each school, the timing of distribution and the selection criteria for recipients can be considered. Step 2: The teaching department uses AI to conduct lessons. For example, the AI ​​asks students questions, and students answer using tablet devices. The AI ​​manages the lesson, allowing teachers to perform other tasks during the lesson. When conducting lessons, the AI ​​technology to be used and the method of conducting the lesson can be considered. Step 3: The platform unit manages and analyzes comprehension and performance. For example, it collects student performance data, and the AI ​​analyzes that data to identify areas of low comprehension. The AI ​​then suggests supplementary lessons for the areas of low comprehension identified. When managing and analyzing comprehension and performance, the data collection method and analysis algorithm can be considered.

[0058] (Example of form 2) An embodiment of the present invention provides an educational support system that reduces the burden on teachers by distributing tablet devices and conducting lessons using AI. The educational support system reduces the burden on teachers by distributing tablet devices and conducting lessons using AI. For example, the educational support system distributes tablet devices to each school. Next, the educational support system conducts lessons using AI. AI is used in parts of each subject, allowing teachers to perform other tasks during lessons. For example, teachers can answer questions or monitor the progress of the lesson. Furthermore, the educational support system builds a platform to manage and analyze students' understanding and performance. This platform allows for understanding individual students' levels of comprehension and providing appropriate guidance. For example, the AI ​​can analyze students' performance data, identify areas of low comprehension, and suggest supplementary lessons for those areas. This system reduces the burden on teachers and improves the quality of education. For example, teachers can reduce the time spent on lesson preparation and dedicate that time to individual instruction. Also, having AI conduct lessons allows teachers to maintain mental and physical well-being. Prior explanation and understanding from parents and students are crucial before introducing this system. Furthermore, the costs associated with developing tablet devices and platforms, as well as ensuring security, are also challenges. By resolving these issues, the system's effectiveness can be maximized. This will allow the educational support system to reduce the burden on teachers and improve the quality of education.

[0059] The educational support system according to this embodiment comprises a distribution unit, a lesson unit, and a platform unit. The distribution unit distributes tablet devices. For example, the distribution unit distributes tablet devices to each school. When distributing tablet devices, the distribution unit can consider the timing of distribution and the selection criteria for recipients. The lesson unit conducts lessons using AI. For example, the lesson unit has the AI ​​ask questions to students, and students answer using tablet devices. For example, the lesson unit has the AI ​​conduct the lesson, allowing teachers to perform other tasks during the lesson. When the AI ​​conducts the lesson, the lesson unit can consider the AI ​​technology to be used and the method of conducting the lesson. The platform unit manages and analyzes comprehension and grades. For example, the platform unit collects student grade data, and the AI ​​analyzes that data to identify areas of low comprehension. For example, the platform unit suggests supplementary lessons for the areas of low comprehension identified by the AI. When managing and analyzing comprehension and grades, the platform unit can consider the data collection method and analysis algorithm. As a result, the educational support system according to this embodiment will be able to distribute tablet devices, conduct AI-driven lessons, and manage and analyze students' comprehension levels and grades.

[0060] The distribution department will distribute tablet devices. Specifically, when distributing tablet devices to each school, the distribution department can consider the timing of distribution and the selection criteria for recipients. For example, tablet devices could be distributed at the start of the new school year or when students advance to a specific grade level. The selection criteria for recipients may include grade level, academic performance, and progress in specific subjects. Furthermore, the distribution department will perform the initial setup of the tablet devices and install necessary applications so that each student can use them immediately. This includes pre-installing educational materials and learning apps tailored to each school's curriculum. Even after the distribution of the tablet devices, the distribution department is required to perform regular maintenance and software updates to keep them up-to-date. In this way, the distribution department can distribute tablet devices efficiently and effectively, creating an environment where students can smoothly begin learning.

[0061] The teaching department uses AI to conduct lessons. Specifically, the AI ​​asks students questions, and students answer using tablet devices. The AI ​​analyzes students' answers in real time and provides immediate feedback. For example, if the answer is correct, it moves on to the next question; if incorrect, it displays an explanation. The AI ​​also adjusts the difficulty of the questions according to the students' level of understanding, providing a individually optimized learning experience. Furthermore, the AI ​​is responsible for managing the lesson, allowing teachers to perform other tasks during the lesson. This allows teachers to focus on individual instruction and student support. The teaching department can consider the AI ​​technology to use and the method of conducting the lesson. For example, it can use natural language processing technology to generate answers to students' questions, or use machine learning algorithms to analyze students' learning patterns and propose the optimal learning plan. In this way, the teaching department can leverage AI to provide efficient and effective lessons and maximize student learning effectiveness.

[0062] The platform unit manages and analyzes students' comprehension and academic performance. Specifically, it collects student performance data and uses AI to analyze that data and identify areas of low comprehension. For example, it stores each student's test results and assignment submission status in a database, and the AI ​​analyzes this data to detect low comprehension in specific subjects or units. The AI ​​suggests supplementary lessons for areas of weakness and supports students in learning efficiently. The platform unit can consider data collection methods and analysis algorithms. For example, it can collect data through regular tests and quizzes and analyze students' learning patterns using machine learning algorithms. The platform unit can also provide teachers and parents with the results of comprehension and academic performance management and analysis, sharing students' learning progress. This allows teachers and parents to understand students' learning progress and provide appropriate support. Furthermore, the platform unit can accumulate long-term learning data and use it to improve future learning plans and revise educational policies. In this way, the platform unit can effectively manage and analyze students' comprehension and academic performance and provide individually optimized learning support.

[0063] The teaching department can use AI to ask students questions, and students can answer them using tablet devices. For example, the teaching department can consider the format, timing, and content of questions when the AI ​​asks students questions. For example, the teaching department can ask students multiple-choice questions, and students can select an answer using their tablet device. For example, the teaching department can ask students open-ended questions, and students can answer them freely using their tablet device. For example, the teaching department can have the AI ​​provide students with real-time feedback and grasp their level of understanding in real time. This means that by using AI, students' level of understanding can be grasped in real time.

[0064] The platform unit can collect student performance data, and AI can analyze that data to identify areas of low understanding. For example, when collecting student performance data, the platform unit can consider the type of data to collect and the timing of collection. For example, when the AI ​​analyzes performance data, the platform unit can use specific algorithms and evaluation criteria. For example, when the AI ​​analyzes performance data to identify areas of low understanding, the platform unit can perform data preprocessing and feature extraction. For example, when the AI ​​analyzes performance data to identify areas of low understanding, the platform unit can refer to teacher feedback. This allows for a detailed understanding of students' comprehension levels and enables appropriate instruction.

[0065] The platform unit can propose supplementary lessons for areas identified by the AI ​​as having low levels of understanding. For example, when proposing supplementary lessons for areas identified by the AI ​​as having low levels of understanding, the platform unit can consider the timing, content, and format of the proposal. For example, the platform unit can propose individualized instruction for areas identified by the AI ​​as having low levels of understanding. For example, the platform unit can propose supplementary lessons using online learning materials for areas identified by the AI ​​as having low levels of understanding. For example, the platform unit can propose group learning supplementary lessons for areas identified by the AI ​​as having low levels of understanding. This improves student learning effectiveness by proposing supplementary lessons for areas of low understanding.

[0066] The distribution department can distribute tablet devices to each school. For example, when distributing tablet devices to each school, the distribution department can consider the timing of distribution and the selection criteria for recipients. For example, when distributing tablet devices, the distribution department can consider the facilities and student population of each school. For example, when distributing tablet devices, the distribution department can consider the grade level and class composition of the students to be distributed. For example, when distributing tablet devices, the distribution department can consider the special needs of the students to be distributed. This makes AI-driven lessons possible by distributing tablet devices to each school.

[0067] The teaching department allows AI to conduct lessons, enabling teachers to perform other tasks during class. For example, the teaching department can consider the teaching methods and AI technologies used when the AI ​​is conducting the lesson. For example, the teaching department can adjust the pace and content of the lesson when the AI ​​is conducting it. For example, the teaching department can monitor students' understanding in real time and adjust the lesson accordingly when the AI ​​is conducting it. For example, the teaching department can provide support to teachers so they can perform other tasks during the lesson when the AI ​​is conducting it. This reduces the burden on teachers by allowing AI to conduct lessons.

[0068] The distribution unit can estimate the user's emotions and adjust the timing of tablet distribution based on the estimated emotions. For example, when estimating user emotions, the distribution unit can consider emotion estimation algorithms and the data used. For example, the distribution unit can use facial recognition technology or voice analysis technology to estimate user emotions. For example, the distribution unit can collect biometric data such as heart rate and skin electrical activity to estimate user emotions. For example, the distribution unit can adjust the timing of tablet distribution based on the estimated emotions. For example, if a student is excited, the distribution unit will wait until they calm down before distributing the tablet. For example, if a student is tired, the distribution unit will distribute the tablet during a break. For example, if a student is focused, the distribution unit will distribute the tablet before the start of class. This allows for effective distribution by adjusting the distribution timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0069] The distribution department can analyze the equipment status of each school and select the optimal distribution method. For example, when analyzing the equipment status of each school, the distribution department can consider the criteria for analysis and the data to be used. For example, the distribution department can analyze the internet connectivity status of each school and select tablet devices suitable for online classes. For example, the distribution department can consider the size of each school's classrooms and distribute an appropriate number of tablet devices. For example, the distribution department can check the power supply equipment of each school and determine the location of charging stations. For example, when selecting the optimal distribution method, the distribution department can consider the criteria and methods for selection. This enables optimal distribution according to the equipment status of each school. Some or all of the above processes in the distribution department may be performed using AI, for example, or not using AI. For example, the distribution department can input equipment status data for each school into a generating AI and have the generating AI select the optimal distribution method.

[0070] The distribution unit can adjust the number of tablet devices distributed at each school based on the number of students and class composition. For example, the distribution unit can calculate the number of tablet devices needed based on the number of students in each class. For example, the distribution unit can distribute additional tablet devices considering the number of students in special education classes. For example, the distribution unit can create a distribution plan for tablet devices based on the class composition of each grade level. For example, the distribution unit can consider the criteria and methods for adjustment when adjusting the quantity. This allows for the distribution of an appropriate number of tablet devices according to the number of students and class composition. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the number of students and class composition data for each school into a generating AI and have the generating AI perform the adjustment of the number of tablet devices.

[0071] The distribution unit can estimate the user's emotions and determine the priority of the tablet devices to distribute based on the estimated emotions. For example, the distribution unit can consider emotion estimation algorithms and the data used when estimating user emotions. For example, the distribution unit can use facial recognition technology or voice analysis technology to estimate user emotions. For example, the distribution unit can collect biometric data such as heart rate and skin electrical activity to estimate user emotions. For example, the distribution unit can determine the priority of the tablet devices to distribute based on the estimated emotions. For example, if a student is feeling anxious, the distribution unit can distribute the tablet device early to reassure them. For example, if a student is excited, the distribution unit can wait until they calm down before distributing the tablet device. For example, if a student is focused, the distribution unit can distribute the tablet device before the start of class. This enables effective distribution by determining the priority of the tablet devices to distribute according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0072] The distribution department can optimize distribution routes by considering the geographical location information of each school during distribution. For example, the distribution department can calculate the shortest route based on the location of each school and create a distribution plan. For example, the distribution department can select a route that avoids congestion by considering traffic conditions. For example, the distribution department can create an efficient distribution schedule by considering the opening hours of the schools to be distributed to. For example, the distribution department can consider optimization criteria and methods when optimizing distribution routes. This enables efficient distribution by selecting the optimal distribution route based on geographical location information. Some or all of the above processes in the distribution department may be performed using AI, for example, or without AI. For example, the distribution department can input geographical location data of each school into a generating AI and have the generating AI perform the optimization of the distribution route.

[0073] The distribution department can analyze each school's past distribution history and create an optimal distribution schedule. For example, the distribution department can determine the optimal distribution time based on past distribution history. For example, the distribution department can predict the time required for distribution based on past distribution history and adjust the schedule. For example, the distribution department can analyze past distribution history and select an efficient distribution route. For example, the distribution department can consider the criteria and methods for creating the distribution schedule. This enables efficient distribution by creating an optimal distribution schedule based on past distribution history. Some or all of the above processes in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input past distribution history data for each school into a generating AI and have the generating AI create the distribution schedule.

[0074] The teaching system can estimate the user's emotions and adjust the presentation of the lesson content based on those emotions. For example, when estimating user emotions, the system can consider emotion estimation algorithms and the data used. For example, the system can use facial recognition technology or voice analysis technology to estimate user emotions. For example, the system can collect biometric data such as heart rate and skin electrical activity to estimate user emotions. For example, the system can adjust the presentation of the lesson content based on the estimated user emotions. For example, if a student is excited, the system will conduct the lesson in a calm tone. For example, if a student is tired, the system will use visually stimulating materials. For example, if a student is focused, the system will add detailed explanations. This allows for more effective lessons by adjusting the presentation of the lesson content according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0075] The teaching department can adjust the pace of the lesson based on the students' level of understanding. For example, if the students' level of understanding is high, the teaching department will speed up the lesson. For example, if the students' level of understanding is low, the teaching department will slow down the lesson. For example, the teaching department will add supplementary explanations according to the students' level of understanding. For example, when adjusting the pace of the lesson, the teaching department can consider the criteria and methods for adjustment. This makes it possible to conduct effective lessons by adjusting the pace of the lesson according to the students' level of understanding. Some or all of the above processes in the teaching department may be performed using AI, for example, or without AI. For example, the teaching department can input student understanding data into a generating AI and have the generating AI adjust the pace of the lesson.

[0076] The teaching department can apply different teaching algorithms to each subject during the course of a lesson, depending on its characteristics. For example, the teaching department might apply a problem-solving algorithm to a mathematics lesson, a storytelling algorithm to a history lesson, or an experiment / observation algorithm to a science lesson. The teaching department can also consider criteria and methods for applying teaching algorithms. This allows for effective lessons by applying teaching algorithms tailored to the characteristics of each subject. Some or all of the above-described processes in the teaching department may be performed using AI, or not. For example, the teaching department can input characteristic data for each subject into a generating AI and have the generating AI execute the application of teaching algorithms.

[0077] The teaching system can estimate the user's emotions and adjust the length of the lesson based on those emotions. For example, when estimating the user's emotions, the system can consider emotion estimation algorithms and the data used. For example, the system can use facial recognition technology or speech analysis technology to estimate the user's emotions. For example, the system can collect biometric data such as heart rate and skin electrical activity to estimate the user's emotions. The system can adjust the length of the lesson based on the estimated user emotions. For example, if students are tired, the system will shorten the lesson. For example, if students are focused, the system will extend the lesson. For example, if students are excited, the system will adjust the lesson length to include a break. This allows for more effective lessons by adjusting the lesson length according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The teaching department can determine lesson priorities based on students' past performance data during lesson progression. For example, the teaching department can prioritize lessons in subjects where students have a low level of understanding, based on their past performance data. For example, the teaching department can analyze students' past performance data and focus on specific units in lessons. For example, the teaching department can allocate time for individual tutoring based on students' past performance data. For example, the teaching department can consider criteria and methods for determining priorities. This enables effective lessons by determining lesson priorities based on students' past performance data. Some or all of the above processes in the teaching department may be performed using AI, for example, or not. For example, the teaching department can input students' past performance data into a generating AI and have the generating AI determine lesson priorities.

[0079] The teaching department can customize lesson content based on students' interests and concerns during the lesson. For example, the teaching department can create lesson content that incorporates topics that will interest students. For example, the teaching department can introduce relevant examples and case studies based on students' interests. For example, the teaching department can adjust the way the lesson is conducted according to students' interests. For example, the teaching department can consider criteria and methods for customization when customizing lesson content. This makes it possible to conduct effective lessons by customizing lesson content based on students' interests and concerns. Some or all of the above processes in the teaching department may be performed using AI, for example, or not using AI. For example, the teaching department can input student interest data into a generating AI and have the generating AI perform the customization of lesson content.

[0080] The platform can estimate the user's emotions and adjust the display method of performance data based on the estimated emotions. For example, the platform can consider emotion estimation algorithms and the data used when estimating the user's emotions. For example, the platform can use facial recognition technology or voice analysis technology to estimate the user's emotions. For example, the platform can collect biometric data such as heart rate and skin electrical activity to estimate the user's emotions. For example, the platform can adjust the display method of performance data based on the estimated emotions. For example, if a student is feeling anxious, the platform can provide a simple and highly visible display method. For example, if a student is relaxed, the platform can display detailed performance data. For example, if a student is excited, the platform can provide a visually stimulating display method. This allows for effective performance management by adjusting the display method of performance data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0081] The platform unit can predict current grades by referring to past data when analyzing grade data. For example, the platform unit predicts current grades based on a student's past grade data. For example, the platform unit analyzes a student's past grade data to understand grade trends. For example, the platform unit predicts future grades by referring to a student's past grade data. For example, the platform unit can consider prediction criteria and methods when predicting grades. This makes it possible to predict current grades based on past data. Some or all of the above processes in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input a student's past grade data into a generating AI and have the generating AI perform a current grade prediction.

[0082] The platform unit can apply different analysis methods to each subject when analyzing performance data. For example, the platform unit can apply statistical analysis methods to mathematics performance data. For example, the platform unit can apply linguistic analysis methods to English performance data. For example, the platform unit can apply experimental results analysis methods to science performance data. For example, the platform unit can consider criteria and methods for application when applying analysis methods. This improves the accuracy of performance data analysis by applying the most appropriate analysis method for each subject. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input performance data for each subject into a generating AI and have the generating AI perform the application of analysis methods.

[0083] The platform can estimate the user's emotions and adjust the importance of performance data based on those emotions. For example, the platform can consider emotion estimation algorithms and the data used when estimating the user's emotions. For example, the platform can use facial recognition technology or voice analysis technology to estimate the user's emotions. For example, the platform can collect biometric data such as heart rate and skin electrical activity to estimate the user's emotions. For example, the platform can adjust the importance of performance data based on the estimated user's emotions. For example, if a student is feeling anxious, the platform can highlight important performance data. For example, if a student is relaxed, the platform can display detailed performance data. For example, if a student is excited, the platform can provide a visually stimulating display method. This enables effective performance management by adjusting the importance of performance data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0084] The platform unit can weight the performance data based on the student's submission timing when analyzing performance data. For example, the platform unit can assign higher weight to performance data for assignments submitted early. For example, the platform unit can assign lower weight to performance data for assignments submitted late. For example, the platform unit can adjust the weighting of the performance data based on the student's submission timing. For example, the platform unit can consider the criteria and methods for weighting the data when performing the weighting. This improves the accuracy of the performance data analysis by weighting the data based on the submission timing. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input student submission timing data into a generating AI and have the generating AI perform the data weighting.

[0085] The platform unit can improve the accuracy of its analysis of academic performance data by referencing relevant student activity data. For example, the platform unit can reference student club activity data and reflect it in the analysis of academic performance data. For example, the platform unit can reference student extracurricular activity data and reflect it in the analysis of academic performance data. For example, the platform unit can reference student volunteer activity data and reflect it in the analysis of academic performance data. For example, when improving the accuracy of the analysis, the platform unit can consider the type of data to reference and the analysis method. As a result, referencing relevant activity data improves the accuracy of the analysis of academic performance data. Some or all of the above processing in the platform unit may be performed using AI, for example, or without AI. For example, the platform unit can input student relevant activity data into a generating AI and have the generating AI perform the analysis of academic performance data.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The educational support system can further analyze students' learning styles and provide individually optimized learning plans. For example, students who prefer visual learning can be provided with materials that heavily utilize videos and diagrams. Students who prefer auditory learning can be provided with audio explanations and podcast-style materials. Students who prefer tactile learning can be provided with materials that include interactive simulations and experiments. This maximizes learning effectiveness by providing the optimal learning plan tailored to each student's learning style.

[0088] The educational support system can further monitor students' learning progress in real time and automatically adjust learning plans as needed. For example, if a student struggles with a particular unit, it can immediately provide supplementary materials related to that unit. Once a student achieves their goals, it presents new challenges to help them move on to the next step. It also adjusts the pace of the learning plan to match the student's learning pace. This allows for improved learning efficiency by providing flexible learning plans tailored to students' progress.

[0089] The educational support system can further predict future learning content based on students' learning history and prepare appropriate materials in advance. For example, when students review a unit they previously struggled with, it provides more detailed explanations and additional practice problems. For areas where students excel, it provides more advanced content and application problems. By analyzing students' learning history and predicting what they should learn next, the system can prepare materials accordingly. This predictive learning support based on students' learning history can enhance the effectiveness of their learning.

[0090] The educational support system can further optimize the student learning environment by adjusting ambient sounds and lighting during study. For example, it can play nature sounds or classical music to provide ambient sounds that help students concentrate. It can also adjust the color temperature and brightness of the lighting to help students relax while studying. By monitoring the student's learning environment and making adjustments to provide the optimal environment, the system can improve learning efficiency by providing an environment in which students can concentrate on their studies.

[0091] Educational support systems can incorporate gamification elements to further enhance students' learning motivation. For example, a system could be implemented where students earn points or badges based on their learning progress. Virtual rewards or titles could be offered when students achieve their goals. Learning content could be presented in a game format, making learning enjoyable. This can increase students' motivation, thereby improving the continuity and effectiveness of their learning.

[0092] The educational support system can further estimate students' emotions and adjust the difficulty level of learning content based on those emotions. For example, if a student is stressed, the difficulty level can be lowered to make learning easier. If a student is relaxed, the difficulty level can be increased to provide challenging tasks. If a student is excited, relaxing content can be provided to enhance their concentration. In this way, by adjusting the difficulty level of learning content according to the student's emotions, effective learning becomes possible.

[0093] The educational support system can further estimate students' emotions and adjust the timing of learning based on those emotions. For example, if a student is tired, it can take a break before resuming learning. If a student is focused, it can continue learning as usual. If a student is excited, it can provide a short break to help them relax. By adjusting the timing of learning according to the student's emotions, effective learning becomes possible.

[0094] The educational support system can further estimate students' emotions and adjust learning feedback based on those estimates. For example, if a student is feeling anxious, it can provide more positive feedback. If a student is relaxed, it can provide detailed feedback. If a student is excited, it can provide concise and clear feedback. This enables effective learning support by providing feedback tailored to the student's emotions.

[0095] The educational support system can further estimate students' emotions and adjust the pace of learning based on those estimates. For example, if a student is stressed, the learning pace can be slowed down. If a student is relaxed, the learning pace can be maintained at a normal rate. If a student is agitated, relaxing content can be provided to enhance their concentration. This allows for more effective learning by adjusting the pace of learning according to the student's emotions.

[0096] The educational support system can further estimate students' emotions and adjust learning goals based on those estimates. For example, if a student is feeling anxious, it can set easily achievable short-term goals. If a student is relaxed, it can set challenging long-term goals. If a student is excited, it can set specific goals to improve their concentration. This allows for more effective learning by adjusting learning goals according to the student's emotions.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The distribution department distributes the tablet devices. For example, when distributing tablet devices to each school, the timing of distribution and the selection criteria for recipients can be considered. Step 2: The teaching department uses AI to conduct lessons. For example, the AI ​​asks students questions, and students answer using tablet devices. The AI ​​manages the lesson, allowing teachers to perform other tasks during the lesson. When conducting lessons, the AI ​​technology to be used and the method of conducting the lesson can be considered. Step 3: The platform unit manages and analyzes comprehension and performance. For example, it collects student performance data, and the AI ​​analyzes that data to identify areas of low comprehension. The AI ​​then suggests supplementary lessons for the areas of low comprehension identified. When managing and analyzing comprehension and performance, the data collection method and analysis algorithm can be considered.

[0099] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0102] Each of the multiple elements described above, including the distribution unit, the lesson unit, and the platform unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the distribution unit is implemented by the smart device 14, with the control unit 46A of the smart device 14 managing the distribution of tablet terminals. The lesson unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where AI conducts lessons and analyzes students' responses. The platform unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where student performance data is managed and analyzed. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 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.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the distribution unit, the lesson unit, and the platform unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the distribution unit is implemented by the smart glasses 214, with the control unit 46A of the smart glasses 214 managing the distribution of tablet terminals. The lesson unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where AI conducts the lesson and analyzes students' responses. The platform unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where student performance data is managed and analyzed. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the distribution unit, the lesson unit, and the platform unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the distribution unit is implemented by the headset terminal 314, and the control unit 46A of the headset terminal 314 manages the distribution of tablet terminals. The lesson unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where AI conducts lessons and analyzes students' responses. The platform unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where student performance data is managed and analyzed. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the distribution unit, the lesson unit, and the platform unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the distribution unit is implemented by the robot 414, with the control unit 46A of the robot 414 managing the distribution of tablet terminals. The lesson unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the AI ​​conducts the lesson and analyzes the students' responses. The platform unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the student performance data is managed and analyzed. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0152] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) The distribution department that distributes tablet devices, The teaching department conducts classes using AI, It includes a platform unit for managing and analyzing understanding and performance. A system characterized by the following features. (Note 2) The aforementioned teaching department, AI is used to ask questions to students, who then answer using tablet devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned platform unit is The system collects student performance data and uses AI to analyze that data and identify areas where students have difficulty understanding. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned platform unit is The AI ​​suggests supplementary lessons for areas where the student's understanding is weak. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned distribution unit is Tablet devices will be distributed to each school. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned teaching department, AI can conduct lessons, allowing teachers to perform other tasks during class. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned distribution unit is The system estimates user sentiment and adjusts the timing of tablet device distribution based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned distribution unit is We will analyze the facilities at each school and select the most suitable distribution method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned distribution unit is The number of tablet devices distributed will be adjusted based on the number of students and class structure at each school. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned distribution unit is It estimates user sentiment and determines the priority of tablet devices to distribute based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned distribution unit is During distribution, the distribution route will be optimized considering the geographical location of each school. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned distribution unit is During distribution, we analyze each school's past distribution history to create the optimal distribution schedule. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned teaching department, The system estimates the user's emotions and adjusts the way the lesson content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned teaching department, During lessons, the pace of instruction is adjusted based on the students' level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned teaching department, During the course, different teaching algorithms are applied according to the characteristics of each subject. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned teaching department, It estimates the user's emotions and adjusts the length of the lesson based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned teaching department, During lessons, the priority of lessons is determined based on students' past academic performance data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned teaching department, During the lesson, customize the content based on the students' interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned platform unit is It estimates the user's emotions and adjusts how performance data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned platform unit is When analyzing performance data, we refer to past data to predict current performance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned platform unit is When analyzing performance data, different analytical methods are applied to each subject. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned platform unit is It estimates user sentiment and adjusts the importance of performance data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned platform unit is When analyzing performance data, the data is weighted based on when students submitted their work. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned platform unit is When analyzing performance data, we improve the accuracy of the analysis by referring to students' related activity data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The distribution department that distributes tablet devices, The teaching department conducts classes using AI, It includes a platform unit for managing and analyzing understanding and performance. A system characterized by the following features.

2. The aforementioned teaching department, AI is used to ask questions to students, who then answer using tablet devices. The system according to feature 1.

3. The aforementioned platform unit is The system collects student performance data, and AI analyzes that data to identify areas where students have difficulty understanding. The system according to feature 1.

4. The aforementioned platform unit is The AI ​​will suggest supplementary lessons for areas where the student's understanding is weak. The system according to feature 1.

5. The aforementioned distribution unit is Tablet devices will be distributed to each school. The system according to feature 1.

6. The aforementioned teaching department, AI can conduct the lesson, allowing teachers to perform other tasks during class. The system according to feature 1.

7. The aforementioned distribution unit is The system estimates user sentiment and adjusts the timing of tablet device distribution based on the estimated sentiment. The system according to feature 1.

8. The aforementioned distribution unit is We will analyze the facilities at each school and select the most suitable distribution method. The system according to feature 1.

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