system

Abstract

The system according to the embodiment aims to efficiently create tests, evaluate them, and provide feedback to students in educational settings. [Solution] The system according to the embodiment includes a setting unit, a question creation unit, a response collection unit, a data accumulation unit, and a feedback generation unit. The setting unit sets curriculum guidelines or school environmental data. The question creation unit creates questions based on the information set by the setting unit. The response collection unit allows students to answer the questions created by the question creation unit. The data accumulation unit accumulates and analyzes the response data collected by the response collection unit. The feedback generation unit generates individual feedback based on the data accumulated by the data accumulation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating tests, evaluating them, and providing feedback to students in educational settings was time-consuming and laborious, making it difficult to do so efficiently.

[0005] The system according to the embodiment aims to efficiently create tests, evaluate them, and provide feedback to students in educational settings. [Means for solving the problem]

[0006] The system according to the embodiment includes a setting unit, a question creation unit, a response collection unit, a data accumulation unit, and a feedback generation unit. The setting unit sets teaching guidelines or school environmental data. The question creation unit creates questions based on the information set by the setting unit. The response collection unit allows students to answer the questions created by the question creation unit. The data accumulation unit accumulates and analyzes the response data collected by the response collection unit. The feedback generation unit generates individual feedback based on the data accumulated by the data accumulation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create tests, evaluate them, and provide feedback to students in educational settings. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI system according to an embodiment of the present invention is a system for efficiently creating tests, evaluating them, and providing feedback to students in educational settings. This AI system sets curriculum guidelines and school environment data, creates questions based on the set information, and provides the questions for students to answer. Furthermore, it accumulates and analyzes answer data to generate individual feedback. This allows the AI ​​system to efficiently create tests, evaluate them, and provide feedback to students in educational settings. For example, teachers can save the effort of creating and grading tests and provide appropriate instruction to each student. Students can also efficiently progress through their studies by working on questions tailored to their academic ability.

[0029] The AI ​​system according to the embodiment includes a setting unit, a question creation unit, a response collection unit, a data accumulation unit, and a feedback generation unit. The setting unit sets curriculum guidelines or school environmental data. For example, a teacher inputs the purpose of the test and information about the target students. The question creation unit creates questions based on the information set by the setting unit. For example, the AI ​​generates questions based on curriculum guidelines or past question trends. The response collection unit allows students to answer the questions created by the question creation unit. For example, students answer using a tablet device or the like, and the answer data is collected in real time. The data accumulation unit accumulates and analyzes the answer data collected by the response collection unit. For example, the collected answer data is saved along with past scores. The feedback generation unit generates individual feedback based on the data accumulated by the data accumulation unit. For example, for a student who is weak in a particular subject, it provides reinforcement questions in that subject. This enables the AI ​​system according to the embodiment to efficiently create tests, evaluate them, and provide feedback to students in educational settings.

[0030] The question creation unit can generate questions based on the curriculum guidelines or past question trends. The curriculum guidelines include, for example, learning goals for each grade and teaching content for each subject. The past question trends include, for example, past exam questions, question frequency, and difficulty level. The question creation unit, for example, creates questions that are balanced from basic calculation questions to applied questions based on the curriculum guidelines. The question creation unit can also create questions based on past questions from the desired school, referring to past question trends. In this way, appropriate questions can be provided by generating questions based on the curriculum guidelines and past question trends.

[0031] The response collection unit can collect student response data in real time. "Real time" refers to data collection in seconds or minutes, for example. The response collection unit can instantly collect data from students' responses on tablet devices, for example. The response collection unit can also collect student response data in real time through an online platform. By collecting student response data in real time, students' grades can be immediately known.

[0032] The data storage unit can store the collected response data and save it together with past scores. Past scores include, for example, grades for each semester and grades for the past several years. The data storage unit, for example, saves the collected response data in cloud storage. The data storage unit can also save data on a local server. In this way, by storing the collected response data together with past scores, data consistency can be maintained.

[0033] The feedback generation unit can analyze the accumulated data and generate individualized feedback. Analysis can include, for example, the use of statistical analysis and machine learning algorithms. For example, the feedback generation unit can provide reinforcement questions for a student who is weak in a particular area. The feedback generation unit can also analyze past grades and provide more challenging questions for students who show improvement in their academic ability. In this way, by analyzing the accumulated data and generating individualized feedback, it is possible to provide appropriate instruction to each student.

[0034] The feedback generation unit can provide reinforcement questions for a student who is weak in a particular area. The particular area can include, for example, mathematical algebra or English grammar. The reinforcement questions can include, for example, additional practice questions or supplementary learning materials. For example, the feedback generation unit can provide reinforcement questions for algebra for a student who is weak in mathematical algebra. The feedback generation unit can also provide reinforcement questions for grammar for a student who is weak in English grammar. In this way, a student who is weak in a particular area can be provided with reinforcement questions for that area to overcome their weaknesses.

[0035] The feedback generation unit can analyze past grades and provide more challenging questions to students who show improvement in academic ability. Improvement in academic ability includes, for example, higher test scores and improved comprehension. Challenge questions include, for example, high-difficulty questions and applied questions. For example, the feedback generation unit can analyze past grades and provide more challenging questions to students who show improvement in academic ability. The feedback generation unit can also provide applied questions to students who show improvement in comprehension. In this way, by providing more challenging questions to students who show improvement in academic ability, it is possible to promote improvement in academic ability.

[0036] The setting unit can suggest optimal settings by referring to the past setting history. The past setting history includes, for example, past setting contents and change history. The setting unit, for example, automatically displays settings that the user has frequently used in the past as candidates. The setting unit can also preferentially suggest setting methods (voice, text, etc.) that the user has used in the past. Furthermore, the setting unit can predict and suggest settings to be used in a specific time period based on the user's past setting history. In this way, optimal settings can be suggested by referring to the past setting history.

[0037] The setting unit can automatically adjust the setting contents taking into account the school's annual schedule. The school's annual schedule includes, for example, event schedules for each semester and exam dates. The setting unit automatically sets test dates based on the school's annual schedule. The setting unit can also adjust the content and scope of the test taking into account school events and other such activities. Furthermore, the setting unit can optimize the timing of the test taking into account school vacation periods. This allows for appropriate settings to be made by taking into account the school's annual schedule.

[0038] The setting unit can customize the setting content based on the teaching style of the teacher. The teaching style of the teacher includes, for example, a lecture format or a discussion format. The setting unit sets the scope of questions to be included in the test based on the teaching points that the teacher emphasizes, for example. The setting unit can also adjust the format and difficulty of the test to match the teaching style of the teacher. Furthermore, the setting unit can also optimize the setting content by reflecting the teacher's past feedback. In this way, customizing the setting content based on the teaching style of the teacher makes it possible to create settings that are easy for the teacher to use.

[0039] The setting unit can adjust the setting contents taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, urban or rural areas and climatic conditions. The setting unit can, for example, optimize the location of the test based on the geographical conditions of the school. The setting unit can also adjust the content and scope of the test taking into account the geographical conditions of the school. Furthermore, the setting unit can optimize the timing of the test based on the geographical conditions of the school. In this way, appropriate settings can be made by taking into account the geographical conditions of the school.

[0040] The setting unit can optimize the setting contents by referring to the setting data of other schools. The setting data of other schools includes, for example, public databases and shared platforms. The setting unit can, for example, propose optimal setting contents based on the setting data of other schools. The setting unit can also adjust the content and scope of the test by referring to the setting data of other schools. Furthermore, the setting unit can optimize the timing of test implementation based on the setting data of other schools. In this way, optimal settings can be achieved by referring to the setting data of other schools.

[0041] The setting unit can customize the setting contents by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and improvement suggestions. The setting unit, for example, sets the scope of questions to be included in the test based on the teacher's past feedback. The setting unit can also adjust the format and difficulty of the test by reflecting the teacher's past feedback. Furthermore, the setting unit can optimize the setting contents based on the teacher's past feedback. In this way, optimal settings can be achieved by reflecting the teacher's past feedback.

[0042] The question creation unit can optimize the balance of questions by analyzing past question trends. Past question trends include, for example, past exam questions, question frequency, and difficulty level. The question creation unit, for example, balances basic questions and applied questions based on past question trends. The question creation unit can also analyze past question trends and create questions that do not lean toward a specific field. Furthermore, the question creation unit can broaden the scope of questions by referring to past question trends. In this way, appropriate questions can be provided by analyzing past question trends.

[0043] The question creation unit can customize questions based on specific learning goals. Specific learning goals include, for example, goals for each unit and skill acquisition goals. The question creation unit can set the scope of questions to be asked based on, for example, the specific learning goals. The question creation unit can also adjust the format and difficulty of questions to match the specific learning goals. Furthermore, the question creation unit can add supplementary questions to achieve the specific learning goals. In this way, by customizing questions based on the specific learning goals, it is possible to provide appropriate questions.

[0044] The question creation unit can adjust the question format based on the teaching style of the teacher. The teaching style of the teacher includes, for example, a lecture format and a discussion format. The question creation unit selects the question format to match, for example, the teaching style of the teacher. The question creation unit can also adjust the difficulty of the questions based on the teaching style of the teacher. Furthermore, the question creation unit can set the range of questions to be asked, reflecting the teaching style of the teacher. In this way, by adjusting the question format based on the teaching style of the teacher, appropriate questions can be provided.

[0045] The question creation unit can adjust the question content taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, urban areas, rural areas, and climatic conditions. The question creation unit, for example, sets the question range based on the geographical conditions of the school. The question creation unit can also adjust the format and difficulty of the questions taking into account the geographical conditions of the school. Furthermore, the question creation unit can optimize the question content based on the geographical conditions of the school. In this way, appropriate questions can be provided by taking into account the geographical conditions of the school.

[0046] The question creation unit can optimize the content of questions by referring to question data from other schools. Question data from other schools includes, for example, public databases and shared platforms. The question creation unit can propose optimal question content, for example, based on the question data from other schools. The question creation unit can also adjust the format and difficulty of questions by referring to the question data from other schools. Furthermore, the question creation unit can set the scope of questions based on the question data from other schools. In this way, optimal questions can be provided by referring to the question data from other schools.

[0047] The question creation unit can customize the question content by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and improvement suggestions. The question creation unit can, for example, set the scope of questions based on the teacher's past feedback. The question creation unit can also adjust the format and difficulty of questions by reflecting the teacher's past feedback. Furthermore, the question creation unit can optimize the question content based on the teacher's past feedback. In this way, the most suitable questions can be provided by reflecting the teacher's past feedback.

[0048] The response collection unit can optimize the collection method by referring to past response data. Past response data includes, for example, past test results and survey results. The response collection unit can, for example, propose an optimal collection method based on the past response data. The response collection unit can also adjust the collection method by referring to the past response data. Furthermore, the response collection unit can also optimize the collection timing based on the past response data. In this way, by referring to the past response data, an optimal collection method can be provided.

[0049] The answer collection unit can customize the collection method based on the student's learning progress. The student's learning progress includes, for example, test scores and assignment submission status. The answer collection unit adjusts the collection method based on, for example, the student's learning progress. The answer collection unit can also optimize the timing of collection in consideration of the student's learning progress. Furthermore, the answer collection unit can customize the collection method based on the student's learning progress. In this way, by customizing the collection method based on the student's learning progress, an appropriate collection method can be provided.

[0050] The answer collection unit can adjust the collection method based on the teaching style of the teacher. The teaching style of the teacher includes, for example, a lecture format and a discussion format. The answer collection unit selects the collection method to suit, for example, the teaching style of the teacher. The answer collection unit can also adjust the timing of collection based on the teaching style of the teacher. Furthermore, the answer collection unit can also optimize the collection method by reflecting the teaching style of the teacher. In this way, an appropriate collection method can be provided by adjusting the collection method based on the teaching style of the teacher.

[0051] The response collection unit can adjust the collection method taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, whether the school is in an urban area or a rural area, and climatic conditions. The response collection unit can optimize the collection method based on, for example, the geographical conditions of the school. The response collection unit can also adjust the collection timing taking into account the geographical conditions of the school. Furthermore, the response collection unit can customize the collection method based on the geographical conditions of the school. In this way, an appropriate collection method can be provided by taking into account the geographical conditions of the school.

[0052] The response collection unit can optimize the collection method by referring to the collected data of other schools. The collected data of other schools includes, for example, public databases and shared platforms. The response collection unit can, for example, propose an optimal collection method based on the collected data of other schools. The response collection unit can also adjust the collection method by referring to the collected data of other schools. Furthermore, the response collection unit can optimize the collection timing based on the collected data of other schools. In this way, the optimal collection method can be provided by referring to the collected data of other schools.

[0053] The answer collection unit can customize the collection method by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and improvement suggestions. The answer collection unit selects the collection method, for example, based on the teacher's past feedback. The answer collection unit can also adjust the collection timing by reflecting the teacher's past feedback. Furthermore, the answer collection unit can optimize the collection method based on the teacher's past feedback. In this way, the optimal collection method can be provided by reflecting the teacher's past feedback.

[0054] The data accumulation unit can optimize the storage method by referring to past data. Past data includes, for example, past test results and survey results. The data accumulation unit can, for example, propose an optimal storage method based on the past data. The data accumulation unit can also adjust the storage method by referring to the past data. Furthermore, the data accumulation unit can also optimize the storage timing based on the past data. In this way, the optimal storage method can be provided by referring to the past data.

[0055] The data accumulation unit can adjust the frequency of data storage based on the student's learning progress. The student's learning progress includes, for example, test scores and assignment submission status. The data accumulation unit adjusts the frequency of storage based on, for example, the student's learning progress. The data accumulation unit can also optimize the timing of storage in consideration of the student's learning progress. Furthermore, the data accumulation unit can customize the storage method based on the student's learning progress. In this way, an appropriate storage frequency can be provided by adjusting the frequency of data storage based on the student's learning progress.

[0056] The data storage unit can adjust the data storage format based on the teacher's teaching style. The teacher's teaching style includes, for example, a lecture format and a discussion format. The data storage unit selects the storage format, for example, to match the teacher's teaching style. The data storage unit can also adjust the storage timing based on the teacher's teaching style. Furthermore, the data storage unit can also optimize the storage method by reflecting the teacher's teaching style. In this way, an appropriate storage format can be provided by adjusting the data storage format based on the teacher's teaching style.

[0057] The data accumulation unit can adjust the storage method taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, whether the school is in an urban area or a rural area, and climatic conditions. The data accumulation unit can optimize the storage method, for example, based on the geographical conditions of the school. The data accumulation unit can also adjust the storage timing taking into account the geographical conditions of the school. Furthermore, the data accumulation unit can customize the storage method based on the geographical conditions of the school. In this way, an appropriate storage method can be provided by taking into account the geographical conditions of the school.

[0058] The data storage unit can optimize the storage method by referring to the data storage methods of other schools. Data storage methods of other schools include, for example, public databases and shared platforms. The data storage unit can, for example, propose an optimal storage method based on the data storage methods of other schools. The data storage unit can also adjust the storage method by referring to the data storage methods of other schools. Furthermore, the data storage unit can optimize the storage timing based on the data storage methods of other schools. In this way, the optimal storage method can be provided by referring to the data storage methods of other schools.

[0059] The data storage unit can customize the storage method by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and suggestions for improvement. The data storage unit selects the storage method, for example, based on the teacher's past feedback. The data storage unit can also adjust the timing of storage by reflecting the teacher's past feedback. Furthermore, the data storage unit can optimize the storage method based on the teacher's past feedback. In this way, the optimal storage method can be provided by reflecting the teacher's past feedback.

[0060] The feedback generation unit can improve the accuracy of feedback by referring to past performance data. Past performance data includes, for example, past test results and assignment evaluations. The feedback generation unit provides highly accurate feedback based on, for example, past performance data. The feedback generation unit can also adjust the content of the feedback by referring to past performance data. Furthermore, the feedback generation unit can also optimize the timing of feedback based on past performance data. In this way, by referring to past performance data, highly accurate feedback can be provided.

[0061] The feedback generation unit can customize the content of the feedback based on the student's learning progress. The student's learning progress includes, for example, test scores and assignment submission status. The feedback generation unit can adjust the content of the feedback based on, for example, the student's learning progress. The feedback generation unit can also optimize the timing of the feedback in consideration of the student's learning progress. Furthermore, the feedback generation unit can customize the format of the feedback based on the student's learning progress. In this way, by customizing the content of the feedback based on the student's learning progress, it is possible to provide appropriate feedback.

[0062] The feedback generation unit can adjust the feedback format based on the teaching style of the teacher. The teaching style of the teacher includes, for example, a lecture format and a discussion format. The feedback generation unit selects the feedback format in accordance with, for example, the teaching style of the teacher. The feedback generation unit can also adjust the content of the feedback based on the teaching style of the teacher. Furthermore, the feedback generation unit can also optimize the timing of the feedback by reflecting the teaching style of the teacher. In this way, appropriate feedback can be provided by adjusting the feedback format based on the teaching style of the teacher.

[0063] The feedback generation unit can adjust the content of the feedback taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, whether the school is in an urban area or a rural area, and climatic conditions. The feedback generation unit can optimize the content of the feedback based on, for example, the geographical conditions of the school. The feedback generation unit can also adjust the timing of the feedback taking into account the geographical conditions of the school. Furthermore, the feedback generation unit can customize the format of the feedback based on the geographical conditions of the school. In this way, appropriate feedback can be provided by taking into account the geographical conditions of the school.

[0064] The feedback generation unit can optimize the content of the feedback by referring to feedback data of other schools. The feedback data of other schools includes, for example, public databases and shared platforms. The feedback generation unit can, for example, suggest optimal feedback content based on the feedback data of other schools. The feedback generation unit can also adjust the format and content of the feedback by referring to the feedback data of other schools. Furthermore, the feedback generation unit can optimize the timing of the feedback based on the feedback data of other schools. In this way, optimal feedback can be provided by referring to the feedback data of other schools.

[0065] The feedback generation unit can customize the content of the feedback by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and improvement suggestions. The feedback generation unit selects the content of the feedback, for example, based on the teacher's past feedback. The feedback generation unit can also adjust the format and timing of the feedback by reflecting the teacher's past feedback. Furthermore, the feedback generation unit can optimize the content of the feedback based on the teacher's past feedback. In this way, optimal feedback can be provided by reflecting the teacher's past feedback.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The question creation unit can analyze the user's learning history and adjust the scope of questions based on the analysis results. For example, questions can be focused on areas that the user was weak in in the past. Also, for areas in which the user is strong, many applied questions can be asked. Furthermore, questions aimed at strengthening specific skills can be added based on the learning history. In this way, by adjusting the scope of questions based on the user's learning history, effective learning can be supported.

[0068] The data storage unit can customize the storage method based on the importance of the data. For example, important data can be backed up multiple times and stored in cloud storage. Less important data can also be stored in local storage. Furthermore, the storage period can be set according to the importance of the data. This allows for efficient data management by customizing the storage method based on the importance of the data.

[0069] The settings unit can analyze the user's device usage and optimize the settings based on the analysis results. For example, a user who mainly uses a desktop PC can be provided with an interface that makes extensive use of keyboard shortcuts. A user who mainly uses a tablet can be provided with an interface optimized for touch operations. Furthermore, a user who mainly uses a smartphone can be provided with an interface that is easy to operate with one hand. In this way, an easy-to-use interface can be provided by optimizing the settings based on the user's device usage.

[0070] The answer collection unit can analyze the user's answer patterns and optimize the answer collection method based on the analysis results. For example, a simple interface can be provided to a user who answers many questions in a short amount of time. Alternatively, an interface including detailed explanations can be provided to a user who takes time to think about their answers. Furthermore, the collection timing can be adjusted depending on the answer pattern. This allows for efficient data collection by optimizing the answer collection method based on the user's answer patterns.

[0071] The data storage unit can customize the storage format based on the type of data. For example, text data can be compressed and stored, and image data can be stored at high resolution. Audio data can also be stored in an appropriate format. Furthermore, the storage location can be selected depending on the type of data. This allows for efficient data management by customizing the storage format based on the type of data.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The setting section sets the curriculum or school environment data. For example, a teacher inputs the purpose of the test and information about the target students. Step 2: The question creation unit creates questions based on the information set by the setting unit. For example, the AI ​​generates questions based on teaching guidelines and past question trends. Step 3: In the answer collection unit, students answer the questions created by the question creation unit. For example, students answer using a tablet device, and the answer data is collected in real time. Step 4: The data storage unit stores and analyzes the response data collected by the response collection unit. For example, the collected response data is saved together with past scores. Step 5: The feedback generator generates individualized feedback based on the data stored by the data store. For example, if a student is weak in a particular area, the feedback generator provides reinforcement questions for that area.

[0074] (Example 2) An AI system according to an embodiment of the present invention is a system for efficiently creating tests, evaluating them, and providing feedback to students in educational settings. This AI system sets curriculum guidelines and school environment data, creates questions based on the set information, and provides the questions for students to answer. Furthermore, it accumulates and analyzes answer data to generate individual feedback. This allows the AI ​​system to efficiently create tests, evaluate them, and provide feedback to students in educational settings. For example, teachers can save the effort of creating and grading tests and provide appropriate instruction to each student. Students can also efficiently progress through their studies by working on questions tailored to their academic ability.

[0075] The AI ​​system according to the embodiment includes a setting unit, a question creation unit, a response collection unit, a data accumulation unit, and a feedback generation unit. The setting unit sets curriculum guidelines or school environmental data. For example, a teacher inputs the purpose of the test and information about the target students. The question creation unit creates questions based on the information set by the setting unit. For example, the AI ​​generates questions based on curriculum guidelines or past question trends. The response collection unit allows students to answer the questions created by the question creation unit. For example, students answer using a tablet device or the like, and the answer data is collected in real time. The data accumulation unit accumulates and analyzes the answer data collected by the response collection unit. For example, the collected answer data is saved along with past scores. The feedback generation unit generates individual feedback based on the data accumulated by the data accumulation unit. For example, for a student who is weak in a particular subject, it provides reinforcement questions in that subject. This enables the AI ​​system according to the embodiment to efficiently create tests, evaluate them, and provide feedback to students in educational settings.

[0076] The question creation unit can generate questions based on the curriculum guidelines or past question trends. The curriculum guidelines include, for example, learning goals for each grade and teaching content for each subject. The past question trends include, for example, past exam questions, question frequency, and difficulty level. The question creation unit, for example, creates questions that are balanced from basic calculation questions to applied questions based on the curriculum guidelines. The question creation unit can also create questions based on past questions from the desired school, referring to past question trends. In this way, appropriate questions can be provided by generating questions based on the curriculum guidelines and past question trends.

[0077] The response collection unit can collect student response data in real time. "Real time" refers to data collection in seconds or minutes, for example. The response collection unit can instantly collect data from students' responses on tablet devices, for example. The response collection unit can also collect student response data in real time through an online platform. By collecting student response data in real time, students' grades can be immediately known.

[0078] The data storage unit can store the collected response data and save it together with past scores. Past scores include, for example, grades for each semester and grades for the past several years. The data storage unit, for example, saves the collected response data in cloud storage. The data storage unit can also save data on a local server. In this way, by storing the collected response data together with past scores, data consistency can be maintained.

[0079] The feedback generation unit can analyze the accumulated data and generate individualized feedback. Analysis can include, for example, the use of statistical analysis and machine learning algorithms. For example, the feedback generation unit can provide reinforcement questions for a student who is weak in a particular area. The feedback generation unit can also analyze past grades and provide more challenging questions for students who show improvement in their academic ability. In this way, by analyzing the accumulated data and generating individualized feedback, it is possible to provide appropriate instruction to each student.

[0080] The feedback generation unit can provide reinforcement questions for a student who is weak in a particular area. The particular area can include, for example, mathematical algebra or English grammar. The reinforcement questions can include, for example, additional practice questions or supplementary learning materials. For example, the feedback generation unit can provide reinforcement questions for algebra for a student who is weak in mathematical algebra. The feedback generation unit can also provide reinforcement questions for grammar for a student who is weak in English grammar. In this way, a student who is weak in a particular area can be provided with reinforcement questions for that area to overcome their weaknesses.

[0081] The feedback generation unit can analyze past grades and provide more challenging questions to students who show improvement in academic ability. Improvement in academic ability includes, for example, higher test scores and improved comprehension. Challenge questions include, for example, high-difficulty questions and applied questions. For example, the feedback generation unit can analyze past grades and provide more challenging questions to students who show improvement in academic ability. The feedback generation unit can also provide applied questions to students who show improvement in comprehension. In this way, by providing more challenging questions to students who show improvement in academic ability, it is possible to promote improvement in academic ability.

[0082] The setting unit can estimate the user's emotions and adjust the settings based on the estimated user emotions. User emotions include, for example, stress, relaxation, and a state of being in a hurry. For example, when the user is feeling stressed, the setting unit provides a simple setting screen and minimizes the setting procedure. Furthermore, when the user is relaxed, the setting unit can provide detailed setting options and suggest a customizable setting method. Furthermore, when the user is in a hurry, the setting unit prioritizes voice input, allowing the user to quickly enter the settings. In this way, by adjusting the settings based on the user's emotions, it is possible to provide settings that are easy for the user to use.

[0083] The setting unit can suggest optimal settings by referring to the past setting history. The past setting history includes, for example, past setting contents and change history. The setting unit, for example, automatically displays settings that the user has frequently used in the past as candidates. The setting unit can also preferentially suggest setting methods (voice, text, etc.) that the user has used in the past. Furthermore, the setting unit can predict and suggest settings to be used in a specific time period based on the user's past setting history. In this way, optimal settings can be suggested by referring to the past setting history.

[0084] The setting unit can automatically adjust the setting contents taking into account the school's annual schedule. The school's annual schedule includes, for example, event schedules for each semester and exam dates. The setting unit automatically sets test dates based on the school's annual schedule. The setting unit can also adjust the content and scope of the test taking into account school events and other such activities. Furthermore, the setting unit can optimize the timing of the test taking into account school vacation periods. This allows for appropriate settings to be made by taking into account the school's annual schedule.

[0085] The setting unit can customize the setting content based on the teaching style of the teacher. The teaching style of the teacher includes, for example, a lecture format or a discussion format. The setting unit sets the scope of questions to be included in the test based on the teaching points that the teacher emphasizes, for example. The setting unit can also adjust the format and difficulty of the test to match the teaching style of the teacher. Furthermore, the setting unit can also optimize the setting content by reflecting the teacher's past feedback. In this way, customizing the setting content based on the teaching style of the teacher makes it possible to create settings that are easy for the teacher to use.

[0086] The setting unit can estimate the user's emotions and determine the priority of settings based on the estimated user's emotions. The user's emotions include, for example, tension, relaxation, and a state of being in a hurry. For example, when the user is tension, the setting unit can display important setting items with priority. Furthermore, when the user is relaxed, the setting unit can sequentially display detailed setting items. Furthermore, when the user is in a hurry, the setting unit can display the most important setting item first. In this way, by determining the priority of settings based on the user's emotions, it is possible to provide settings that are easy for the user to use.

[0087] The setting unit can adjust the setting contents taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, urban or rural areas and climatic conditions. The setting unit can, for example, optimize the location of the test based on the geographical conditions of the school. The setting unit can also adjust the content and scope of the test taking into account the geographical conditions of the school. Furthermore, the setting unit can optimize the timing of the test based on the geographical conditions of the school. In this way, appropriate settings can be made by taking into account the geographical conditions of the school.

[0088] The setting unit can optimize the setting contents by referring to the setting data of other schools. The setting data of other schools includes, for example, public databases and shared platforms. The setting unit can, for example, propose optimal setting contents based on the setting data of other schools. The setting unit can also adjust the content and scope of the test by referring to the setting data of other schools. Furthermore, the setting unit can optimize the timing of test implementation based on the setting data of other schools. In this way, optimal settings can be achieved by referring to the setting data of other schools.

[0089] The setting unit can customize the setting contents by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and improvement suggestions. The setting unit, for example, sets the scope of questions to be included in the test based on the teacher's past feedback. The setting unit can also adjust the format and difficulty of the test by reflecting the teacher's past feedback. Furthermore, the setting unit can optimize the setting contents based on the teacher's past feedback. In this way, optimal settings can be achieved by reflecting the teacher's past feedback.

[0090] The question creation unit can estimate the user's emotions and adjust the difficulty of the questions based on the estimated user's emotions. User emotions include, for example, relaxation, tension, and excitement. For example, if the user is relaxed, the question creation unit can present questions with a moderate level of difficulty. Furthermore, if the user is tense, the question creation unit can present questions with a balanced level of difficulty. In this way, appropriate questions can be provided by adjusting the difficulty of the questions based on the user's emotions.

[0091] The question creation unit can optimize the balance of questions by analyzing past question trends. Past question trends include, for example, past exam questions, question frequency, and difficulty level. The question creation unit, for example, balances basic questions and applied questions based on past question trends. The question creation unit can also analyze past question trends and create questions that do not lean toward a specific field. Furthermore, the question creation unit can broaden the scope of questions by referring to past question trends. In this way, appropriate questions can be provided by analyzing past question trends.

[0092] The question creation unit can customize questions based on specific learning goals. Specific learning goals include, for example, goals for each unit and skill acquisition goals. The question creation unit can set the scope of questions to be asked based on, for example, the specific learning goals. The question creation unit can also adjust the format and difficulty of questions to match the specific learning goals. Furthermore, the question creation unit can add supplementary questions to achieve the specific learning goals. In this way, by customizing questions based on the specific learning goals, it is possible to provide appropriate questions.

[0093] The question creation unit can adjust the question format based on the teaching style of the teacher. The teaching style of the teacher includes, for example, a lecture format and a discussion format. The question creation unit selects the question format to match, for example, the teaching style of the teacher. The question creation unit can also adjust the difficulty of the questions based on the teaching style of the teacher. Furthermore, the question creation unit can set the range of questions to be asked, reflecting the teaching style of the teacher. In this way, by adjusting the question format based on the teaching style of the teacher, appropriate questions can be provided.

[0094] The question creation unit can estimate the user's emotions and adjust the order in which questions are presented based on the estimated user's emotions. User emotions include, for example, tension, relaxation, and excitement. For example, if the user is nervous, the question creation unit presents questions in order from easier to harder. Furthermore, if the user is relaxed, the question creation unit can present questions with a higher level of difficulty first. Furthermore, if the user is excited, the question creation unit can present questions in an order that balances the level of difficulty. In this way, by adjusting the order in which questions are presented based on the user's emotions, appropriate questions can be provided.

[0095] The question creation unit can adjust the question content taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, urban areas, rural areas, and climatic conditions. The question creation unit, for example, sets the question range based on the geographical conditions of the school. The question creation unit can also adjust the format and difficulty of the questions taking into account the geographical conditions of the school. Furthermore, the question creation unit can optimize the question content based on the geographical conditions of the school. In this way, appropriate questions can be provided by taking into account the geographical conditions of the school.

[0096] The question creation unit can optimize the content of questions by referring to question data from other schools. Question data from other schools includes, for example, public databases and shared platforms. The question creation unit can propose optimal question content, for example, based on the question data from other schools. The question creation unit can also adjust the format and difficulty of questions by referring to the question data from other schools. Furthermore, the question creation unit can set the scope of questions based on the question data from other schools. In this way, optimal questions can be provided by referring to the question data from other schools.

[0097] The question creation unit can customize the question content by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and improvement suggestions. The question creation unit can, for example, set the scope of questions based on the teacher's past feedback. The question creation unit can also adjust the format and difficulty of questions by reflecting the teacher's past feedback. Furthermore, the question creation unit can optimize the question content based on the teacher's past feedback. In this way, the most suitable questions can be provided by reflecting the teacher's past feedback.

[0098] The answer collection unit can estimate the user's emotions and adjust the timing of answer collection based on the estimated user's emotions. User emotions include, for example, relaxation, tension, and excitement. For example, the answer collection unit flexibly sets the timing of answer collection when the user is relaxed. Furthermore, the answer collection unit can also shorten the timing of answer collection when the user is tense. Furthermore, the answer collection unit can adjust the timing of answer collection to collect answers at an appropriate timing when the user is excited. In this way, by adjusting the timing of answer collection based on the user's emotions, answers can be collected at an appropriate timing.

[0099] The response collection unit can optimize the collection method by referring to past response data. Past response data includes, for example, past test results and survey results. The response collection unit can, for example, propose an optimal collection method based on the past response data. The response collection unit can also adjust the collection method by referring to the past response data. Furthermore, the response collection unit can also optimize the collection timing based on the past response data. In this way, by referring to the past response data, an optimal collection method can be provided.

[0100] The answer collection unit can customize the collection method based on the student's learning progress. The student's learning progress includes, for example, test scores and assignment submission status. The answer collection unit adjusts the collection method based on, for example, the student's learning progress. The answer collection unit can also optimize the timing of collection in consideration of the student's learning progress. Furthermore, the answer collection unit can customize the collection method based on the student's learning progress. In this way, by customizing the collection method based on the student's learning progress, an appropriate collection method can be provided.

[0101] The answer collection unit can adjust the collection method based on the teaching style of the teacher. The teaching style of the teacher includes, for example, a lecture format and a discussion format. The answer collection unit selects the collection method to suit, for example, the teaching style of the teacher. The answer collection unit can also adjust the timing of collection based on the teaching style of the teacher. Furthermore, the answer collection unit can also optimize the collection method by reflecting the teaching style of the teacher. In this way, an appropriate collection method can be provided by adjusting the collection method based on the teaching style of the teacher.

[0102] The answer collection unit can estimate the user's emotions and determine the priority of answer collection based on the estimated user's emotions. The user's emotions include, for example, tension, relaxation, and a state of being in a hurry. For example, when the user is tense, the answer collection unit prioritizes collecting important answers. Furthermore, when the user is relaxed, the answer collection unit can also collect detailed answers sequentially. Furthermore, when the user is in a hurry, the answer collection unit can collect the most important answers first. In this way, by determining the priority of answer collection based on the user's emotions, answers can be collected with appropriate priority.

[0103] The response collection unit can adjust the collection method taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, whether the school is in an urban area or a rural area, and climatic conditions. The response collection unit can optimize the collection method based on, for example, the geographical conditions of the school. The response collection unit can also adjust the collection timing taking into account the geographical conditions of the school. Furthermore, the response collection unit can customize the collection method based on the geographical conditions of the school. In this way, an appropriate collection method can be provided by taking into account the geographical conditions of the school.

[0104] The response collection unit can optimize the collection method by referring to the collected data of other schools. The collected data of other schools includes, for example, public databases and shared platforms. The response collection unit can, for example, propose an optimal collection method based on the collected data of other schools. The response collection unit can also adjust the collection method by referring to the collected data of other schools. Furthermore, the response collection unit can optimize the collection timing based on the collected data of other schools. In this way, the optimal collection method can be provided by referring to the collected data of other schools.

[0105] The answer collection unit can customize the collection method by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and improvement suggestions. The answer collection unit selects the collection method, for example, based on the teacher's past feedback. The answer collection unit can also adjust the collection timing by reflecting the teacher's past feedback. Furthermore, the answer collection unit can optimize the collection method based on the teacher's past feedback. In this way, the optimal collection method can be provided by reflecting the teacher's past feedback.

[0106] The data storage unit can estimate the user's emotions and adjust the data storage method based on the estimated user's emotions. The user's emotions include, for example, relaxation, tension, and a state of being in a hurry. For example, the data storage unit stores detailed data when the user is relaxed. Furthermore, the data storage unit can also store important data preferentially when the user is tense. Furthermore, the data storage unit can store the most important data first when the user is in a hurry. In this way, an appropriate storage method can be provided by adjusting the data storage method based on the user's emotions.

[0107] The data accumulation unit can optimize the storage method by referring to past data. Past data includes, for example, past test results and survey results. The data accumulation unit can, for example, propose an optimal storage method based on the past data. The data accumulation unit can also adjust the storage method by referring to the past data. Furthermore, the data accumulation unit can also optimize the storage timing based on the past data. In this way, the optimal storage method can be provided by referring to the past data.

[0108] The data accumulation unit can adjust the frequency of data storage based on the student's learning progress. The student's learning progress includes, for example, test scores and assignment submission status. The data accumulation unit adjusts the frequency of storage based on, for example, the student's learning progress. The data accumulation unit can also optimize the timing of storage in consideration of the student's learning progress. Furthermore, the data accumulation unit can customize the storage method based on the student's learning progress. In this way, an appropriate storage frequency can be provided by adjusting the frequency of data storage based on the student's learning progress.

[0109] The data storage unit can adjust the data storage format based on the teacher's teaching style. The teacher's teaching style includes, for example, a lecture format and a discussion format. The data storage unit selects the storage format, for example, to match the teacher's teaching style. The data storage unit can also adjust the storage timing based on the teacher's teaching style. Furthermore, the data storage unit can also optimize the storage method by reflecting the teacher's teaching style. In this way, an appropriate storage format can be provided by adjusting the data storage format based on the teacher's teaching style.

[0110] The data storage unit can estimate the user's emotions and determine the data storage priority based on the estimated user's emotions. User emotions include, for example, tension, relaxation, and a state of being in a hurry. For example, when the user is tense, the data storage unit prioritizes saving important data. Furthermore, when the user is relaxed, the data storage unit can sequentially save detailed data. Furthermore, when the user is in a hurry, the data storage unit can save the most important data first. In this way, by determining the data storage priority based on the user's emotions, data can be saved with appropriate priority.

[0111] The data accumulation unit can adjust the storage method taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, whether the school is in an urban area or a rural area, and climatic conditions. The data accumulation unit can optimize the storage method, for example, based on the geographical conditions of the school. The data accumulation unit can also adjust the storage timing taking into account the geographical conditions of the school. Furthermore, the data accumulation unit can customize the storage method based on the geographical conditions of the school. In this way, an appropriate storage method can be provided by taking into account the geographical conditions of the school.

[0112] The data storage unit can optimize the storage method by referring to the data storage methods of other schools. Data storage methods of other schools include, for example, public databases and shared platforms. The data storage unit can, for example, propose an optimal storage method based on the data storage methods of other schools. The data storage unit can also adjust the storage method by referring to the data storage methods of other schools. Furthermore, the data storage unit can optimize the storage timing based on the data storage methods of other schools. In this way, the optimal storage method can be provided by referring to the data storage methods of other schools.

[0113] The data storage unit can customize the storage method by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and suggestions for improvement. The data storage unit selects the storage method, for example, based on the teacher's past feedback. The data storage unit can also adjust the timing of storage by reflecting the teacher's past feedback. Furthermore, the data storage unit can optimize the storage method based on the teacher's past feedback. In this way, the optimal storage method can be provided by reflecting the teacher's past feedback.

[0114] The feedback generation unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. The user's emotion includes, for example, relaxation, tension, and excitement. For example, the feedback generation unit provides detailed feedback when the user is relaxed. Furthermore, the feedback generation unit can also provide brief feedback when the user is tension. Furthermore, the feedback generation unit can also provide visually stimulating feedback when the user is excited. In this way, by adjusting the content of the feedback based on the user's emotion, appropriate feedback can be provided.

[0115] The feedback generation unit can improve the accuracy of feedback by referring to past performance data. Past performance data includes, for example, past test results and assignment evaluations. The feedback generation unit provides highly accurate feedback based on, for example, past performance data. The feedback generation unit can also adjust the content of the feedback by referring to past performance data. Furthermore, the feedback generation unit can also optimize the timing of feedback based on past performance data. In this way, by referring to past performance data, highly accurate feedback can be provided.

[0116] The feedback generation unit can customize the content of the feedback based on the student's learning progress. The student's learning progress includes, for example, test scores and assignment submission status. The feedback generation unit can adjust the content of the feedback based on, for example, the student's learning progress. The feedback generation unit can also optimize the timing of the feedback in consideration of the student's learning progress. Furthermore, the feedback generation unit can customize the format of the feedback based on the student's learning progress. In this way, by customizing the content of the feedback based on the student's learning progress, it is possible to provide appropriate feedback.

[0117] The feedback generation unit can adjust the feedback format based on the teaching style of the teacher. The teaching style of the teacher includes, for example, a lecture format and a discussion format. The feedback generation unit selects the feedback format in accordance with, for example, the teaching style of the teacher. The feedback generation unit can also adjust the content of the feedback based on the teaching style of the teacher. Furthermore, the feedback generation unit can also optimize the timing of the feedback by reflecting the teaching style of the teacher. In this way, appropriate feedback can be provided by adjusting the feedback format based on the teaching style of the teacher.

[0118] The feedback generation unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The user's emotions include, for example, tension, relaxation, and a state of being in a hurry. For example, when the user is tense, the feedback generation unit can provide important feedback preferentially. Furthermore, when the user is relaxed, the feedback generation unit can sequentially provide detailed feedback. Furthermore, when the user is in a hurry, the feedback generation unit can provide the most important feedback first. In this way, by determining the priority of feedback based on the user's emotions, it is possible to provide feedback with appropriate priority.

[0119] The feedback generation unit can adjust the content of the feedback taking into account the geographical conditions of the school. The geographical conditions of the school include, for example, whether the school is in an urban area or a rural area, and climatic conditions. The feedback generation unit can optimize the content of the feedback based on, for example, the geographical conditions of the school. The feedback generation unit can also adjust the timing of the feedback taking into account the geographical conditions of the school. Furthermore, the feedback generation unit can customize the format of the feedback based on the geographical conditions of the school. In this way, appropriate feedback can be provided by taking into account the geographical conditions of the school.

[0120] The feedback generation unit can optimize the content of the feedback by referring to feedback data of other schools. The feedback data of other schools includes, for example, public databases and shared platforms. The feedback generation unit can, for example, suggest optimal feedback content based on the feedback data of other schools. The feedback generation unit can also adjust the format and content of the feedback by referring to the feedback data of other schools. Furthermore, the feedback generation unit can optimize the timing of the feedback based on the feedback data of other schools. In this way, optimal feedback can be provided by referring to the feedback data of other schools.

[0121] The feedback generation unit can customize the content of the feedback by reflecting the teacher's past feedback. The teacher's past feedback includes, for example, evaluation comments and improvement suggestions. The feedback generation unit selects the content of the feedback, for example, based on the teacher's past feedback. The feedback generation unit can also adjust the format and timing of the feedback by reflecting the teacher's past feedback. Furthermore, the feedback generation unit can optimize the content of the feedback based on the teacher's past feedback. In this way, optimal feedback can be provided by reflecting the teacher's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the setting unit, question creation unit, answer collection unit, data storage unit, and feedback generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart device 14 and adjusts the setting content based on the user's emotions. The question creation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of questions and the order in which questions are presented based on the user's emotions. The answer collection unit is realized by the control unit 46A of the smart device 14 and adjusts the timing and priority of answer collection based on the user's emotions. The data storage unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the data storage method and storage priority based on the user's emotions. The feedback generation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the content and priority of feedback based on the user's emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the setting unit, question creation unit, answer collection unit, data accumulation unit, and feedback generation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart glasses 214 and adjusts the setting content based on the user's emotions. The question creation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of questions and the order of questions based on the user's emotions. The answer collection unit is realized by the control unit 46A of the smart glasses 214 and adjusts the timing and priority of answer collection based on the user's emotions. The data accumulation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the data storage method and storage priority based on the user's emotions. The feedback generation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the content and priority of feedback based on the user's emotions. === Hard Collateral 1-3 === Each of the multiple elements including the setting unit, question creation unit, answer collection unit, data storage unit, and feedback generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the headset-type terminal 314 and adjusts the setting content based on the user's emotions. The question creation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of questions and the order in which questions are presented based on the user's emotions. The answer collection unit is realized by the control unit 46A of the headset-type terminal 314 and adjusts the timing and priority of answer collection based on the user's emotions. The data storage unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the data storage method and storage priority based on the user's emotions. The feedback generation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the content and priority of feedback based on the user's emotions. === Hard Collateral 1-4 === Each of the multiple elements including the setting unit, question creation unit, answer collection unit, data accumulation unit, and feedback generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the robot 414 and adjusts the setting content based on the user's emotions. The question creation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty level of questions and the order in which questions are presented based on the user's emotions. The answer collection unit is realized by the control unit 46A of the robot 414 and adjusts the timing and priority of answer collection based on the user's emotions. The data accumulation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the data storage method and storage priority based on the user's emotions. The feedback generation unit is realized by the specific processing unit 290 of the data processing device 12 and adjusts the content and priority of feedback based on the user's emotions.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The setting unit can estimate a user's learning style and customize the settings based on the estimated learning style. For example, a graphical interface can be provided for a user who prefers visual learning. Audio guides can be provided for a user who prefers auditory learning. Furthermore, interactive setting options can be provided for a user who prefers hands-on learning. This allows the setting unit to customize the settings based on the user's learning style, providing a more effective learning environment.

[0124] The question creation unit can analyze the user's learning history and adjust the scope of questions based on the analysis results. For example, questions can be focused on areas that the user was weak in in the past. Also, for areas in which the user is strong, many applied questions can be asked. Furthermore, questions aimed at strengthening specific skills can be added based on the learning history. In this way, by adjusting the scope of questions based on the user's learning history, effective learning can be supported.

[0125] The response collection unit can estimate the user's learning environment and adjust the response collection method based on the estimated learning environment. For example, notifications can be minimized to help a user who is learning in a quiet environment maintain focus. An interface optimized for mobile devices can also be provided to a user who is on the move. Furthermore, an interface that facilitates collaboration can be provided to a user who is studying in a group. This allows for efficient data collection by adjusting the response collection method based on the user's learning environment.

[0126] The data storage unit can customize the storage method based on the importance of the data. For example, important data can be backed up multiple times and stored in cloud storage. Less important data can also be stored in local storage. Furthermore, the storage period can be set according to the importance of the data. This allows for efficient data management by customizing the storage method based on the importance of the data.

[0127] The feedback generator can estimate the user's learning goals and adjust the content of the feedback based on the estimated learning goals. For example, a specific action plan can be provided to a user with short-term goals. Feedback showing progress can also be provided to a user with long-term goals. Furthermore, feedback specific to a specific skill can be provided to a user who wants to master that skill. This allows for effective learning support by adjusting the content of the feedback based on the user's learning goals.

[0128] The settings unit can analyze the user's device usage and optimize the settings based on the analysis results. For example, a user who mainly uses a desktop PC can be provided with an interface that makes extensive use of keyboard shortcuts. A user who mainly uses a tablet can be provided with an interface optimized for touch operations. Furthermore, a user who mainly uses a smartphone can be provided with an interface that is easy to operate with one hand. In this way, an easy-to-use interface can be provided by optimizing the settings based on the user's device usage.

[0129] The question creation unit can estimate the user's learning pace and adjust the frequency of questions based on the estimated learning pace. For example, new questions can be provided frequently to a user who learns at a fast pace. Also, more review questions can be provided to a user who learns at a slow pace. Furthermore, the difficulty of questions can be adjusted according to the learning pace. In this way, by adjusting the frequency of questions based on the user's learning pace, effective learning can be supported.

[0130] The answer collection unit can analyze the user's answer patterns and optimize the answer collection method based on the analysis results. For example, a simple interface can be provided to a user who answers many questions in a short amount of time. Alternatively, an interface including detailed explanations can be provided to a user who takes time to think about their answers. Furthermore, the collection timing can be adjusted depending on the answer pattern. This allows for efficient data collection by optimizing the answer collection method based on the user's answer patterns.

[0131] The data storage unit can customize the storage format based on the type of data. For example, text data can be compressed and stored, and image data can be stored at high resolution. Audio data can also be stored in an appropriate format. Furthermore, the storage location can be selected depending on the type of data. This allows for efficient data management by customizing the storage format based on the type of data.

[0132] The feedback generator can estimate a user's learning style and adjust the form of feedback based on the estimated learning style. For example, feedback using graphs and charts can be provided to a user who prefers visual learning. Audio feedback can also be provided to a user who prefers auditory learning. Furthermore, feedback including a specific action plan can be provided to a user who prefers practical learning. This makes it possible to provide effective learning support by adjusting the form of feedback based on the user's learning style.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The setting section sets the curriculum or school environment data. For example, a teacher inputs the purpose of the test and information about the target students. Step 2: The question creation unit creates questions based on the information set by the setting unit. For example, the AI ​​generates questions based on teaching guidelines and past question trends. Step 3: In the answer collection unit, students answer the questions created by the question creation unit. For example, students answer using a tablet device, and the answer data is collected in real time. Step 4: The data storage unit stores and analyzes the response data collected by the response collection unit. For example, the collected response data is saved together with past scores. Step 5: The feedback generator generates individualized feedback based on the data stored by the data store. For example, if a student is weak in a particular area, the feedback generator provides reinforcement questions for that area.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0179] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0180] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0181] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0190] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0191] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0196] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0197] 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.

[0198] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0199] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0200] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0201] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0203] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0204] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0205] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0206] [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a setting unit for setting the curriculum or school environment data; a question creation unit that creates questions based on the information set by the setting unit; an answer collection unit in which students answer questions created by the question creation unit; a data storage unit that stores and analyzes the response data collected by the response collection unit; a feedback generating unit that generates individual feedback based on the data accumulated by the data accumulation unit. A system characterized by:

2. The question creation unit: Generate questions based on teaching guidelines or past question trends 2. The system of claim 1.

3. The response collection unit Collect student response data in real time 2. The system of claim 1.

4. The data storage unit Collected response data is accumulated and saved along with past scores.

2. The system of claim 1.

5. The feedback generation unit Analyze the accumulated data and generate personalized feedback 2. The system of claim 1.

6. The feedback generation unit For students who are weak in a particular area, provide reinforcement questions in that area.

2. The system of claim 1.

7. The feedback generation unit Analyze past performance and provide students who show improvement with more challenging questions 2. The system of claim 1.

8. The setting unit Inferring user emotions and adjusting settings based on the estimated user emotions 2. The system of claim 1.

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