system

Abstract

The system according to the embodiment aims to propose the most suitable educational path for each student and support their continued motivation to learn. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a monitoring unit. The collection unit collects data on students' learning history, interests, and learning styles. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests an appropriate educational path based on the analysis results obtained by the analysis unit. The monitoring unit monitors the learning progress in real time.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find the optimal educational path for each student, making it difficult to maintain their motivation to learn.

[0005] The system according to the embodiment aims to propose the most suitable educational path for each student and support their continued motivation to learn. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a monitoring unit. The collection unit collects data on students' learning history, interests, and learning styles. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests an appropriate educational path based on the analysis results obtained by the analysis unit. The monitoring unit monitors the learning progress in real time. [Effects of the Invention]

[0007] The system according to the embodiment can propose the most suitable educational path for each student and support their continued motivation to learn. [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 education optimization system according to an embodiment of the present invention uses AI to optimize each student's educational experience. This education optimization system collects data such as students' learning history, interests, and learning styles, and uses AI to analyze this data to propose the optimal educational path for each student. For example, if a student is interested in a particular subject, it provides learning materials and assignments related to that subject to enhance their motivation to learn. Furthermore, the system monitors their learning progress in real time and adjusts their learning plan as needed. This allows students to quickly identify the optimal educational path for self-realization and maintain their motivation to learn. First, data such as students' learning history, interests, and learning styles is collected. This includes detailed data such as the student's grades, past learning history, subjects of interest, and learning style (e.g., visual, auditory). For example, it is possible to determine which subjects the student is interested in and what learning style is effective for them. Next, the collected data is analyzed using AI. The AI ​​analyzes the collected data and proposes the optimal educational path for each student. For example, if a student is interested in a particular subject, it provides learning materials and assignments related to that subject to enhance their motivation to learn. Furthermore, the system monitors their learning progress in real time. For example, sensors and applications can be used to monitor students' learning status in real time and adjust their learning plans as needed. This allows students to find the optimal educational path for self-realization early on and maintain their motivation to learn. This system provides the optimal educational path tailored to each student, increasing their motivation to learn. For example, a student who is interested in a particular subject can be provided with learning materials and assignments related to that subject, increasing their motivation to learn. Furthermore, by monitoring their learning progress in real time and adjusting their learning plans as needed, students can find the optimal educational path for self-realization early on. This allows the education optimization system to suggest the optimal educational path for each student and support their motivation to learn.

[0029] An education optimization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a monitoring unit. The collection unit collects data on students' learning histories, interests, and learning styles. For example, the collection unit can acquire students' grades and past learning histories from a database. The collection unit can also identify students' interests based on questionnaires and behavioral histories. The collection unit can also collect data to identify learning styles, such as visual, auditory, and experiential. For example, the collection unit can survey students to determine their preferred learning methods and store the results in a database. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use AI to analyze students' learning histories, interests, and learning styles and generate information for proposing the optimal educational path for each student. For example, the analysis unit can identify students' strong and weak subjects based on past grade data. The analysis unit can also analyze questionnaire results and behavioral histories to identify students' interests. The analysis unit can also analyze data on learning styles and generate information for proposing the optimal learning method for each student. The suggestion unit proposes an appropriate educational path based on the analysis results obtained by the analysis unit. For example, the suggestion unit can provide teaching materials and assignments related to a particular subject to a student who is interested in that subject. The suggestion unit can also suggest a learning method according to the student's learning style. For example, it can provide visual materials to a visual student and audio materials to an auditory student. The suggestion unit can also adjust the learning plan according to the student's learning progress. For example, the suggestion unit can suggest more difficult assignments if the student is making progress and more basic assignments if the student is stagnating. The monitoring unit instantly monitors the learning progress. For example, the monitoring unit can monitor the student's learning status in real time using sensors or applications. The monitoring unit can, for example, record the student's study time and learning content and send the data to the analysis unit. The monitoring unit can also monitor the student's learning attitude and concentration level and provide information to adjust the learning plan as necessary.Furthermore, the monitoring unit can monitor the student's learning environment and collect information to provide an appropriate learning environment. For example, the monitoring unit can monitor whether the student is studying in a quiet environment and adjust the environment as necessary. This allows the education optimization system according to the embodiment to propose an optimal educational path for each student and support their continued motivation to learn.

[0030] The suggestion unit may include a provision unit that provides teaching materials and assignments related to a particular subject to a student who is interested in that subject. For example, the provision unit provides teaching materials and assignments related to that subject to a student who is interested in that subject. For example, the provision unit may search a database for teaching materials related to the subject in which the student is interested and provide them to the student. The provision unit may also provide appropriate assignments according to the student's learning progress. For example, the provision unit may provide more difficult assignments if the student is making progress, and more basic assignments if the student is stagnating. Furthermore, the provision unit may provide teaching materials according to the student's learning style. For example, it may provide visual teaching materials to visual students and audio teaching materials to auditory students. In this way, providing teaching materials and assignments based on the student's interests increases the student's motivation to learn.

[0031] The monitoring unit can monitor the student's learning status in real time using sensors or applications. The monitoring unit, for example, uses sensors to monitor the student's learning status in real time. For example, the monitoring unit can monitor the student's study time and concentration level using a wearable device. The monitoring unit can also monitor the student's learning environment using an environmental sensor. For example, the monitoring unit can monitor whether the student is studying in a quiet environment and adjust the environment as needed. Furthermore, the monitoring unit can monitor the student's learning status using an application. For example, the monitoring unit can monitor the student's learning progress in real time using a learning management system or a mobile app and adjust the learning plan as needed. In this way, by monitoring the learning status in real time, the learning plan can be adjusted at the appropriate time.

[0032] The suggestion unit can adjust the study plan according to the student's learning progress. For example, the suggestion unit can adjust the study plan according to the student's learning progress. For example, the suggestion unit can suggest more difficult tasks if the student is making progress, and more basic tasks if the student is stagnating. The suggestion unit can also monitor the student's learning attitude and concentration level and adjust the study plan as needed. For example, the suggestion unit can extend the study time if the student is concentrating, and suggest taking a break if the student is tired. Furthermore, the suggestion unit can monitor the student's learning environment and collect information to provide an appropriate learning environment. For example, the suggestion unit can monitor whether the student is studying in a quiet environment and adjust the environment as needed. This makes it possible to support effective learning by flexibly adjusting the study plan according to the student's learning progress.

[0033] The providing unit may include a feedback unit that provides feedback to increase motivation to learn. The feedback unit provides, for example, feedback to increase motivation to learn of students. For example, the feedback unit can provide positive comments for goals that a student has achieved. The feedback unit can also provide encouraging messages when a student is facing difficulties. Furthermore, the feedback unit can provide feedback that indicates specific areas for improvement according to the student's learning progress. For example, when a student is struggling with a particular assignment, the feedback unit can provide specific advice for that assignment. This makes it possible to increase motivation to learn through feedback.

[0034] The collection unit can analyze the student's past learning history and select the optimal data collection method. The collection unit, for example, analyzes the student's past learning history and selects the optimal data collection method. For example, the collection unit can collect data using a similar method based on a learning method that was effective for the student in the past. The collection unit can also collect data using a different approach for subjects that the student was weak at in the past. Furthermore, the collection unit can collect data at the most efficient time period based on the student's past learning history. For example, the collection unit can collect data at the time period when the student was most able to concentrate in the past, thereby achieving efficient data collection. In this way, efficient data collection is possible by selecting the optimal data collection method based on the student's past learning history.

[0035] The collection unit can filter data based on the student's current learning environment and areas of interest when collecting data. For example, the collection unit can filter data based on the student's current learning environment and areas of interest when collecting data. For example, if a student is studying in a quiet environment, the collection unit can collect data appropriate for that environment. Also, if a student is interested in a particular subject, the collection unit can preferentially collect data related to that subject. Furthermore, if students are studying in a group, the collection unit can collect data for the entire group. For example, the collection unit can collect data related to a project that students are working on as a group to understand the learning status of the entire group. In this way, by filtering data based on the learning environment and areas of interest, highly relevant data can be collected.

[0036] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the student's geographical location information. For example, when collecting data, the collection unit prioritizes collecting highly relevant data by taking into account the student's geographical location information. For example, when a student is in a specific area, the collection unit can collect learning data related to that area. Also, when a student is traveling, the collection unit can collect cultural and historical data related to the travel destination. Furthermore, when a student is at home, the collection unit can collect data suitable for home study. For example, when a student is studying at home, the collection unit can collect data for providing learning materials and assignments suitable for home study. In this way, by collecting highly relevant data based on geographical location information, more appropriate data can be provided.

[0037] The collection unit can analyze students' social media activities and collect related data when collecting data. For example, the collection unit can analyze students' social media activities and collect related data when collecting data. For example, the collection unit can collect data related to topics that students are interested in on social media. The collection unit can also collect useful information from education-related accounts that students follow. Furthermore, the collection unit can analyze activities in online communities in which students participate and collect related data. For example, the collection unit can analyze activities in online communities in which students participate and collect related learning data. In this way, by collecting related data based on social media activities, it is possible to provide data tailored to students' interests.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit can achieve efficient analysis by performing a detailed analysis on data of high importance and a simplified analysis on data of low importance. In this way, efficient analysis is possible by adjusting the level of detail of the analysis according to the importance of the data.

[0039] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply an analysis algorithm based on past grades to learning history data. The analysis unit can also apply a clustering algorithm to interest data. The analysis unit can also apply a pattern recognition algorithm to learning style data. For example, the analysis unit can achieve more accurate analysis by applying an analysis algorithm based on past grades to learning history data and a clustering algorithm to interest data. This makes it possible to achieve more accurate analysis by applying an analysis algorithm depending on the category of data.

[0040] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize analyzing the latest data. The analysis unit can also emphasize the latest data while referring to past data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. For example, the analysis unit can achieve efficient analysis by analyzing the latest data with priority and referring to past data. In this way, by determining the analysis priority based on the time of data collection, the latest data can be prioritized.

[0041] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. For example, the analysis unit can achieve efficient analysis by analyzing highly relevant data with priority and postponing analysis of less relevant data. In this way, efficient analysis is possible by adjusting the order of analysis based on the relevance of data.

[0042] The suggestion unit can adjust the level of detail of the proposal based on the importance of the educational path when making the suggestion. For example, the suggestion unit can make a detailed suggestion for an educational path with high importance. The suggestion unit can also make a simplified suggestion for an educational path with low importance. Furthermore, the suggestion unit can make a suggestion with an appropriate level of detail for an educational path with medium importance. For example, the suggestion unit can make a detailed suggestion for an educational path with high importance and a simplified suggestion for an educational path with low importance, thereby realizing efficient suggestions. As a result, efficient suggestions can be made by adjusting the level of detail of the suggestion according to the importance of the educational path.

[0043] The suggestion unit can apply different suggestion algorithms depending on the category of the educational path when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the educational path when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm specialized for science to science subjects. The suggestion unit can also apply a suggestion algorithm specialized for humanities to humanities subjects. The suggestion unit can also apply a suggestion algorithm specialized for arts to arts subjects. For example, the suggestion unit can realize more accurate suggestions by applying a suggestion algorithm specialized for science to science subjects and a suggestion algorithm specialized for humanities to humanities subjects. This makes it possible to make more accurate suggestions by applying a suggestion algorithm depending on the category of the educational path.

[0044] The suggestion unit can determine the priority of the proposals based on the submission time of the educational paths at the time of proposal. The suggestion unit, for example, determines the priority of the proposals based on the submission time of the educational paths at the time of proposal. For example, the suggestion unit can preferentially suggest educational paths with a near submission deadline. The suggestion unit can also postpone educational paths with a distant submission deadline. Furthermore, the suggestion unit can dynamically adjust the priority of the proposals according to the submission time. For example, the suggestion unit can provide proposals at an appropriate time by preferentially proposing educational paths with a near submission deadline and postponing educational paths with a distant submission deadline. In this way, by determining the priority of the proposals based on the submission time, it is possible to provide proposals at an appropriate time.

[0045] The suggestion unit can adjust the order of proposals based on the relevance of the educational paths when making a proposal. The suggestion unit, for example, adjusts the order of proposals based on the relevance of the educational paths when making a proposal. For example, the suggestion unit can prioritize proposing highly relevant educational paths. The suggestion unit can also postpone less relevant educational paths. Furthermore, the suggestion unit can dynamically adjust the order of proposals according to the relevance of the educational paths. For example, the suggestion unit can prioritize proposing highly relevant educational paths and postpone less relevant educational paths, thereby achieving efficient proposals. As a result, efficient proposals are possible by adjusting the order of proposals based on the relevance of the educational paths.

[0046] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between learning activities when monitoring. For example, the monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between learning activities when monitoring. For example, when a student is studying multiple subjects simultaneously, the monitoring unit can monitor the progress of each subject by correlating them with each other. Furthermore, when students are studying in a group, the monitoring unit can also monitor the learning status of the entire group. Furthermore, when a student is studying project-based learning, the monitoring unit can monitor the progress of the project. For example, when a student is studying multiple subjects simultaneously, the monitoring unit can improve the accuracy of monitoring by monitoring the progress of each subject by correlating them with each other. In this way, the accuracy of monitoring is improved by taking into account the interrelationships between learning activities.

[0047] The monitoring unit can perform monitoring taking into account the attribute information of students during monitoring. For example, the monitoring unit can perform monitoring taking into account the attribute information of students during monitoring. For example, the monitoring unit can adjust the monitoring criteria according to the age of the student. The monitoring unit can also adjust the monitoring criteria according to the gender of the student. Furthermore, the monitoring unit can adjust the monitoring criteria according to the year of the student. For example, the monitoring unit can adjust the monitoring criteria according to the age of the student and the gender of the student, thereby achieving more appropriate monitoring. This makes it possible to perform more appropriate monitoring by performing monitoring based on the attribute information of the student.

[0048] The monitoring unit can perform monitoring taking into account the geographical distribution of learning when monitoring. For example, the monitoring unit performs monitoring taking into account the geographical distribution of learning when monitoring. For example, if students are studying in different regions, the monitoring unit can monitor the learning situation in each region individually. In addition, if a student is studying abroad, the monitoring unit can also monitor taking into account the learning environment of the student's study abroad destination. Furthermore, if a student is studying online, the monitoring unit can monitor without taking geographical constraints into account. For example, if students are studying in different regions, the monitoring unit can monitor the learning situation in each region individually, and if a student is studying abroad, the monitoring unit can monitor taking into account the learning environment of the student's study abroad destination, thereby achieving more appropriate monitoring. In this way, more appropriate monitoring is possible by taking into account the geographical distribution of learning.

[0049] The monitoring unit may improve the accuracy of monitoring by referring to literature related to the learning during monitoring. For example, the monitoring unit may improve the accuracy of monitoring by referring to literature related to the learning during monitoring. For example, the monitoring unit may monitor by referring to the latest research papers related to the subject the student is studying. The monitoring unit may also monitor based on the references of the learning materials the student is using. Furthermore, the monitoring unit may monitor by referring to literature related to the online course the student is participating in. For example, the monitoring unit may improve the accuracy of monitoring by referring to the latest research papers related to the subject the student is studying and by monitoring based on the references of the learning materials the student is using. In this way, the accuracy of monitoring is improved by referring to related literature.

[0050] The provision unit can improve the accuracy of provision by taking into account the interrelationships between learning subjects when providing the information. For example, the provision unit can improve the accuracy of provision by taking into account the interrelationships between learning subjects when providing the information. For example, when a student is studying multiple subjects simultaneously, the provision unit can provide teaching materials by taking into account the relevance of each subject. Furthermore, when a student is studying in a group, the provision unit can provide teaching materials by taking into account the learning status of the entire group. Furthermore, when a student is studying in a project-based learning, the provision unit can provide teaching materials according to the progress of the project. For example, when a student is studying multiple subjects simultaneously, the provision unit can provide teaching materials by taking into account the relevance of each subject, and when a student is studying in a group, the provision unit can improve the accuracy of provision by providing teaching materials by taking into account the learning status of the entire group. In this way, the accuracy of provision is improved by taking into account the interrelationships between learning subjects.

[0051] The providing unit can provide the teaching materials taking into consideration the student's attribute information when providing the teaching materials. For example, the providing unit can provide teaching materials of an appropriate level of difficulty depending on the student's age. The providing unit can also provide teaching materials that attract the student's interest depending on the student's gender. Furthermore, the providing unit can provide teaching materials appropriate for the student's grade level depending on the student's grade level. For example, the providing unit can provide teaching materials of an appropriate level of difficulty depending on the student's age and teaching materials that attract the student's interest depending on the student's gender, thereby providing more appropriate teaching materials and assignments. In this way, more appropriate teaching materials and assignments can be provided by providing the teaching materials based on the student's attribute information.

[0052] The provision unit can provide the teaching materials taking into consideration the geographical distribution of learning when providing the teaching materials. For example, the provision unit provides the teaching materials taking into consideration the geographical distribution of learning when providing the teaching materials. For example, if students are studying in different regions, the provision unit can provide teaching materials appropriate for each region. Furthermore, if a student is studying abroad, the provision unit can provide teaching materials appropriate for the learning environment of the student's study abroad destination. Furthermore, if a student is studying online, the provision unit can provide teaching materials without considering geographical constraints. For example, if a student is studying in different regions, the provision unit can provide teaching materials appropriate for each region, and if a student is studying abroad, the provision unit can provide teaching materials appropriate for the learning environment of the student's study abroad destination, thereby providing more appropriate teaching materials and assignments. In this way, more appropriate teaching materials and assignments can be provided by taking into consideration the geographical distribution of learning.

[0053] The providing unit can improve the accuracy of the provision by referring to literature related to the study when providing the teaching materials. For example, the providing unit can improve the accuracy of the provision by referring to literature related to the study when providing the teaching materials. For example, the providing unit can provide the teaching materials by referring to the latest research papers related to the subject the student is studying. The providing unit can also provide the teaching materials based on the references of the teaching materials used by the student. Furthermore, the providing unit can provide the teaching materials by referring to literature related to the online course in which the student is participating. For example, the providing unit can improve the accuracy of the provision by referring to the latest research papers related to the subject the student is studying and providing the teaching materials based on the references of the teaching materials used by the student. In this way, the accuracy of the provision is improved by referring to the related literature.

[0054] The feedback unit can select the optimal feedback method by analyzing the student's past learning behavior when providing feedback. For example, the feedback unit can select the optimal feedback method by analyzing the student's past learning behavior when providing feedback. For example, the feedback unit can provide feedback using a similar method based on feedback methods that have been effective for the student in the past. The feedback unit can also provide feedback using a different approach, avoiding feedback methods that the student has struggled with in the past. Furthermore, the feedback unit can select the most efficient feedback method based on the student's past learning behavior. For example, the feedback unit can provide feedback using a similar method based on feedback methods that have been effective for the student in the past, and provide feedback using a different approach, avoiding feedback methods that the student has struggled with in the past, thereby achieving effective feedback. In this way, effective feedback is possible by selecting the optimal feedback method based on the student's past learning behavior.

[0055] The feedback unit can customize the means of feedback based on the student's current learning situation when providing feedback. For example, the feedback unit customizes the means of feedback based on the student's current learning situation when providing feedback. For example, the feedback unit can provide praising feedback if the student is making progress. Furthermore, the feedback unit can provide encouraging feedback if the student is stagnating. Furthermore, the feedback unit can provide feedback indicating specific areas for improvement if the student is regressing. For example, the feedback unit can provide praising feedback if the student is making progress, and encouraging feedback if the student is stagnating, thereby providing more appropriate feedback. This allows for more appropriate feedback to be provided by customizing the means of feedback based on the student's current learning situation.

[0056] The feedback unit can select the optimal feedback method by taking into account the student's geographical location information when providing feedback. For example, the feedback unit selects the optimal feedback method by taking into account the student's geographical location information when providing feedback. For example, if students are studying in different regions, the feedback unit can provide feedback appropriate for each region. Furthermore, if a student is studying abroad, the feedback unit can provide feedback appropriate for the learning environment of the student's study abroad destination. Furthermore, if a student is studying online, the feedback unit can provide feedback without taking geographical constraints into account. For example, if a student is studying in different regions, the feedback unit can provide feedback appropriate for each region, and if a student is studying abroad, the feedback unit can provide feedback appropriate for the learning environment of the student's study abroad destination, thereby providing more appropriate feedback. This enables more appropriate feedback by selecting the optimal feedback method based on the geographical location information.

[0057] The feedback unit can analyze the student's social media activity and suggest a means of providing feedback when providing feedback. For example, the feedback unit can analyze the student's social media activity and suggest a means of providing feedback when providing feedback. For example, the feedback unit can provide feedback related to topics that the student is interested in on social media. The feedback unit can also incorporate useful information from education-related accounts that the student follows into the feedback. Furthermore, the feedback unit can analyze the activity of online communities in which the student participates and provide related feedback. For example, the feedback unit can provide feedback related to topics that the student is interested in on social media and incorporate useful information from education-related accounts that the student follows into the feedback, thereby providing more appropriate feedback. This enables more appropriate feedback to be provided by suggesting a means of providing feedback based on social media activity.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The education optimization system may further include a health monitoring unit that monitors the student's health status. For example, the health monitoring unit may use a wearable device to monitor the student's heart rate and sleep patterns to understand their health status. The health monitoring unit may also measure the student's stress level and provide relaxation advice as needed. Furthermore, the health monitoring unit may record the student's dietary and exercise habits and provide suggestions for maintaining healthy lifestyles. This may allow the system to propose an optimal education path that takes into account the student's health status.

[0060] The monitoring unit may include an environmental adjustment unit that adjusts the student's learning environment in real time. The environmental adjustment unit can provide an optimal learning environment by adjusting environmental elements such as lighting, temperature, and volume. The environmental adjustment unit can also monitor the student's concentration level and adjust the environment to restore concentration if their concentration wanes. Furthermore, the environmental adjustment unit can customize the environment according to the student's learning style, providing bright lighting for visual students and a quiet environment for auditory students. This maximizes the student's learning efficiency.

[0061] The collection unit can collect not only students' learning history, but also data on their hobbies and special skills. For example, the collection unit can understand what hobbies and special skills a student has and customize a study plan based on that information. The collection unit can also collect data on the club activities and volunteer activities in which students participate, allowing it to provide comprehensive study support. Furthermore, the collection unit can collect data on students' family structures and living environments, allowing it to provide study support tailored to individual needs. This makes it possible to propose optimal educational paths that suit students' diverse backgrounds.

[0062] The collection unit can analyze the student's past learning history and select the optimal data collection method. For example, the collection unit can collect data using a similar learning method based on a student's past effective learning method. The collection unit can also collect data using a different approach for subjects that the student was weak at in the past. Furthermore, the collection unit can collect data at the most efficient time period based on the student's past learning history. This allows for efficient data collection by selecting the optimal data collection method based on the student's past learning history.

[0063] The monitoring unit can be equipped with an evaluation unit that not only monitors students' learning status but also evaluates the quality of their learning. The evaluation unit can, for example, evaluate how much a student understands and provide feedback according to the level of understanding. The evaluation unit can also evaluate students' learning attitudes and concentration levels and adjust their learning plans as necessary. Furthermore, the evaluation unit can regularly evaluate students' learning outcomes and grasp their progress. This makes it possible to provide more effective learning support by evaluating the quality of learning.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects data on students' learning history, interests, and learning styles. For example, the collection unit can retrieve students' grades and past learning history from a database. The collection unit can also understand students' interests based on questionnaires and behavioral history. Furthermore, the collection unit can collect data to identify learning styles such as visual, auditory, and experiential. For example, the collection unit can survey students to find out what learning methods they prefer and store the results in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze students' learning history, interests, and learning styles, and generates information to suggest the optimal educational path for each student. For example, the analysis unit can identify students' strong and weak subjects based on past grade data. The analysis unit can also analyze survey results and behavioral history to understand students' interests. Furthermore, the analysis unit can analyze data related to learning styles and generate information to suggest the optimal learning method for each student. Step 3: The suggestion unit proposes an appropriate educational path based on the analysis results obtained by the analysis unit. For example, the suggestion unit can provide learning materials and assignments related to a particular subject to a student who is interested in that subject. The suggestion unit can also propose a learning method according to the student's learning style. For example, it can provide visual learning materials to a visual student and audio learning materials to an auditory student. Furthermore, the suggestion unit can adjust the learning plan according to the student's learning progress. For example, the suggestion unit can propose more difficult assignments if the student is making progress, and more basic assignments if the student is stagnating. Step 4: The monitoring unit monitors the progress of students' learning in real time. For example, the monitoring unit can monitor students' learning status in real time using sensors and applications. For example, the monitoring unit can record students' study time and learning content and send the data to the analysis unit. The monitoring unit can also monitor students' study attitudes and concentration levels and provide information to adjust study plans as needed. Furthermore, the monitoring unit can monitor students' learning environments and collect information to provide an appropriate learning environment. For example, the monitoring unit can monitor whether students are studying in a quiet environment and adjust the environment as needed.

[0066] (Example 2) An education optimization system according to an embodiment of the present invention uses AI to optimize each student's educational experience. This education optimization system collects data such as students' learning history, interests, and learning styles, and uses AI to analyze this data to propose the optimal educational path for each student. For example, if a student is interested in a particular subject, it provides learning materials and assignments related to that subject to enhance their motivation to learn. Furthermore, the system monitors their learning progress in real time and adjusts their learning plan as needed. This allows students to quickly identify the optimal educational path for self-realization and maintain their motivation to learn. First, data such as students' learning history, interests, and learning styles is collected. This includes detailed data such as the student's grades, past learning history, subjects of interest, and learning style (e.g., visual, auditory). For example, it is possible to determine which subjects the student is interested in and what learning style is effective for them. Next, the collected data is analyzed using AI. The AI ​​analyzes the collected data and proposes the optimal educational path for each student. For example, if a student is interested in a particular subject, it provides learning materials and assignments related to that subject to enhance their motivation to learn. Furthermore, the system monitors their learning progress in real time. For example, sensors and applications can be used to monitor students' learning status in real time and adjust their learning plans as needed. This allows students to find the optimal educational path for self-realization early on and maintain their motivation to learn. This system provides the optimal educational path tailored to each student, increasing their motivation to learn. For example, a student who is interested in a particular subject can be provided with learning materials and assignments related to that subject, increasing their motivation to learn. Furthermore, by monitoring their learning progress in real time and adjusting their learning plans as needed, students can find the optimal educational path for self-realization early on. This allows the education optimization system to suggest the optimal educational path for each student and support their motivation to learn.

[0067] An education optimization system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a monitoring unit. The collection unit collects data on students' learning histories, interests, and learning styles. For example, the collection unit can acquire students' grades and past learning histories from a database. The collection unit can also identify students' interests based on questionnaires and behavioral histories. The collection unit can also collect data to identify learning styles, such as visual, auditory, and experiential. For example, the collection unit can survey students to determine their preferred learning methods and store the results in a database. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can use AI to analyze students' learning histories, interests, and learning styles and generate information for proposing the optimal educational path for each student. For example, the analysis unit can identify students' strong and weak subjects based on past grade data. The analysis unit can also analyze questionnaire results and behavioral histories to identify students' interests. The analysis unit can also analyze data on learning styles and generate information for proposing the optimal learning method for each student. The suggestion unit proposes an appropriate educational path based on the analysis results obtained by the analysis unit. For example, the suggestion unit can provide teaching materials and assignments related to a particular subject to a student who is interested in that subject. The suggestion unit can also suggest a learning method according to the student's learning style. For example, it can provide visual materials to a visual student and audio materials to an auditory student. The suggestion unit can also adjust the learning plan according to the student's learning progress. For example, the suggestion unit can suggest more difficult assignments if the student is making progress and more basic assignments if the student is stagnating. The monitoring unit instantly monitors the learning progress. For example, the monitoring unit can monitor the student's learning status in real time using sensors or applications. The monitoring unit can, for example, record the student's study time and learning content and send the data to the analysis unit. The monitoring unit can also monitor the student's learning attitude and concentration level and provide information to adjust the learning plan as necessary.Furthermore, the monitoring unit can monitor the student's learning environment and collect information to provide an appropriate learning environment. For example, the monitoring unit can monitor whether the student is studying in a quiet environment and adjust the environment as necessary. This allows the education optimization system according to the embodiment to propose an optimal educational path for each student and support their continued motivation to learn.

[0068] The suggestion unit may include a provision unit that provides teaching materials and assignments related to a particular subject to a student who is interested in that subject. For example, the provision unit provides teaching materials and assignments related to that subject to a student who is interested in that subject. For example, the provision unit may search a database for teaching materials related to the subject in which the student is interested and provide them to the student. The provision unit may also provide appropriate assignments according to the student's learning progress. For example, the provision unit may provide more difficult assignments if the student is making progress, and more basic assignments if the student is stagnating. Furthermore, the provision unit may provide teaching materials according to the student's learning style. For example, it may provide visual teaching materials to visual students and audio teaching materials to auditory students. In this way, providing teaching materials and assignments based on the student's interests increases the student's motivation to learn.

[0069] The monitoring unit can monitor the student's learning status in real time using sensors or applications. The monitoring unit, for example, uses sensors to monitor the student's learning status in real time. For example, the monitoring unit can monitor the student's study time and concentration level using a wearable device. The monitoring unit can also monitor the student's learning environment using an environmental sensor. For example, the monitoring unit can monitor whether the student is studying in a quiet environment and adjust the environment as needed. Furthermore, the monitoring unit can monitor the student's learning status using an application. For example, the monitoring unit can monitor the student's learning progress in real time using a learning management system or a mobile app and adjust the learning plan as needed. In this way, by monitoring the learning status in real time, the learning plan can be adjusted at the appropriate time.

[0070] The suggestion unit can adjust the study plan according to the student's learning progress. For example, the suggestion unit can adjust the study plan according to the student's learning progress. For example, the suggestion unit can suggest more difficult tasks if the student is making progress, and more basic tasks if the student is stagnating. The suggestion unit can also monitor the student's learning attitude and concentration level and adjust the study plan as needed. For example, the suggestion unit can extend the study time if the student is concentrating, and suggest taking a break if the student is tired. Furthermore, the suggestion unit can monitor the student's learning environment and collect information to provide an appropriate learning environment. For example, the suggestion unit can monitor whether the student is studying in a quiet environment and adjust the environment as needed. This makes it possible to support effective learning by flexibly adjusting the study plan according to the student's learning progress.

[0071] The providing unit may include a feedback unit that provides feedback to increase motivation to learn. The feedback unit provides, for example, feedback to increase motivation to learn of students. For example, the feedback unit can provide positive comments for goals that a student has achieved. The feedback unit can also provide encouraging messages when a student is facing difficulties. Furthermore, the feedback unit can provide feedback that indicates specific areas for improvement according to the student's learning progress. For example, when a student is struggling with a particular assignment, the feedback unit can provide specific advice for that assignment. This makes it possible to increase motivation to learn through feedback.

[0072] The collection unit can estimate the student's emotions and adjust the timing of data collection based on the estimated student's emotions. The collection unit, for example, estimates the student's emotions and adjusts the timing of data collection based on the estimated student's emotions. For example, if the student is feeling stressed, the collection unit can delay data collection until the student is relaxed. Also, if the student is concentrating, the collection unit can collect detailed learning data at that time. Furthermore, if the student is tired, the collection unit can collect data after a break. For example, the collection unit can collect data when the student is relaxed, thereby collecting more accurate data. In this way, more appropriate data can be collected by adjusting the timing of data collection according to the student's emotions.

[0073] The collection unit can analyze the student's past learning history and select the optimal data collection method. The collection unit, for example, analyzes the student's past learning history and selects the optimal data collection method. For example, the collection unit can collect data using a similar method based on a learning method that was effective for the student in the past. The collection unit can also collect data using a different approach for subjects that the student was weak at in the past. Furthermore, the collection unit can collect data at the most efficient time period based on the student's past learning history. For example, the collection unit can collect data at the time period when the student was most able to concentrate in the past, thereby achieving efficient data collection. In this way, efficient data collection is possible by selecting the optimal data collection method based on the student's past learning history.

[0074] The collection unit can filter data based on the student's current learning environment and areas of interest when collecting data. For example, the collection unit can filter data based on the student's current learning environment and areas of interest when collecting data. For example, if a student is studying in a quiet environment, the collection unit can collect data appropriate for that environment. Also, if a student is interested in a particular subject, the collection unit can preferentially collect data related to that subject. Furthermore, if students are studying in a group, the collection unit can collect data for the entire group. For example, the collection unit can collect data related to a project that students are working on as a group to understand the learning status of the entire group. In this way, by filtering data based on the learning environment and areas of interest, highly relevant data can be collected.

[0075] The collection unit can estimate the student's emotions and determine the priority of data to be collected based on the estimated student's emotions. The collection unit, for example, estimates the student's emotions and determines the priority of data to be collected based on the estimated student's emotions. For example, if the student is excited, the collection unit can prioritize collecting data related to subjects that the student is interested in. Also, if the student is relaxed, the collection unit can collect overall learning data in a balanced manner. Furthermore, if the student is tired, the collection unit can prioritize collecting data that is less burdensome. For example, the collection unit can collect data when the student is relaxed, thereby collecting more accurate data. In this way, by determining the priority of data according to the student's emotions, important data can be collected preferentially.

[0076] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the student's geographical location information. For example, when collecting data, the collection unit prioritizes collecting highly relevant data by taking into account the student's geographical location information. For example, when a student is in a specific area, the collection unit can collect learning data related to that area. Also, when a student is traveling, the collection unit can collect cultural and historical data related to the travel destination. Furthermore, when a student is at home, the collection unit can collect data suitable for home study. For example, when a student is studying at home, the collection unit can collect data for providing learning materials and assignments suitable for home study. In this way, by collecting highly relevant data based on geographical location information, more appropriate data can be provided.

[0077] The collection unit can analyze students' social media activities and collect related data when collecting data. For example, the collection unit can analyze students' social media activities and collect related data when collecting data. For example, the collection unit can collect data related to topics that students are interested in on social media. The collection unit can also collect useful information from education-related accounts that students follow. Furthermore, the collection unit can analyze activities in online communities in which students participate and collect related data. For example, the collection unit can analyze activities in online communities in which students participate and collect related learning data. In this way, by collecting related data based on social media activities, it is possible to provide data tailored to students' interests.

[0078] The analysis unit can estimate the student's emotions and adjust the way the analysis is presented based on the estimated student's emotions. The analysis unit, for example, estimates the student's emotions and adjusts the way the analysis is presented based on the estimated student's emotions. For example, if the student is nervous, the analysis unit can provide simple, highly visible analysis results. Also, if the student is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the student is excited, the analysis unit can provide visually appealing analysis results. For example, if the student is excited, the analysis unit can provide visually appealing analysis results using graphs and charts. In this way, by adjusting the way the analysis is presented according to the student's emotions, it is possible to provide analysis results that are easier to understand.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit can achieve efficient analysis by performing a detailed analysis on data of high importance and a simplified analysis on data of low importance. In this way, efficient analysis is possible by adjusting the level of detail of the analysis according to the importance of the data.

[0080] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply an analysis algorithm based on past grades to learning history data. The analysis unit can also apply a clustering algorithm to interest data. The analysis unit can also apply a pattern recognition algorithm to learning style data. For example, the analysis unit can achieve more accurate analysis by applying an analysis algorithm based on past grades to learning history data and a clustering algorithm to interest data. This makes it possible to achieve more accurate analysis by applying an analysis algorithm depending on the category of data.

[0081] The analysis unit can estimate the student's emotions and adjust the length of the analysis based on the estimated student's emotions. The analysis unit, for example, estimates the student's emotions and adjusts the length of the analysis based on the estimated student's emotions. For example, if the student is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Also, if the student is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the student is excited, the analysis unit can provide a visually appealing analysis result. For example, if the student is in a hurry, the analysis unit can provide a short and to-the-point analysis result, and if the student is relaxed, the analysis unit can provide a detailed analysis result, thereby providing an analysis result of an appropriate length. In this way, by adjusting the length of the analysis according to the student's emotions, an analysis result of an appropriate length can be provided.

[0082] The analysis unit can determine the analysis priority based on the time of data collection during analysis. The analysis unit, for example, determines the analysis priority based on the time of data collection during analysis. For example, the analysis unit can prioritize analyzing the latest data. The analysis unit can also emphasize the latest data while referring to past data. Furthermore, the analysis unit can prioritize analyzing data collected during a specific period. For example, the analysis unit can achieve efficient analysis by analyzing the latest data with priority and referring to past data. In this way, by determining the analysis priority based on the time of data collection, the latest data can be prioritized.

[0083] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of data. For example, the analysis unit can achieve efficient analysis by analyzing highly relevant data with priority and postponing analysis of less relevant data. In this way, efficient analysis is possible by adjusting the order of analysis based on the relevance of data.

[0084] The suggestion unit can estimate the student's emotions and adjust the way in which the suggestion is expressed based on the estimated student's emotions. The suggestion unit, for example, estimates the student's emotions and adjusts the way in which the suggestion is expressed based on the estimated student's emotions. For example, if the student is nervous, the suggestion unit can make a simple and highly visible suggestion. Also, if the student is relaxed, the suggestion unit can make a detailed suggestion. Furthermore, if the student is excited, the suggestion unit can make a visually appealing suggestion. For example, if the student is nervous, the suggestion unit can make a simple and highly visible suggestion, and if the student is relaxed, the suggestion unit can make a detailed suggestion, thereby providing a more understandable suggestion. In this way, by adjusting the way in which the suggestion is expressed according to the student's emotions, a more understandable suggestion can be provided.

[0085] The suggestion unit can adjust the level of detail of the proposal based on the importance of the educational path when making the suggestion. For example, the suggestion unit can make a detailed suggestion for an educational path with high importance. The suggestion unit can also make a simplified suggestion for an educational path with low importance. Furthermore, the suggestion unit can make a suggestion with an appropriate level of detail for an educational path with medium importance. For example, the suggestion unit can make a detailed suggestion for an educational path with high importance and a simplified suggestion for an educational path with low importance, thereby realizing efficient suggestions. As a result, efficient suggestions can be made by adjusting the level of detail of the suggestion according to the importance of the educational path.

[0086] The suggestion unit can apply different suggestion algorithms depending on the category of the educational path when making a suggestion. For example, the suggestion unit can apply different suggestion algorithms depending on the category of the educational path when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm specialized for science to science subjects. The suggestion unit can also apply a suggestion algorithm specialized for humanities to humanities subjects. The suggestion unit can also apply a suggestion algorithm specialized for arts to arts subjects. For example, the suggestion unit can realize more accurate suggestions by applying a suggestion algorithm specialized for science to science subjects and a suggestion algorithm specialized for humanities to humanities subjects. This makes it possible to make more accurate suggestions by applying a suggestion algorithm depending on the category of the educational path.

[0087] The suggestion unit can estimate the student's emotions and adjust the length of the suggestion based on the estimated student's emotions. The suggestion unit, for example, estimates the student's emotions and adjusts the length of the suggestion based on the estimated student's emotions. For example, the suggestion unit can provide a short and to-the-point suggestion when the student is in a hurry. Also, the suggestion unit can provide a detailed suggestion when the student is relaxed. Furthermore, the suggestion unit can provide a visually appealing suggestion when the student is excited. For example, the suggestion unit can provide a short and to-the-point suggestion when the student is in a hurry, and a detailed suggestion when the student is relaxed, thereby providing a suggestion of appropriate length. In this way, by adjusting the length of the suggestion according to the student's emotions, a suggestion of appropriate length can be provided.

[0088] The suggestion unit can determine the priority of the proposals based on the submission time of the educational paths at the time of proposal. The suggestion unit, for example, determines the priority of the proposals based on the submission time of the educational paths at the time of proposal. For example, the suggestion unit can preferentially suggest educational paths with a near submission deadline. The suggestion unit can also postpone educational paths with a distant submission deadline. Furthermore, the suggestion unit can dynamically adjust the priority of the proposals according to the submission time. For example, the suggestion unit can provide proposals at an appropriate time by preferentially proposing educational paths with a near submission deadline and postponing educational paths with a distant submission deadline. In this way, by determining the priority of the proposals based on the submission time, it is possible to provide proposals at an appropriate time.

[0089] The suggestion unit can adjust the order of proposals based on the relevance of the educational paths when making a proposal. The suggestion unit, for example, adjusts the order of proposals based on the relevance of the educational paths when making a proposal. For example, the suggestion unit can prioritize proposing highly relevant educational paths. The suggestion unit can also postpone less relevant educational paths. Furthermore, the suggestion unit can dynamically adjust the order of proposals according to the relevance of the educational paths. For example, the suggestion unit can prioritize proposing highly relevant educational paths and postpone less relevant educational paths, thereby achieving efficient proposals. As a result, efficient proposals are possible by adjusting the order of proposals based on the relevance of the educational paths.

[0090] The monitoring unit can estimate the student's emotions and adjust the monitoring standards based on the estimated student's emotions. The monitoring unit, for example, estimates the student's emotions and adjusts the monitoring standards based on the estimated student's emotions. For example, the monitoring unit can reduce the frequency of monitoring when the student is nervous. Also, the monitoring unit can increase the frequency of monitoring when the student is relaxed. Furthermore, the monitoring unit can tighten the monitoring standards when the student is excited. For example, the monitoring unit can reduce the frequency of monitoring when the student is nervous and increase the frequency of monitoring when the student is relaxed, thereby achieving appropriate monitoring. In this way, appropriate monitoring is possible by adjusting the monitoring standards according to the student's emotions.

[0091] The monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between learning activities when monitoring. For example, the monitoring unit can improve the accuracy of monitoring by taking into account the interrelationships between learning activities when monitoring. For example, when a student is studying multiple subjects simultaneously, the monitoring unit can monitor the progress of each subject by correlating them with each other. Furthermore, when students are studying in a group, the monitoring unit can also monitor the learning status of the entire group. Furthermore, when a student is studying project-based learning, the monitoring unit can monitor the progress of the project. For example, when a student is studying multiple subjects simultaneously, the monitoring unit can improve the accuracy of monitoring by monitoring the progress of each subject by correlating them with each other. In this way, the accuracy of monitoring is improved by taking into account the interrelationships between learning activities.

[0092] The monitoring unit can perform monitoring taking into account the attribute information of students during monitoring. For example, the monitoring unit can perform monitoring taking into account the attribute information of students during monitoring. For example, the monitoring unit can adjust the monitoring criteria according to the age of the student. The monitoring unit can also adjust the monitoring criteria according to the gender of the student. Furthermore, the monitoring unit can adjust the monitoring criteria according to the year of the student. For example, the monitoring unit can adjust the monitoring criteria according to the age of the student and the gender of the student, thereby achieving more appropriate monitoring. This makes it possible to perform more appropriate monitoring by performing monitoring based on the attribute information of the student.

[0093] The monitoring unit can estimate the student's emotions and adjust the order in which the monitoring results are displayed based on the estimated student's emotions. The monitoring unit can, for example, estimate the student's emotions and adjust the order in which the monitoring results are displayed based on the estimated student's emotions. For example, if the student is nervous, the monitoring unit can display important results first. Also, if the student is relaxed, the monitoring unit can sequentially display detailed results. Furthermore, if the student is excited, the monitoring unit can display visually appealing results first. For example, if the student is nervous, the monitoring unit can display important results first, and if the student is relaxed, the monitoring unit can sequentially display detailed results, thereby providing results that are easier to understand. In this way, by adjusting the display order of the monitoring results according to the student's emotions, it is possible to provide results that are easier to understand.

[0094] The monitoring unit can perform monitoring taking into account the geographical distribution of learning when monitoring. For example, the monitoring unit performs monitoring taking into account the geographical distribution of learning when monitoring. For example, if students are studying in different regions, the monitoring unit can monitor the learning situation in each region individually. In addition, if a student is studying abroad, the monitoring unit can also monitor taking into account the learning environment of the student's study abroad destination. Furthermore, if a student is studying online, the monitoring unit can monitor without taking geographical constraints into account. For example, if students are studying in different regions, the monitoring unit can monitor the learning situation in each region individually, and if a student is studying abroad, the monitoring unit can monitor taking into account the learning environment of the student's study abroad destination, thereby achieving more appropriate monitoring. In this way, more appropriate monitoring is possible by taking into account the geographical distribution of learning.

[0095] The monitoring unit may improve the accuracy of monitoring by referring to literature related to the learning during monitoring. For example, the monitoring unit may improve the accuracy of monitoring by referring to literature related to the learning during monitoring. For example, the monitoring unit may monitor by referring to the latest research papers related to the subject the student is studying. The monitoring unit may also monitor based on the references of the learning materials the student is using. Furthermore, the monitoring unit may monitor by referring to literature related to the online course the student is participating in. For example, the monitoring unit may improve the accuracy of monitoring by referring to the latest research papers related to the subject the student is studying and by monitoring based on the references of the learning materials the student is using. In this way, the accuracy of monitoring is improved by referring to related literature.

[0096] The provision unit can estimate a student's emotions and determine the priority of teaching materials and assignments to be provided based on the estimated student's emotions. The provision unit, for example, estimates a student's emotions and determines the priority of teaching materials and assignments to be provided based on the estimated student's emotions. For example, if the student is excited, the provision unit can prioritize providing teaching materials related to subjects in which the student is interested. Furthermore, if the student is relaxed, the provision unit can also provide teaching materials based on an overall learning plan. Furthermore, if the student is tired, the provision unit can prioritize providing assignments that are less burdensome. For example, if the student is excited, the provision unit prioritizes providing teaching materials related to subjects in which the student is interested, and if the student is relaxed, the provision unit provides teaching materials based on an overall learning plan, thereby supporting more effective learning. In this way, more effective learning can be supported by determining the priority of teaching materials and assignments according to the student's emotions.

[0097] The provision unit can improve the accuracy of provision by taking into account the interrelationships between learning subjects when providing the information. For example, the provision unit can improve the accuracy of provision by taking into account the interrelationships between learning subjects when providing the information. For example, when a student is studying multiple subjects simultaneously, the provision unit can provide teaching materials by taking into account the relevance of each subject. Furthermore, when a student is studying in a group, the provision unit can provide teaching materials by taking into account the learning status of the entire group. Furthermore, when a student is studying in a project-based learning, the provision unit can provide teaching materials according to the progress of the project. For example, when a student is studying multiple subjects simultaneously, the provision unit can provide teaching materials by taking into account the relevance of each subject, and when a student is studying in a group, the provision unit can improve the accuracy of provision by providing teaching materials by taking into account the learning status of the entire group. In this way, the accuracy of provision is improved by taking into account the interrelationships between learning subjects.

[0098] The providing unit can provide the teaching materials taking into consideration the student's attribute information when providing the teaching materials. For example, the providing unit can provide teaching materials of an appropriate level of difficulty depending on the student's age. The providing unit can also provide teaching materials that attract the student's interest depending on the student's gender. Furthermore, the providing unit can provide teaching materials appropriate for the student's grade level depending on the student's grade level. For example, the providing unit can provide teaching materials of an appropriate level of difficulty depending on the student's age and teaching materials that attract the student's interest depending on the student's gender, thereby providing more appropriate teaching materials and assignments. In this way, more appropriate teaching materials and assignments can be provided by providing the teaching materials based on the student's attribute information.

[0099] The providing unit can estimate a student's emotions and adjust the display method of the teaching materials and assignments to be provided based on the estimated student's emotions. The providing unit, for example, estimates a student's emotions and adjusts the display method of the teaching materials and assignments to be provided based on the estimated student's emotions. For example, if the student is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the student is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the student is excited, the providing unit can provide a visually appealing display method. For example, if the student is nervous, the providing unit can provide a simple, highly visible display method, and if the student is relaxed, the providing unit can provide a display method including detailed information, thereby providing teaching materials and assignments that are easier to understand. In this way, by adjusting the display method according to the student's emotions, teaching materials and assignments that are easier to understand can be provided.

[0100] The provision unit can provide the teaching materials taking into consideration the geographical distribution of learning when providing the teaching materials. For example, the provision unit provides the teaching materials taking into consideration the geographical distribution of learning when providing the teaching materials. For example, if students are studying in different regions, the provision unit can provide teaching materials appropriate for each region. Furthermore, if a student is studying abroad, the provision unit can provide teaching materials appropriate for the learning environment of the student's study abroad destination. Furthermore, if a student is studying online, the provision unit can provide teaching materials without considering geographical constraints. For example, if a student is studying in different regions, the provision unit can provide teaching materials appropriate for each region, and if a student is studying abroad, the provision unit can provide teaching materials appropriate for the learning environment of the student's study abroad destination, thereby providing more appropriate teaching materials and assignments. In this way, more appropriate teaching materials and assignments can be provided by taking into consideration the geographical distribution of learning.

[0101] The providing unit can improve the accuracy of the provision by referring to literature related to the study when providing the teaching materials. For example, the providing unit can improve the accuracy of the provision by referring to literature related to the study when providing the teaching materials. For example, the providing unit can provide the teaching materials by referring to the latest research papers related to the subject the student is studying. The providing unit can also provide the teaching materials based on the references of the teaching materials used by the student. Furthermore, the providing unit can provide the teaching materials by referring to literature related to the online course in which the student is participating. For example, the providing unit can improve the accuracy of the provision by referring to the latest research papers related to the subject the student is studying and providing the teaching materials based on the references of the teaching materials used by the student. In this way, the accuracy of the provision is improved by referring to the related literature.

[0102] The feedback unit can estimate the student's emotions and adjust the feedback method based on the estimated student's emotions. The feedback unit, for example, estimates the student's emotions and adjusts the feedback method based on the estimated student's emotions. For example, if the student is nervous, the feedback unit can provide feedback in gentle words. Also, if the student is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the student is excited, the feedback unit can provide visually appealing feedback. For example, if the student is nervous, the feedback unit can provide feedback in gentle words, and if the student is relaxed, the feedback unit can provide detailed feedback, thereby providing more effective feedback. In this way, more effective feedback can be provided by adjusting the feedback method according to the student's emotions.

[0103] The feedback unit can select the optimal feedback method by analyzing the student's past learning behavior when providing feedback. For example, the feedback unit can select the optimal feedback method by analyzing the student's past learning behavior when providing feedback. For example, the feedback unit can provide feedback using a similar method based on feedback methods that have been effective for the student in the past. The feedback unit can also provide feedback using a different approach, avoiding feedback methods that the student has struggled with in the past. Furthermore, the feedback unit can select the most efficient feedback method based on the student's past learning behavior. For example, the feedback unit can provide feedback using a similar method based on feedback methods that have been effective for the student in the past, and provide feedback using a different approach, avoiding feedback methods that the student has struggled with in the past, thereby achieving effective feedback. In this way, effective feedback is possible by selecting the optimal feedback method based on the student's past learning behavior.

[0104] The feedback unit can customize the means of feedback based on the student's current learning situation when providing feedback. For example, the feedback unit customizes the means of feedback based on the student's current learning situation when providing feedback. For example, the feedback unit can provide praising feedback if the student is making progress. Furthermore, the feedback unit can provide encouraging feedback if the student is stagnating. Furthermore, the feedback unit can provide feedback indicating specific areas for improvement if the student is regressing. For example, the feedback unit can provide praising feedback if the student is making progress, and encouraging feedback if the student is stagnating, thereby providing more appropriate feedback. This allows for more appropriate feedback to be provided by customizing the means of feedback based on the student's current learning situation.

[0105] The feedback unit can estimate the student's emotions and determine the priority of feedback based on the estimated student's emotions. The feedback unit, for example, estimates the student's emotions and determines the priority of feedback based on the estimated student's emotions. For example, if the student is excited, the feedback unit can prioritize feedback related to subjects that the student is interested in. Also, if the student is relaxed, the feedback unit can provide feedback based on the overall learning plan. Furthermore, if the student is tired, the feedback unit can prioritize feedback that is less burdensome. For example, if the student is excited, the feedback unit can prioritize feedback related to subjects that the student is interested in, and if the student is relaxed, the feedback unit can provide feedback based on the overall learning plan, thereby providing more effective feedback. In this way, more effective feedback can be provided by determining the priority of feedback according to the student's emotions.

[0106] The feedback unit can select the optimal feedback method by taking into account the student's geographical location information when providing feedback. For example, the feedback unit selects the optimal feedback method by taking into account the student's geographical location information when providing feedback. For example, if students are studying in different regions, the feedback unit can provide feedback appropriate for each region. Furthermore, if a student is studying abroad, the feedback unit can provide feedback appropriate for the learning environment of the student's study abroad destination. Furthermore, if a student is studying online, the feedback unit can provide feedback without taking geographical constraints into account. For example, if a student is studying in different regions, the feedback unit can provide feedback appropriate for each region, and if a student is studying abroad, the feedback unit can provide feedback appropriate for the learning environment of the student's study abroad destination, thereby providing more appropriate feedback. This enables more appropriate feedback by selecting the optimal feedback method based on the geographical location information.

[0107] The feedback unit can analyze the student's social media activity and suggest a means of providing feedback when providing feedback. For example, the feedback unit can analyze the student's social media activity and suggest a means of providing feedback when providing feedback. For example, the feedback unit can provide feedback related to topics that the student is interested in on social media. The feedback unit can also incorporate useful information from education-related accounts that the student follows into the feedback. Furthermore, the feedback unit can analyze the activity of online communities in which the student participates and provide related feedback. For example, the feedback unit can provide feedback related to topics that the student is interested in on social media and incorporate useful information from education-related accounts that the student follows into the feedback, thereby providing more appropriate feedback. This enables more appropriate feedback to be provided by suggesting a means of providing feedback based on social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and monitoring unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects students' learning histories and interests using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate an optimal educational path for each student. The suggestion unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and suggests appropriate teaching materials and assignments based on the analysis results. The monitoring unit, for example, monitors students' learning status in real time using sensors and applications of the smart device 14 and adjusts the learning plan as needed. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and monitoring 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 collection unit collects students' learning histories and interests using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and generates an optimal educational path for each student. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests appropriate teaching materials and assignments based on the analysis results. The monitoring unit, for example, monitors students' learning status in real time using sensors and applications of the smart glasses 214 and adjusts the learning plan as necessary. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and monitoring unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects students' learning histories and interests using the camera 42 and microphone 238 of the headset terminal 314 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate an optimal educational path for each student. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate learning materials and assignments based on the analysis results. The monitoring unit monitors students' learning status in real time using, for example, sensors and applications of the headset terminal 314 and adjusts the learning plan as needed. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and monitoring unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects students' learning histories and interests using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to generate an optimal educational path for each student. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests appropriate teaching materials and assignments based on the analysis results. The monitoring unit monitors students' learning situations in real time using, for example, sensors and applications of the robot 414 and adjusts the learning plan as needed.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The education optimization system may further include a health monitoring unit that monitors the student's health status. For example, the health monitoring unit may use a wearable device to monitor the student's heart rate and sleep patterns to understand their health status. The health monitoring unit may also measure the student's stress level and provide relaxation advice as needed. Furthermore, the health monitoring unit may record the student's dietary and exercise habits and provide suggestions for maintaining healthy lifestyles. This may allow the system to propose an optimal education path that takes into account the student's health status.

[0110] The suggestion unit can estimate the student's emotions and evaluate the student's learning progress based on the estimated student emotions. For example, if the suggestion unit determines that the student is excited, he or she has a high motivation to learn, and can suggest a more difficult assignment. If the student is relaxed, the suggestion unit can determine that the student's concentration is high, and can suggest an assignment that requires concentration. Furthermore, if the student is tired, the suggestion unit can suggest a break and encourage the student to start learning again after refreshing. This makes it possible to provide a flexible learning plan that corresponds to the student's emotions.

[0111] The monitoring unit may include an environmental adjustment unit that adjusts the student's learning environment in real time. The environmental adjustment unit can provide an optimal learning environment by adjusting environmental elements such as lighting, temperature, and volume. The environmental adjustment unit can also monitor the student's concentration level and adjust the environment to restore concentration if their concentration wanes. Furthermore, the environmental adjustment unit can customize the environment according to the student's learning style, providing bright lighting for visual students and a quiet environment for auditory students. This maximizes the student's learning efficiency.

[0112] The suggestion unit can estimate the student's emotions and evaluate the student's learning progress based on the estimated student emotions. For example, if the suggestion unit determines that the student is excited, he or she has a high motivation to learn, and can suggest a more difficult assignment. If the student is relaxed, the suggestion unit can determine that the student's concentration is high, and can suggest an assignment that requires concentration. Furthermore, if the student is tired, the suggestion unit can suggest a break and encourage the student to start learning again after refreshing. This makes it possible to provide a flexible learning plan that corresponds to the student's emotions.

[0113] The provision unit can estimate the student's emotions and determine the priority of teaching materials and assignments to provide based on the estimated student's emotions. For example, if the student is excited, the provision unit can prioritize providing teaching materials related to subjects that the student is interested in. Also, if the student is relaxed, the provision unit can provide teaching materials based on the overall learning plan. Furthermore, if the student is tired, the provision unit can prioritize providing assignments that are less burdensome. In this way, more effective learning can be supported by prioritizing teaching materials and assignments according to the student's emotions.

[0114] The collection unit can collect not only students' learning history, but also data on their hobbies and special skills. For example, the collection unit can understand what hobbies and special skills a student has and customize a study plan based on that information. The collection unit can also collect data on the club activities and volunteer activities in which students participate, allowing it to provide comprehensive study support. Furthermore, the collection unit can collect data on students' family structures and living environments, allowing it to provide study support tailored to individual needs. This makes it possible to propose optimal educational paths that suit students' diverse backgrounds.

[0115] The analysis unit can estimate the student's emotions and adjust the way the analysis is presented based on the estimated student's emotions. For example, if the student is nervous, the analysis unit can provide a simple, highly visible analysis result. If the student is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the student is excited, the analysis unit can also provide a visually appealing analysis result. In this way, by adjusting the way the analysis is presented according to the student's emotions, it is possible to provide analysis results that are easier to understand.

[0116] The collection unit can analyze the student's past learning history and select the optimal data collection method. For example, the collection unit can collect data using a similar learning method based on a student's past effective learning method. The collection unit can also collect data using a different approach for subjects that the student was weak at in the past. Furthermore, the collection unit can collect data at the most efficient time period based on the student's past learning history. This allows for efficient data collection by selecting the optimal data collection method based on the student's past learning history.

[0117] The monitoring unit can be equipped with an evaluation unit that not only monitors students' learning status but also evaluates the quality of their learning. The evaluation unit can, for example, evaluate how much a student understands and provide feedback according to the level of understanding. The evaluation unit can also evaluate students' learning attitudes and concentration levels and adjust their learning plans as necessary. Furthermore, the evaluation unit can regularly evaluate students' learning outcomes and grasp their progress. This makes it possible to provide more effective learning support by evaluating the quality of learning.

[0118] The suggestion unit can estimate the student's emotions and adjust the way the suggestions are expressed based on the estimated student's emotions. For example, if the student is nervous, the suggestion unit can make a simple, highly visible suggestion. If the student is relaxed, the suggestion unit can also make a detailed suggestion. Furthermore, if the student is excited, the suggestion unit can also make a visually appealing suggestion. In this way, by adjusting the way the suggestions are expressed according to the student's emotions, it is possible to provide suggestions that are easier to understand.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects data on students' learning history, interests, and learning styles. For example, the collection unit can retrieve students' grades and past learning history from a database. The collection unit can also understand students' interests based on questionnaires and behavioral history. Furthermore, the collection unit can collect data to identify learning styles such as visual, auditory, and experiential. For example, the collection unit can survey students to find out what learning methods they prefer and store the results in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze students' learning history, interests, and learning styles, and generates information to suggest the optimal educational path for each student. For example, the analysis unit can identify students' strong and weak subjects based on past grade data. The analysis unit can also analyze survey results and behavioral history to understand students' interests. Furthermore, the analysis unit can analyze data related to learning styles and generate information to suggest the optimal learning method for each student. Step 3: The suggestion unit proposes an appropriate educational path based on the analysis results obtained by the analysis unit. For example, the suggestion unit can provide learning materials and assignments related to a particular subject to a student who is interested in that subject. The suggestion unit can also propose a learning method according to the student's learning style. For example, it can provide visual learning materials to a visual student and audio learning materials to an auditory student. Furthermore, the suggestion unit can adjust the learning plan according to the student's learning progress. For example, the suggestion unit can propose more difficult assignments if the student is making progress, and more basic assignments if the student is stagnating. Step 4: The monitoring unit monitors the progress of students' learning in real time. For example, the monitoring unit can monitor students' learning status in real time using sensors and applications. For example, the monitoring unit can record students' study time and learning content and send the data to the analysis unit. The monitoring unit can also monitor students' study attitudes and concentration levels and provide information to adjust study plans as needed. Furthermore, the monitoring unit can monitor students' learning environments and collect information to provide an appropriate learning environment. For example, the monitoring unit can monitor whether students are studying in a quiet environment and adjust the environment as needed.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 collection department that collects data on students' learning history, interests, and learning styles; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests an appropriate educational path based on the analysis results obtained by the analysis unit; A monitoring unit that monitors the progress of learning in real time. A system characterized by:

2. The proposal unit Provide a department that provides course materials and assignments related to a particular subject to students who are interested in that subject. The system of claim 1 .

3. The monitoring unit Real-time monitoring of student learning using sensors or applications The system of claim 1 .

4. The proposal unit Adjust your study plan according to your progress The system of claim 1 .

5. The providing unit Equipped with a feedback section that provides feedback to enhance motivation to learn 3. The system of claim 2.

6. The collecting unit Estimate student emotions and adjust the timing of data collection based on the estimated student emotions. The system of claim 1 .

7. The collecting unit Analyze students' past learning histories and select the most appropriate data collection method The system of claim 1 .

8. The collecting unit Filter data collection based on students' current learning environment and areas of interest The system of claim 1 .

9. The collecting unit Estimate student sentiment and prioritize data collection based on the estimated student sentiment The system of claim 1 .

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