system

Abstract

To provide a system that accurately determines learning proficiency of students and provides appropriate learning support.SOLUTION: A system according to the embodiment comprises a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects learning data of students. The determination unit determines proficiency of the student based on the learning data collected by the collection unit. The provision unit provides the students with explanations and practice problems in areas in which the proficiency determined by the determination unit is lacking. The presentation unit presents the students with the proficiency determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to accurately assess students' learning proficiency and provide appropriate learning support.

[0005] The system according to the embodiment aims to accurately determine the learning proficiency of students and provide appropriate learning support. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects learning data of students. The determination unit determines the proficiency level of the students based on the learning data collected by the collection unit. The provision unit provides the students with explanations and practice problems in areas where the proficiency level determined by the determination unit is lacking. The presentation unit presents the proficiency level determined by the determination unit to the students. [Effects of the Invention]

[0007] The system according to the embodiment can accurately determine the learning proficiency of students and provide appropriate learning support. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention is a system for cram schools and schools that assesses a student's learning proficiency and provides explanations and practice problems at an appropriate level for areas where the student's proficiency is low. This learning support system first collects the student's learning data and then assesses the student's proficiency based on the collected learning data. Explanations and practice problems are provided for areas where the student's assessed proficiency is lacking, and the assessed proficiency level is then presented to the student, thereby improving the student's motivation. For example, the learning support system collects data such as the student's test scores, homework submission status, and comments made during class. This data is collected by a collection unit. Next, the student's proficiency is assessed based on the collected learning data. The assessment unit analyzes the collected data and assesses the student's proficiency. For example, areas where the student's test scores are low or the student's homework submission status is poor are assessed. Explanations and practice problems are provided for areas where the student's assessed proficiency is lacking. The provision unit provides explanations and practice problems at an appropriate level for areas where the student's proficiency is lacking as assessed by the assessment unit. This allows the student to overcome difficult problems. Furthermore, the determined proficiency level is presented to the student. The presentation unit presents the proficiency level determined by the assessment unit to the student. The presentation is visualized using graphs and charts. This allows students to understand their own proficiency level and improve their motivation. This system can effectively assess a student's learning proficiency level and provide appropriate support. For example, by providing explanations and practice questions for areas in which students have low test scores, students can overcome their weaknesses. Furthermore, visualizing proficiency level can improve students' motivation. This realizes individualized learning support. This allows the learning support system to effectively assess a student's learning proficiency level and provide appropriate support.

[0029] A learning support system according to an embodiment includes a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects learning data of students. The learning data of students includes, for example, test scores, homework submission status, and comments made during class, but is not limited to these examples. For example, the collection unit automatically obtains test scores from an online platform. The collection unit can also obtain homework submission status from a learning management system. Furthermore, the collection unit can convert comments made during class into text data using speech recognition technology and collect the text data. For example, the collection unit obtains test scores from an online platform and stores them in a database. The homework submission status is obtained from the learning management system and recorded in the database. The comments made during class are converted into text data in real time using speech recognition technology and collected. The determination unit determines the proficiency of students based on the learning data collected by the collection unit. The determination is made based on, for example, test scores, homework submission status, and comments made during class, but is not limited to these examples. For example, the determination unit may identify a subject area where test scores are low and determine that the student's proficiency in that subject area is insufficient. The determination unit may also identify a subject area where homework submission rates are poor and determine that the student's proficiency in that subject area is insufficient. Furthermore, the determination unit may analyze comments made during class, identify a subject area where few comments are made, and determine that the student's proficiency in that subject area is insufficient. For example, the determination unit may determine that a subject area where test scores are less than 50 points is insufficient. The determination unit may determine that a subject area where less than 50% of homework is submitted is insufficient. The determination unit may determine that a subject area where few comments are made during class is insufficient. The provision unit provides explanations and practice problems to the student in the subject area where the determination unit determines that the student's proficiency is insufficient. The provision may be in, for example, a text format, a video format, an interactive question format, or the like, but is not limited to these examples. For example, the provision unit may provide text-format explanations on an online platform. The provision unit may also provide video-format explanations through a streaming service. The provision unit may also provide practice problems in the form of interactive questions.For example, the providing unit provides text-format explanations on an online platform so that students can freely view them. Video-format explanations are provided through a streaming service so that students can watch them. Interactive question-format practice questions are provided on an online platform so that students can answer them. The presenting unit presents the proficiency level assessed by the assessing unit to the students. The presentation may be visualized using, for example, a graph or chart, but is not limited to such examples. For example, the presenting unit may display the proficiency level as a bar graph so that students can visually understand it. The presenting unit may also display the proficiency level as a pie chart so that students can grasp the overall balance. Furthermore, the presenting unit may display the proficiency level as a line graph so that students can understand changes over time. For example, the presenting unit may display the proficiency level as a bar graph so that students can compare proficiency levels in each area. The presenting unit may display the proficiency level as a pie chart so that students can grasp the overall balance. The presenting unit may display the proficiency level as a line graph so that students can understand changes over time. As a result, the learning support system according to the embodiment can effectively assess a student's learning proficiency and provide appropriate support. Some or all of the above-described processing in the assessment unit may be performed, for example, using AI, or may be performed without AI. For example, the assessment unit may assess proficiency using an AI model that receives learning data collected by the collection unit as input and outputs proficiency. Some or all of the above-described processing in the provision unit may be performed, for example, using AI, or may be performed without AI. For example, the provision unit may receive input of areas in which the student's proficiency is lacking, as determined by the assessment unit, and provide explanations and practice questions using an AI model that outputs explanations and practice questions. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI, or may be performed without AI. For example, the presentation unit may visualize proficiency using an AI model that receives input of the proficiency determined by the assessment unit and outputs graphs or charts.

[0030] The collection unit collects student learning data. Examples of student learning data include, but are not limited to, test scores, homework submission status, and comments made during class. For example, the collection unit automatically obtains test scores from an online platform. The collection unit can also obtain homework submission status from a learning management system. Furthermore, the collection unit can convert comments made during class into text data using speech recognition technology and collect the text data. For example, the collection unit obtains test scores from an online platform and stores them in a database. The homework submission status is obtained from the learning management system and recorded in a database. The comments made during class are converted into text data in real time using speech recognition technology and collected. The collection unit can centrally manage this data and connect with other systems or departments as needed. For example, the collected data can be stored on a cloud server and accessed by the assessment unit and provision unit. Furthermore, the frequency and accuracy of data collection can be adjusted to accommodate specific situations and conditions. Furthermore, the collection unit can implement encryption technology and access control to ensure data privacy and security. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0031] The determination unit determines the student's proficiency level based on the learning data collected by the collection unit. The determination is made based on, for example, test scores, homework submission status, and comments made during class, but is not limited to these examples. For example, the determination unit identifies a subject area in which the student has low test scores and determines that the student's proficiency level in that subject area is insufficient. The determination unit can also identify a subject area in which the student has poor homework submission status and determine that the student's proficiency level in that subject area is insufficient. Furthermore, the determination unit can analyze comments made during class, identify a subject area in which the student makes few comments, and determine that the student's proficiency level in that subject area is insufficient. For example, the determination unit determines that a subject area in which the student has a test score of less than 50 points is insufficient. The determination unit determines that a subject area in which the student has submitted less than 50% of their homework is insufficient. The determination unit determines that a subject area in which the student makes few comments during class is insufficient. The determination unit can analyze these data using AI to determine the student's proficiency level. For example, the assessment unit can assess proficiency levels using an AI model that inputs learning data collected by the collection unit and outputs proficiency levels. The AI ​​model can make more accurate assessments by learning from past data and understanding each student's learning patterns and tendencies. This allows the assessment unit to quickly and accurately assess students' proficiency levels and build a foundation for providing appropriate support.

[0032] The providing unit provides explanations and practice problems to students in areas where proficiency is lacking, as determined by the determining unit. The provision may be in the form of, for example, text, video, or interactive questions, but is not limited to these examples. For example, the providing unit may provide text-format explanations on an online platform. The providing unit may also provide video-format explanations through a streaming service. Furthermore, the providing unit may also provide practice problems in the form of interactive questions. For example, the providing unit may provide text-format explanations on an online platform so that students can freely view them. Video-format explanations may be provided through a streaming service so that students can watch them. Interactive practice problems may be provided on an online platform so that students can answer them. The providing unit may analyze this data using AI and provide optimal explanations and practice problems. For example, the providing unit may provide explanations and practice problems using an AI model that inputs the areas where proficiency is lacking, as determined by the determining unit, and outputs explanations and practice problems. The AI ​​model may generate individually optimized learning content taking into account the student's learning history and performance. This allows the department to provide effective learning support tailored to each student.

[0033] The presentation unit presents the proficiency level determined by the determination unit to the student. The presentation is visualized using, for example, a graph or chart, but is not limited to such examples. For example, the presentation unit may display the proficiency level as a bar graph to allow the student to visually understand. The presentation unit may also display the proficiency level as a pie chart to allow the student to grasp the overall balance. Furthermore, the presentation unit may display the proficiency level as a line graph to allow the student to understand changes over time. For example, the presentation unit may display the proficiency level as a bar graph to allow the student to compare proficiency levels in each area. The presentation unit may display the proficiency level as a pie chart to allow the student to grasp the overall balance. The presentation unit may display the proficiency level as a line graph to allow the student to understand changes over time. The presentation unit may analyze this data using AI and provide an optimal visualization method. For example, the presentation unit may visualize the proficiency level using an AI model that inputs the proficiency level determined by the determination unit and outputs a graph or chart. The AI ​​model may select the optimal display format taking into account the student's level of understanding and visual preferences. This allows the presentation unit to support students in intuitively understanding their own learning situation and creating effective learning plans.

[0034] The collection unit can collect data on students' test scores, homework submission status, and comments made during class using specific methods. Specific methods include, but are not limited to, automatic acquisition from an online platform, acquisition of data from a learning management system, and collection of comments made during class using voice recognition technology. For example, the collection unit automatically acquires test scores from an online platform and stores them in a database. The collection unit can also acquire homework submission status from a learning management system and record the submission status in a database. Furthermore, the collection unit can convert comments made during class into text data in real time using voice recognition technology and collect the text. For example, the collection unit acquires test scores from an online platform and stores them in a database. The homework submission status is acquired from a learning management system and recorded in a database. The comments made during class are converted into text data in real time using voice recognition technology and collected. This enables more accurate proficiency assessment by collecting students' learning data from multiple angles. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI or without AI. For example, the collection unit can input test scores obtained from an online platform into the generation AI and have the generation AI store the scores in a database.

[0035] The presentation unit can visualize the proficiency level using a graph or chart. Specific types of graphs or charts include, but are not limited to, bar graphs, pie charts, and line graphs. For example, the presentation unit can display the proficiency level as a bar graph to allow students to visually understand. The presentation unit can also display the proficiency level as a pie chart to allow students to grasp the overall balance. The presentation unit can also display the proficiency level as a line graph to allow students to understand changes over time. For example, the presentation unit can display the proficiency level as a bar graph to allow students to compare proficiency levels in each area. The presentation unit can display the proficiency level as a pie chart to allow students to grasp the overall balance. The presentation unit can display the proficiency level as a line graph to allow students to understand changes over time. In this way, visually displaying the proficiency level can help students understand and improve their motivation. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can visualize the proficiency level using an AI model that inputs the proficiency level determined by the determination unit and outputs a graph or chart.

[0036] The collection unit can analyze the student's past learning history and select a data collection method. For example, the collection unit collects data in a similar format based on the format of a test on which the student previously achieved a high score. The collection unit can also analyze the content of homework submitted by the student in the past and collect homework of a similar difficulty level. The collection unit can also analyze the content of students' comments during class and prioritize collecting data from classes in which students frequently comment. For example, the collection unit collects data in a similar format based on the format of a test on which the student previously achieved a high score. The collection unit analyzes the content of homework submitted by the student in the past and collects homework of a similar difficulty level. The collection unit analyzes the content of students' comments during class and prioritizes collecting data from classes in which students frequently comment. This enables efficient data collection by selecting an optimal data collection method based on the student's past learning history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the student's past learning history data into a generation AI and have the generation AI select an optimal data collection method.

[0037] When collecting learning data, the collection unit can filter the data based on the student's current learning situation and areas of interest. For example, the collection unit prioritizes collecting data related to the subject the student is currently studying. The collection unit can also collect data related to areas the student is interested in to increase the student's motivation to study. Furthermore, the collection unit can collect only necessary data according to the student's learning progress. For example, the collection unit prioritizes collecting data related to the subject the student is currently studying. The collection unit collects data related to areas the student is interested in to increase the student's motivation to study. Only necessary data is collected according to the student's learning progress. In this way, by filtering the data based on the student's current learning situation and areas of interest, more relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the student's current learning situation and areas of interest to the generation AI and have the generation AI perform the filtering.

[0038] When collecting learning data, the collection unit can prioritize collecting highly relevant data by taking into account the student's geographical location information. For example, if the student lives in a specific area, the collection unit can prioritize collecting data related to that area. Also, if the student attends a specific school, the collection unit can prioritize collecting data related to the school's curriculum. Furthermore, if the student participates in an event held in a specific area, the collection unit can prioritize collecting data related to the event. For example, if the student lives in a specific area, the collection unit prioritizes collecting data related to that area. If the student attends a specific school, the collection unit prioritizes collecting data related to the school's curriculum. If the student participates in an event held in a specific area, the collection unit prioritizes collecting data related to the event. This allows for more relevant data to be collected by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's geographical location information into the generation AI and cause the generation AI to prioritize collecting highly relevant data.

[0039] The collection unit can analyze the student's social media activity and collect related data when collecting learning data. For example, the collection unit collects related data based on learning content shared by the student on social media. The collection unit can also collect related data from education-related accounts followed by the student on social media. The collection unit can also collect related data based on the activities of learning groups in which the student participates on social media. For example, the collection unit collects related data based on learning content shared by the student on social media. The collection unit collects related data from education-related accounts followed by the student on social media. The collection unit collects related data based on the activities of learning groups in which the student participates on social media. This enables more diversified data collection by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the student's social media activity data into the generation AI and cause the generation AI to collect related data.

[0040] The judgment unit can adjust the level of detail of the judgment based on the importance of the learning data when making the judgment. The judgment unit makes a detailed judgment based on, for example, the score of an important test. The judgment unit can also make a simplified judgment based on the status of homework submission. The judgment unit can also make a judgment with an appropriate level of detail based on the content of comments made during class. For example, the judgment unit makes a detailed judgment based on the score of an important test. The judgment unit makes a simplified judgment based on the status of homework submission. The judgment unit makes a judgment with an appropriate level of detail based on the content of comments made during class. In this way, by adjusting the level of detail of the judgment based on the importance of the learning data, more accurate judgment is possible. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the importance of the learning data to the generation AI and cause the generation AI to adjust the level of detail of the judgment.

[0041] The judgment unit can apply different judgment algorithms depending on the category of the learning data when making the judgment. For example, the judgment unit applies a specific algorithm based on test scores to make the judgment. The judgment unit can also apply a different algorithm based on the status of homework submission to make the judgment. Furthermore, the judgment unit can apply yet another algorithm based on the content of utterances made during class to make the judgment. For example, the judgment unit applies a specific algorithm based on test scores to make the judgment. Another algorithm based on the status of homework submission to make the judgment. Another algorithm based on the content of utterances made during class to make the judgment. In this way, by applying different algorithms depending on the category of the learning data, more appropriate judgment is possible. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the category of the learning data to the generation AI and cause the generation AI to apply different judgment algorithms.

[0042] The judgment unit can determine the priority of judgment based on the time of submission of the learning data during judgment. For example, the judgment unit prioritizes the scores of recently submitted tests. The judgment unit can also prioritize data that is submitted late based on the time of submission of homework. The judgment unit can also prioritize the latest data based on the time of submission of comments made during class. For example, the judgment unit prioritizes the scores of recently submitted tests. The judgment unit prioritizes data that is submitted late based on the time of submission of homework. The judgment unit prioritizes the latest data based on the time of submission of comments made during class. This enables more efficient judgment by determining the priority based on the time of submission. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the time of submission of the learning data to the generation AI and have the generation AI determine the priority of judgment.

[0043] The judgment unit can adjust the order of judgment based on the relevance of the learning data during judgment. For example, the judgment unit prioritizes judgment of data with high relevance in test scores. The judgment unit can also prioritize judgment of data with high relevance in homework submission status. The judgment unit can also prioritize judgment of data with high relevance in utterances made during class. For example, the judgment unit prioritizes judgment of data with high relevance in test scores. The judgment unit prioritizes judgment of data with high relevance in homework submission status. The judgment unit prioritizes judgment of data with high relevance in utterances made during class. This allows for more appropriate judgment by adjusting the order of judgment based on relevance. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the relevance of the learning data to the generation AI and cause the generation AI to adjust the order of judgment.

[0044] The providing unit can adjust the level of detail of the provided content based on the importance of the learning content when providing the learning content. For example, the providing unit provides detailed explanations and practice questions for important learning content. The providing unit can also provide simple explanations and practice questions for less important learning content. Furthermore, the providing unit can provide explanations and practice questions with an appropriate level of detail according to the importance of the learning content. For example, the providing unit provides detailed explanations and practice questions for important learning content. The providing unit provides simple explanations and practice questions for less important learning content. The providing unit provides explanations and practice questions with an appropriate level of detail according to the importance of the learning content. This enables more appropriate learning support by adjusting the level of detail based on the importance of the learning content. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the learning content to the generating AI and cause the generating AI to adjust the level of detail of the provided content.

[0045] The providing unit can apply different provision algorithms depending on the category of the learning content when providing the learning content. For example, the providing unit can apply a specific algorithm to mathematics learning content to provide explanations and practice problems. The providing unit can also apply a different algorithm to English learning content to provide explanations and practice problems. The providing unit can also apply yet another algorithm to science learning content to provide explanations and practice problems. For example, the providing unit can apply a specific algorithm to mathematics learning content to provide explanations and practice problems. For English learning content to provide explanations and practice problems. For science learning content to provide explanations and practice problems, the providing unit can apply yet another algorithm to provide explanations and practice problems. This enables more appropriate learning support by applying different algorithms depending on the category of the learning content. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the category of the learning content to the generation AI and cause the generation AI to apply different provision algorithms.

[0046] The providing unit can determine the priority of provision based on the submission date of the learning content when providing the learning content. For example, the providing unit prioritizes providing explanations and practice questions for recently submitted learning content. The providing unit can also prioritize providing explanations and practice questions for lately submitted learning content. Furthermore, the providing unit can provide explanations and practice questions in an appropriate order of priority based on the submission date. For example, the providing unit prioritizes providing explanations and practice questions for recently submitted learning content. For example, the providing unit prioritizes providing explanations and practice questions for lately submitted learning content. The providing unit provides explanations and practice questions in an appropriate order of priority based on the submission date. This enables more effective learning support by determining the priority based on the submission date. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the submission date of the learning content to the generating AI and cause the generating AI to determine the provision priority.

[0047] The providing unit can adjust the order of provision based on the relevance of the learning content when providing the learning content. For example, the providing unit prioritizes providing explanations and practice questions for important learning content. The providing unit can also prioritize providing explanations and practice questions for highly relevant learning content. Furthermore, the providing unit can provide explanations and practice questions in an appropriate order based on the relevance of the learning content. For example, the providing unit prioritizes providing explanations and practice questions for important learning content. The providing unit prioritizes providing explanations and practice questions for highly relevant learning content. The providing unit provides explanations and practice questions in an appropriate order based on the relevance of the learning content. This enables more effective learning support by adjusting the order of provision based on the relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the learning content to a generating AI and cause the generating AI to adjust the order of provision.

[0048] The presentation unit can select a display method by referring to the student's past learning history when presenting the data. The presentation unit, for example, provides an optimal display method based on a display method that the student has previously preferred. The presentation unit can also select a display method with high visibility from the student's past learning history. The presentation unit can also analyze the student's past learning history and provide the most effective display method. For example, the presentation unit provides an optimal display method based on a display method that the student has previously preferred. The presentation unit selects a display method with high visibility from the student's past learning history. The presentation unit analyzes the student's past learning history and provides the most effective display method. This enables more effective feedback by selecting an optimal display method based on the student's past learning history. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the student's past learning history data into a generation AI and have the generation AI select an optimal display method.

[0049] The presentation unit can apply different display algorithms depending on the proficiency category when presenting. For example, the presentation unit applies a specific display algorithm to mathematics proficiency. The presentation unit can also apply a different display algorithm to English proficiency. The presentation unit can also apply yet another display algorithm to science proficiency. For example, the presentation unit applies a specific display algorithm to mathematics proficiency. A different display algorithm to English proficiency. A still another display algorithm to science proficiency. This enables more effective feedback by applying different display algorithms depending on the proficiency category. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit can input the proficiency category to the generation AI and cause the generation AI to apply different display algorithms.

[0050] The presentation unit can select a display method by taking into account the student's geographical location information when presenting the information. For example, if a student lives in a specific area, the presentation unit can provide a display method related to the area. Furthermore, if a student attends a specific school, the presentation unit can provide a display method related to the school's curriculum. Furthermore, if a student is participating in an event held in a specific area, the presentation unit can provide a display method related to the event. For example, if a student lives in a specific area, the presentation unit can provide a display method related to the area. If a student attends a specific school, the presentation unit can provide a display method related to the school's curriculum. If a student is participating in an event held in a specific area, the presentation unit can provide a display method related to the event. This allows for a more relevant display method to be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the student's geographical location information into the generation AI and cause the generation AI to select the optimal display method.

[0051] The presentation unit can analyze the student's social media activity and display related information when presenting the information. The presentation unit can display related information based on, for example, learning content shared by the student on social media. The presentation unit can also display related information from education-related accounts followed by the student on social media. The presentation unit can also display related information based on the activities of a learning group in which the student participates on social media. For example, the presentation unit can display related information based on learning content shared by the student on social media. The presentation unit can display related information from education-related accounts followed by the student on social media. The presentation unit can display related information based on the activities of a learning group in which the student participates on social media. This makes it possible to provide more diverse information by analyzing social media activity. Some or all of the above-described processing by the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the student's social media activity data into a generation AI and cause the generation AI to display related information.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The learning support system can also analyze students' learning styles and provide individually optimized learning plans. For example, a student who prefers visual learning can be provided with learning materials that make heavy use of videos and infographics. A student who prefers auditory learning can be provided with learning materials in the form of audio commentary or podcasts. Furthermore, a student who prefers hands-on learning can be provided with learning materials that include interactive simulations and experiments. This maximizes learning effectiveness by providing an optimal learning plan tailored to each student's learning style.

[0054] The learning support system can also predict future learning content based on a student's learning history and support proactive learning. For example, it can analyze past test results and homework submission status to predict what content should be studied next. It can also provide appropriate learning materials and practice questions based on the predicted learning content. It can also conduct tests to check the student's level of understanding of the predicted learning content and adjust the learning plan based on the results. This makes it possible to anticipate students' learning progress and support efficient learning.

[0055] The learning support system can also monitor students' learning environments to provide an optimal learning environment. For example, it can use sensors to detect noise levels and lighting brightness during learning and provide advice on maintaining an appropriate environment. It can also monitor posture and break timing during learning and provide advice on promoting healthy study habits. It can also adjust the temperature and humidity of the learning environment to provide a comfortable learning environment. This can help students study in the optimal environment.

[0056] The learning support system can also set learning goals for students and monitor their progress. For example, it can record the short-term and long-term learning goals set by students and periodically check their progress. It can also visualize progress toward achieving goals and provide feedback to students. It can also propose action plans for achieving goals and support students in achieving them effectively. This allows students to work effectively toward their learning goals.

[0057] Learning support systems can also evaluate students' learning performance in real time and provide immediate feedback. For example, they can instantly analyze the results of online tests and quizzes and provide feedback to students. They can also monitor learning progress in real time and provide advice and support as needed. They can also provide chatbots and online support to resolve questions and problems students may have during their studies in real time. This can quickly resolve any problems students may encounter during their studies and support effective learning.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects student learning data. Student learning data includes, for example, test scores, homework submission status, and what students say during class. The collection unit automatically obtains test scores from the online platform and homework submission status from the learning management system. In addition, the collection unit converts what students say during class into text data using speech recognition technology and collects it. Step 2: The assessment unit assesses the student's proficiency based on the learning data collected by the collection unit. The assessment is based on test scores, homework submission status, and comments made during class. For example, it identifies areas where students have low test scores, poor homework submission status, or little commentary during class, and determines that the student's proficiency in those areas is insufficient. Step 3: The provision unit provides students with explanations and practice problems in areas where the proficiency level is lacking as determined by the assessment unit. The provision may be in the form of text, video, interactive questions, etc. For example, text-based explanations may be provided on an online platform, video-based explanations may be provided through a streaming service, and practice problems may be provided in the form of interactive questions. Step 4: The presentation unit presents the proficiency level determined by the assessment unit to the student. The presentation is visualized using graphs or charts. For example, the proficiency level can be displayed as a bar graph, pie chart, or line graph to help the student understand visually.

[0060] (Example 2) A learning support system according to an embodiment of the present invention is a system for cram schools and schools that assesses a student's learning proficiency and provides explanations and practice problems at an appropriate level for areas where the student's proficiency is low. This learning support system first collects the student's learning data and then assesses the student's proficiency based on the collected learning data. Explanations and practice problems are provided for areas where the student's assessed proficiency is lacking, and the assessed proficiency level is then presented to the student, thereby improving the student's motivation. For example, the learning support system collects data such as the student's test scores, homework submission status, and comments made during class. This data is collected by a collection unit. Next, the student's proficiency is assessed based on the collected learning data. The assessment unit analyzes the collected data and assesses the student's proficiency. For example, areas where the student's test scores are low or the student's homework submission status is poor are assessed. Explanations and practice problems are provided for areas where the student's assessed proficiency is lacking. The provision unit provides explanations and practice problems at an appropriate level for areas where the student's proficiency is lacking as assessed by the assessment unit. This allows the student to overcome difficult problems. Furthermore, the determined proficiency level is presented to the student. The presentation unit presents the proficiency level determined by the assessment unit to the student. The presentation is visualized using graphs and charts. This allows students to understand their own proficiency level and improve their motivation. This system can effectively assess a student's learning proficiency level and provide appropriate support. For example, by providing explanations and practice questions for areas in which students have low test scores, students can overcome their weaknesses. Furthermore, visualizing proficiency level can improve students' motivation. This realizes individualized learning support. This allows the learning support system to effectively assess a student's learning proficiency level and provide appropriate support.

[0061] A learning support system according to an embodiment includes a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects learning data of students. The learning data of students includes, for example, test scores, homework submission status, and comments made during class, but is not limited to these examples. For example, the collection unit automatically obtains test scores from an online platform. The collection unit can also obtain homework submission status from a learning management system. Furthermore, the collection unit can convert comments made during class into text data using speech recognition technology and collect the text data. For example, the collection unit obtains test scores from an online platform and stores them in a database. The homework submission status is obtained from the learning management system and recorded in the database. The comments made during class are converted into text data in real time using speech recognition technology and collected. The determination unit determines the proficiency of students based on the learning data collected by the collection unit. The determination is made based on, for example, test scores, homework submission status, and comments made during class, but is not limited to these examples. For example, the determination unit may identify a subject area where test scores are low and determine that the student's proficiency in that subject area is insufficient. The determination unit may also identify a subject area where homework submission rates are poor and determine that the student's proficiency in that subject area is insufficient. Furthermore, the determination unit may analyze comments made during class, identify a subject area where few comments are made, and determine that the student's proficiency in that subject area is insufficient. For example, the determination unit may determine that a subject area where test scores are less than 50 points is insufficient. The determination unit may determine that a subject area where less than 50% of homework is submitted is insufficient. The determination unit may determine that a subject area where few comments are made during class is insufficient. The provision unit provides explanations and practice problems to the student in the subject area where the determination unit determines that the student's proficiency is insufficient. The provision may be in, for example, a text format, a video format, an interactive question format, or the like, but is not limited to these examples. For example, the provision unit may provide text-format explanations on an online platform. The provision unit may also provide video-format explanations through a streaming service. The provision unit may also provide practice problems in the form of interactive questions.For example, the providing unit provides text-format explanations on an online platform so that students can freely view them. Video-format explanations are provided through a streaming service so that students can watch them. Interactive question-format practice questions are provided on an online platform so that students can answer them. The presenting unit presents the proficiency level assessed by the assessing unit to the students. The presentation may be visualized using, for example, a graph or chart, but is not limited to such examples. For example, the presenting unit may display the proficiency level as a bar graph so that students can visually understand it. The presenting unit may also display the proficiency level as a pie chart so that students can grasp the overall balance. Furthermore, the presenting unit may display the proficiency level as a line graph so that students can understand changes over time. For example, the presenting unit may display the proficiency level as a bar graph so that students can compare proficiency levels in each area. The presenting unit may display the proficiency level as a pie chart so that students can grasp the overall balance. The presenting unit may display the proficiency level as a line graph so that students can understand changes over time. As a result, the learning support system according to the embodiment can effectively assess a student's learning proficiency and provide appropriate support. Some or all of the above-described processing in the assessment unit may be performed, for example, using AI, or may be performed without AI. For example, the assessment unit may assess proficiency using an AI model that receives learning data collected by the collection unit as input and outputs proficiency. Some or all of the above-described processing in the provision unit may be performed, for example, using AI, or may be performed without AI. For example, the provision unit may receive input of areas in which the student's proficiency is lacking, as determined by the assessment unit, and provide explanations and practice questions using an AI model that outputs explanations and practice questions. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI, or may be performed without AI. For example, the presentation unit may visualize proficiency using an AI model that receives input of the proficiency determined by the assessment unit and outputs graphs or charts.

[0062] The collection unit collects student learning data. Examples of student learning data include, but are not limited to, test scores, homework submission status, and comments made during class. For example, the collection unit automatically obtains test scores from an online platform. The collection unit can also obtain homework submission status from a learning management system. Furthermore, the collection unit can convert comments made during class into text data using speech recognition technology and collect the text data. For example, the collection unit obtains test scores from an online platform and stores them in a database. The homework submission status is obtained from the learning management system and recorded in a database. The comments made during class are converted into text data in real time using speech recognition technology and collected. The collection unit can centrally manage this data and connect with other systems or departments as needed. For example, the collected data can be stored on a cloud server and accessed by the assessment unit and provision unit. Furthermore, the frequency and accuracy of data collection can be adjusted to accommodate specific situations and conditions. Furthermore, the collection unit can implement encryption technology and access control to ensure data privacy and security. This allows the collection unit to collect data efficiently and effectively, improving overall system performance.

[0063] The determination unit determines the student's proficiency level based on the learning data collected by the collection unit. The determination is made based on, for example, test scores, homework submission status, and comments made during class, but is not limited to these examples. For example, the determination unit identifies a subject area in which the student has low test scores and determines that the student's proficiency level in that subject area is insufficient. The determination unit can also identify a subject area in which the student has poor homework submission status and determine that the student's proficiency level in that subject area is insufficient. Furthermore, the determination unit can analyze comments made during class, identify a subject area in which the student makes few comments, and determine that the student's proficiency level in that subject area is insufficient. For example, the determination unit determines that a subject area in which the student has a test score of less than 50 points is insufficient. The determination unit determines that a subject area in which the student has submitted less than 50% of their homework is insufficient. The determination unit determines that a subject area in which the student makes few comments during class is insufficient. The determination unit can analyze these data using AI to determine the student's proficiency level. For example, the assessment unit can assess proficiency levels using an AI model that inputs learning data collected by the collection unit and outputs proficiency levels. The AI ​​model can make more accurate assessments by learning from past data and understanding each student's learning patterns and tendencies. This allows the assessment unit to quickly and accurately assess students' proficiency levels and build a foundation for providing appropriate support.

[0064] The providing unit provides explanations and practice problems to students in areas where proficiency is lacking, as determined by the determining unit. The provision may be in the form of, for example, text, video, or interactive questions, but is not limited to these examples. For example, the providing unit may provide text-format explanations on an online platform. The providing unit may also provide video-format explanations through a streaming service. Furthermore, the providing unit may also provide practice problems in the form of interactive questions. For example, the providing unit may provide text-format explanations on an online platform so that students can freely view them. Video-format explanations may be provided through a streaming service so that students can watch them. Interactive practice problems may be provided on an online platform so that students can answer them. The providing unit may analyze this data using AI and provide optimal explanations and practice problems. For example, the providing unit may provide explanations and practice problems using an AI model that inputs the areas where proficiency is lacking, as determined by the determining unit, and outputs explanations and practice problems. The AI ​​model may generate individually optimized learning content taking into account the student's learning history and performance. This allows the department to provide effective learning support tailored to each student.

[0065] The presentation unit presents the proficiency level determined by the determination unit to the student. The presentation is visualized using, for example, a graph or chart, but is not limited to such examples. For example, the presentation unit may display the proficiency level as a bar graph to allow the student to visually understand. The presentation unit may also display the proficiency level as a pie chart to allow the student to grasp the overall balance. Furthermore, the presentation unit may display the proficiency level as a line graph to allow the student to understand changes over time. For example, the presentation unit may display the proficiency level as a bar graph to allow the student to compare proficiency levels in each area. The presentation unit may display the proficiency level as a pie chart to allow the student to grasp the overall balance. The presentation unit may display the proficiency level as a line graph to allow the student to understand changes over time. The presentation unit may analyze this data using AI and provide an optimal visualization method. For example, the presentation unit may visualize the proficiency level using an AI model that inputs the proficiency level determined by the determination unit and outputs a graph or chart. The AI ​​model may select the optimal display format taking into account the student's level of understanding and visual preferences. This allows the presentation unit to support students in intuitively understanding their own learning situation and creating effective learning plans.

[0066] The collection unit can collect data on students' test scores, homework submission status, and comments made during class using specific methods. Specific methods include, but are not limited to, automatic acquisition from an online platform, acquisition of data from a learning management system, and collection of comments made during class using voice recognition technology. For example, the collection unit automatically acquires test scores from an online platform and stores them in a database. The collection unit can also acquire homework submission status from a learning management system and record the submission status in a database. Furthermore, the collection unit can convert comments made during class into text data in real time using voice recognition technology and collect the text. For example, the collection unit acquires test scores from an online platform and stores them in a database. The homework submission status is acquired from a learning management system and recorded in a database. The comments made during class are converted into text data in real time using voice recognition technology and collected. This enables more accurate proficiency assessment by collecting students' learning data from multiple angles. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI or without AI. For example, the collection unit can input test scores obtained from an online platform into the generation AI and have the generation AI store the scores in a database.

[0067] The presentation unit can visualize the proficiency level using a graph or chart. Specific types of graphs or charts include, but are not limited to, bar graphs, pie charts, and line graphs. For example, the presentation unit can display the proficiency level as a bar graph to allow students to visually understand. The presentation unit can also display the proficiency level as a pie chart to allow students to grasp the overall balance. The presentation unit can also display the proficiency level as a line graph to allow students to understand changes over time. For example, the presentation unit can display the proficiency level as a bar graph to allow students to compare proficiency levels in each area. The presentation unit can display the proficiency level as a pie chart to allow students to grasp the overall balance. The presentation unit can display the proficiency level as a line graph to allow students to understand changes over time. In this way, visually displaying the proficiency level can help students understand and improve their motivation. Some or all of the above-described processing in the presentation unit may be performed, for example, using AI or without AI. For example, the presentation unit can visualize the proficiency level using an AI model that inputs the proficiency level determined by the determination unit and outputs a graph or chart.

[0068] The collection unit can estimate the student's emotions and adjust the timing of learning data collection based on the estimated student's emotions. For example, if the student is feeling stressed, the collection unit can provide a break to collect learning data in a relaxed state. Furthermore, if the student is concentrating, the collection unit can also collect test scores and homework submission status at that time. Furthermore, if the student is tired, the collection unit can adjust the schedule to collect learning data the next day. For example, if the student is feeling stressed, the collection unit can provide a break to collect learning data in a relaxed state. If the student is concentrating, the collection unit can collect test scores and homework submission status at that time. If the student is tired, the collection unit can adjust the schedule to collect learning data the next day. This allows for more appropriate data collection by adjusting the timing of data collection according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input emotion data of students to the generation AI and have the generation AI perform emotion estimation.

[0069] The collection unit can analyze the student's past learning history and select a data collection method. For example, the collection unit collects data in a similar format based on the format of a test on which the student previously achieved a high score. The collection unit can also analyze the content of homework submitted by the student in the past and collect homework of a similar difficulty level. The collection unit can also analyze the content of students' comments during class and prioritize collecting data from classes in which students frequently comment. For example, the collection unit collects data in a similar format based on the format of a test on which the student previously achieved a high score. The collection unit analyzes the content of homework submitted by the student in the past and collects homework of a similar difficulty level. The collection unit analyzes the content of students' comments during class and prioritizes collecting data from classes in which students frequently comment. This enables efficient data collection by selecting an optimal data collection method based on the student's past learning history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the student's past learning history data into a generation AI and have the generation AI select an optimal data collection method.

[0070] When collecting learning data, the collection unit can filter the data based on the student's current learning situation and areas of interest. For example, the collection unit prioritizes collecting data related to the subject the student is currently studying. The collection unit can also collect data related to areas the student is interested in to increase the student's motivation to study. Furthermore, the collection unit can collect only necessary data according to the student's learning progress. For example, the collection unit prioritizes collecting data related to the subject the student is currently studying. The collection unit collects data related to areas the student is interested in to increase the student's motivation to study. Only necessary data is collected according to the student's learning progress. In this way, by filtering the data based on the student's current learning situation and areas of interest, more relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the student's current learning situation and areas of interest to the generation AI and have the generation AI perform the filtering.

[0071] 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. For example, if the student is stressed, the collection unit can prioritize collecting data with easy questions or relaxing content. Furthermore, if the student is concentrating, the collection unit can prioritize collecting data with difficult questions or important data. Furthermore, if the student is tired, the collection unit can prioritize collecting data with light content. For example, if the student is stressed, the collection unit can prioritize collecting data with easy questions or relaxing content. If the student is concentrating, the collection unit can prioritize collecting data with difficult questions or important data. If the student is tired, the collection unit can prioritize collecting data with light content. This allows for more effective data collection by prioritizing data according to the student's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input students' emotional data into the generation AI and have the generation AI perform emotion estimation.

[0072] When collecting learning data, the collection unit can prioritize collecting highly relevant data by taking into account the student's geographical location information. For example, if the student lives in a specific area, the collection unit can prioritize collecting data related to that area. Also, if the student attends a specific school, the collection unit can prioritize collecting data related to the school's curriculum. Furthermore, if the student participates in an event held in a specific area, the collection unit can prioritize collecting data related to the event. For example, if the student lives in a specific area, the collection unit prioritizes collecting data related to that area. If the student attends a specific school, the collection unit prioritizes collecting data related to the school's curriculum. If the student participates in an event held in a specific area, the collection unit prioritizes collecting data related to the event. This allows for more relevant data to be collected by taking the geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the student's geographical location information into the generation AI and cause the generation AI to prioritize collecting highly relevant data.

[0073] The collection unit can analyze the student's social media activity and collect related data when collecting learning data. For example, the collection unit collects related data based on learning content shared by the student on social media. The collection unit can also collect related data from education-related accounts followed by the student on social media. The collection unit can also collect related data based on the activities of learning groups in which the student participates on social media. For example, the collection unit collects related data based on learning content shared by the student on social media. The collection unit collects related data from education-related accounts followed by the student on social media. The collection unit collects related data based on the activities of learning groups in which the student participates on social media. This enables more diversified data collection by analyzing social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the student's social media activity data into the generation AI and cause the generation AI to collect related data.

[0074] The judgment unit can estimate the student's emotions and adjust the proficiency assessment criteria based on the estimated student's emotions. For example, if the student is stressed, the judgment unit can relax the assessment criteria and increase positive feedback. Furthermore, if the student is relaxed, the judgment unit can tighten the assessment criteria and provide an accurate assessment. Furthermore, if the student is concentrating, the judgment unit can appropriately adjust the assessment criteria and provide a balanced assessment. For example, if the student is stressed, the judgment unit can relax the assessment criteria and increase positive feedback. If the student is relaxed, the judgment unit can tighten the assessment criteria and provide an accurate assessment. If the student is concentrating, the judgment unit can appropriately adjust the assessment criteria and provide a balanced assessment. This allows for more appropriate proficiency assessment by adjusting the assessment criteria according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input emotion data of the student to the generation AI and cause the generation AI to estimate the emotion.

[0075] The judgment unit can adjust the level of detail of the judgment based on the importance of the learning data when making the judgment. The judgment unit makes a detailed judgment based on, for example, the score of an important test. The judgment unit can also make a simplified judgment based on the status of homework submission. The judgment unit can also make a judgment with an appropriate level of detail based on the content of comments made during class. For example, the judgment unit makes a detailed judgment based on the score of an important test. The judgment unit makes a simplified judgment based on the status of homework submission. The judgment unit makes a judgment with an appropriate level of detail based on the content of comments made during class. In this way, by adjusting the level of detail of the judgment based on the importance of the learning data, more accurate judgment is possible. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the importance of the learning data to the generation AI and cause the generation AI to adjust the level of detail of the judgment.

[0076] The judgment unit can apply different judgment algorithms depending on the category of the learning data when making the judgment. For example, the judgment unit applies a specific algorithm based on test scores to make the judgment. The judgment unit can also apply a different algorithm based on the status of homework submission to make the judgment. Furthermore, the judgment unit can apply yet another algorithm based on the content of utterances made during class to make the judgment. For example, the judgment unit applies a specific algorithm based on test scores to make the judgment. Another algorithm based on the status of homework submission to make the judgment. Another algorithm based on the content of utterances made during class to make the judgment. In this way, by applying different algorithms depending on the category of the learning data, more appropriate judgment is possible. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the category of the learning data to the generation AI and cause the generation AI to apply different judgment algorithms.

[0077] The determination unit can estimate the student's emotions and adjust the display method of the determination result based on the estimated student's emotions. For example, if the student is nervous, the determination unit provides a simple, highly visible display method. Furthermore, if the student is relaxed, the determination unit can provide a display method including detailed information. Furthermore, if the student is in a hurry, the determination unit can provide a display method that focuses on the main points. For example, if the student is nervous, the determination unit provides a simple, highly visible display method. If the student is relaxed, the determination unit provides a display method including detailed information. If the student is in a hurry, the determination unit provides a display method that focuses on the main points. This enables more effective feedback by adjusting the display method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the determination unit can be performed using, for example, AI, or without AI. For example, the judgment unit can input the student's emotional data into the generation AI and have the generation AI perform emotion estimation.

[0078] The judgment unit can determine the priority of judgment based on the time of submission of the learning data during judgment. For example, the judgment unit prioritizes the scores of recently submitted tests. The judgment unit can also prioritize data that is submitted late based on the time of submission of homework. The judgment unit can also prioritize the latest data based on the time of submission of comments made during class. For example, the judgment unit prioritizes the scores of recently submitted tests. The judgment unit prioritizes data that is submitted late based on the time of submission of homework. The judgment unit prioritizes the latest data based on the time of submission of comments made during class. This enables more efficient judgment by determining the priority based on the time of submission. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the time of submission of the learning data to the generation AI and have the generation AI determine the priority of judgment.

[0079] The judgment unit can adjust the order of judgment based on the relevance of the learning data during judgment. For example, the judgment unit prioritizes judgment of data with high relevance in test scores. The judgment unit can also prioritize judgment of data with high relevance in homework submission status. The judgment unit can also prioritize judgment of data with high relevance in utterances made during class. For example, the judgment unit prioritizes judgment of data with high relevance in test scores. The judgment unit prioritizes judgment of data with high relevance in homework submission status. The judgment unit prioritizes judgment of data with high relevance in utterances made during class. This allows for more appropriate judgment by adjusting the order of judgment based on relevance. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the relevance of the learning data to the generation AI and cause the generation AI to adjust the order of judgment.

[0080] The providing unit can estimate the student's emotions and adjust the method of providing explanations and exercises based on the estimated student's emotions. For example, if the student is feeling stressed, the providing unit can provide relaxing explanations and easy exercises. Furthermore, if the student is concentrating, the providing unit can provide more difficult explanations and exercises. Furthermore, if the student is tired, the providing unit can provide explanations and exercises with light content. For example, if the student is feeling stressed, the providing unit can provide relaxing explanations and easy exercises. If the student is concentrating, the providing unit can provide more difficult explanations and exercises. If the student is tired, the providing unit can provide light explanations and exercises. This enables more effective learning support by adjusting the method of provision according to the student's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or without AI. For example, the providing unit can input emotional data of students into the generating AI and have the generating AI perform emotion estimation.

[0081] The providing unit can adjust the level of detail of the provided content based on the importance of the learning content when providing the learning content. For example, the providing unit provides detailed explanations and practice questions for important learning content. The providing unit can also provide simple explanations and practice questions for less important learning content. Furthermore, the providing unit can provide explanations and practice questions with an appropriate level of detail according to the importance of the learning content. For example, the providing unit provides detailed explanations and practice questions for important learning content. The providing unit provides simple explanations and practice questions for less important learning content. The providing unit provides explanations and practice questions with an appropriate level of detail according to the importance of the learning content. This enables more appropriate learning support by adjusting the level of detail based on the importance of the learning content. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the importance of the learning content to the generating AI and cause the generating AI to adjust the level of detail of the provided content.

[0082] The providing unit can apply different provision algorithms depending on the category of the learning content when providing the learning content. For example, the providing unit can apply a specific algorithm to mathematics learning content to provide explanations and practice problems. The providing unit can also apply a different algorithm to English learning content to provide explanations and practice problems. The providing unit can also apply yet another algorithm to science learning content to provide explanations and practice problems. For example, the providing unit can apply a specific algorithm to mathematics learning content to provide explanations and practice problems. For English learning content to provide explanations and practice problems. For science learning content to provide explanations and practice problems, the providing unit can apply yet another algorithm to provide explanations and practice problems. This enables more appropriate learning support by applying different algorithms depending on the category of the learning content. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the category of the learning content to the generation AI and cause the generation AI to apply different provision algorithms.

[0083] The providing unit can estimate the student's emotions and determine the priority of the explanations and exercises to be provided based on the estimated student's emotions. For example, if the student is stressed, the providing unit can prioritize providing easy explanations and exercises. Furthermore, if the student is concentrating, the providing unit can prioritize providing more difficult explanations and exercises. Furthermore, if the student is tired, the providing unit can prioritize providing explanations and exercises with light content. For example, if the student is stressed, the providing unit can prioritize providing easy explanations and exercises. If the student is concentrating, the providing unit can prioritize providing more difficult explanations and exercises. If the student is tired, the providing unit can prioritize providing explanations and exercises with light content. This enables more effective learning support by determining the priority according to the student's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed, for example, using AI, or without AI. For example, the providing unit can input emotional data of students into the generating AI and have the generating AI perform emotion estimation.

[0084] The providing unit can determine the priority of provision based on the submission date of the learning content when providing the learning content. For example, the providing unit prioritizes providing explanations and practice questions for recently submitted learning content. The providing unit can also prioritize providing explanations and practice questions for lately submitted learning content. Furthermore, the providing unit can provide explanations and practice questions in an appropriate order of priority based on the submission date. For example, the providing unit prioritizes providing explanations and practice questions for recently submitted learning content. For example, the providing unit prioritizes providing explanations and practice questions for lately submitted learning content. The providing unit provides explanations and practice questions in an appropriate order of priority based on the submission date. This enables more effective learning support by determining the priority based on the submission date. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the submission date of the learning content to the generating AI and cause the generating AI to determine the provision priority.

[0085] The providing unit can adjust the order of provision based on the relevance of the learning content when providing the learning content. For example, the providing unit prioritizes providing explanations and practice questions for important learning content. The providing unit can also prioritize providing explanations and practice questions for highly relevant learning content. Furthermore, the providing unit can provide explanations and practice questions in an appropriate order based on the relevance of the learning content. For example, the providing unit prioritizes providing explanations and practice questions for important learning content. The providing unit prioritizes providing explanations and practice questions for highly relevant learning content. The providing unit provides explanations and practice questions in an appropriate order based on the relevance of the learning content. This enables more effective learning support by adjusting the order of provision based on the relevance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the relevance of the learning content to a generating AI and cause the generating AI to adjust the order of provision.

[0086] The presentation unit can estimate the student's emotions and adjust the display method of the proficiency level based on the estimated student's emotions. For example, if the student is nervous, the presentation unit provides a simple, highly visible display method. Furthermore, if the student is relaxed, the presentation unit can provide a display method including detailed information. Furthermore, if the student is in a hurry, the presentation unit can provide a display method that focuses on the main points. For example, if the student is nervous, the presentation unit provides a simple, highly visible display method. If the student is relaxed, the presentation unit provides a display method including detailed information. If the student is in a hurry, the presentation unit provides a display method that focuses on the main points. This enables more effective feedback by adjusting the display method according to the student's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the student's emotional data into the generation AI and have the generation AI perform emotion estimation.

[0087] The presentation unit can select a display method by referring to the student's past learning history when presenting the data. The presentation unit, for example, provides an optimal display method based on a display method that the student has previously preferred. The presentation unit can also select a display method with high visibility from the student's past learning history. The presentation unit can also analyze the student's past learning history and provide the most effective display method. For example, the presentation unit provides an optimal display method based on a display method that the student has previously preferred. The presentation unit selects a display method with high visibility from the student's past learning history. The presentation unit analyzes the student's past learning history and provides the most effective display method. This enables more effective feedback by selecting an optimal display method based on the student's past learning history. Some or all of the above-described processing in the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the student's past learning history data into a generation AI and have the generation AI select an optimal display method.

[0088] The presentation unit can apply different display algorithms depending on the proficiency category when presenting. For example, the presentation unit applies a specific display algorithm to mathematics proficiency. The presentation unit can also apply a different display algorithm to English proficiency. The presentation unit can also apply yet another display algorithm to science proficiency. For example, the presentation unit applies a specific display algorithm to mathematics proficiency. A different display algorithm to English proficiency. A still another display algorithm to science proficiency. This enables more effective feedback by applying different display algorithms depending on the proficiency category. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or may be performed without using AI. For example, the presentation unit can input the proficiency category to the generation AI and cause the generation AI to apply different display algorithms.

[0089] The presentation unit can estimate the student's emotions and adjust the display order of the proficiency levels based on the estimated student's emotions. For example, if the student is feeling stressed, the presentation unit can display the levels in order from easiest to hardest. Furthermore, if the student is concentrating, the presentation unit can also display the levels in order from hardest to hardest. Furthermore, if the student is tired, the presentation unit can display the levels in order from lightest to lightest. For example, if the student is feeling stressed, the presentation unit can display the levels in order from easiest to hardest. If the student is concentrating, the presentation unit can display the levels in order from hardest to hardest. This allows for more effective feedback by adjusting the display order according to the student's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the presentation unit can be performed using, for example, an AI. For example, the presentation unit can input the student's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0090] The presentation unit can select a display method by taking into account the student's geographical location information when presenting the information. For example, if a student lives in a specific area, the presentation unit can provide a display method related to the area. Furthermore, if a student attends a specific school, the presentation unit can provide a display method related to the school's curriculum. Furthermore, if a student is participating in an event held in a specific area, the presentation unit can provide a display method related to the event. For example, if a student lives in a specific area, the presentation unit can provide a display method related to the area. If a student attends a specific school, the presentation unit can provide a display method related to the school's curriculum. If a student is participating in an event held in a specific area, the presentation unit can provide a display method related to the event. This allows for a more relevant display method to be provided by taking the geographical location information into consideration. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the student's geographical location information into the generation AI and cause the generation AI to select the optimal display method.

[0091] The presentation unit can analyze the student's social media activity and display related information when presenting the information. The presentation unit can display related information based on, for example, learning content shared by the student on social media. The presentation unit can also display related information from education-related accounts followed by the student on social media. The presentation unit can also display related information based on the activities of a learning group in which the student participates on social media. For example, the presentation unit can display related information based on learning content shared by the student on social media. The presentation unit can display related information from education-related accounts followed by the student on social media. The presentation unit can display related information based on the activities of a learning group in which the student participates on social media. This makes it possible to provide more diverse information by analyzing social media activity. Some or all of the above-described processing by the presentation unit can be performed using, for example, AI, or without AI. For example, the presentation unit can input the student's social media activity data into a generation AI and cause the generation AI to display related information.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The learning support system can also analyze students' learning styles and provide individually optimized learning plans. For example, a student who prefers visual learning can be provided with learning materials that make heavy use of videos and infographics. A student who prefers auditory learning can be provided with learning materials in the form of audio commentary or podcasts. Furthermore, a student who prefers hands-on learning can be provided with learning materials that include interactive simulations and experiments. This maximizes learning effectiveness by providing an optimal learning plan tailored to each student's learning style.

[0094] The learning support system can also predict future learning content based on a student's learning history and support proactive learning. For example, it can analyze past test results and homework submission status to predict what content should be studied next. It can also provide appropriate learning materials and practice questions based on the predicted learning content. It can also conduct tests to check the student's level of understanding of the predicted learning content and adjust the learning plan based on the results. This makes it possible to anticipate students' learning progress and support efficient learning.

[0095] The learning support system can also monitor students' learning environments to provide an optimal learning environment. For example, it can use sensors to detect noise levels and lighting brightness during learning and provide advice on maintaining an appropriate environment. It can also monitor posture and break timing during learning and provide advice on promoting healthy study habits. It can also adjust the temperature and humidity of the learning environment to provide a comfortable learning environment. This can help students study in the optimal environment.

[0096] The learning support system can also set learning goals for students and monitor their progress. For example, it can record the short-term and long-term learning goals set by students and periodically check their progress. It can also visualize progress toward achieving goals and provide feedback to students. It can also propose action plans for achieving goals and support students in achieving them effectively. This allows students to work effectively toward their learning goals.

[0097] Learning support systems can also evaluate students' learning performance in real time and provide immediate feedback. For example, they can instantly analyze the results of online tests and quizzes and provide feedback to students. They can also monitor learning progress in real time and provide advice and support as needed. They can also provide chatbots and online support to resolve questions and problems students may have during their studies in real time. This can quickly resolve any problems students may encounter during their studies and support effective learning.

[0098] The learning support system can also estimate the student's emotions and adjust the learning progress based on the estimated emotions. For example, if a student is feeling stressed, the learning pace can be slowed down. If the student is relaxed, the learning pace can be increased. Furthermore, if the student is concentrating, the learning support system can support more effective learning by adjusting the learning progress according to the student's emotions.

[0099] The learning support system can also estimate the student's emotions and personalize the learning content based on the estimated emotions. For example, if a student is feeling stressed, it can provide learning materials with relaxing content. If the student is relaxed, it can provide learning materials with more difficult content. Furthermore, if the student is concentrating, it can provide challenging tasks. In this way, by personalizing the learning content according to the student's emotions, it is possible to support more effective learning.

[0100] The learning support system can also estimate a student's emotions and provide feedback to improve their motivation to learn based on the estimated emotions. For example, if a student is feeling stressed, it can provide an encouraging message. If a student is relaxed, it can provide feedback that makes the student feel a sense of accomplishment. Furthermore, if a student is concentrating, it can set challenging goals and support them in achieving them. In this way, it is possible to increase students' motivation by providing feedback to improve their motivation according to their emotions.

[0101] The learning support system can also estimate students' emotions and adjust the timing of study breaks based on the estimated emotions. For example, if a student is tired, it can encourage them to take a break at an appropriate time. Also, if a student is concentrating, it can encourage them to postpone the break and continue studying. Furthermore, if a student is feeling stressed, it can suggest a break that will allow them to relax. In this way, learning efficiency can be improved by adjusting the timing of breaks according to the student's emotions.

[0102] The learning support system can further estimate the student's emotions and adjust the way it reports learning progress based on the estimated emotions. For example, if a student is feeling stressed, it can report positive feedback. If a student is relaxed, it can report detailed progress. Furthermore, if a student is concentrating, it can provide specific advice for taking the next step. This allows for more effective feedback by adjusting the way progress is reported depending on the student's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects student learning data. Student learning data includes, for example, test scores, homework submission status, and what students say during class. The collection unit automatically obtains test scores from the online platform and homework submission status from the learning management system. In addition, the collection unit converts what students say during class into text data using speech recognition technology and collects it. Step 2: The assessment unit assesses the student's proficiency based on the learning data collected by the collection unit. The assessment is based on test scores, homework submission status, and comments made during class. For example, it identifies areas where students have low test scores, poor homework submission status, or little commentary during class, and determines that the student's proficiency in those areas is insufficient. Step 3: The provision unit provides students with explanations and practice problems in areas where the proficiency level is lacking as determined by the assessment unit. The provision may be in the form of text, video, interactive questions, etc. For example, text-based explanations may be provided on an online platform, video-based explanations may be provided through a streaming service, and practice problems may be provided in the form of interactive questions. Step 4: The presentation unit presents the proficiency level determined by the assessment unit to the student. The presentation is visualized using graphs or charts. For example, the proficiency level can be displayed as a bar graph, pie chart, or line graph to help the student understand visually.

[0105] 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.

[0106] 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> ) and other generation AIs. 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 voice 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 including voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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. The AIs other than the generation AI are, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but are not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or in whole by AI, but are not limited to these examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0107] 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.

[0108] For example, the collection unit can collect the student's learning data using the camera 42 or microphone 38B of the smart device 14. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected learning data to determine the student's proficiency. For example, the provision unit is realized by the control unit 46A of the smart device 14 and provides appropriate explanations and practice questions for areas where the determined proficiency is lacking. For example, the presentation unit can visualize and present the student's proficiency using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 and inference data such as voice 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 voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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.

[0123] 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.

[0124] For example, the collection unit can collect the student's learning data using the camera 42 and microphone 238 of the smart glasses 214. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected learning data to determine the student's proficiency. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 and provides appropriate explanations and practice questions for areas where the determined proficiency is lacking. For example, the presentation unit can visualize and present the student's proficiency using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 and inference data such as voice 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 voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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 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.

[0140] For example, the collection unit can collect learning data of the student using the camera 42 or microphone 238 of the headset terminal 314. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected learning data to determine the student's proficiency. For example, the provision unit is realized by the control unit 46A of the headset terminal 314, and provides appropriate explanations and practice questions for areas in which the determined proficiency is lacking. For example, the presentation unit can visualize and present the proficiency to the student using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 and inference data such as voice 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 voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. 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.

[0156] 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.

[0157] For example, the collection unit can collect learning data of the student using the camera 42 or microphone 238 of the robot 414. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected learning data to determine the student's proficiency. For example, the provision unit is realized by the control unit 46A of the robot 414, and provides appropriate explanations and practice questions for areas where the determined proficiency is lacking. For example, the presentation unit can visualize and present the proficiency to the student using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Appendix 1) a collection unit that collects learning data of students; a determination unit that determines the proficiency level of the student based on the learning data collected by the collection unit; a providing unit that provides the student with explanations and practice questions in the field in which the proficiency level determined by the determining unit is lacking; a presentation unit that presents the proficiency level determined by the determination unit to the student. A system characterized by: (Appendix 2) The collecting unit Collect data on student test scores, homework submissions, and class discussions in a concrete way 2. The system of claim 1. (Appendix 3) The presentation unit Visualize proficiency with graphs or charts 2. The system of claim 1. (Appendix 4) The collecting unit Estimate students' emotions in a specific way and adjust the timing of learning data collection based on the estimated student emotions. 2. The system of claim 1. (Appendix 5) The collecting unit Analyze students' past learning history and select data collection methods 2. The system of claim 1. (Appendix 6) The collecting unit When collecting learning data, filter it in specific ways based on the student's current learning status and areas of interest. 2. The system of claim 1. (Appendix 7) The collecting unit Estimate student emotions and prioritize data collection based on the estimated student emotions. 2. The system of claim 1. (Appendix 8) The collecting unit When collecting learning data, consider students' geographic locations in specific ways to prioritize the collection of relevant data. 2. The system of claim 1. (Appendix 9) The collecting unit When collecting learning data, analyze students' social media activities in a specific way and collect relevant data. 2. The system of claim 1. (Appendix 10) The determination unit Estimate students' emotions in a specific way and adjust the proficiency criteria based on the estimated student emotions. 2. The system of claim 1. (Appendix 11) The determination unit At the time of judgment, the level of detail of the judgment is adjusted based on the importance of the training data. 2. The system of claim 1. (Appendix 12) The determination unit When making a decision, different decision algorithms are applied depending on the category of the training data. 2. The system of claim 1. (Appendix 13) The determination unit Estimate students' emotions in a specific way and adjust the way assessment results are displayed based on the estimated student emotions. 2. The system of claim 1. (Appendix 14) The determination unit At the time of evaluation, the priority of the evaluation is determined based on the time of submission of the training data. 2. The system of claim 1. (Appendix 15) The determination unit At the time of judgment, the order of judgments is adjusted based on the relevance of the training data. 2. The system of claim 1. (Appendix 16) The providing unit Estimate students' emotions in a specific way and adjust the way explanations and practice questions are provided based on the estimated student emotions. 2. The system of claim 1. (Appendix 17) The providing unit At the time of delivery, adjust the level of detail of the delivery based on the importance of the learning content. 2. The system of claim 1. (Appendix 18) The providing unit At the time of delivery, different delivery algorithms are applied depending on the category of learning content. 2. The system of claim 1. (Appendix 19) The providing unit Estimate students' feelings in a specific way and prioritize the explanations and exercises to provide based on the estimated students' feelings. 2. The system of claim 1. (Appendix 20) The providing unit At the time of delivery, delivery will be prioritized based on the time of submission of learning content. 2. The system of claim 1. (Appendix 21) The providing unit As it is delivered, adjust the order of delivery based on the relevance of the learning content. 2. The system of claim 1. (Appendix 22) The presentation unit Estimate student emotions in a specific way and adjust the way proficiency is displayed based on the estimated student emotions. 2. The system of claim 1. (Appendix 23) The presentation unit When presenting, the display method is selected by referring to the student's past learning history. 2. The system of claim 1. (Appendix 24) The presentation unit At presentation time, different display algorithms are applied depending on the proficiency category. 2. The system of claim 1. (Appendix 25) The presentation unit Estimate students' emotions in a specific way and adjust the display order of proficiency levels based on the estimated student emotions. 2. The system of claim 1. (Appendix 26) The presentation unit When presenting, the display method is selected taking into account the student's geographic location information. 2. The system of claim 1. (Appendix 27) The presentation unit Analyze students' social media activity and display relevant information during presentation 2. The system of claim 1. [Explanation of symbols]

[0177] 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 system comprising: a collection unit that collects student learning data using at least one of an online platform, a learning management system, and voice recognition technology; a determination unit that determines the proficiency level of the student based on the learning data collected by the collection unit; a providing unit that provides the student with explanations and practice questions in the field in which the proficiency level determined by the determining unit is lacking; a presentation unit that presents the proficiency level determined by the determination unit on a display, The determination unit estimates the emotion of the student using an emotion identification model, and adjusts the criteria for determining the proficiency level so that the criteria are relaxed when the estimated emotion of the student is a stressful state, and the criteria are tightened when the estimated emotion of the student is a relaxed state, and then determines the proficiency level of the student based on the learning data. A system characterized by:

2. The collecting unit Collect data on the student's test scores, homework submission status, and class content.

2. The system of claim 1.

3. The presentation unit Visualize proficiency with graphs or charts 2. The system of claim 1.

4. The collecting unit Based on the student's emotion estimated by the emotion identification model, the timing of collecting learning data is adjusted so that if the student is feeling stressed, a break is provided to delay the timing of collection, and if the student is concentrating, the timing of collection is advanced.

2. The system of claim 1.

5. The collecting unit Analyze the student's past learning history and select a data collection method.

2. The system of claim 1.

6. The collecting unit When collecting learning data, filtering is performed based on the student's current learning status and areas of interest.

2. The system of claim 1.

7. The collecting unit Based on the student's emotions estimated by the emotion identification model, a priority order of data to be collected is determined, such that data on easy questions is preferentially collected when the student is feeling stressed, and data on difficult questions is preferentially collected when the student is concentrating.

2. The system of claim 1.

8. The collecting unit When collecting learning data, the geographical location information of the student is taken into consideration to preferentially collect highly relevant data.

2. The system of claim 1.

9. The collecting unit When collecting learning data, analyze the social media activity of the student and collect relevant data.

2. The system of claim 1.

10. The determination unit The method for displaying the judgment result is adjusted based on the emotion of the student estimated by the emotion identification model, and a simple and highly visible display method is provided when the student is nervous, and a display method including detailed information is provided when the student is relaxed.

2. The system of claim 1.

Images