JP7770502B2 - system - Google Patents

Description

The technology disclosed herein relates to a system.

Patent document 1 discloses a persona chatbot control method executed 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's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Japanese Patent Application Laid-Open No. 2022-180282

Conventional technology has had the challenge of making it difficult to accurately assess students' learning proficiency and provide appropriate learning support.

The system according to this embodiment aims to accurately assess students' learning proficiency and provide appropriate learning support.

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 from students. The determination unit determines the student's level of proficiency based on the learning data collected by the collection unit. The provision unit provides the student with explanations and practice questions in areas where the student's proficiency level is lacking, as determined by the determination unit. The presentation unit presents the student with the level of proficiency determined by the determination unit.

The system according to the embodiment can accurately assess students' learning proficiency and provide appropriate learning support.

1 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a first embodiment. 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. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a second embodiment. 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. FIG. 10 is a conceptual diagram illustrating an example of the configuration of a data processing system according to a third embodiment. 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. FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. 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. 1 shows an emotion map onto which multiple emotions are mapped. 1 shows an emotion map onto which multiple emotions are mapped.

Below, an example of an embodiment of a system relating to the technology disclosed herein will be described with reference to the accompanying drawings.

First, let me explain the terminology used in the following explanation.

In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, a processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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).

In the following embodiments, coded random access memory (RAM) is memory in which information is temporarily stored and is used as work memory by the processor.

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

In the following embodiments, a communication I/F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I/F controls communication between multiple computers. Examples of communication standards that can be applied to the communication I/F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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 A alone, B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and/or B" also applies when three or more things are expressed connected by "and/or."

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

As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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).

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, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with an indicator (e.g., a pen or finger) to receive user input by the indicator. 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 microphone 38B to the data processing device 12. In the data processing device 12, the identification processing unit 290 (see Figure 2) acquires the data indicating the user input.

The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form that the user can perceive (e.g., audio and/or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

The communication I/F 44 is connected to the 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.

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

As shown in FIG. 2, in the data processing device 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" 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 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.

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 the 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 emotions, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

(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.

The learning support system according to the embodiment includes a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects student learning data. The student learning data 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. The collection unit can also convert comments made during class into text data using speech recognition technology and collect the text. 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. Comments made during class are converted into text data in real time using speech recognition technology and collected. The determination unit determines the student's proficiency 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 by the assessment unit may be performed using, for example, 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 by the provision unit may be performed using, for example, AI, or may be performed without AI. For example, the provision unit may receive input of areas in which 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 by the presentation unit may be performed using, for example, 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.

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 voice recognition technology and collect the 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. Comments made during class are converted into text data in real time using voice recognition technology and collected. The collection unit can centrally manage this data and connect with other systems and 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, by adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. Additionally, collection units can implement encryption technologies and access controls to ensure data privacy and security, allowing them to collect data efficiently and effectively and improving overall system performance.

The judgment unit judges the student's proficiency level based on the learning data collected by the collection unit. The judgment 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 judgment unit identifies a subject area in which test scores are low and judges that the student's proficiency in that subject area is insufficient. The judgment unit can also identify a subject area in which homework submission status is poor and judge that the student's proficiency in that subject area is insufficient. Furthermore, the judgment unit can analyze the content of comments made during class, identify a subject area in which there are few comments, and judge that the student's proficiency in that subject area is insufficient. For example, the judgment unit judges that a subject area in which test scores are less than 50 points is insufficient in proficiency. A subject area in which homework submission status is less than 50% is insufficient in proficiency. A subject area in which there are few comments made during class is insufficient in proficiency. The judgment unit can analyze this data using AI to judge 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 studying past data and understanding each student's learning patterns and tendencies. This allows the assessment unit to quickly and accurately assess students' proficiency levels, building a foundation for providing appropriate support.

The providing unit provides students with explanations and practice problems in the 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. 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 to 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.

The presentation unit presents the proficiency level determined by the determination unit to the student. Presentation may be 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. The presentation unit may also 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. Displaying it as a pie chart allows the student to grasp the overall balance. Displaying it as a line graph allows the student to understand changes in proficiency levels 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 help students intuitively understand their own learning situation and create effective study plans.

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 data. 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 student 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.

The presentation unit can visualize proficiency 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 proficiency as a bar graph to allow students to visually understand. The presentation unit can also display proficiency as a pie chart to allow students to grasp the overall balance. The presentation unit can also display proficiency as a line graph to allow students to understand changes over time. For example, the presentation unit can display proficiency as a bar graph to allow students to compare proficiency in each area. It can also display it as a pie chart to allow students to grasp the overall balance. It can also display it as a line graph to allow students to understand changes in proficiency over time. In this way, visually displaying proficiency can aid students' understanding 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 graphs or charts.

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 tests on which the student previously achieved high scores. The collection unit can also analyze the content of homework submitted by the student in the past and collect homework of a similar level of difficulty. 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 tests on which the student previously achieved high scores. The collection unit analyzes the content of homework submitted by the student in the past and collects homework of a similar level of difficulty. 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 the optimal data collection method based on the student's past learning history. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the student's past learning history data into the generation AI and have the generation AI select the optimal data collection method.

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 their motivation to learn. Furthermore, the collection unit can collect only the necessary data depending on the student's learning progress. For example, the collection unit prioritizes collecting data related to the subject the student is currently studying. Collecting data related to areas the student is interested in can increase their motivation to learn. Only the necessary data is collected depending on 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 AI, for example, 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.

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 a student lives in a particular area, the collection unit can prioritize collecting data related to that area. Furthermore, if a student attends a particular school, the collection unit can also prioritize collecting data related to that school's curriculum. Furthermore, if a student is participating in an event held in a particular area, the collection unit can prioritize collecting data related to that event. For example, if a student lives in a particular area, the collection unit prioritizes collecting data related to that area. If a student attends a particular school, the collection unit prioritizes collecting data related to that school's curriculum. If a student is participating in an event held in a particular area, the collection unit prioritizes collecting data related to that event. In this way, by taking geographical location information into account, more relevant data can be collected. Some or all of the above-mentioned processing in 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 to the generation AI and cause the generation AI to prioritize collecting highly relevant data.

When collecting learning data, the collection unit can analyze the student's social media activity and collect related 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. Collects related data from education-related accounts followed by the student on social media. Collects related data based on the activities of learning groups in which the student participates on social media. This enables more diverse data collection by analyzing social media activity. Some or all of the above-mentioned processing in 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 social media activity data into the generation AI and cause the generation AI to collect related data.

When making a judgment, the judgment unit can adjust the level of detail of the judgment based on the importance of the learning data. The judgment unit, for example, makes a detailed judgment based on the score of an important test. The judgment unit can also make a simple 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. A simple judgment based on the status of homework submission. 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 judgments can be made. Some or all of the above-mentioned processing in the judgment unit may be performed using AI, for example, 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.

When making a judgment, the judgment unit can apply different judgment algorithms depending on the category of the learning data. For example, the judgment unit can apply a specific algorithm based on test scores to make a judgment. The judgment unit can also apply a different algorithm based on the status of homework submission to make a judgment. The judgment unit can also apply yet another algorithm based on the content of comments made during class to make a judgment. For example, the judgment unit can apply a specific algorithm based on test scores to make a judgment. A different algorithm can be applied based on the status of homework submission to make a judgment. A still further algorithm can be applied based on the content of comments made during class to make a judgment. In this way, by applying different algorithms depending on the category of the learning data, more appropriate judgments can be made. Some or all of the above-mentioned processing by the judgment unit may be performed using AI, for example, 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.

When making a judgment, the judgment unit can determine the priority of the judgment based on the time of submission of the learning data. For example, the judgment unit may prioritize the scores of recently submitted tests. The judgment unit may also prioritize lately submitted data based on the time of submission of homework. The judgment unit may also prioritize the most recent data based on the time of submission of comments made during class. For example, the judgment unit may prioritize the scores of recently submitted tests. The judgment unit may prioritize lately submitted data based on the time of submission of homework. The judgment unit may prioritize the most recent 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 AI, for example, or may be performed without using AI. For example, the judgment unit may input the time of submission of the learning data to the generation AI and have the generation AI determine the priority of the judgment.

When making a judgment, the judgment unit can adjust the order of judgment based on the relevance of the learning data. For example, the judgment unit may prioritize judgment of data with a high relevance in test scores. The judgment unit may also prioritize judgment of data with a high relevance in homework submission status. The judgment unit may also prioritize judgment of data with a high relevance in the content of comments made during class. For example, the judgment unit may prioritize judgment of data with a high relevance in test scores. The judgment unit may prioritize judgment of data with a high relevance in homework submission status. The judgment unit may prioritize judgment of data with a high relevance in the content of comments 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 AI, for example, or may be performed without using AI. For example, the judgment unit may input the relevance of the learning data to the generation AI and cause the generation AI to adjust the order of judgment.

The providing unit can adjust the level of detail of the provision based on the importance of the learning content when providing it. 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 depending on the importance of the learning content. For example, the providing unit provides detailed explanations and practice questions for important learning content. It provides simple explanations and practice questions for less important learning content. It provides explanations and practice questions with an appropriate level of detail depending on 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-mentioned processing by the providing unit may be performed using AI, for example, or without 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 provision.

When providing the learning content, the providing unit can apply different provision algorithms depending on the category of 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, it can apply yet another algorithm to provide explanations and practice problems. In this way, by applying different algorithms depending on the category of the learning content, more appropriate learning support is possible. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the learning content to the generation AI and cause the generation AI to apply different provision algorithms.

When providing learning content, the providing unit can determine the priority of provision based on the time of submission of 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 late submitted learning content. Furthermore, the providing unit can provide explanations and practice questions in an appropriate order of priority based on the time of submission. For example, the providing unit prioritizes providing explanations and practice questions for recently submitted learning content. The providing unit prioritizes providing explanations and practice questions for late submitted learning content. The providing unit provides explanations and practice questions in an appropriate order of priority based on the time of submission. In this way, determining the priority based on the time of submission enables more effective learning support. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the time of submission of the learning content to the generation AI and have the generation AI determine the priority of provision.

The providing unit can adjust the order of provision based on the relevance of the learning content when providing it. For example, the providing unit can prioritize 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 relevance. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the learning content to the generating AI and cause the generating AI to adjust the order of provision.

When presenting, the presentation unit can select the display method by referring to the student's past learning history. The presentation unit, for example, provides the optimal display method based on the student's preferred display method in the past. 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 the optimal display method based on the student's preferred display method in the past. A display method with high visibility is selected from the student's past learning history. The student's past learning history is analyzed and the most effective display method is provided. This enables more effective feedback by selecting the optimal display method based on the student's past learning history. 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 student's past learning history data into the generation AI and have the generation AI select the optimal display method.

When presenting, the presentation unit can apply different display algorithms depending on the proficiency category. For example, the presentation unit can apply 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 can apply a specific display algorithm to mathematics proficiency. A different display algorithm can be applied to English proficiency. A still another display algorithm can be applied to science proficiency. In this way, by applying different display algorithms depending on the proficiency category, more effective feedback can be provided. Some or all of the above-mentioned processing in the presentation unit can be performed using AI, for example, or without 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.

When presenting, the presentation unit can select a display method taking into account the student's geographical location information. For example, if a student lives in a specific area, the presentation unit can provide a display method related to that area. Furthermore, if a student attends a specific school, the presentation unit can also provide a display method related to that school's curriculum. Furthermore, if a student is participating in an event held in a specific area, the presentation unit can also provide a display method related to that event. For example, if a student lives in a specific area, the presentation unit can provide a display method related to that area. If a student attends a specific school, the presentation unit can provide a display method related to that 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 that event. In this way, by taking geographical location information into consideration, a more relevant display method can be provided. Some or all of the above-mentioned processing in the presentation unit may be performed using, or without, AI, for example. For example, the presentation unit can input the student's geographical location information to the generation AI and have the generation AI select the optimal display method.

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

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

Learning support systems can also analyze students' learning styles and provide individually optimized learning plans. For example, students who prefer visual learning can be provided with learning materials that make heavy use of videos and infographics. Students who prefer auditory learning can be provided with learning materials in the form of audio commentary or podcasts. Furthermore, students who prefer hands-on learning can be provided with learning materials that include interactive simulations and experiments. This makes it possible to maximize learning effectiveness by providing the optimal learning plan based on each student's learning style.

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.

The learning support system can also monitor students' learning environments to provide an optimal learning environment. For example, sensors can 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.

The learning support system can also set learning goals for students and monitor their progress toward achieving them. For example, it can record the short-term and long-term learning goals set by students and periodically check their progress toward achieving them. 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.

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.

The processing flow for Example 1 is briefly explained below.

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 an online platform and homework submission status from a learning management system. Furthermore, 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 providing unit provides the student with explanations and practice problems in the areas where the proficiency level is lacking as determined by the determining 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 enable the student to understand visually.

(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.

The learning support system according to the embodiment includes a collection unit, a determination unit, a provision unit, and a presentation unit. The collection unit collects student learning data. The student learning data 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. The collection unit can also convert comments made during class into text data using speech recognition technology and collect the text. 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. Comments made during class are converted into text data in real time using speech recognition technology and collected. The determination unit determines the student's proficiency 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 by the assessment unit may be performed using, for example, 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 by the provision unit may be performed using, for example, AI, or may be performed without AI. For example, the provision unit may receive input of areas in which 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 by the presentation unit may be performed using, for example, 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.

The collection unit collects student learning data. Student learning data includes, but is 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 voice recognition technology and collect the 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. Comments made during class are converted into text data in real time using voice recognition technology and collected. The collection unit can centrally manage this data and connect with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the assessment unit and provision unit. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. Additionally, collection units can implement encryption technologies and access controls to ensure data privacy and security, allowing them to collect data efficiently and effectively and improving overall system performance.

The judgment unit judges the student's proficiency level based on the learning data collected by the collection unit. The judgment 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 judgment unit identifies a subject area in which test scores are low and judges that the student's proficiency in that subject area is insufficient. The judgment unit can also identify a subject area in which homework submission status is poor and judge that the student's proficiency in that subject area is insufficient. Furthermore, the judgment unit can analyze the content of comments made during class, identify a subject area in which there are few comments, and judge that the student's proficiency in that subject area is insufficient. For example, the judgment unit judges that a subject area in which test scores are less than 50 points is insufficient in proficiency. A subject area in which homework submission status is less than 50% is insufficient in proficiency. A subject area in which there are few comments made during class is insufficient in proficiency. The judgment unit can analyze this data using AI to judge 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 studying past data and understanding each student's learning patterns and tendencies. This allows the assessment unit to quickly and accurately assess students' proficiency levels, building a foundation for providing appropriate support.

The providing unit provides students with explanations and practice problems in the 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 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.

The presentation unit presents the proficiency level determined by the determination unit to the student. Presentation may be 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. The presentation unit may also 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. Displaying it as a pie chart allows the student to grasp the overall balance. Displaying it as a line graph allows the student to understand changes in proficiency levels 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 help students intuitively understand their own learning situation and create effective study plans.

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. The collection unit can also convert comments made during class into text data in real time using voice recognition technology and collect the data. 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 student 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.

The presentation unit can visualize proficiency 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 proficiency as a bar graph to allow students to visually understand. The presentation unit can also display proficiency as a pie chart to allow students to grasp the overall balance. The presentation unit can also display proficiency as a line graph to allow students to understand changes over time. For example, the presentation unit can display proficiency as a bar graph to allow students to compare proficiency in each area. It can also display it as a pie chart to allow students to grasp the overall balance. It can also display it as a line graph to allow students to understand changes in proficiency over time. In this way, visually displaying proficiency can aid students' understanding 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 graphs or charts.

The collection unit can estimate a student's emotions and adjust the timing of learning data collection based on the estimated student emotions. For example, if a student is feeling stressed, the collection unit can provide a break to collect learning data in a relaxed state. Furthermore, if a student is concentrating, the collection unit can also collect test scores and homework submission status at that time. Furthermore, if a student is tired, the collection unit can adjust the schedule to collect learning data the next day. For example, if a student is feeling stressed, the collection unit can provide a break to collect learning data in a relaxed state. If a student is concentrating, the collection unit can collect test scores and homework submission status at that time. If a 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, text generation AI (e.g., LLM) or 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 student emotion data into the generation AI and have the generation AI perform emotion estimation.

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 tests on which the student previously achieved high scores. 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 tests on which the student previously achieved high scores. 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 the optimal data collection method based on the student's past learning history. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the student's past learning history data into the generation AI and have the generation AI select the optimal data collection method.

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 their motivation to learn. Furthermore, the collection unit can collect only the necessary data depending on the student's learning progress. For example, the collection unit prioritizes collecting data related to the subject the student is currently studying. Collecting data related to areas the student is interested in can increase their motivation to learn. Only the necessary data is collected depending on 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 AI, for example, 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.

The collection unit can estimate the student's emotions and determine the priority of data to be collected based on the estimated student emotions. For example, if the student is stressed, the collection unit can prioritize collecting easy questions and data with relaxing content. Furthermore, if the student is concentrating, the collection unit can also prioritize collecting difficult questions and 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 easy questions and data with relaxing content. If the student is concentrating, the collection unit can prioritize collecting difficult questions and important data. If the student is tired, the collection unit can prioritize collecting data with light content. This enables more effective data collection by prioritizing data 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, text generation AI (e.g., LLM) or 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 student emotion data into the generation AI and have the generation AI perform emotion estimation.

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 a student lives in a particular area, the collection unit can prioritize collecting data related to that area. Furthermore, if a student attends a particular school, the collection unit can also prioritize collecting data related to that school's curriculum. Furthermore, if a student is participating in an event held in a particular area, the collection unit can prioritize collecting data related to that event. For example, if a student lives in a particular area, the collection unit prioritizes collecting data related to that area. If a student attends a particular school, the collection unit prioritizes collecting data related to that school's curriculum. If a student is participating in an event held in a particular area, the collection unit prioritizes collecting data related to that event. In this way, by taking geographical location information into account, more relevant data can be collected. Some or all of the above-mentioned processing in 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 to the generation AI and cause the generation AI to prioritize collecting highly relevant data.

When collecting learning data, the collection unit can analyze the student's social media activity and collect related 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. Collects related data from education-related accounts followed by the student on social media. Collects related data based on the activities of learning groups in which the student participates on social media. This enables more diverse data collection by analyzing social media activity. Some or all of the above-mentioned processing in 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 social media activity data into the generation AI and cause the generation AI to collect related data.

The judgment unit can estimate the student's emotions and adjust the proficiency assessment criteria based on the estimated student 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 evaluation. Furthermore, if the student is concentrating, the judgment unit can appropriately adjust the assessment criteria to provide a balanced evaluation. 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 criteria can be tightened and provide an accurate evaluation. If the student is concentrating, the judgment unit can appropriately adjust the assessment criteria to provide a balanced evaluation. 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. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit may input student emotion data into the generation AI and have the generation AI estimate the emotion.

When making a judgment, the judgment unit can adjust the level of detail of the judgment based on the importance of the learning data. The judgment unit, for example, makes a detailed judgment based on the score of an important test. The judgment unit can also make a simple 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. A simple judgment based on the status of homework submission. 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 judgments can be made. Some or all of the above-mentioned processing in the judgment unit may be performed using AI, for example, 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.

When making a judgment, the judgment unit can apply different judgment algorithms depending on the category of the learning data. For example, the judgment unit can apply a specific algorithm based on test scores to make a judgment. The judgment unit can also apply a different algorithm based on the status of homework submission to make a judgment. The judgment unit can also apply yet another algorithm based on the content of comments made during class to make a judgment. For example, the judgment unit can apply a specific algorithm based on test scores to make a judgment. A different algorithm can be applied based on the status of homework submission to make a judgment. A still further algorithm can be applied based on the content of comments made during class to make a judgment. In this way, by applying different algorithms depending on the category of the learning data, more appropriate judgments can be made. Some or all of the above-mentioned processing by the judgment unit may be performed using AI, for example, 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.

The judgment unit can estimate the student's emotions and adjust the display method of the judgment results based on the estimated student's emotions. For example, if the student is nervous, the judgment unit provides a simple, highly visible display method. Furthermore, if the student is relaxed, the judgment unit can also provide a display method that includes detailed information. Furthermore, if the student is in a hurry, the judgment unit can also provide a display method that focuses on the main points. For example, if the student is nervous, the judgment unit provides a simple, highly visible display method. If the student is relaxed, the judgment unit provides a display method that includes detailed information. If the student is in a hurry, the judgment 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 generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or without AI. For example, the judgment unit can input the student's emotion data into the generation AI and have the generation AI perform emotion estimation.

When making a judgment, the judgment unit can determine the priority of the judgment based on the time of submission of the learning data. For example, the judgment unit may prioritize the scores of recently submitted tests. The judgment unit may also prioritize lately submitted data based on the time of submission of homework. The judgment unit may also prioritize the most recent data based on the time of submission of comments made during class. For example, the judgment unit may prioritize the scores of recently submitted tests. The judgment unit may prioritize lately submitted data based on the time of submission of homework. The judgment unit may prioritize the most recent 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 AI, for example, or may be performed without using AI. For example, the judgment unit may input the time of submission of the learning data to the generation AI and have the generation AI determine the priority of the judgment.

When making a judgment, the judgment unit can adjust the order of judgment based on the relevance of the learning data. For example, the judgment unit may prioritize judgment of data with a high relevance in test scores. The judgment unit may also prioritize judgment of data with a high relevance in homework submission status. The judgment unit may also prioritize judgment of data with a high relevance in the content of comments made during class. For example, the judgment unit may prioritize judgment of data with a high relevance in test scores. The judgment unit may prioritize judgment of data with a high relevance in homework submission status. The judgment unit may prioritize judgment of data with a high relevance in the content of comments 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 AI, for example, or may be performed without using AI. For example, the judgment unit may input the relevance of the learning data to the generation AI and cause the generation AI to adjust the order of judgment.

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 also provide more difficult explanations and exercises. Furthermore, if the student is tired, the providing unit can also provide explanations and exercises with lighter 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 less difficult explanations and exercises. This allows for more effective learning support by adjusting the method of provision according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 student emotion data into the generation AI and have the generation AI perform emotion estimation.

The providing unit can adjust the level of detail of the provision based on the importance of the learning content when providing it. 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 depending on the importance of the learning content. For example, the providing unit provides detailed explanations and practice questions for important learning content. It provides simple explanations and practice questions for less important learning content. It provides explanations and practice questions with an appropriate level of detail depending on 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-mentioned processing by the providing unit may be performed using AI, for example, or without 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 provision.

When providing the learning content, the providing unit can apply different provision algorithms depending on the category of 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, it can apply yet another algorithm to provide explanations and practice problems. In this way, by applying different algorithms depending on the category of the learning content, more appropriate learning support is possible. Some or all of the above-mentioned processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the learning content to the generation AI and cause the generation AI to apply different provision algorithms.

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 feeling stressed, the providing unit can prioritize providing easy explanations and exercises. Furthermore, if the student is concentrating, the providing unit can prioritize providing 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 feeling stressed, the providing unit can prioritize providing easy explanations and exercises. If the student is concentrating, the providing unit can prioritize providing 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 priorities 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, text generation AI (e.g., LLM) or multimodal generation AI. 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 may input student emotion data into the generating AI and have the generating AI estimate the emotions.

When providing learning content, the providing unit can determine the priority of provision based on the submission date of 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 late 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. The providing unit prioritizes providing explanations and practice questions for late submitted learning content. The providing unit provides explanations and practice questions in an appropriate order of priority based on the submission date. In this way, more effective learning support can be achieved 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 without AI. For example, the providing unit may input the submission date of the learning content to the generation AI and cause the generation AI to determine the provision priority.

The providing unit can adjust the order of provision based on the relevance of the learning content when providing it. For example, the providing unit can prioritize 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 relevance. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the relevance of the learning content to the generating AI and cause the generating AI to adjust the order of provision.

The presentation unit can estimate the student's emotions and adjust the display method of the proficiency level based on the estimated student 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 also provide a display method including detailed information. Furthermore, if the student is in a hurry, the presentation unit can also 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. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or without AI. For example, the presentation unit can input the student's emotion data into the generation AI and have the generation AI perform emotion estimation.

When presenting, the presentation unit can select the display method by referring to the student's past learning history. The presentation unit, for example, provides the optimal display method based on the student's preferred display method in the past. 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 the optimal display method based on the student's preferred display method in the past. A display method with high visibility is selected from the student's past learning history. The student's past learning history is analyzed and the most effective display method is provided. This enables more effective feedback by selecting the optimal display method based on the student's past learning history. Some or all of the above-mentioned processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the student's past learning history data to the generation AI and have the generation AI select the optimal display method.

When presenting, the presentation unit can apply different display algorithms depending on the proficiency category. For example, the presentation unit can apply 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 can apply a specific display algorithm to mathematics proficiency. A different display algorithm can be applied to English proficiency. A still another display algorithm can be applied to science proficiency. In this way, by applying different display algorithms depending on the proficiency category, more effective feedback can be provided. Some or all of the above-mentioned processing in the presentation unit can be performed using AI, for example, or without 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.

The presentation unit can estimate the student's emotions and adjust the display order of the proficiency levels based on the estimated student emotions. For example, if the student is stressed, the presentation unit can display the levels in order of ease. Furthermore, if the student is concentrating, the presentation unit can also display the levels in order of difficulty. Furthermore, if the student is tired, the presentation unit can display the levels in order of ease. For example, if the student is stressed, the presentation unit can display the levels in order of ease. If the student is concentrating, the presentation unit can display the levels in order of difficulty. If the student is tired, the presentation unit can display the levels in order of ease. This allows for more effective feedback by adjusting the display order according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. 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 emotion data into the generation AI and have the generation AI perform emotion estimation.

When presenting, the presentation unit can select a display method taking into account the student's geographical location information. For example, if a student lives in a specific area, the presentation unit can provide a display method related to that area. Furthermore, if a student attends a specific school, the presentation unit can also provide a display method related to that school's curriculum. Furthermore, if a student is participating in an event held in a specific area, the presentation unit can also provide a display method related to that event. For example, if a student lives in a specific area, the presentation unit can provide a display method related to that area. If a student attends a specific school, the presentation unit can provide a display method related to that 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 that event. In this way, by taking geographical location information into consideration, a more relevant display method can be provided. Some or all of the above-mentioned processing in the presentation unit may be performed using, or without, AI, for example. For example, the presentation unit can input the student's geographical location information to the generation AI and have the generation AI select the optimal display method.

When presenting information, the presentation unit can analyze the student's social media activity and display related information. The presentation unit can, for example, display related information based on 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 study groups 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. It can display related information from education-related accounts followed by the student on social media. It can display related information based on the activities of study groups 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-mentioned processing in the presentation unit can be performed using AI, for example, or without AI. For example, the presentation unit can input the student's social media activity data into the generation AI and cause the generation AI to display related information.

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

Learning support systems can also analyze students' learning styles and provide individually optimized learning plans. For example, students who prefer visual learning can be provided with learning materials that make heavy use of videos and infographics. Students who prefer auditory learning can be provided with learning materials in the form of audio commentary or podcasts. Furthermore, students who prefer hands-on learning can be provided with learning materials that include interactive simulations and experiments. This makes it possible to maximize learning effectiveness by providing the optimal learning plan based on each student's learning style.

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.

The learning support system can also monitor students' learning environments to provide an optimal learning environment. For example, sensors can 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 support students in learning in an optimal environment.

The learning support system can also set learning goals for students and monitor their progress toward achieving them. For example, it can record the short-term and long-term learning goals set by students and periodically check their progress toward achieving them. 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.

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.

The learning support system can also estimate students' emotions and adjust their learning progress based on the estimated emotions. For example, if a student is feeling stressed, the learning pace can be slowed down. Alternatively, if a student is relaxed, the learning pace can be increased. Furthermore, if a student is concentrating, they can be given more difficult tasks. This makes it possible to support more effective learning by adjusting the learning progress according to the student's emotions.

The learning support system can also estimate a student's emotions and personalize the learning content based on the estimated emotions. For example, if a student is feeling stressed, it can provide them with learning materials that will help them relax. Alternatively, if a student is relaxed, it can provide them with learning materials with more difficult content. Furthermore, if a student is concentrating, it can provide them with challenging tasks. This makes it possible to support more effective learning by personalizing the learning content according to the student's emotions.

The learning support system can also estimate students' 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 them feel a sense of accomplishment. Furthermore, if a student is concentrating, it can set challenging goals and support them in achieving them. This makes it possible to increase students' motivation by providing feedback to improve their motivation according to their emotions.

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.

The learning support system can also estimate a student's emotions and adjust how it reports their learning progress based on the estimated emotions. For example, if a student is feeling stressed, it can report them with a focus on positive feedback. If a student is relaxed, it can report detailed progress. Furthermore, if a student is focused, it can provide specific advice on how to proceed to the next step. This allows it to provide more effective feedback by adjusting how it reports progress based on the student's emotions.

The processing flow for Example 2 is briefly explained below.

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 an online platform and homework submission status from a learning management system. Furthermore, 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 providing unit provides the student with explanations and practice problems in the areas where the proficiency level is lacking as determined by the determining 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 enable the student to understand visually.

The specific processing unit 290 transmits the results 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 results of the specific processing. The microphone 38B acquires audio indicating the user input regarding the results 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.

The data generation model 58 is what is known as generative AI (artificial intelligence). An example of a data generation model 58 is generative AI such as ChatGPT (registered trademark) (Internet search <URL: https://openai.com/blog/chatgpt>). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data) is also input. The data generation model 58 performs inference on the input inference data in accordance with the instructions indicated by the prompt and outputs the inference results in one or more data formats, such as voice data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and/or summarization. The 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 that outputs inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12 or the like includes multiple types of data generation models 58, and the data generation model 58 includes AI other than the generation AI. Examples of AI other than the generation AI include 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, and can perform various processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. In addition, processing performed by AI, including generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generation AI.

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, etc., and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

For example, the collection unit can collect the student's learning data using the camera 42 and 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 level of 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 level of proficiency is lacking. For example, the presentation unit can visualize and present the student's level of 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 example described above, and various modifications are possible.

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

As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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.

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, RAM 48, and storage 50. The processor 46, RAM 48, and 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.

The microphone 238 receives instructions and other information 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 in accordance with instructions from the processor 46.

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).

The communication I/F 44 is connected to the 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 manner.

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

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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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 the 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 emotions, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

In the smart glasses 214, 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 emotion identification model similar to the data generation model 58 and emotion identification model 59, and can use these models to perform processing similar to that of the specific processing unit 290.

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 processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the results 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 results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

The data generation model 58 is what is known as generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data) is input. 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, text generation AI, image generation AI, and 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 that outputs inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12 or the like includes multiple types of data generation models 58, and the data generation model 58 includes AI other than the generation AI. Examples of AI other than the generation AI include 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, and can perform various processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. In addition, processing performed by AI, including generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generation AI.

The data processing system 210 according to the second embodiment performs processing similar to that of the data processing system 10 according to the first embodiment. 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. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device, etc.

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 level of 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 level of proficiency is lacking. For example, the presentation unit can visualize and present the student's level of 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 example described above, and various modifications are possible.

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

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

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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.

The headset 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, RAM 48, and storage 50. The processor 46, RAM 48, and 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.

The microphone 238 receives instructions and other information 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 in accordance with instructions from the processor 46.

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).

The communication I/F 44 is connected to the 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 manner.

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

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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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 the 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 emotions, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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 processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the results of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

The data generation model 58 is what is known as generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data) is input. 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, text generation AI, image generation AI, and 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 that outputs inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12 or the like includes multiple types of data generation models 58, and the data generation model 58 includes AI other than the generation AI. Examples of AI other than the generation AI include 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, and can perform various processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. In addition, processing performed by AI, including generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generation AI.

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. 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 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 terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

For example, the collection unit can collect student learning data using the camera 42 and 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 level of 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 where the determined level of proficiency is lacking. For example, the presentation unit can visualize and present the student's level of proficiency 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.

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

As shown in FIG. 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.

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, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and 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.

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, RAM 48, and storage 50. The processor 46, RAM 48, and 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.

The microphone 238 receives instructions and other information 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 in accordance with instructions from the processor 46.

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 CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

The communication I/F 44 is connected to the 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 manner.

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.

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

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 the specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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 the 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 emotions, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

In robot 414, specific processing is performed by processor 46. Specific program 60 is stored in storage 50. Processor 46 reads specific program 60 from storage 50 and executes the read specific program 60 on RAM 48. The specific processing is realized by processor 46 operating as control unit 46A in accordance with specific program 60 executed on RAM 48. Robot 414 also has a data generation model and emotion identification model similar to data generation model 58 and emotion identification model 59, and can use these models to perform processing similar to that of identification processing unit 290.

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 processing results (such as prediction results) using the data generation model 58. The data processing device 12 may also be a server device, or a terminal device owned by a user (for example, a mobile phone, robot, home appliance, etc.).

The specific processing unit 290 transmits the results 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 results of the specific processing. The microphone 238 acquires audio indicating the user input regarding the results 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.

The data generation model 58 is what is known as generative AI. An example of a data generation model 58 is generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data) is input. 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, text generation AI, image generation AI, and 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 that outputs inference results from prompts that do not include instructions. In this case, the data generation model 58 can output inference results from prompts that do not include instructions. The data processing device 12 or the like includes multiple types of data generation models 58, and the data generation model 58 includes AI other than the generation AI. Examples of AI other than the generation AI include 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, and can perform various processes, but are not limited to these examples. The AI may also be an AI agent. When the processing of each of the above-mentioned parts is performed by AI, the processing may be performed in part or in whole by AI, but is not limited to these examples. In addition, processing performed by AI, including generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generation AI.

The data processing system 410 according to the fourth embodiment performs processing similar to that of the data processing system 10 according to the first embodiment. 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.

For example, the collection unit can collect student learning data using the camera 42 and 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 level of 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 level of proficiency is lacking. For example, the presentation unit can visualize and present the student's level of proficiency 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.

The emotion identification model 59, which serves 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 Figure 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.

Figure 9 shows an emotion map 400 on which multiple emotions are mapped. In 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both emotions and mental states. Emotions that are generally generated from reactions that occur in the brain are arranged on the left side of the concentric circles. Emotions that are generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions that are generally generated from reactions that occur in the brain and are 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, emotion map 400 maps multiple emotions based on the structure by which emotions are generated, with emotions that tend to occur simultaneously being mapped close together.

These emotions are distributed in the 3 o'clock direction on emotion map 400, and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness takes precedence over internal sensations, resulting in a calm impression.

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 out you go on emotion map 400, the more visible (expressed in behavior) the emotion becomes.

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

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

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 close to each other have similar values, as in the emotion map 900 shown in Figure 10. Figure 10 shows an example in which multiple emotions, such as "relieved," "calm," and "reassuring," have similar emotion values.

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

In the above embodiment, an example was described in which the specific processing program 56 is stored in the storage 32, 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.

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.

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

The following types of processors can be used as hardware resources for executing specific processes. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource for executing specific processes by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which are processors with a circuit configuration designed specifically for executing specific processes. All processors have built-in or connected memory, and all use the memory to execute specific processes.

The hardware resource that executes the specific processing may be composed of one of these various processors, or may be composed of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). The hardware resource that executes the specific processing may also be a single processor.

As an example of a configuration using a single processor, first, there is a configuration in which one processor is configured using a combination of one or more CPUs and software, and this processor functions as a hardware resource that executes specific processing. Second, there is a configuration in which a processor is used to realize the functions of an entire system, including multiple hardware resources that execute specific processing, on a single IC chip, as typified by SoC (System-on-a-chip). In this way, specific processing is realized using one or more of the various processors listed above as hardware resources.

More specifically, the hardware structure of these various processors can be an electrical circuit that combines circuit elements such as semiconductor devices. Furthermore, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps can be added, or the processing order can be rearranged, all within the scope of the spirit of the invention.

Furthermore, in the above examples, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. Furthermore, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are only examples, and they may be combined, or other devices may be used. Furthermore, in the above examples, the first and second embodiments have been described separately, but these may also be combined.

The above-described written content 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 explanation 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 substitutions may be made to the above-described written content and illustrations, as long as they do not deviate from the spirit 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 written content 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.

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

(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.
(Appendix 2)
The collecting unit
The system according to claim 1, characterized in that data on students' test scores, homework submission status, and comments made during class are collected in a specific manner.
(Appendix 3)
The presentation unit
2. The system of claim 1, wherein proficiency is visualized using a graph or chart.
(Appendix 4)
The collecting unit
2. The system according to claim 1, wherein the system estimates the student's emotions in a specific manner and adjusts the timing of collecting learning data based on the estimated student's emotions.
(Appendix 5)
The collecting unit
2. The system of claim 1, further comprising: analyzing a student's past learning history and selecting a data collection method.
(Appendix 6)
The collecting unit
The system of claim 1, wherein when collecting learning data, the learning data is filtered in a specific manner based on the student's current learning situation and areas of interest.
(Appendix 7)
The collecting unit
2. The system of claim 1, further comprising: estimating student emotions; and prioritizing data to be collected based on the estimated student emotions.
(Appendix 8)
The collecting unit
The system of claim 1, wherein when collecting learning data, the system takes into account the geographical location information of students in a specific manner to prioritize the collection of highly relevant data.
(Appendix 9)
The collecting unit
The system described in Appendix 1, characterized in that when collecting learning data, students' social media activities are analyzed in a specific manner and related data is collected.
(Appendix 10)
The determination unit
2. The system of claim 1, wherein the student's emotions are estimated in a specific manner, and the proficiency assessment criteria are adjusted based on the estimated student emotions.
(Appendix 11)
The determination unit
The system according to claim 1, wherein the level of detail of the judgment is adjusted based on the importance of the learning data at the time of judgment.
(Appendix 12)
The determination unit
The system according to claim 1, wherein different determination algorithms are applied depending on the category of the training data during the determination.
(Appendix 13)
The determination unit
The system according to claim 1, characterized in that it estimates the student's emotion in a specific manner and adjusts the display method of the judgment result based on the estimated student's emotion.
(Appendix 14)
The determination unit
The system according to claim 1, wherein, at the time of judgment, a priority of judgment is determined based on the time of submission of the learning data.
(Appendix 15)
The determination unit
The system of claim 1, wherein the order of judgment is adjusted based on the relevance of the training data during judgment.
(Appendix 16)
The providing unit
2. The system according to claim 1, wherein the system estimates the student's emotions in a specific manner and adjusts the method of providing explanations and practice questions based on the estimated student's emotions.
(Appendix 17)
The providing unit
The system of claim 1, wherein the level of detail of the content provided is adjusted based on the importance of the learning content at the time of provision.
(Appendix 18)
The providing unit
The system of claim 1, wherein different provision algorithms are applied depending on the category of the learning content during provision.
(Appendix 19)
The providing unit
The system according to claim 1, characterized in that it estimates a student's emotions in a specific manner and determines the priority of explanations and exercises to be provided based on the estimated student's emotions.
(Appendix 20)
The providing unit
The system according to claim 1, wherein, at the time of provision, the priority of provision is determined based on the time of submission of the learning content.
(Appendix 21)
The providing unit
2. The system of claim 1, wherein the order of presentation is adjusted based on the relevance of the learning content during presentation.
(Appendix 22)
The presentation unit
The system according to claim 1, wherein the student's emotions are estimated in a specific manner, and the method of displaying the proficiency level is adjusted based on the estimated student's emotions.
(Appendix 23)
The presentation unit
The system described in Appendix 1, characterized in that when presenting, the display method is selected by referring to the student's past learning history.
(Appendix 24)
The presentation unit
2. The system of claim 1, wherein different display algorithms are applied depending on the proficiency category during presentation.
(Appendix 25)
The presentation unit
The system according to claim 1, characterized in that it estimates the student's emotions in a specific manner and adjusts the display order of the proficiency levels based on the estimated student's emotions.
(Appendix 26)
The presentation unit
The system according to claim 1, wherein the display method is selected taking into consideration the geographical location information of the student at the time of presentation.
(Appendix 27)
The presentation unit
2. The system of claim 1, wherein, at the time of presentation, the system analyzes the student's social media activity and displays relevant information.

10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims (10)

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: The collecting unit
2. The system according to claim 1, wherein data on the students' test scores, homework submission status, and comments made during class are collected. The presentation unit
The system of claim 1 , wherein the proficiency is visualized using a graph or chart. The collecting unit
The system according to claim 1 , further comprising: adjusting a timing for collecting learning data based on the student's emotion estimated by the emotion identification model, so as to delay the timing for collection by providing a break if the student is feeling stressed, and to advance the timing for collection if the student is concentrating . The collecting unit
The system of claim 1, further comprising: analyzing the student's past learning history and selecting a data collection method. The collecting unit
The system according to claim 1, wherein when collecting learning data, filtering is performed based on the student's current learning situation and areas of interest. The collecting unit
2. The system according to claim 1, further comprising: determining a priority of data to be collected based on the student's emotion estimated by the emotion identification model , such that data on easy questions is collected preferentially when the student is feeling stressed, and data on difficult questions is collected preferentially when the student is concentrating . The collecting unit
The system according to claim 1, wherein when collecting learning data, highly relevant data is collected with priority given to geographical location information of the student. The collecting unit
The system of claim 1 , wherein when collecting learning data, the system analyzes the student's social media activity and collects related data. 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.