system

Abstract

The system according to this embodiment aims to promote collaborative learning among students and to cultivate communication skills and teamwork. [Solution] The system according to the embodiment comprises a group formation unit, a provision unit, a monitoring unit, an advice unit, a feedback unit, and a sharing unit. The group formation unit forms optimal groups according to the students' learning content and areas of expertise. The provision unit presents collaborative tasks to the groups formed by the group formation unit. The monitoring unit monitors the progress of group activities based on the collaborative tasks presented by the provision unit. The advice unit provides necessary advice based on the progress monitored by the monitoring unit. The feedback unit encourages constructive feedback among students based on the advice given by the advice unit. The sharing unit shares the status of group activities with the teacher based on the feedback encouraged by the feedback unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is room for improvement in effectively promoting collaborative learning among students and cultivating communication skills and teamwork.

[0005] The system according to the embodiment aims to promote collaborative learning among students and cultivate communication skills and teamwork.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a group formation unit, a provision unit, a monitoring unit, an advice unit, a feedback unit, and a sharing unit. The group formation unit forms optimal groups according to the students' learning content and areas of expertise. The provision unit presents collaborative tasks to the groups formed by the group formation unit. The monitoring unit monitors the progress of group activities based on the collaborative tasks presented by the provision unit. The advice unit provides necessary advice based on the progress monitored by the monitoring unit. The feedback unit encourages constructive feedback among students based on the advice given by the advice unit. The sharing unit shares the status of group activities with the teacher based on the feedback encouraged by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can promote collaborative learning among students and cultivate communication skills and teamwork. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The collaborative learning promotion system according to an embodiment of the present invention is an AI agent that promotes collaborative learning among students. This collaborative learning promotion system not only deepens understanding of learning content but also cultivates communication skills and teamwork. The system aims to enhance autonomy in order to create an environment in which students proactively learn together. The collaborative learning promotion system forms learning groups, provides collaborative tasks, manages and supports progress, facilitates feedback, and reports to teachers. For example, the collaborative learning promotion system forms optimal groups according to the students' learning content and areas of expertise. For example, by putting students who are good at mathematics and students who are good at English in the same group, they can leverage each other's strengths. Next, the collaborative learning promotion system presents tasks to be worked on by the groups and cultivates their thinking skills. For example, in a history class, it can present a task in which each group researches a different historical period and presents their findings. Furthermore, the collaborative learning promotion system monitors the progress of group activities and provides advice as needed. For example, if a group is falling behind, the collaborative learning promotion system provides advice to promote progress. The collaborative learning promotion system encourages constructive feedback among students. For example, after a group presentation, the collaborative learning promotion system provides feedback points and encourages students to exchange opinions with each other. Finally, the collaborative learning promotion system shares the status of the group activities with the teacher and facilitates appropriate support. For example, it reports the group's progress and the degree to which they have achieved their tasks to the teacher so that the teacher can provide the necessary support. In this way, the collaborative learning promotion system can promote collaborative learning among students and support proactive learning.

[0029] The collaborative learning promotion system according to this embodiment comprises a grouping unit, a provision unit, a monitoring unit, an advice unit, a feedback unit, and a sharing unit. The grouping unit forms optimal groups according to the students' learning content and areas of expertise. For example, the grouping unit can group students who are good at mathematics with students who are good at English, allowing them to leverage each other's strengths. The grouping unit can also form groups considering students' learning history and current learning status. For example, the grouping unit can analyze students' past performance and assignment submission status to form well-balanced groups. The provision unit presents collaborative tasks to the groups formed by the grouping unit. For example, the provision unit can present a task in a history class where each group researches a different historical period and presents their findings. The provision unit can also generate collaborative tasks and learning content using generative AI. For example, the provision unit can automatically generate different tasks for each group using generative AI. The monitoring unit monitors the progress of group activities based on the collaborative tasks presented by the provision unit. For example, the monitoring unit monitors the progress of the groups in real time and provides advice as needed. The Advice Department provides necessary advice based on the progress monitored by the Monitoring Department. For example, if a group is behind schedule, the Advice Department provides advice to accelerate its progress. The Feedback Department encourages constructive feedback among students based on the advice given by the Advice Department. For example, the Feedback Department provides feedback points after group presentations and encourages students to exchange opinions with each other. The Sharing Department shares the status of group activities with the teacher based on the feedback encouraged by the Feedback Department. For example, the Sharing Department reports the group's progress and the degree of achievement of tasks to the teacher, enabling the teacher to provide necessary support. In this way, the collaborative learning promotion system according to the embodiment can promote collaborative learning among students and support proactive learning.

[0030] The group formation department creates optimal groups based on students' learning content and strengths. Specifically, it comprehensively analyzes students' performance data, learning history, interests, and personality traits to create the most effective group formations. For example, grouping students who excel in mathematics with those who excel in English allows them to leverage each other's strengths. The group formation department can also form groups considering students' learning history and current learning status. For instance, it analyzes students' past performance and assignment submission history to create balanced groups. Furthermore, the group formation department can use AI to evaluate students' learning patterns and collaboration skills, automatically creating optimal groups. The AI ​​uses an algorithm that analyzes students' learning data and predicts compatibility and effectiveness in collaborative learning. This allows the group formation department to create groups that maximize the individual characteristics of each student, thereby enhancing the effectiveness of collaborative learning. The group formation department can also periodically reorganize groups to adapt to students' growth and changes in their learning status. For example, reviewing groups each semester and proposing new member compositions can maintain students' motivation and sustain the effectiveness of collaborative learning.

[0031] The provision department presents collaborative assignments to groups formed by the organization department. Specifically, the provision department designs and presents collaborative assignments tailored to the characteristics and learning objectives of each group. For example, in a history class, it could present an assignment where each group researches and presents on a different historical period. The provision department can also generate collaborative assignments and learning content using generative AI. The generative AI automatically generates optimal assignments, taking into account each group's learning situation and interests. For example, the provision department can use generative AI to automatically generate different assignments for each group. The generative AI uses natural language processing technology to adjust the content and difficulty level of assignments to suit each group, providing an individualized learning experience. Furthermore, the provision department can monitor the progress and achievement of collaborative assignments in real time and adjust the content and method of assignments as needed. This allows the provision department to provide appropriate assignments to each group and maximize the effectiveness of collaborative learning. In addition, the provision department can share the results of collaborative assignments on a digital platform, promoting peer evaluation and feedback among students. This allows students to deepen their learning by referring to the results of other groups.

[0032] The Monitoring Department monitors the progress of group activities based on collaborative tasks provided by the Provision Department. Specifically, the Monitoring Department monitors the activity status of each group in real time and evaluates their progress and the degree to which tasks have been completed. For example, the Monitoring Department monitors the progress of groups in real time and provides advice as needed. The Monitoring Department can use AI to analyze group activity data and automatically evaluate delays in progress and the degree to which tasks have been completed. The AI ​​uses an algorithm that analyzes each group's activity logs and submissions and evaluates their progress and the degree to which tasks have been completed in real time. This allows the Monitoring Department to quickly and accurately grasp the progress of each group and provide the necessary support. In addition, the Monitoring Department can evaluate the communication status and improvement of collaborative skills within the groups and comprehensively evaluate the effectiveness of collaborative learning. For example, the Monitoring Department analyzes the number of times members speak and the frequency of opinion exchanges within the groups to evaluate improvements in collaborative skills. This allows the Monitoring Department to comprehensively evaluate the progress and effectiveness of collaborative learning and provide the necessary support.

[0033] The Advice Department provides necessary advice based on the progress monitored by the Monitoring Department. Specifically, the Advice Department evaluates the progress and achievement of tasks for each group and provides necessary advice. For example, if a group is behind schedule, the Advice Department provides advice to accelerate progress. The Advice Department can use AI to analyze the progress of each group and automatically generate optimal advice. The AI ​​uses an algorithm that analyzes the activity data of each group and evaluates their progress and achievement of tasks. This allows the Advice Department to provide appropriate advice to each group quickly and accurately. Furthermore, the Advice Department can provide individualized advice tailored to the characteristics and learning objectives of each group. For example, if there is a lack of communication within a group, the Advice Department provides advice to promote communication. This allows the Advice Department to evaluate the progress and achievement of tasks for each group and provide necessary support. In addition, the Advice Department can regularly evaluate the effectiveness of the advice and improve the content of the advice. This allows the Advice Department to maximize the effectiveness of collaborative learning and increase students' motivation to learn.

[0034] The Feedback Department encourages constructive feedback among students based on the advice given by the Advice Department. Specifically, the Feedback Department presents feedback points after group presentations and encourages students to exchange opinions with each other. For example, the Feedback Department can use AI to analyze the content of each group's presentation and automatically generate feedback points. The AI ​​uses an algorithm that analyzes the content of each group's presentation and automatically generates feedback points. This allows the Feedback Department to provide appropriate feedback points to each group quickly and accurately. Furthermore, the Feedback Department can encourage constructive feedback among students and enhance the effectiveness of collaborative learning. For example, the Feedback Department can hold workshops and discussion sessions to facilitate the exchange of opinions among students. This allows the Feedback Department to encourage constructive feedback among students and enhance the effectiveness of collaborative learning. In addition, the Feedback Department can evaluate the effectiveness of the feedback and improve the content of the feedback. This allows the Feedback Department to maximize the effectiveness of collaborative learning and increase students' motivation to learn.

[0035] The sharing department shares the status of group activities with teachers based on feedback encouraged by the feedback department. Specifically, the sharing department reports the progress and achievement of tasks for each group to teachers so that teachers can provide necessary support. For example, the sharing department can use AI to analyze the activity data of each group and automatically evaluate the progress and achievement of tasks. The AI ​​uses an algorithm that analyzes the activity data of each group and evaluates the progress and achievement of tasks. This allows the sharing department to quickly and accurately grasp the progress and achievement of tasks for each group and report it to teachers. In addition, the sharing department can provide individualized support tailored to the characteristics and learning objectives of each group so that teachers can provide the necessary support. For example, if there is a lack of communication within a group, the sharing department can provide support to facilitate communication. This allows the sharing department to evaluate the progress and achievement of tasks for each group and provide the necessary support. Furthermore, the sharing department can periodically evaluate the effectiveness of the shared content and improve the content accordingly. This allows the shared area to maximize the effectiveness of collaborative learning and enhance students' motivation to learn.

[0036] The service provider can generate collaborative assignments and learning content using generative AI. For example, the service provider can use generative AI to automatically generate different assignments for each group. The generative AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to these examples. For example, the service provider can input a prompt to the generative AI, "Generate different history assignments for each group," and the generative AI will generate assignments suitable for each group. The service provider can also use generative AI to generate learning content. For example, the service provider can input a prompt to the generative AI, "Generate math practice problems," and the generative AI will generate math practice problems. In this way, using generative AI makes the generation of collaborative assignments and learning content more efficient.

[0037] The feedback unit can facilitate communication within a group using natural language processing. For example, the feedback unit uses natural language processing techniques to support the exchange of opinions among students. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the feedback unit uses natural language processing techniques to analyze the content of students' statements and provide appropriate feedback. The feedback unit can also use natural language processing techniques to provide advice to facilitate communication among students. For example, the feedback unit analyzes the content of students' statements and points out areas for improvement in communication. In this way, communication within the group is facilitated by using natural language processing. Some or all of the above processing in the feedback unit may be performed using, for example, generative AI, or without generative AI. For example, the feedback unit inputs the content of students' statements into a generative AI, and the generative AI generates appropriate feedback.

[0038] The grouping department can analyze students' past learning history and select the optimal grouping method. For example, the department can create balanced groups based on students' past performance. It can also analyze students' past assignment submission history and group cooperative students together. Furthermore, it can consider students' past learning styles and group students who are compatible with each other. This makes it possible to create optimal groups by analyzing past learning history. Some or all of the above processes in the grouping department may be performed using, for example, a generative AI, or not. For example, the grouping department inputs students' past learning history data into a generative AI, and the generative AI selects the optimal grouping method.

[0039] The grouping unit can filter students based on their current learning status and areas of interest when forming groups. For example, the unit can consider students' current learning progress and group students at the same progress. It can also group students with common interests based on their areas of interest. Furthermore, it can group students who can complement each other based on their understanding of current assignments. This allows for more appropriate group formation by filtering based on current learning status and areas of interest. Some or all of the above processing in the grouping unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the grouping unit inputs students' current learning status data into a generative AI, and the generative AI performs the filtering.

[0040] The grouping unit can prioritize grouping students based on their geographical location, taking into account their geographical location. For example, the unit can prioritize grouping students who live in the same area. It can also group students who share the same commuting route. Furthermore, it can group students who are geographically close to each other. This allows for the prioritization of highly relevant students by considering their geographical location. Some or all of the above processing in the grouping unit may be performed using, for example, a generative AI, or without one. For example, the grouping unit inputs students' geographical location information into a generative AI, which then prioritizes grouping highly relevant students.

[0041] The grouping department can analyze students' social media activities and group related students together when forming groups. For example, the grouping department can group students who interact frequently on social media together. It can also group students who share similar interests on social media. Furthermore, the grouping department can group students who have cooperative relationships on social media. In this way, related students can be grouped by analyzing social media activities. Some or all of the above processing in the grouping department may be performed using, for example, a generative AI, or not using a generative AI. For example, the grouping department inputs students' social media activity data into a generative AI, and the generative AI groups related students together.

[0042] The service provider can adjust the level of detail provided based on the importance of the task when providing it. For example, the service provider can provide a detailed explanation for high-importance tasks, and a concise explanation for low-importance tasks. Furthermore, the service provider can adjust the length of the task explanation according to its importance. By adjusting the level of detail based on the importance of the task, it becomes possible to provide more appropriate tasks. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs task importance data into a generative AI, and the generative AI adjusts the level of detail provided.

[0043] The service provider can apply different service provision algorithms depending on the category of the assignment when providing assignments. For example, the service provider can apply an algorithm that includes experimental videos to science assignments. It can also apply an algorithm that includes step-by-step explanations to mathematics assignments. Furthermore, it can apply an algorithm that includes listening materials to English assignments. By applying different service provision algorithms depending on the category of the assignment, it becomes possible to provide more appropriate assignments. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the assignment category data into a generative AI, and the generative AI applies different service provision algorithms.

[0044] The assignment provider can determine the priority of assignments based on their submission dates. For example, the provider can prioritize assignments with approaching deadlines. It can also postpone assignments with later deadlines. Furthermore, the provider can adjust the order of assignment delivery according to the submission dates. This allows for more appropriate assignment delivery by determining the priority based on the submission dates. Some or all of the above processing in the assignment provider may be performed using, for example, a generative AI, or without one. For example, the provider inputs assignment submission date data into a generative AI, which then determines the priority of assignment delivery.

[0045] The delivery unit can adjust the order in which tasks are delivered based on their relevance. For example, the delivery unit can deliver highly relevant tasks consecutively. It can also postpone less relevant tasks. Furthermore, the delivery unit can adjust the order in which tasks are delivered according to their relevance. By adjusting the order in which tasks are delivered based on their relevance, it becomes possible to deliver more appropriate tasks. Some or all of the above processing in the delivery unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the delivery unit inputs task relevance data into a generative AI, and the generative AI adjusts the order in which tasks are delivered.

[0046] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships within the group during monitoring. For example, the monitoring unit can adjust the accuracy of monitoring based on the frequency of communication within the group. The monitoring unit can also set monitoring criteria by considering the cooperative relationships within the group. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the division of roles within the group. In this way, the accuracy of monitoring is improved by considering the interrelationships within the group. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit inputs communication data within the group into a generative AI, and the generative AI adjusts the accuracy of monitoring.

[0047] The monitoring unit can perform monitoring while considering the student's attribute information. For example, the monitoring unit can set monitoring criteria considering the student's age. The monitoring unit can also adjust the monitoring method considering the student's gender. Furthermore, the monitoring unit can improve the accuracy of monitoring by considering the student's learning style. This makes it possible to perform more appropriate monitoring by considering the student's attribute information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit inputs student attribute information data into a generative AI, and the generative AI sets the monitoring criteria.

[0048] The monitoring unit can perform monitoring while considering the geographical distribution of the groups. For example, the monitoring unit can prioritize monitoring students who are geographically close to each other. It can also postpone monitoring students who are geographically far apart. Furthermore, the monitoring unit can adjust the frequency of monitoring according to the geographical distribution. This allows for more appropriate monitoring by considering the geographical distribution. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit inputs the geographical distribution data of the groups into a generative AI, and the generative AI adjusts the frequency of monitoring.

[0049] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature during monitoring. For example, the monitoring unit sets monitoring criteria based on relevant literature. The monitoring unit can also improve the monitoring method by referring to relevant literature. Furthermore, the monitoring unit can improve the accuracy of monitoring by utilizing the knowledge from relevant literature. Thus, the accuracy of monitoring is improved by referring to relevant literature. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit inputs relevant literature data into a generative AI, and the generative AI sets the monitoring criteria.

[0050] The advice unit can provide optimal advice by referring to past advice data when giving advice. For example, the advice unit can provide optimal advice for similar situations based on past advice data. The advice unit can also analyze past advice data and extract effective advice. Furthermore, the advice unit can provide individually customized advice by referring to past advice data. This allows for the provision of optimal advice by referring to past advice data. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit inputs past advice data into a generative AI, and the generative AI provides optimal advice.

[0051] The advice unit can provide advice while considering the student's attribute information. For example, the advice unit can provide appropriate advice considering the student's age. It can also adjust the content of the advice considering the student's gender. Furthermore, the advice unit can provide optimal advice considering the student's learning style. This makes it possible to provide more appropriate advice by considering the student's attribute information. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit inputs student attribute information data into a generative AI, and the generative AI adjusts the content of the advice.

[0052] The advice unit can provide advice while considering the geographical distribution of the groups. For example, the advice unit can provide advice that encourages cooperation among students who are geographically close to each other. It can also provide advice that strengthens collaboration among students who are geographically far apart. Furthermore, the advice unit can adjust the content of the advice according to the geographical distribution. This makes it possible to provide more appropriate advice by considering the geographical distribution. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the advice unit inputs the geographical distribution data of the groups into a generative AI, and the generative AI adjusts the content of the advice.

[0053] The advice unit can improve the accuracy of its advice by referring to relevant literature. For example, the advice unit provides evidence-based advice based on relevant literature. The advice unit can also improve the content of its advice by referring to relevant literature. Furthermore, the advice unit can improve the accuracy of its advice by utilizing the insights from relevant literature. Thus, the accuracy of the advice is improved by referring to relevant literature. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit inputs relevant literature data into a generative AI, and the generative AI improves the content of the advice.

[0054] The feedback unit can provide optimal feedback by referring to past feedback data during the feedback process. For example, the feedback unit can provide optimal feedback for similar situations based on past feedback data. The feedback unit can also analyze past feedback data and extract effective feedback. Furthermore, the feedback unit can provide individually customized feedback by referring to past feedback data. This allows for the provision of optimal feedback by referring to past feedback data. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs past feedback data into a generative AI, and the generative AI provides optimal feedback.

[0055] The feedback unit can provide feedback while considering the student's attribute information. For example, the feedback unit can provide appropriate feedback considering the student's age. It can also adjust the content of the feedback considering the student's gender. Furthermore, the feedback unit can provide optimal feedback considering the student's learning style. This makes it possible to provide more appropriate feedback by considering the student's attribute information. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs student attribute information data into a generative AI, and the generative AI adjusts the content of the feedback.

[0056] The feedback unit can provide feedback while considering the geographical distribution of the group. For example, the feedback unit can provide feedback that encourages cooperation among students who are geographically close to each other. It can also provide feedback that strengthens collaboration among students who are geographically far apart. Furthermore, the feedback unit can adjust the content of the feedback according to the geographical distribution. This makes it possible to provide more appropriate feedback by considering the geographical distribution. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs the geographical distribution data of the group into a generative AI, and the generative AI adjusts the content of the feedback.

[0057] The feedback unit can improve the accuracy of its feedback by referring to relevant literature during the feedback process. For example, the feedback unit provides scientifically-based feedback based on relevant literature. The feedback unit can also improve the content of its feedback by referring to relevant literature. Furthermore, the feedback unit can improve the accuracy of its feedback by utilizing the insights from relevant literature. Thus, the accuracy of the feedback is improved by referring to relevant literature. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs relevant literature data into a generative AI, and the generative AI improves the content of the feedback.

[0058] The sharing unit can provide optimal information by referring to past shared data during the sharing process. For example, the sharing unit can provide optimal information for similar situations based on past shared data. The sharing unit can also analyze past shared data and extract effective information. Furthermore, the sharing unit can provide individually customized information by referring to past shared data. This allows for the provision of optimal information by referring to past shared data. Some or all of the above-described processes in the sharing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing unit inputs past shared data into a generative AI, which then provides optimal information.

[0059] The sharing unit can share information while considering the student's attribute information. For example, the sharing unit can share appropriate information considering the student's age. It can also adjust the content of the information considering the student's gender. Furthermore, the sharing unit can share optimal information considering the student's learning style. This makes it possible to share information more appropriately by considering the student's attribute information. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing unit inputs student attribute information data into a generative AI, and the generative AI adjusts the content of the information.

[0060] The sharing function can share information while considering the geographical distribution of the groups. For example, the sharing function can share information that encourages cooperation among students who are geographically close to each other. It can also share information that strengthens collaboration among students who are geographically far apart. Furthermore, the sharing function can adjust the content of the information according to the geographical distribution. This makes it possible to share information more appropriately by considering geographical distribution. Some or all of the above processing in the sharing function may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing function inputs the geographical distribution data of the groups into a generative AI, and the generative AI adjusts the content of the information.

[0061] The sharing unit can improve the accuracy of sharing by referring to relevant literature during the sharing process. For example, the sharing unit shares scientifically-based information based on relevant literature. The sharing unit can also improve the content of the information by referring to relevant literature. Furthermore, the sharing unit can improve the accuracy of sharing by utilizing the insights from relevant literature. As a result, the accuracy of sharing is improved by referring to relevant literature. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing unit inputs relevant literature data into a generative AI, and the generative AI improves the content of the information.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The grouping department can create groups based not only on students' academic history but also on their personal interests, such as hobbies and special skills. For example, the department could group students who enjoy music together and have them work on music-related tasks. They could also group students who excel at sports together and have them work on sports-related projects. Furthermore, they could group students interested in art and promote collaborative learning through art projects. This allows for the creation of more highly motivated groups by considering students' personal interests.

[0064] The system provider can adjust the format of collaborative assignments according to students' learning styles. For example, students who prefer visual learning can be provided with assignments that make extensive use of diagrams and graphs. Students who prefer auditory learning can be provided with assignments that include audio guidance. Furthermore, students who prefer experiential learning can be provided with assignments that include practical activities. By adjusting the format of assignments according to students' learning styles, more effective learning becomes possible.

[0065] The monitoring unit can monitor not only students' learning progress but also their health. For example, it can monitor students' sleep patterns and eating habits, and adjust their learning progress according to their health. It can also monitor students' physical activity levels and provide appropriate advice if they are not getting enough exercise. Furthermore, it can monitor students' stress levels and provide advice on relaxation if stress levels are high. This allows for more appropriate learning support by taking students' health into consideration.

[0066] The group formation department can create groups based not only on students' learning history but also on the results of past collaborative learning. For example, the department can group students who have been cooperative in the past to enhance the effectiveness of collaborative learning. They can also appoint students who have demonstrated leadership in the past as group leaders. Furthermore, they can group students who have shown strong problem-solving abilities in the past to improve the efficiency of problem-solving. In this way, considering the results of past collaborative learning makes it possible to create more effective groups.

[0067] The assignment provider can adjust the difficulty level of collaborative assignments according to the students' learning objectives. For example, students with basic learning objectives can be given basic assignments. Students with advanced learning objectives can be given challenging assignments. Furthermore, students who want to improve a specific skill can be given assignments tailored to that skill. By adjusting the difficulty level of assignments according to the students' learning objectives, more effective learning becomes possible.

[0068] The monitoring department can monitor not only students' learning progress but also the quality of collaborative learning. For example, it can monitor the quality of communication within groups and provide advice for improvement as needed. It can also monitor the appropriateness of role assignments within groups and provide advice for adjustment if they are unbalanced. Furthermore, it can monitor cooperative relationships within groups and provide advice to encourage cooperation if it is lacking. By monitoring the quality of collaborative learning, more effective learning support becomes possible.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The grouping department forms optimal groups based on students' learning content and areas of expertise. For example, students who are good at math and students who are good at English can be grouped together to leverage each other's strengths. Groups can also be formed considering students' learning history and current learning status. Step 2: The provisioning department presents collaborative tasks to the groups formed by the organization department. For example, in a history class, the assignment could be to have each group research and present on a different historical period. It is also possible to generate collaborative tasks and learning content using generative AI. Step 3: The monitoring department monitors the progress of group activities based on the collaborative tasks presented by the provision department. For example, it monitors the group's progress in real time and provides advice as needed. Step 4: The Advice Department provides necessary advice based on the progress monitored by the Monitoring Department. For example, if a group is behind schedule, the Advice Department will provide advice to help accelerate progress. Step 5: The Feedback team encourages constructive feedback among students based on the advice given by the Advice team. For example, they might provide feedback points after group presentations and encourage students to exchange opinions with each other. Step 6: The sharing team shares the status of the group activity with the teacher based on the feedback encouraged by the feedback team. For example, they report the group's progress and the degree to which they have achieved their tasks so that the teacher can provide any necessary support.

[0071] (Example of form 2) The collaborative learning promotion system according to an embodiment of the present invention is an AI agent that promotes collaborative learning among students. This collaborative learning promotion system not only deepens understanding of learning content but also cultivates communication skills and teamwork. The system aims to enhance autonomy in order to create an environment in which students proactively learn together. The collaborative learning promotion system forms learning groups, provides collaborative tasks, manages and supports progress, facilitates feedback, and reports to teachers. For example, the collaborative learning promotion system forms optimal groups according to the students' learning content and areas of expertise. For example, by putting students who are good at mathematics and students who are good at English in the same group, they can leverage each other's strengths. Next, the collaborative learning promotion system presents tasks to be worked on by the groups and cultivates their thinking skills. For example, in a history class, it can present a task in which each group researches a different historical period and presents their findings. Furthermore, the collaborative learning promotion system monitors the progress of group activities and provides advice as needed. For example, if a group is falling behind, the collaborative learning promotion system provides advice to promote progress. The collaborative learning promotion system encourages constructive feedback among students. For example, after a group presentation, the collaborative learning promotion system provides feedback points and encourages students to exchange opinions with each other. Finally, the collaborative learning promotion system shares the status of the group activities with the teacher and facilitates appropriate support. For example, it reports the group's progress and the degree to which they have achieved their tasks to the teacher so that the teacher can provide the necessary support. In this way, the collaborative learning promotion system can promote collaborative learning among students and support proactive learning.

[0072] The collaborative learning promotion system according to this embodiment comprises a grouping unit, a provision unit, a monitoring unit, an advice unit, a feedback unit, and a sharing unit. The grouping unit forms optimal groups according to the students' learning content and areas of expertise. For example, the grouping unit can group students who are good at mathematics with students who are good at English, allowing them to leverage each other's strengths. The grouping unit can also form groups considering students' learning history and current learning status. For example, the grouping unit can analyze students' past performance and assignment submission status to form well-balanced groups. The provision unit presents collaborative tasks to the groups formed by the grouping unit. For example, the provision unit can present a task in a history class where each group researches a different historical period and presents their findings. The provision unit can also generate collaborative tasks and learning content using generative AI. For example, the provision unit can automatically generate different tasks for each group using generative AI. The monitoring unit monitors the progress of group activities based on the collaborative tasks presented by the provision unit. For example, the monitoring unit monitors the progress of the groups in real time and provides advice as needed. The Advice Department provides necessary advice based on the progress monitored by the Monitoring Department. For example, if a group is behind schedule, the Advice Department provides advice to accelerate its progress. The Feedback Department encourages constructive feedback among students based on the advice given by the Advice Department. For example, the Feedback Department provides feedback points after group presentations and encourages students to exchange opinions with each other. The Sharing Department shares the status of group activities with the teacher based on the feedback encouraged by the Feedback Department. For example, the Sharing Department reports the group's progress and the degree of achievement of tasks to the teacher, enabling the teacher to provide necessary support. In this way, the collaborative learning promotion system according to the embodiment can promote collaborative learning among students and support proactive learning.

[0073] The group formation department creates optimal groups based on students' learning content and strengths. Specifically, it comprehensively analyzes students' performance data, learning history, interests, and personality traits to create the most effective group formations. For example, grouping students who excel in mathematics with those who excel in English allows them to leverage each other's strengths. The group formation department can also form groups considering students' learning history and current learning status. For instance, it analyzes students' past performance and assignment submission history to create balanced groups. Furthermore, the group formation department can use AI to evaluate students' learning patterns and collaboration skills, automatically creating optimal groups. The AI ​​uses an algorithm that analyzes students' learning data and predicts compatibility and effectiveness in collaborative learning. This allows the group formation department to create groups that maximize the individual characteristics of each student, thereby enhancing the effectiveness of collaborative learning. The group formation department can also periodically reorganize groups to adapt to students' growth and changes in their learning status. For example, reviewing groups each semester and proposing new member compositions can maintain students' motivation and sustain the effectiveness of collaborative learning.

[0074] The provision department presents collaborative assignments to groups formed by the organization department. Specifically, the provision department designs and presents collaborative assignments tailored to the characteristics and learning objectives of each group. For example, in a history class, it could present an assignment where each group researches and presents on a different historical period. The provision department can also generate collaborative assignments and learning content using generative AI. The generative AI automatically generates optimal assignments, taking into account each group's learning situation and interests. For example, the provision department can use generative AI to automatically generate different assignments for each group. The generative AI uses natural language processing technology to adjust the content and difficulty level of assignments to suit each group, providing an individualized learning experience. Furthermore, the provision department can monitor the progress and achievement of collaborative assignments in real time and adjust the content and method of assignments as needed. This allows the provision department to provide appropriate assignments to each group and maximize the effectiveness of collaborative learning. In addition, the provision department can share the results of collaborative assignments on a digital platform, promoting peer evaluation and feedback among students. This allows students to deepen their learning by referring to the results of other groups.

[0075] The Monitoring Department monitors the progress of group activities based on collaborative tasks provided by the Provision Department. Specifically, the Monitoring Department monitors the activity status of each group in real time and evaluates their progress and the degree to which tasks have been completed. For example, the Monitoring Department monitors the progress of groups in real time and provides advice as needed. The Monitoring Department can use AI to analyze group activity data and automatically evaluate delays in progress and the degree to which tasks have been completed. The AI ​​uses an algorithm that analyzes each group's activity logs and submissions and evaluates their progress and the degree to which tasks have been completed in real time. This allows the Monitoring Department to quickly and accurately grasp the progress of each group and provide the necessary support. In addition, the Monitoring Department can evaluate the communication status and improvement of collaborative skills within the groups and comprehensively evaluate the effectiveness of collaborative learning. For example, the Monitoring Department analyzes the number of times members speak and the frequency of opinion exchanges within the groups to evaluate improvements in collaborative skills. This allows the Monitoring Department to comprehensively evaluate the progress and effectiveness of collaborative learning and provide the necessary support.

[0076] The Advice Department provides necessary advice based on the progress monitored by the Monitoring Department. Specifically, the Advice Department evaluates the progress and achievement of tasks for each group and provides necessary advice. For example, if a group is behind schedule, the Advice Department provides advice to accelerate progress. The Advice Department can use AI to analyze the progress of each group and automatically generate optimal advice. The AI ​​uses an algorithm that analyzes the activity data of each group and evaluates their progress and achievement of tasks. This allows the Advice Department to provide appropriate advice to each group quickly and accurately. Furthermore, the Advice Department can provide individualized advice tailored to the characteristics and learning objectives of each group. For example, if there is a lack of communication within a group, the Advice Department provides advice to promote communication. This allows the Advice Department to evaluate the progress and achievement of tasks for each group and provide necessary support. In addition, the Advice Department can regularly evaluate the effectiveness of the advice and improve the content of the advice. This allows the Advice Department to maximize the effectiveness of collaborative learning and increase students' motivation to learn.

[0077] The Feedback Department encourages constructive feedback among students based on the advice given by the Advice Department. Specifically, the Feedback Department presents feedback points after group presentations and encourages students to exchange opinions with each other. For example, the Feedback Department can use AI to analyze the content of each group's presentation and automatically generate feedback points. The AI ​​uses an algorithm that analyzes the content of each group's presentation and automatically generates feedback points. This allows the Feedback Department to provide appropriate feedback points to each group quickly and accurately. Furthermore, the Feedback Department can encourage constructive feedback among students and enhance the effectiveness of collaborative learning. For example, the Feedback Department can hold workshops and discussion sessions to facilitate the exchange of opinions among students. This allows the Feedback Department to encourage constructive feedback among students and enhance the effectiveness of collaborative learning. In addition, the Feedback Department can evaluate the effectiveness of the feedback and improve the content of the feedback. This allows the Feedback Department to maximize the effectiveness of collaborative learning and increase students' motivation to learn.

[0078] The sharing department shares the status of group activities with teachers based on feedback encouraged by the feedback department. Specifically, the sharing department reports the progress and achievement of tasks for each group to teachers so that teachers can provide necessary support. For example, the sharing department can use AI to analyze the activity data of each group and automatically evaluate the progress and achievement of tasks. The AI ​​uses an algorithm that analyzes the activity data of each group and evaluates the progress and achievement of tasks. This allows the sharing department to quickly and accurately grasp the progress and achievement of tasks for each group and report it to teachers. In addition, the sharing department can provide individualized support tailored to the characteristics and learning objectives of each group so that teachers can provide the necessary support. For example, if there is a lack of communication within a group, the sharing department can provide support to facilitate communication. This allows the sharing department to evaluate the progress and achievement of tasks for each group and provide the necessary support. Furthermore, the sharing department can periodically evaluate the effectiveness of the shared content and improve the content accordingly. This allows the shared area to maximize the effectiveness of collaborative learning and enhance students' motivation to learn.

[0079] The service provider can generate collaborative assignments and learning content using generative AI. For example, the service provider can use generative AI to automatically generate different assignments for each group. The generative AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to these examples. For example, the service provider can input a prompt to the generative AI, "Generate different history assignments for each group," and the generative AI will generate assignments suitable for each group. The service provider can also use generative AI to generate learning content. For example, the service provider can input a prompt to the generative AI, "Generate math practice problems," and the generative AI will generate math practice problems. In this way, using generative AI makes the generation of collaborative assignments and learning content more efficient.

[0080] The feedback unit can facilitate communication within a group using natural language processing. For example, the feedback unit uses natural language processing techniques to support the exchange of opinions among students. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. For example, the feedback unit uses natural language processing techniques to analyze the content of students' statements and provide appropriate feedback. The feedback unit can also use natural language processing techniques to provide advice to facilitate communication among students. For example, the feedback unit analyzes the content of students' statements and points out areas for improvement in communication. In this way, communication within the group is facilitated by using natural language processing. Some or all of the above processing in the feedback unit may be performed using, for example, generative AI, or without generative AI. For example, the feedback unit inputs the content of students' statements into a generative AI, and the generative AI generates appropriate feedback.

[0081] The grouping unit can estimate students' emotions and adjust the timing of group formation based on the estimated emotions. For example, if students are feeling stressed, the grouping unit can form groups at a time when they can relax. Alternatively, if students are concentrating, the unit can immediately form groups to capitalize on their concentration. Furthermore, if students are tired, the unit can form groups after a break. This allows for more appropriate group formation by adjusting the timing of group formation according to students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the grouping unit may be performed using, for example, a generative AI, or not. For example, the grouping unit inputs student emotion data into a generative AI, which then adjusts the timing of group formation.

[0082] The grouping department can analyze students' past learning history and select the optimal grouping method. For example, the department can create balanced groups based on students' past performance. It can also analyze students' past assignment submission history and group cooperative students together. Furthermore, it can consider students' past learning styles and group students who are compatible with each other. This makes it possible to create optimal groups by analyzing past learning history. Some or all of the above processes in the grouping department may be performed using, for example, a generative AI, or not. For example, the grouping department inputs students' past learning history data into a generative AI, and the generative AI selects the optimal grouping method.

[0083] The grouping unit can filter students based on their current learning status and areas of interest when forming groups. For example, the unit can consider students' current learning progress and group students at the same progress. It can also group students with common interests based on their areas of interest. Furthermore, it can group students who can complement each other based on their understanding of current assignments. This allows for more appropriate group formation by filtering based on current learning status and areas of interest. Some or all of the above processing in the grouping unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the grouping unit inputs students' current learning status data into a generative AI, and the generative AI performs the filtering.

[0084] The grouping unit can estimate students' emotions and determine the priority of groups based on those estimated emotions. For example, if a student is feeling anxious, the grouping unit may group them with trustworthy students to provide a sense of security. If a student is highly motivated, the grouping unit may group them with a group where they can demonstrate leadership to leverage their motivation. Furthermore, if a student is feeling lonely, the grouping unit may group them with sociable students. This allows for more appropriate group formation by prioritizing groups according to students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the grouping unit may be performed using or without a generative AI. For example, the grouping unit inputs student emotion data into a generative AI, which then determines the group priorities.

[0085] The grouping unit can prioritize grouping students based on their geographical location, taking into account their geographical location. For example, the unit can prioritize grouping students who live in the same area. It can also group students who share the same commuting route. Furthermore, it can group students who are geographically close to each other. This allows for the prioritization of highly relevant students by considering their geographical location. Some or all of the above processing in the grouping unit may be performed using, for example, a generative AI, or without one. For example, the grouping unit inputs students' geographical location information into a generative AI, which then prioritizes grouping highly relevant students.

[0086] The grouping department can analyze students' social media activities and group related students together when forming groups. For example, the grouping department can group students who interact frequently on social media together. It can also group students who share similar interests on social media. Furthermore, the grouping department can group students who have cooperative relationships on social media. In this way, related students can be grouped by analyzing social media activities. Some or all of the above processing in the grouping department may be performed using, for example, a generative AI, or not using a generative AI. For example, the grouping department inputs students' social media activity data into a generative AI, and the generative AI groups related students together.

[0087] The service provider can estimate a student's emotions and adjust the way tasks are presented based on those emotions. For example, if a student is stressed, the service provider can provide a simple and easy-to-understand task. If a student is relaxed, the service provider can also provide a task with detailed explanations. Furthermore, if a student is excited, the service provider can provide a challenging task. By adjusting the way tasks are presented according to a student's emotions, more appropriate tasks can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider inputs student emotion data into a generative AI, and the generative AI adjusts the way tasks are presented.

[0088] The service provider can adjust the level of detail provided based on the importance of the task when providing it. For example, the service provider can provide a detailed explanation for high-importance tasks, and a concise explanation for low-importance tasks. Furthermore, the service provider can adjust the length of the task explanation according to its importance. By adjusting the level of detail based on the importance of the task, it becomes possible to provide more appropriate tasks. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs task importance data into a generative AI, and the generative AI adjusts the level of detail provided.

[0089] The service provider can apply different service provision algorithms depending on the category of the assignment when providing assignments. For example, the service provider can apply an algorithm that includes experimental videos to science assignments. It can also apply an algorithm that includes step-by-step explanations to mathematics assignments. Furthermore, it can apply an algorithm that includes listening materials to English assignments. By applying different service provision algorithms depending on the category of the assignment, it becomes possible to provide more appropriate assignments. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider inputs the assignment category data into a generative AI, and the generative AI applies different service provision algorithms.

[0090] The task provider can estimate a student's emotions and adjust the length of the assignment based on the estimated emotions. For example, if a student is tired, the task provider can provide a short assignment. It can also provide a longer assignment if the student is focused. Furthermore, if the student is relaxed, it can provide an assignment of appropriate length. This allows for more appropriate assignment delivery by adjusting the assignment length 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task provider may be performed using or without a generative AI. For example, the task provider inputs student emotion data into a generative AI, which then adjusts the assignment length.

[0091] The assignment provider can determine the priority of assignments based on their submission dates. For example, the provider can prioritize assignments with approaching deadlines. It can also postpone assignments with later deadlines. Furthermore, the provider can adjust the order of assignment delivery according to the submission dates. This allows for more appropriate assignment delivery by determining the priority based on the submission dates. Some or all of the above processing in the assignment provider may be performed using, for example, a generative AI, or without one. For example, the provider inputs assignment submission date data into a generative AI, which then determines the priority of assignment delivery.

[0092] The delivery unit can adjust the order in which tasks are delivered based on their relevance. For example, the delivery unit can deliver highly relevant tasks consecutively. It can also postpone less relevant tasks. Furthermore, the delivery unit can adjust the order in which tasks are delivered according to their relevance. By adjusting the order in which tasks are delivered based on their relevance, it becomes possible to deliver more appropriate tasks. Some or all of the above processing in the delivery unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the delivery unit inputs task relevance data into a generative AI, and the generative AI adjusts the order in which tasks are delivered.

[0093] The monitoring unit can estimate the student's emotions and adjust the monitoring criteria based on the estimated emotions. For example, if the student is stressed, the monitoring unit can reduce the frequency of monitoring. Conversely, if the student is relaxed, the monitoring unit can increase the frequency of monitoring. Furthermore, if the student is focused, the monitoring unit can tighten the monitoring criteria. This allows for more appropriate monitoring by adjusting the monitoring 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 is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the monitoring unit inputs student emotion data into a generative AI, and the generative AI adjusts the monitoring criteria.

[0094] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships within the group during monitoring. For example, the monitoring unit can adjust the accuracy of monitoring based on the frequency of communication within the group. The monitoring unit can also set monitoring criteria by considering the cooperative relationships within the group. Furthermore, the monitoring unit can improve the accuracy of monitoring based on the division of roles within the group. In this way, the accuracy of monitoring is improved by considering the interrelationships within the group. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit inputs communication data within the group into a generative AI, and the generative AI adjusts the accuracy of monitoring.

[0095] The monitoring unit can perform monitoring while considering the student's attribute information. For example, the monitoring unit can set monitoring criteria considering the student's age. The monitoring unit can also adjust the monitoring method considering the student's gender. Furthermore, the monitoring unit can improve the accuracy of monitoring by considering the student's learning style. This makes it possible to perform more appropriate monitoring by considering the student's attribute information. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit inputs student attribute information data into a generative AI, and the generative AI sets the monitoring criteria.

[0096] The monitoring unit can estimate the student's emotions and adjust the order in which the monitoring results are displayed based on the estimated emotions. For example, if the student is feeling anxious, the monitoring unit can display positive results first. It can also display detailed results if the student is relaxed. Furthermore, if the student is in a hurry, the monitoring unit can display concise results first. This allows for more appropriate result display by adjusting the order in which the monitoring results are displayed 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or not. For example, the monitoring unit inputs student emotion data into a generative AI, which then adjusts the order in which the monitoring results are displayed.

[0097] The monitoring unit can perform monitoring while considering the geographical distribution of the groups. For example, the monitoring unit can prioritize monitoring students who are geographically close to each other. It can also postpone monitoring students who are geographically far apart. Furthermore, the monitoring unit can adjust the frequency of monitoring according to the geographical distribution. This allows for more appropriate monitoring by considering the geographical distribution. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit inputs the geographical distribution data of the groups into a generative AI, and the generative AI adjusts the frequency of monitoring.

[0098] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature during monitoring. For example, the monitoring unit sets monitoring criteria based on relevant literature. The monitoring unit can also improve the monitoring method by referring to relevant literature. Furthermore, the monitoring unit can improve the accuracy of monitoring by utilizing the knowledge from relevant literature. Thus, the accuracy of monitoring is improved by referring to relevant literature. Some or all of the above processing in the monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the monitoring unit inputs relevant literature data into a generative AI, and the generative AI sets the monitoring criteria.

[0099] The advice unit can estimate a student's emotions and adjust the content of the advice based on the estimated emotions. For example, if the advice unit is stressed, it can provide advice that includes words of encouragement. If the student is relaxed, it can also provide detailed advice. Furthermore, if the student is focused, it can provide advice that includes specific areas for improvement. By adjusting the content of the advice according to the student's emotions, more appropriate advice becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the advice unit may be performed using a generative AI, or not using a generative AI. For example, the advice unit inputs the student's emotion data into a generative AI, and the generative AI adjusts the content of the advice.

[0100] The advice unit can provide optimal advice by referring to past advice data when giving advice. For example, the advice unit can provide optimal advice for similar situations based on past advice data. The advice unit can also analyze past advice data and extract effective advice. Furthermore, the advice unit can provide individually customized advice by referring to past advice data. This allows for the provision of optimal advice by referring to past advice data. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit inputs past advice data into a generative AI, and the generative AI provides optimal advice.

[0101] The advice unit can provide advice while considering the student's attribute information. For example, the advice unit can provide appropriate advice considering the student's age. It can also adjust the content of the advice considering the student's gender. Furthermore, the advice unit can provide optimal advice considering the student's learning style. This makes it possible to provide more appropriate advice by considering the student's attribute information. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit inputs student attribute information data into a generative AI, and the generative AI adjusts the content of the advice.

[0102] The advice unit can estimate a student's emotions and prioritize advice based on those emotions. For example, if a student is feeling anxious, the advice unit will prioritize reassuring advice. It can also prioritize challenging advice if the student is highly motivated. Furthermore, if the student is tired, it can prioritize advice encouraging rest. This allows for more appropriate advice by prioritizing it 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or not. For example, the advice unit inputs student emotion data into a generative AI, which then determines the priority of advice.

[0103] The advice unit can provide advice while considering the geographical distribution of the groups. For example, the advice unit can provide advice that encourages cooperation among students who are geographically close to each other. It can also provide advice that strengthens collaboration among students who are geographically far apart. Furthermore, the advice unit can adjust the content of the advice according to the geographical distribution. This makes it possible to provide more appropriate advice by considering the geographical distribution. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the advice unit inputs the geographical distribution data of the groups into a generative AI, and the generative AI adjusts the content of the advice.

[0104] The advice unit can improve the accuracy of its advice by referring to relevant literature. For example, the advice unit provides evidence-based advice based on relevant literature. The advice unit can also improve the content of its advice by referring to relevant literature. Furthermore, the advice unit can improve the accuracy of its advice by utilizing the insights from relevant literature. Thus, the accuracy of the advice is improved by referring to relevant literature. Some or all of the above processing in the advice unit may be performed using, for example, a generative AI, or without a generative AI. For example, the advice unit inputs relevant literature data into a generative AI, and the generative AI improves the content of the advice.

[0105] The feedback unit can estimate a student's emotions and adjust the content of the feedback based on the estimated emotions. For example, if a student is feeling stressed, the feedback unit will provide positive feedback. It can also provide detailed feedback if the student is relaxed. Furthermore, if the student is focused, it can provide feedback that includes specific areas for improvement. This allows for more appropriate feedback by adjusting the content 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 may 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 feedback unit may be performed using or without a generative AI. For example, the feedback unit inputs student emotion data into a generative AI, which then adjusts the content of the feedback.

[0106] The feedback unit can provide optimal feedback by referring to past feedback data during the feedback process. For example, the feedback unit can provide optimal feedback for similar situations based on past feedback data. The feedback unit can also analyze past feedback data and extract effective feedback. Furthermore, the feedback unit can provide individually customized feedback by referring to past feedback data. This allows for the provision of optimal feedback by referring to past feedback data. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs past feedback data into a generative AI, and the generative AI provides optimal feedback.

[0107] The feedback unit can provide feedback while considering the student's attribute information. For example, the feedback unit can provide appropriate feedback considering the student's age. It can also adjust the content of the feedback considering the student's gender. Furthermore, the feedback unit can provide optimal feedback considering the student's learning style. This makes it possible to provide more appropriate feedback by considering the student's attribute information. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs student attribute information data into a generative AI, and the generative AI adjusts the content of the feedback.

[0108] The feedback unit can estimate a student's emotions and prioritize feedback based on those emotions. For example, if a student is feeling anxious, the feedback unit will prioritize reassuring feedback. It can also prioritize challenging feedback if the student is highly motivated. Furthermore, if the student is tired, it can prioritize rest-enhancing feedback. This allows for more appropriate feedback by prioritizing it 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 may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the feedback unit may be performed using or without a generative AI. For example, the feedback unit inputs student emotion data into a generative AI, which then determines the priority of the feedback.

[0109] The feedback unit can provide feedback while considering the geographical distribution of the group. For example, the feedback unit can provide feedback that encourages cooperation among students who are geographically close to each other. It can also provide feedback that strengthens collaboration among students who are geographically far apart. Furthermore, the feedback unit can adjust the content of the feedback according to the geographical distribution. This makes it possible to provide more appropriate feedback by considering the geographical distribution. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs the geographical distribution data of the group into a generative AI, and the generative AI adjusts the content of the feedback.

[0110] The feedback unit can improve the accuracy of its feedback by referring to relevant literature during the feedback process. For example, the feedback unit provides scientifically-based feedback based on relevant literature. The feedback unit can also improve the content of its feedback by referring to relevant literature. Furthermore, the feedback unit can improve the accuracy of its feedback by utilizing the insights from relevant literature. Thus, the accuracy of the feedback is improved by referring to relevant literature. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs relevant literature data into a generative AI, and the generative AI improves the content of the feedback.

[0111] The sharing unit can estimate students' emotions and adjust the content of the information it shares based on the estimated emotions. For example, if a student is stressed, the sharing unit prioritizes sharing positive information. If a student is relaxed, the sharing unit can also share more detailed information. Furthermore, if a student is focused, the sharing unit can share information that includes specific areas for improvement. This allows for more appropriate information sharing by adjusting the content of the information shared 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using a generative AI, or not. For example, the sharing unit inputs student emotion data into a generative AI, and the generative AI adjusts the content of the information it shares.

[0112] The sharing unit can provide optimal information by referring to past shared data during the sharing process. For example, the sharing unit can provide optimal information for similar situations based on past shared data. The sharing unit can also analyze past shared data and extract effective information. Furthermore, the sharing unit can provide individually customized information by referring to past shared data. This allows for the provision of optimal information by referring to past shared data. Some or all of the above-described processes in the sharing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing unit inputs past shared data into a generative AI, which then provides optimal information.

[0113] The sharing unit can share information while considering the student's attribute information. For example, the sharing unit can share appropriate information considering the student's age. It can also adjust the content of the information considering the student's gender. Furthermore, the sharing unit can share optimal information considering the student's learning style. This makes it possible to share information more appropriately by considering the student's attribute information. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing unit inputs student attribute information data into a generative AI, and the generative AI adjusts the content of the information.

[0114] The sharing unit can estimate students' emotions and determine sharing priorities based on those estimated emotions. For example, if a student is feeling anxious, the sharing unit will prioritize sharing information that provides reassurance. It can also prioritize sharing challenging information if a student is highly motivated. Furthermore, if a student is tired, it can prioritize sharing information that encourages rest. This allows for more appropriate information sharing by determining sharing priorities according to students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using a generative AI, or not. For example, the sharing unit inputs student emotion data into a generative AI, which then determines the sharing priorities.

[0115] The sharing function can share information while considering the geographical distribution of the groups. For example, the sharing function can share information that encourages cooperation among students who are geographically close to each other. It can also share information that strengthens collaboration among students who are geographically far apart. Furthermore, the sharing function can adjust the content of the information according to the geographical distribution. This makes it possible to share information more appropriately by considering geographical distribution. Some or all of the above processing in the sharing function may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing function inputs the geographical distribution data of the groups into a generative AI, and the generative AI adjusts the content of the information.

[0116] The sharing unit can improve the accuracy of sharing by referring to relevant literature during the sharing process. For example, the sharing unit shares scientifically-based information based on relevant literature. The sharing unit can also improve the content of the information by referring to relevant literature. Furthermore, the sharing unit can improve the accuracy of sharing by utilizing the insights from relevant literature. As a result, the accuracy of sharing is improved by referring to relevant literature. Some or all of the above processing in the sharing unit may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing unit inputs relevant literature data into a generative AI, and the generative AI improves the content of the information.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The grouping department can create groups based not only on students' academic history but also on their personal interests, such as hobbies and special skills. For example, the department could group students who enjoy music together and have them work on music-related tasks. They could also group students who excel at sports together and have them work on sports-related projects. Furthermore, they could group students interested in art and promote collaborative learning through art projects. This allows for the creation of more highly motivated groups by considering students' personal interests.

[0119] The system provider can adjust the format of collaborative assignments according to students' learning styles. For example, students who prefer visual learning can be provided with assignments that make extensive use of diagrams and graphs. Students who prefer auditory learning can be provided with assignments that include audio guidance. Furthermore, students who prefer experiential learning can be provided with assignments that include practical activities. By adjusting the format of assignments according to students' learning styles, more effective learning becomes possible.

[0120] The monitoring unit can monitor not only students' learning progress but also their health. For example, it can monitor students' sleep patterns and eating habits, and adjust their learning progress according to their health. It can also monitor students' physical activity levels and provide appropriate advice if they are not getting enough exercise. Furthermore, it can monitor students' stress levels and provide advice on relaxation if stress levels are high. This allows for more appropriate learning support by taking students' health into consideration.

[0121] The advice unit can estimate a student's emotions and adjust the timing of advice based on that estimation. For example, if a student is feeling down, the advice unit can immediately provide encouraging advice. If a student is focused, it can also provide advice that includes specific areas for improvement to help them maintain their focus. Furthermore, if a student is relaxed, it can provide more detailed advice. By adjusting the timing of advice according to the student's emotions, more effective advice becomes possible.

[0122] The feedback system can estimate a student's emotions and adjust the format of the feedback based on that estimation. For example, if a student is stressed, the feedback system will prioritize providing positive feedback. If the student is relaxed, it can provide more detailed feedback. Furthermore, if the student is focused, it can provide feedback that includes specific areas for improvement. This allows for more appropriate feedback by adjusting the format of the feedback according to the student's emotions.

[0123] The sharing function can estimate students' emotions and adjust the priority of information shared based on those estimates. For example, if a student is feeling anxious, the sharing function will prioritize sharing information that provides reassurance. If a student is highly motivated, it can prioritize sharing challenging information. Furthermore, if a student is tired, it can prioritize sharing information that encourages rest. By adjusting the priority of information shared according to students' emotions, more appropriate information sharing becomes possible.

[0124] The group formation department can create groups based not only on students' learning history but also on the results of past collaborative learning. For example, the department can group students who have been cooperative in the past to enhance the effectiveness of collaborative learning. They can also appoint students who have demonstrated leadership in the past as group leaders. Furthermore, they can group students who have shown strong problem-solving abilities in the past to improve the efficiency of problem-solving. In this way, considering the results of past collaborative learning makes it possible to create more effective groups.

[0125] The assignment provider can adjust the difficulty level of collaborative assignments according to the students' learning objectives. For example, students with basic learning objectives can be given basic assignments. Students with advanced learning objectives can be given challenging assignments. Furthermore, students who want to improve a specific skill can be given assignments tailored to that skill. By adjusting the difficulty level of assignments according to the students' learning objectives, more effective learning becomes possible.

[0126] The monitoring department can monitor not only students' learning progress but also the quality of collaborative learning. For example, it can monitor the quality of communication within groups and provide advice for improvement as needed. It can also monitor the appropriateness of role assignments within groups and provide advice for adjustment if they are unbalanced. Furthermore, it can monitor cooperative relationships within groups and provide advice to encourage cooperation if it is lacking. By monitoring the quality of collaborative learning, more effective learning support becomes possible.

[0127] The advice function can estimate a student's emotions and adjust the format of the advice based on that estimation. For example, if a student is stressed, the advice function will provide concise and easy-to-understand advice. If the student is relaxed, it can provide more detailed advice. Furthermore, if the student is focused, it can provide advice that includes specific areas for improvement. By adjusting the format of advice according to the student's emotions, more appropriate advice can be provided.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The grouping department forms optimal groups based on students' learning content and areas of expertise. For example, students who are good at math and students who are good at English can be grouped together to leverage each other's strengths. Groups can also be formed considering students' learning history and current learning status. Step 2: The provisioning department presents collaborative tasks to the groups formed by the organization department. For example, in a history class, the assignment could be to have each group research and present on a different historical period. It is also possible to generate collaborative tasks and learning content using generative AI. Step 3: The monitoring department monitors the progress of group activities based on the collaborative tasks presented by the provision department. For example, it monitors the group's progress in real time and provides advice as needed. Step 4: The Advice Department provides necessary advice based on the progress monitored by the Monitoring Department. For example, if a group is behind schedule, the Advice Department will provide advice to help accelerate progress. Step 5: The Feedback team encourages constructive feedback among students based on the advice given by the Advice team. For example, they might provide feedback points after group presentations and encourage students to exchange opinions with each other. Step 6: The sharing team shares the status of the group activity with the teacher based on the feedback encouraged by the feedback team. For example, they report the group's progress and the degree to which they have achieved their tasks so that the teacher can provide any necessary support.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the organization unit, provision unit, monitoring unit, advice unit, feedback unit, and sharing unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the organization unit is implemented by the control unit 46A of the smart device 14 and forms optimal groups according to the students' learning content and areas of expertise. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates collaborative tasks and learning content using generation AI. The monitoring unit is implemented by, for example, the control unit 46A of the smart device 14 and monitors the progress of group activities. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides necessary advice based on progress. The feedback unit is implemented by, for example, the control unit 46A of the smart device 14 and encourages constructive feedback among students. The sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and shares the status of group activities with the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the grouping unit, provision unit, monitoring unit, advice unit, feedback unit, and sharing unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the grouping unit is implemented by the control unit 46A of the smart glasses 214 and forms optimal groups according to the students' learning content and areas of expertise. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates collaborative tasks and learning content using generation AI. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 and monitors the progress of group activities. The advice unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and provides necessary advice based on progress. The feedback unit is implemented, for example, by the control unit 46A of the smart glasses 214 and encourages constructive feedback among students. The sharing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and shares the status of group activities with the teacher. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the grouping unit, provision unit, monitoring unit, advice unit, feedback unit, and sharing unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the grouping unit is implemented by the control unit 46A of the headset terminal 314 and forms optimal groups according to the students' learning content and areas of expertise. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates collaborative tasks and learning content using generation AI. The monitoring unit is implemented by, for example, the control unit 46A of the headset terminal 314 and monitors the progress of group activities. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides necessary advice based on progress. The feedback unit is implemented by, for example, the control unit 46A of the headset terminal 314 and encourages constructive feedback among students. The sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and shares the status of group activities with the teacher. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the organization unit, provision unit, monitoring unit, advice unit, feedback unit, and sharing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the organization unit is implemented by the control unit 46A of the robot 414 and forms optimal groups according to the students' learning content and areas of expertise. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates collaborative tasks and learning content using generation AI. The monitoring unit is implemented by, for example, the control unit 46A of the robot 414 and monitors the progress of group activities. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides necessary advice based on progress. The feedback unit is implemented by, for example, the control unit 46A of the robot 414 and encourages constructive feedback among students. The sharing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and shares the status of group activities with the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0183] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) The group formation department creates the optimal group for each student according to their learning content and areas of expertise, A provision unit presents collaborative tasks to the groups formed by the aforementioned organization unit, A monitoring unit monitors the progress of group activities based on the collaborative tasks presented by the aforementioned provision unit, An advice unit provides necessary advice based on the progress monitored by the monitoring unit, A feedback section that encourages constructive feedback among students based on the advice given by the aforementioned advice section, The system includes a sharing unit that shares the status of group activities with the teacher based on the feedback encouraged by the aforementioned feedback unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Generative AI is used to create collaborative tasks and learning content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is Using natural language processing to facilitate communication within a group. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned organizational unit, The system estimates students' emotions and adjusts the timing of group formation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned organizational unit, Analyze students' past learning history and select the optimal group formation method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned organizational unit, When forming groups, filtering is performed based on students' current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned organizational unit, Estimate students' emotions and determine the priority of groups to form based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned organizational unit, When forming groups, students' geographical locations are taken into consideration, and priority is given to creating groups of students with high relevance. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned organizational unit, When forming groups, analyze students' social media activity and group them together based on relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned supply unit is, The system estimates students' emotions and adjusts the way the assignment is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned supply unit is, When providing a problem, adjust the level of detail provided based on the importance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, When providing a task, a different provision algorithm is applied depending on the category of the task. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, The system estimates students' emotions and adjusts the length of the assignment based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When assigning tasks, we will determine the priority of assignments based on the submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing tasks, adjust the order in which they are provided based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 16) The monitoring unit, Estimate students' emotions and adjust monitoring criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The monitoring unit, When monitoring, consider the interrelationships within the group to improve the accuracy of the monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, During monitoring, the monitoring process will take into account the students' attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, The system estimates students' emotions and adjusts the order in which monitoring results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, When monitoring, the geographical distribution of the group should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, During monitoring, refer to relevant literature to improve the accuracy of the monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, The system estimates the student's emotions and adjusts the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, When providing advice, we refer to past advice data to provide the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When giving advice, take into account the student's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, The system estimates the students' emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, When giving advice, take into account the geographical distribution of the group. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When giving advice, refer to relevant literature to improve the accuracy of the advice. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is The system estimates the student's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we refer to past feedback data to provide the most optimal feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, take into account the student's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is The system estimates students' emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback unit is When providing feedback, consider the geographical distribution of the group. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback unit is When providing feedback, refer to relevant literature to improve the accuracy of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned shared portion is, The system estimates students' emotions and adjusts the content of the information shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned shared portion is, When sharing data, it provides the most relevant information by referencing previously shared data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shared portion is, When sharing information, consider the student's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned shared portion is, The system estimates students' emotions and determines the priority of shared activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned shared portion is, When sharing information, take into account the geographical distribution of the group. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned shared portion is, When sharing, refer to relevant literature to improve the accuracy of the sharing process. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The group formation department creates the optimal group for each student according to their learning content and areas of expertise, A provision unit presents collaborative tasks to the groups formed by the aforementioned organization unit, A monitoring unit monitors the progress of group activities based on the collaborative tasks presented by the aforementioned provision unit, An advice unit provides necessary advice based on the progress monitored by the aforementioned monitoring unit, A feedback section that encourages constructive feedback among students based on the advice given by the aforementioned advice section, The system includes a sharing unit that shares the status of group activities with the teacher based on the feedback encouraged by the aforementioned feedback unit. A system characterized by the following features.

2. The aforementioned supply unit is, Generative AI is used to generate collaborative tasks and learning content. The system according to feature 1.

3. The aforementioned feedback unit is Using natural language processing to facilitate communication within a group. The system according to feature 1.

4. The aforementioned organizational unit, The system estimates students' emotions and adjusts the timing of group formation based on those estimated emotions. The system according to feature 1.

5. The aforementioned organizational unit, Analyze students' past learning history and select the optimal group formation method. The system according to feature 1.

6. The aforementioned organizational unit, When forming groups, filtering is performed based on students' current learning status and areas of interest. The system according to feature 1.

7. The aforementioned organizational unit, Estimate students' emotions and determine the priority of groups to form based on those estimated emotions. The system according to feature 1.

8. The aforementioned organizational unit, When forming groups, students' geographical locations are taken into consideration, and priority is given to creating groups of students with high relevance. The system according to feature 1.

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