system

Abstract

The system according to this embodiment aims to effectively conduct debates and cultivate students' argumentative skills without requiring specialized training. [Solution] The system according to the embodiment comprises a generation unit, a progress unit, a summary unit, a feedback unit, and an evaluation unit. The generation unit automatically generates debate themes. The progress unit supports the progress of the debate based on the debate themes generated by the generation unit and manages the speaking time for each student. The summary unit summarizes the content of the statements managed by the progress unit in real time and identifies the points of contention. The feedback unit provides feedback on the content of the statements based on the points of contention identified by the summary unit. The evaluation unit evaluates the content of the debate based on the feedback provided by the feedback unit and evaluates the students' argumentative skills.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of 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 conventional technology, there is a problem that the progress of a debate and the management of students' speech contents are complicated, and it is difficult to effectively implement them without a leader receiving specialized training.

[0005] The system according to the embodiment aims to effectively conduct a debate and cultivate students' argumentative ability without receiving specialized training.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a generation unit, a progress unit, a summary unit, a feedback unit, and an evaluation unit. The generation unit automatically generates debate themes. The progress unit supports the progress of the debate based on the debate themes generated by the generation unit and manages the speaking time for each student. The summary unit summarizes the content of the statements managed by the progress unit in real time and identifies the points of contention. The feedback unit provides feedback on the content of the statements based on the points of contention identified by the summary unit. The evaluation unit evaluates the content of the debate based on the feedback provided by the feedback unit and assesses the students' argumentative skills. [Effects of the Invention]

[0007] The system according to this embodiment can effectively conduct debates and cultivate students' argumentative skills without requiring specialized training. [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 signed storage is one or more non-volatile storage devices that store various programs and various parameters. 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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.

[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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the 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, 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 debate support system according to an embodiment of the present invention is a tool to make it easier to incorporate debate, which is a skill that Japanese people often find difficult, into classroom lessons. This debate support system is designed to be immediately and easily implemented by instructors without requiring any special training. Specifically, a generative AI automatically generates appropriate debate themes based on the content of the lesson and the interests of the students. Next, the AI ​​supports the progress of the debate and manages the speaking time for each student. Furthermore, the AI ​​organizes the students' opinions in real time, clarifies the points of contention, and provides feedback on the content of their statements. Finally, the AI ​​evaluates the content of the debate and quantifies the students' argumentative skills. This makes it easier for students to feel a sense of their own growth. In addition, it provides a user interface that anyone can use immediately, allowing instructors and students to operate it intuitively. By using this tool, students can develop the ability to confidently express their opinions, making it possible to cultivate Japanese people who can play an active role on the world stage in the future. For example, the generative AI automatically generates debate themes based on the content of the lesson and the interests of the students. Next, the AI ​​supports the progress of the debate and manages the speaking time for each student. Furthermore, the AI ​​organizes students' opinions in real time, clarifies the points of discussion, and provides feedback on their statements. Finally, the AI ​​evaluates the content of the debate and quantifies the students' argumentative skills. This makes it easier for students to feel a sense of their own growth. It also provides a user interface that anyone can use immediately, allowing instructors and students to operate it intuitively. By using this tool, students can develop the ability to confidently express their opinions, making it possible to cultivate Japanese people who can thrive on the world stage in the future. In this way, the debate support system can effectively cultivate students' argumentative skills.

[0029] The debate support system according to this embodiment comprises a generation unit, a progress unit, an organization unit, a feedback unit, and an evaluation unit. The generation unit automatically generates debate themes using a generation AI. The generation unit generates debate themes based, for example, on the content of the lesson or the interests of the students. The generation AI analyzes, for example, the lesson curriculum and the students' past speaking history to select an appropriate debate theme. The progress unit supports the progress of the debate and manages the speaking time of each student. The progress unit has, for example, a timer function to manage the speaking time of each student equally. The progress unit uses AI to monitor speaking time in real time and designates the next speaker at the appropriate time. The organization unit organizes the students' opinions in real time and clarifies the points of contention. The organization unit analyzes the content of the students' statements using, for example, natural language processing technology and extracts the points of contention. The organization unit uses AI to classify the content of the statements and highlight important points of contention. The feedback unit provides feedback on the content of the statements. The feedback unit evaluates, for example, the logic and persuasiveness of the statements and suggests specific areas for improvement. The feedback unit uses AI to analyze the content of the statements in detail and provides appropriate feedback. The evaluation unit evaluates the content of the debate and quantifies the students' argumentative skills. The evaluation unit uses, for example, the logic and persuasiveness of the statements as evaluation criteria and calculates a score. The evaluation unit uses AI to adjust the evaluation criteria and conduct a fair evaluation. As a result, the debate support system according to this embodiment can effectively cultivate students' argumentative skills.

[0030] The generation unit automatically generates debate themes using generative AI. For example, it generates debate themes based on the content of the lesson or the students' interests. The generative AI analyzes, for example, the lesson curriculum and students' past speaking history to select appropriate debate themes. Specifically, the generative AI utilizes natural language processing technology to import lesson curriculum data and students' past speaking history as text data and analyzes this data. During the analysis process, topic modeling and clustering techniques are used to extract highly relevant themes. For example, if environmental issues are addressed in the lesson curriculum, the generative AI generates specific debate themes such as "methods for reducing plastic waste" or "promotion of renewable energy." Furthermore, by analyzing students' past speaking history, it is possible to identify topics that individual students are interested in or excel at, and customize themes accordingly. Based on this data, the generative AI generates multiple candidate themes and provides an interface for teachers and system administrators to make the final selection. In addition, the generation unit has a feedback loop to evaluate whether the generated themes are appropriate, allowing for continuous improvement of the generative AI's algorithm. This allows the generation unit to consistently provide appropriate debate topics based on the latest information and students' interests, thereby improving the quality of the debates.

[0031] The facilitator supports the progress of the debate and manages each student's speaking time. For example, the facilitator has a timer function to ensure that each student's speaking time is equal. The facilitator uses AI to monitor speaking time in real time and designate the next speaker at the appropriate time. Specifically, the facilitator sets the speaking order for each student at the start of the debate and starts the timer. The AI ​​measures each student's speaking time in milliseconds and predicts when their speaking time will end. Just before the end of a student's speaking time, it notifies the next speaker to support smooth progress. The facilitator can also make adjustments in real time if it is necessary to extend or shorten speaking time. For example, if a particular student is making an important point or if the discussion is heated, the AI ​​can assess the situation and issue an instruction to extend the speaking time. Furthermore, the facilitator provides an interface to visualize the progress of the debate, allowing teachers and students to grasp the current progress at a glance. This enables the facilitator to facilitate the progress of the debate and achieve a fair and efficient discussion.

[0032] The organization unit organizes students' opinions in real time and clarifies the points of discussion. For example, the organization unit uses natural language processing technology to analyze the content of students' statements and extract the points of discussion. The organization unit uses AI to classify the content of statements and highlight important points. Specifically, the organization unit takes in students' statements as text data and analyzes the content of the statements using natural language processing technology. In the analysis process, topic modeling and keyword extraction technology are used to identify the main theme and important points of the statements. For example, if a student speaks about the "benefits of renewable energy," the organization unit extracts keywords such as "renewable energy" and "benefits" and organizes the points of discussion based on these. In addition, clustering technology is used to group similar statements in order to classify the content of statements, and the flow of the discussion can be visualized. Furthermore, the organization unit provides an interface to highlight important points, allowing teachers and students to grasp the progress of the discussion at a glance. In this way, the organization unit can effectively organize students' opinions and improve the quality of the discussion.

[0033] The feedback department provides feedback on the content of statements. For example, it evaluates the logic and persuasiveness of statements and suggests specific areas for improvement. The feedback department uses AI to analyze the content of statements in detail and provide appropriate feedback. Specifically, the feedback department uses natural language processing technology to analyze the content of statements and evaluate the logical structure and persuasiveness. For example, if there are logical leaps in a statement or insufficient evidence, the AI ​​will detect this and suggest specific areas for improvement. In addition, the feedback department can evaluate not only the content of statements but also the tone and expression. For example, if a statement is too emotional or contains too much jargon and is difficult to understand, it will point this out and provide advice for improvement. Furthermore, the feedback department can track student growth based on past feedback history and support continuous improvement. This allows the feedback department to provide specific advice to effectively improve students' argumentation skills and enhance the quality of debates.

[0034] The evaluation department assesses the content of debates and quantifies students' argumentative skills. For example, the evaluation department calculates scores based on criteria such as the logical coherence and persuasiveness of statements. The evaluation department uses AI to adjust the evaluation criteria and ensure fair evaluations. Specifically, the evaluation department uses natural language processing technology to analyze the content of statements and evaluate their logical structure and persuasiveness. For example, it calculates scores based on criteria such as whether the logic is consistent within the statement and whether the evidence is clear. In addition to the content of the statements, the evaluation department can also evaluate the tone and expression of the statements. For example, it evaluates whether the statements are too emotional or whether they contain too much jargon that makes them difficult to understand, and adjusts the score based on this. Furthermore, the evaluation department can track students' growth based on past evaluation history and support continuous improvement. The evaluation department uses AI to continuously improve the evaluation criteria and provide fair and accurate evaluations. As a result, the evaluation department can objectively assess students' argumentative skills and support their growth by suggesting specific areas for improvement.

[0035] The generation unit can automatically generate debate topics based on the lesson content and students' interests. For example, the generation unit analyzes the lesson curriculum and students' past speaking history to select appropriate debate topics. The generation AI takes into account the lesson content and students' interests to automatically generate debate topics. For example, the generation AI selects topics related to the lesson theme and generates topics that will attract students' interest. This allows for the automatic generation of topics that match the lesson content and students' interests, thereby enhancing the effectiveness of debates.

[0036] The facilitator has a timer function that allows for fair management of each student's speaking time. For example, the facilitator uses the timer function to ensure that each student's speaking time is equal. The facilitator uses AI to monitor speaking time in real time and nominate the next speaker at the appropriate time. For example, the facilitator sets a speaking time and prompts the next student to speak when the timer runs out. The facilitator also adjusts the distribution of speaking time to ensure a fair debate. In this way, a fair debate can be achieved by managing each student's speaking time equally.

[0037] The analysis unit can analyze students' statements using natural language processing technology and extract key points. For example, the analysis unit uses natural language processing technology to analyze students' statements and extract key points. The analysis unit uses AI to classify statements and highlight important points. For example, the analysis unit analyzes statements in real time, extracts keywords, and identifies key points. The analysis unit also analyzes the structure of statements and clarifies the relationships between points. In this way, by using natural language processing technology, the analysis of statements and extraction of key points can be performed efficiently.

[0038] The feedback system can evaluate the logic and persuasiveness of statements and provide feedback. For example, the feedback system can evaluate the logic and persuasiveness of statements and suggest specific areas for improvement. The feedback system uses AI to analyze the content of statements in detail and provide appropriate feedback. For example, the feedback system evaluates the logical structure of statements and provides feedback based on logical consistency and persuasiveness. The feedback system also analyzes the content of statements and suggests specific areas for improvement. In this way, by evaluating the logic and persuasiveness of statements, students' argumentative skills can be improved.

[0039] The evaluation unit can calculate a score based on the logical coherence and persuasiveness of a statement. For example, the evaluation unit uses AI to adjust the evaluation criteria and ensure fair evaluation. For instance, the evaluation unit analyzes the content of a statement in detail and calculates a score based on its logical coherence and persuasiveness. Furthermore, the evaluation unit uses multiple evaluation criteria to calculate an overall evaluation score. This allows for the quantification of student growth by calculating a score based on the logical coherence and persuasiveness of a statement.

[0040] The generation unit can analyze the history of past debate topics and select the most suitable topic. For example, the generation unit uses a generation AI to analyze the history of past debate topics and select the most suitable topic. The generation AI analyzes the success rate of previously used topics and selects the most effective topic. For example, the generation AI adjusts the difficulty level of the topic based on the results of past debates. The generation AI also analyzes the popularity of past topics and selects topics that will attract students' interest. In this way, by analyzing past history, it is possible to select an effective topic.

[0041] The generation unit can be equipped with a function to change the theme in real time according to the progress of the lesson. For example, the generation unit monitors the progress of the lesson using a generation AI and changes the theme as needed. The generation AI grasps the progress of the lesson in real time and changes the theme at the appropriate time. For example, the generation AI analyzes student reactions and changes the theme at the appropriate time. In addition, the generation AI adjusts the difficulty level of the theme in accordance with the progress of the lesson. This makes flexible debate possible by changing the theme according to the progress of the lesson.

[0042] The facilitator can analyze students' comments in real time and support the progress of the discussion. For example, the facilitator can use AI to analyze students' comments in real time and support the progress. The AI ​​analyzes students' comments and appropriately selects the next speaker. For example, the AI ​​analyzes students' comments and adjusts the direction of the discussion. The AI ​​also supports the progress based on the students' comments. In this way, by analyzing comments in real time, it is possible to support a smooth progress.

[0043] The facilitator can automatically optimize the order of speaking, ensuring a smooth debate flow. For example, the facilitator can use AI to automatically optimize the order of speaking, streamlining the debate. The AI ​​determines the optimal speaking order based on the content of each student's statement. For instance, the AI ​​analyzes the flow of each student's statement to support a smooth progression. Furthermore, the AI ​​automatically adjusts the order of students' statements to maintain consistency in the argument. By optimizing the order of speaking, the debate can flow more smoothly.

[0044] The editing department can analyze the content of the discussions in real time and evaluate the importance of the points. For example, the editing department can use AI to analyze the content of the discussions in real time and evaluate the importance of the points. The AI ​​analyzes the content of the students' discussions in real time and extracts important points. For example, the AI ​​evaluates the importance of the points based on the content of the students' discussions. The AI ​​also analyzes the content of the students' discussions and highlights important points. In this way, by analyzing the content of the discussions in real time, important points can be clearly identified.

[0045] The organization function can automatically integrate multiple points of discussion and make the overall argument coherent. For example, the organization function uses AI to automatically integrate multiple points of discussion and make the overall argument coherent. The AI ​​integrates multiple points of discussion based on the content of students' statements. For example, the AI ​​analyzes the content of students' statements and constructs a coherent argument. The AI ​​also organizes the content of students' statements to make the overall argument coherent. In this way, the coherence of the argument can be maintained by integrating multiple points of discussion.

[0046] The feedback unit can analyze the content of statements in detail and suggest specific areas for improvement. For example, the feedback unit can use AI to analyze the content of statements in detail and suggest specific areas for improvement. The AI ​​analyzes the content of students' statements in detail and suggests specific areas for improvement. For example, the AI ​​specifically indicates areas for improvement based on the content of students' statements. Furthermore, the AI ​​analyzes the content of students' statements and provides specific feedback. This allows for the suggestion of specific areas for improvement through a detailed analysis of the content of statements.

[0047] The feedback system can save feedback history and support long-term growth. For example, the feedback system can use AI to save feedback history and support long-term growth. The AI ​​saves students' feedback history and supports their long-term growth. For instance, the AI ​​visualizes the growth process based on the student's feedback history. The AI ​​also organizes the student's feedback history and provides it as a record of growth. In this way, saving feedback history can support students' long-term growth.

[0048] The evaluation unit can analyze the content of statements in detail and calculate a score using multiple evaluation criteria. For example, the evaluation unit can use AI to analyze the content of statements in detail and calculate a score using multiple evaluation criteria. The AI ​​analyzes the content of students' statements in detail and calculates a score using multiple evaluation criteria. For example, the AI ​​calculates a score based on the content of students' statements using evaluation criteria such as logic and persuasiveness. The AI ​​also analyzes the content of students' statements and calculates an overall evaluation score. In this way, an overall evaluation score can be calculated by using multiple evaluation criteria.

[0049] The evaluation department can track evaluation results over the long term and visualize student growth. For example, the evaluation department can use AI to track evaluation results over the long term and visualize student growth. The AI ​​tracks student evaluation results over the long term and visualizes the growth process. For example, the AI ​​provides growth records based on student evaluation results. The AI ​​also organizes student evaluation results and displays the growth process in graphs and charts. This allows for the visualization of student growth by tracking evaluation results over the long term.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The generation unit can also consider local social and current events when generating debate themes. For example, the generation AI can analyze local news and social issues to generate debate themes relevant to the region. Furthermore, the generation unit can incorporate international current events to provide debate themes from a global perspective. In addition, the generation unit can customize themes according to the characteristics of specific grades or classes, generating themes that will capture students' interest. This makes it possible to generate themes that consider local and current events, thereby attracting student attention.

[0052] The facilitator can translate students' statements in real time during the debate, providing multilingual support. For example, it can translate statements made in English into Japanese to make them easier for other students to understand. Furthermore, for students learning a foreign language, the facilitator can translate their statements into their language of study, enhancing their learning effectiveness. In addition, the facilitator can support debates between students who speak different languages, providing a platform for international debate. This enables multilingual support and allows for debates from an international perspective.

[0053] The analysis unit can analyze students' statements and automatically provide relevant reference materials and literature. For example, it can search for relevant academic papers and news articles based on the statements and provide them in real time. It can also provide videos and images related to the statements to support visual understanding. Furthermore, it can provide links to relevant books and websites based on the statements, offering students resources for deeper learning. In this way, the quality of debates can be improved by providing reference materials related to the statements.

[0054] The evaluation unit can analyze students' statements and assess their creativity and originality. For example, it can evaluate whether a statement offers a new perspective or idea and calculate a creativity score. It can also evaluate how different a statement is from other students' statements and calculate an originality score. Furthermore, it can assess the degree of originality of a statement and provide an overall creativity score. In this way, by evaluating the creativity and originality of statements, it is possible to improve students' communication skills.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The generation unit automatically generates debate topics using a generation AI. The generation unit generates debate topics based on the content of the lesson and the students' interests. The generation AI analyzes the lesson curriculum and the students' past speaking history to select an appropriate debate topic. Step 2: The facilitator supports the debate and manages each student's speaking time. The facilitator has a timer function to ensure that each student's speaking time is equal. The facilitator uses AI to monitor speaking time in real time and designates the next speaker at the appropriate time. Step 3: The editing team organizes students' opinions in real time and clarifies the main points. The editing team uses natural language processing technology to analyze the content of students' statements and extract the main points. The editing team uses AI to classify the content of the statements and highlight the important points. Step 4: The feedback department provides feedback on the content of the statement. The feedback department evaluates the logic and persuasiveness of the statement and suggests specific areas for improvement. The feedback department uses AI to analyze the content of the statement in detail and provides appropriate feedback. Step 5: The evaluation team assesses the content of the debate and quantifies the students' argumentative skills. The evaluation team uses the logical coherence and persuasiveness of the statements as evaluation criteria and calculates a score. The evaluation team uses AI to adjust the evaluation criteria and ensure fair evaluation.

[0057] (Example of form 2) The debate support system according to an embodiment of the present invention is a tool to make it easier to incorporate debate, which is a skill that Japanese people often find difficult, into classroom lessons. This debate support system is designed to be immediately and easily implemented by instructors without requiring any special training. Specifically, a generative AI automatically generates appropriate debate themes based on the content of the lesson and the interests of the students. Next, the AI ​​supports the progress of the debate and manages the speaking time for each student. Furthermore, the AI ​​organizes the students' opinions in real time, clarifies the points of contention, and provides feedback on the content of their statements. Finally, the AI ​​evaluates the content of the debate and quantifies the students' argumentative skills. This makes it easier for students to feel a sense of their own growth. In addition, it provides a user interface that anyone can use immediately, allowing instructors and students to operate it intuitively. By using this tool, students can develop the ability to confidently express their opinions, making it possible to cultivate Japanese people who can play an active role on the world stage in the future. For example, the generative AI automatically generates debate themes based on the content of the lesson and the interests of the students. Next, the AI ​​supports the progress of the debate and manages the speaking time for each student. Furthermore, the AI ​​organizes students' opinions in real time, clarifies the points of discussion, and provides feedback on their statements. Finally, the AI ​​evaluates the content of the debate and quantifies the students' argumentative skills. This makes it easier for students to feel a sense of their own growth. It also provides a user interface that anyone can use immediately, allowing instructors and students to operate it intuitively. By using this tool, students can develop the ability to confidently express their opinions, making it possible to cultivate Japanese people who can thrive on the world stage in the future. In this way, the debate support system can effectively cultivate students' argumentative skills.

[0058] The debate support system according to this embodiment comprises a generation unit, a progress unit, an organization unit, a feedback unit, and an evaluation unit. The generation unit automatically generates debate themes using a generation AI. The generation unit generates debate themes based, for example, on the content of the lesson or the interests of the students. The generation AI analyzes, for example, the lesson curriculum and the students' past speaking history to select an appropriate debate theme. The progress unit supports the progress of the debate and manages the speaking time of each student. The progress unit has, for example, a timer function to manage the speaking time of each student equally. The progress unit uses AI to monitor speaking time in real time and designates the next speaker at the appropriate time. The organization unit organizes the students' opinions in real time and clarifies the points of contention. The organization unit analyzes the content of the students' statements using, for example, natural language processing technology and extracts the points of contention. The organization unit uses AI to classify the content of the statements and highlight important points of contention. The feedback unit provides feedback on the content of the statements. The feedback unit evaluates, for example, the logic and persuasiveness of the statements and suggests specific areas for improvement. The feedback unit uses AI to analyze the content of the statements in detail and provides appropriate feedback. The evaluation unit evaluates the content of the debate and quantifies the students' argumentative skills. The evaluation unit uses, for example, the logic and persuasiveness of the statements as evaluation criteria and calculates a score. The evaluation unit uses AI to adjust the evaluation criteria and conduct a fair evaluation. As a result, the debate support system according to this embodiment can effectively cultivate students' argumentative skills.

[0059] The generation unit automatically generates debate themes using generative AI. For example, it generates debate themes based on the content of the lesson or the students' interests. The generative AI analyzes, for example, the lesson curriculum and students' past speaking history to select appropriate debate themes. Specifically, the generative AI utilizes natural language processing technology to import lesson curriculum data and students' past speaking history as text data and analyzes this data. During the analysis process, topic modeling and clustering techniques are used to extract highly relevant themes. For example, if environmental issues are addressed in the lesson curriculum, the generative AI generates specific debate themes such as "methods for reducing plastic waste" or "promotion of renewable energy." Furthermore, by analyzing students' past speaking history, it is possible to identify topics that individual students are interested in or excel at, and customize themes accordingly. Based on this data, the generative AI generates multiple candidate themes and provides an interface for teachers and system administrators to make the final selection. In addition, the generation unit has a feedback loop to evaluate whether the generated themes are appropriate, allowing for continuous improvement of the generative AI's algorithm. This allows the generation unit to consistently provide appropriate debate topics based on the latest information and students' interests, thereby improving the quality of the debates.

[0060] The facilitator supports the progress of the debate and manages each student's speaking time. For example, the facilitator has a timer function to ensure that each student's speaking time is equal. The facilitator uses AI to monitor speaking time in real time and designate the next speaker at the appropriate time. Specifically, the facilitator sets the speaking order for each student at the start of the debate and starts the timer. The AI ​​measures each student's speaking time in milliseconds and predicts when their speaking time will end. Just before the end of a student's speaking time, it notifies the next speaker to support smooth progress. The facilitator can also make adjustments in real time if it is necessary to extend or shorten speaking time. For example, if a particular student is making an important point or if the discussion is heated, the AI ​​can assess the situation and issue an instruction to extend the speaking time. Furthermore, the facilitator provides an interface to visualize the progress of the debate, allowing teachers and students to grasp the current progress at a glance. This enables the facilitator to facilitate the progress of the debate and achieve a fair and efficient discussion.

[0061] The organization unit organizes students' opinions in real time and clarifies the points of discussion. For example, the organization unit uses natural language processing technology to analyze the content of students' statements and extract the points of discussion. The organization unit uses AI to classify the content of statements and highlight important points. Specifically, the organization unit takes in students' statements as text data and analyzes the content of the statements using natural language processing technology. In the analysis process, topic modeling and keyword extraction technology are used to identify the main theme and important points of the statements. For example, if a student speaks about the "benefits of renewable energy," the organization unit extracts keywords such as "renewable energy" and "benefits" and organizes the points of discussion based on these. In addition, clustering technology is used to group similar statements in order to classify the content of statements, and the flow of the discussion can be visualized. Furthermore, the organization unit provides an interface to highlight important points, allowing teachers and students to grasp the progress of the discussion at a glance. In this way, the organization unit can effectively organize students' opinions and improve the quality of the discussion.

[0062] The feedback department provides feedback on the content of statements. For example, it evaluates the logic and persuasiveness of statements and suggests specific areas for improvement. The feedback department uses AI to analyze the content of statements in detail and provide appropriate feedback. Specifically, the feedback department uses natural language processing technology to analyze the content of statements and evaluate the logical structure and persuasiveness. For example, if there are logical leaps in a statement or insufficient evidence, the AI ​​will detect this and suggest specific areas for improvement. In addition, the feedback department can evaluate not only the content of statements but also the tone and expression. For example, if a statement is too emotional or contains too much jargon and is difficult to understand, it will point this out and provide advice for improvement. Furthermore, the feedback department can track student growth based on past feedback history and support continuous improvement. This allows the feedback department to provide specific advice to effectively improve students' argumentation skills and enhance the quality of debates.

[0063] The evaluation department assesses the content of debates and quantifies students' argumentative skills. For example, the evaluation department calculates scores based on criteria such as the logical coherence and persuasiveness of statements. The evaluation department uses AI to adjust the evaluation criteria and ensure fair evaluations. Specifically, the evaluation department uses natural language processing technology to analyze the content of statements and evaluate their logical structure and persuasiveness. For example, it calculates scores based on criteria such as whether the logic is consistent within the statement and whether the evidence is clear. In addition to the content of the statements, the evaluation department can also evaluate the tone and expression of the statements. For example, it evaluates whether the statements are too emotional or whether they contain too much jargon that makes them difficult to understand, and adjusts the score based on this. Furthermore, the evaluation department can track students' growth based on past evaluation history and support continuous improvement. The evaluation department uses AI to continuously improve the evaluation criteria and provide fair and accurate evaluations. As a result, the evaluation department can objectively assess students' argumentative skills and support their growth by suggesting specific areas for improvement.

[0064] The generation unit can automatically generate debate topics based on the lesson content and students' interests. For example, the generation unit analyzes the lesson curriculum and students' past speaking history to select appropriate debate topics. The generation AI takes into account the lesson content and students' interests to automatically generate debate topics. For example, the generation AI selects topics related to the lesson theme and generates topics that will attract students' interest. This allows for the automatic generation of topics that match the lesson content and students' interests, thereby enhancing the effectiveness of debates.

[0065] The facilitator has a timer function that allows for fair management of each student's speaking time. For example, the facilitator uses the timer function to ensure that each student's speaking time is equal. The facilitator uses AI to monitor speaking time in real time and nominate the next speaker at the appropriate time. For example, the facilitator sets a speaking time and prompts the next student to speak when the timer runs out. The facilitator also adjusts the distribution of speaking time to ensure a fair debate. In this way, a fair debate can be achieved by managing each student's speaking time equally.

[0066] The analysis unit can analyze students' statements using natural language processing technology and extract key points. For example, the analysis unit uses natural language processing technology to analyze students' statements and extract key points. The analysis unit uses AI to classify statements and highlight important points. For example, the analysis unit analyzes statements in real time, extracts keywords, and identifies key points. The analysis unit also analyzes the structure of statements and clarifies the relationships between points. In this way, by using natural language processing technology, the analysis of statements and extraction of key points can be performed efficiently.

[0067] The feedback system can evaluate the logic and persuasiveness of statements and provide feedback. For example, the feedback system can evaluate the logic and persuasiveness of statements and suggest specific areas for improvement. The feedback system uses AI to analyze the content of statements in detail and provide appropriate feedback. For example, the feedback system evaluates the logical structure of statements and provides feedback based on logical consistency and persuasiveness. The feedback system also analyzes the content of statements and suggests specific areas for improvement. In this way, by evaluating the logic and persuasiveness of statements, students' argumentative skills can be improved.

[0068] The evaluation unit can calculate a score based on the logical coherence and persuasiveness of a statement. For example, the evaluation unit uses AI to adjust the evaluation criteria and ensure fair evaluation. For instance, the evaluation unit analyzes the content of a statement in detail and calculates a score based on its logical coherence and persuasiveness. Furthermore, the evaluation unit uses multiple evaluation criteria to calculate an overall evaluation score. This allows for the quantification of student growth by calculating a score based on the logical coherence and persuasiveness of a statement.

[0069] The generation unit can estimate students' emotions and adjust the difficulty of debate topics based on the estimated emotions. For example, the generation unit uses a generation AI to estimate students' emotions and adjusts the difficulty of debate topics based on the estimated emotions. The generation AI estimates emotions by analyzing students' facial expressions and voice, for example. For example, if a student is nervous, the generation AI selects an easy topic to lower the difficulty of the debate. If a student is relaxed, the generation AI selects a difficult topic to encourage a challenging debate. Furthermore, if a student is excited, the generation AI selects an interesting topic to liven up the debate. In this way, the effectiveness of the debate can be maximized by adjusting the difficulty of the topic according to the students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0070] The generation unit can analyze the history of past debate topics and select the most suitable topic. For example, the generation unit uses a generation AI to analyze the history of past debate topics and select the most suitable topic. The generation AI analyzes the success rate of previously used topics and selects the most effective topic. For example, the generation AI adjusts the difficulty level of the topic based on the results of past debates. The generation AI also analyzes the popularity of past topics and selects topics that will attract students' interest. In this way, by analyzing past history, it is possible to select an effective topic.

[0071] The generation unit can be equipped with a function to change the theme in real time according to the progress of the lesson. For example, the generation unit monitors the progress of the lesson using a generation AI and changes the theme as needed. The generation AI grasps the progress of the lesson in real time and changes the theme at the appropriate time. For example, the generation AI analyzes student reactions and changes the theme at the appropriate time. In addition, the generation AI adjusts the difficulty level of the theme in accordance with the progress of the lesson. This makes flexible debate possible by changing the theme according to the progress of the lesson.

[0072] The facilitator can estimate students' emotions and adjust the allocation of speaking time based on the estimated emotions. For example, the facilitator might use AI to estimate students' emotions and adjust the allocation of speaking time based on the estimated emotions. The AI ​​might analyze students' facial expressions and voices to estimate their emotions. For example, if a student is nervous, the speaking time might be shortened to reduce their burden. If a student is relaxed, the speaking time might be lengthened to allow them to express their opinions freely. Furthermore, if a student is excited, the speaking time might be adjusted appropriately to maintain balance in the discussion. In this way, the balance of the debate can be maintained by adjusting speaking time according to students' emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI could be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0073] The facilitator can analyze students' comments in real time and support the progress of the discussion. For example, the facilitator can use AI to analyze students' comments in real time and support the progress. The AI ​​analyzes students' comments and appropriately selects the next speaker. For example, the AI ​​analyzes students' comments and adjusts the direction of the discussion. The AI ​​also supports the progress based on the students' comments. In this way, by analyzing comments in real time, it is possible to support a smooth progress.

[0074] The facilitator can automatically optimize the order of speaking, ensuring a smooth debate flow. For example, the facilitator can use AI to automatically optimize the order of speaking, streamlining the debate. The AI ​​determines the optimal speaking order based on the content of each student's statement. For instance, the AI ​​analyzes the flow of each student's statement to support a smooth progression. Furthermore, the AI ​​automatically adjusts the order of students' statements to maintain consistency in the argument. By optimizing the order of speaking, the debate can flow more smoothly.

[0075] The organization unit can estimate students' emotions and adjust the method of organizing the points based on the estimated emotions. For example, the organization unit can use AI to estimate students' emotions and adjust the method of organizing the points based on the estimated emotions. The AI ​​analyzes students' facial expressions and voice to estimate their emotions. For example, if a student is nervous, it provides a simple method of organizing the points. If a student is relaxed, it provides a detailed method of organizing the points. Furthermore, if a student is excited, it provides a visually easy-to-understand method of organizing the points. By adjusting the method of organizing the points according to the students' emotions, understanding of the discussion can be deepened. 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.

[0076] The editing department can analyze the content of the discussions in real time and evaluate the importance of the points. For example, the editing department can use AI to analyze the content of the discussions in real time and evaluate the importance of the points. The AI ​​analyzes the content of the students' discussions in real time and extracts important points. For example, the AI ​​evaluates the importance of the points based on the content of the students' discussions. The AI ​​also analyzes the content of the students' discussions and highlights important points. In this way, by analyzing the content of the discussions in real time, important points can be clearly identified.

[0077] The organization function can automatically integrate multiple points of discussion and make the overall argument coherent. For example, the organization function uses AI to automatically integrate multiple points of discussion and make the overall argument coherent. The AI ​​integrates multiple points of discussion based on the content of students' statements. For example, the AI ​​analyzes the content of students' statements and constructs a coherent argument. The AI ​​also organizes the content of students' statements to make the overall argument coherent. In this way, the coherence of the argument can be maintained by integrating multiple points of discussion.

[0078] The feedback unit can estimate a student's emotions and adjust the content of the feedback based on those emotions. For example, the feedback unit might use AI to estimate a student's emotions and adjust the content of the feedback based on those emotions. The AI ​​analyzes the student's facial expressions and voice to estimate their emotions. For example, if a student is nervous, the feedback is provided in a gentle tone. If a student is relaxed, detailed feedback is provided. Furthermore, if a student is excited, positive feedback is provided. This allows for effective 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The feedback unit can analyze the content of statements in detail and suggest specific areas for improvement. For example, the feedback unit can use AI to analyze the content of statements in detail and suggest specific areas for improvement. The AI ​​analyzes the content of students' statements in detail and suggests specific areas for improvement. For example, the AI ​​specifically indicates areas for improvement based on the content of students' statements. Furthermore, the AI ​​analyzes the content of students' statements and provides specific feedback. This allows for the suggestion of specific areas for improvement through a detailed analysis of the content of statements.

[0080] The feedback system can save feedback history and support long-term growth. For example, the feedback system can use AI to save feedback history and support long-term growth. The AI ​​saves students' feedback history and supports their long-term growth. For instance, the AI ​​visualizes the growth process based on the student's feedback history. The AI ​​also organizes the student's feedback history and provides it as a record of growth. In this way, saving feedback history can support students' long-term growth.

[0081] The evaluation unit can estimate students' emotions and adjust evaluation criteria based on the estimated emotions. For example, the evaluation unit can use AI to estimate students' emotions and adjust evaluation criteria based on the estimated emotions. The AI ​​analyzes students' facial expressions and voice to estimate emotions. For example, if a student is nervous, the evaluation criteria are relaxed to reduce the burden. If a student is relaxed, the evaluation criteria are made stricter to encourage challenges. Furthermore, if a student is excited, the evaluation criteria are appropriately adjusted to ensure a fair evaluation. In this way, fair evaluations can be made by adjusting evaluation criteria according to students' 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 these examples.

[0082] The evaluation unit can analyze the content of statements in detail and calculate a score using multiple evaluation criteria. For example, the evaluation unit can use AI to analyze the content of statements in detail and calculate a score using multiple evaluation criteria. The AI ​​analyzes the content of students' statements in detail and calculates a score using multiple evaluation criteria. For example, the AI ​​calculates a score based on the content of students' statements using evaluation criteria such as logic and persuasiveness. The AI ​​also analyzes the content of students' statements and calculates an overall evaluation score. In this way, an overall evaluation score can be calculated by using multiple evaluation criteria.

[0083] The evaluation department can track evaluation results over the long term and visualize student growth. For example, the evaluation department can use AI to track evaluation results over the long term and visualize student growth. The AI ​​tracks student evaluation results over the long term and visualizes the growth process. For example, the AI ​​provides growth records based on student evaluation results. The AI ​​also organizes student evaluation results and displays the growth process in graphs and charts. This allows for the visualization of student growth by tracking evaluation results over the long term.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The generation unit can also consider local social and current events when generating debate themes. For example, the generation AI can analyze local news and social issues to generate debate themes relevant to the region. Furthermore, the generation unit can incorporate international current events to provide debate themes from a global perspective. In addition, the generation unit can customize themes according to the characteristics of specific grades or classes, generating themes that will capture students' interest. This makes it possible to generate themes that consider local and current events, thereby attracting student attention.

[0086] The facilitator can translate students' statements in real time during the debate, providing multilingual support. For example, it can translate statements made in English into Japanese to make them easier for other students to understand. Furthermore, for students learning a foreign language, the facilitator can translate their statements into their language of study, enhancing their learning effectiveness. In addition, the facilitator can support debates between students who speak different languages, providing a platform for international debate. This enables multilingual support and allows for debates from an international perspective.

[0087] The analysis unit can analyze students' statements and automatically provide relevant reference materials and literature. For example, it can search for relevant academic papers and news articles based on the statements and provide them in real time. It can also provide videos and images related to the statements to support visual understanding. Furthermore, it can provide links to relevant books and websites based on the statements, offering students resources for deeper learning. In this way, the quality of debates can be improved by providing reference materials related to the statements.

[0088] The feedback department can analyze students' statements and evaluate their emotional impact. For example, it can assess the emotional impact a student's statements have on other students and provide appropriate feedback. Furthermore, if a statement has a positive impact, the feedback department can emphasize that aspect. Additionally, if a statement has a negative impact, the feedback department can point that out and suggest areas for improvement. This allows for more effective feedback by evaluating the emotional impact of statements.

[0089] The evaluation unit can analyze students' statements and assess their creativity and originality. For example, it can evaluate whether a statement offers a new perspective or idea and calculate a creativity score. It can also evaluate how different a statement is from other students' statements and calculate an originality score. Furthermore, it can assess the degree of originality of a statement and provide an overall creativity score. In this way, by evaluating the creativity and originality of statements, it is possible to improve students' communication skills.

[0090] The generation unit can estimate students' emotions and select debate topics based on those estimates. For example, if a student is excited, the generation unit will select a challenging topic to stimulate debate. If a student is relaxed, it will select a topic that suits the relaxed atmosphere, allowing the debate to proceed smoothly. Furthermore, if a student is nervous, it will select a topic that eases their tension, lowering the barrier to debate. This makes it possible to select topics that match the students' emotions, maximizing the effectiveness of the debate.

[0091] The facilitator can estimate the students' emotions and adjust the order of their statements based on those estimates. For example, if a student is nervous, the facilitator can postpone their turn to speak, giving them time to relax. Conversely, if a student is relaxed, allowing them to speak earlier can help keep the debate flowing smoothly. Furthermore, if a student is excited, the facilitator can adjust the order of their statements to maintain balance in the discussion. In this way, by adjusting the order of statements according to the students' emotions, the debate can proceed smoothly.

[0092] The organization function can estimate students' emotions and adjust the method of organizing the arguments based on those estimates. For example, if a student is nervous, the organization function can provide a simple method of organizing the arguments. If a student is relaxed, it can provide a more detailed method. Furthermore, if a student is excited, it can provide a visually easy-to-understand method of organizing the arguments. By providing an argument organization method that is tailored to the student's emotions, it can deepen their understanding of the discussion.

[0093] The feedback system can estimate a student's emotions and adjust the content of the feedback based on those emotions. For example, if a student is nervous, the feedback system can provide feedback in a gentle tone. If the student is relaxed, it can provide detailed feedback. Furthermore, if the student is excited, it can provide positive feedback. This allows for more effective feedback by tailoring the content to the student's emotions.

[0094] The evaluation unit can estimate students' emotions and adjust evaluation criteria based on those estimates. For example, if a student is nervous, the evaluation unit can ease the evaluation criteria to reduce the burden. Conversely, if a student is relaxed, it can make the evaluation criteria stricter to encourage challenges. Furthermore, if a student is excited, it can appropriately adjust the evaluation criteria to ensure a fair evaluation. In this way, fair evaluations can be made by providing evaluation criteria that are tailored to the student's emotions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The generation unit automatically generates debate topics using a generation AI. The generation unit generates debate topics based on the content of the lesson and the students' interests. The generation AI analyzes the lesson curriculum and the students' past speaking history to select an appropriate debate topic. Step 2: The facilitator supports the debate and manages each student's speaking time. The facilitator has a timer function to ensure that each student's speaking time is equal. The facilitator uses AI to monitor speaking time in real time and designates the next speaker at the appropriate time. Step 3: The editing team organizes students' opinions in real time and clarifies the main points. The editing team uses natural language processing technology to analyze the content of students' statements and extract the main points. The editing team uses AI to classify the content of the statements and highlight the important points. Step 4: The feedback department provides feedback on the content of the statement. The feedback department evaluates the logic and persuasiveness of the statement and suggests specific areas for improvement. The feedback department uses AI to analyze the content of the statement in detail and provides appropriate feedback. Step 5: The evaluation team assesses the content of the debate and quantifies the students' argumentative skills. The evaluation team uses the logical coherence and persuasiveness of the statements as evaluation criteria and calculates a score. The evaluation team uses AI to adjust the evaluation criteria and ensure fair evaluation.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] For example, the generation unit is implemented in either the smart device 14 or the data processing device 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, and automatically generates debate themes using generation AI. The progress unit is implemented by, for example, the control unit 46A of the smart device 14, and supports the progress of the debate and manages the speaking time of each student. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and organizes the students' opinions in real time and clarifies the points of contention. The feedback unit is implemented by, for example, the control unit 46A of the smart device 14, and provides feedback on the content of the statements. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, and evaluates the content of the debate and quantifies the students' argumentative skills. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] For example, the generation unit is implemented by either the smart glasses 214 or the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically generates debate themes using generation AI. The progress unit is implemented by, for example, the control unit 46A of the smart glasses 214, which supports the progress of the debate and manages the speaking time of each student. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which organizes the students' opinions in real time and clarifies the points of contention. The feedback unit is implemented by, for example, the control unit 46A of the smart glasses 214, which provides feedback on the content of the statements. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the content of the debate and quantifies the students' argumentative skills. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] For example, the generation unit is implemented in either the headset terminal 314 or the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, and automatically generates debate themes using generation AI. The progress unit is implemented by, for example, the control unit 46A of the headset terminal 314, and supports the progress of the debate and manages the speaking time of each student. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and organizes the students' opinions in real time and clarifies the points of contention. The feedback unit is implemented by, for example, the control unit 46A of the headset terminal 314, and provides feedback on the content of the statements. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and evaluates the content of the debate and quantifies the students' argumentative skills. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[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 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.

[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 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.

[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 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.

[0149] For example, the generation unit is implemented by either the robot 414 or the data processing unit 12. For example, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically generates debate themes using a generation AI. The progress unit is implemented by, for example, the control unit 46A of the robot 414, which supports the progress of the debate and manages the speaking time for each student. The organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which organizes the students' opinions in real time and clarifies the points of contention. The feedback unit is implemented by, for example, the control unit 46A of the robot 414, which provides feedback on the content of the statements. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which evaluates the content of the debate and quantifies the students' argumentative skills. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A system characterized by comprising: a generation unit that automatically generates debate themes; a progress unit that supports the progress of the debate based on the debate themes generated by the generation unit and manages the speaking time of each student; a summarization unit that organizes the content of the statements managed by the progress unit in real time and identifies the points of contention; a feedback unit that provides feedback on the content of the statements based on the points of contention identified by the summarization unit; and an evaluation unit that evaluates the content of the debate based on the feedback provided by the feedback unit and evaluates the students' argumentative skills. (Note 2) The system described in Appendix 1 is characterized in that the generation unit automatically generates debate themes based on the content of the lesson and the interests of the students. (Note 3) The system described in Appendix 1 is characterized in that the progress unit has a timer function and fairly manages the speaking time of each student. (Note 4) The aforementioned editing unit, We will use natural language processing technology to analyze students' statements and extract key points. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Evaluate the logic and persuasiveness of the statements and provide feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, The logical and persuasive nature of the statements will be used as evaluation criteria, and a score will be calculated. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is The system estimates students' emotions and adjusts the difficulty level of the debate topic based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We analyze the history of past debate topics and select the most suitable topic. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is It features a function that allows you to change the topic in real time according to the progress of the lesson. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned progress section is, The system estimates students' emotions and adjusts the allocation of speaking time based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned progress section is, The system analyzes students' comments in real time and provides support for the progress of the discussion. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned progress section is, Automatically optimizes the order of speaking to ensure a smooth debate flow. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned editing unit, We estimate the students' emotions and adjust the way we organize the points of discussion based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editing unit, The content of the statements is analyzed in real time, and the importance of the points being discussed is evaluated. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editing unit, It automatically integrates multiple points of contention, making the overall argument coherent. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned feedback unit is Analyze the content of the statements in detail and suggest specific areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback unit is Save feedback history to support long-term growth The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, Estimate students' emotions and adjust evaluation criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, The content of the statements is analyzed in detail, and a score is calculated using multiple evaluation criteria. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, Track evaluation results over the long term to visualize student growth. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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. A generation unit that automatically generates debate topics, A progress unit supports the progress of the debate based on the debate theme generated by the generation unit and manages the speaking time for each student, The aforementioned progress management unit organizes the content of the statements in real time and identifies the points of discussion, A feedback unit provides feedback on the content of the statements based on the issues identified by the aforementioned editing unit, The system includes an evaluation unit that evaluates the content of the debate based on the feedback provided by the aforementioned feedback unit and assesses the students' argumentative skills. A system characterized by the following features.

2. The generating unit is The system automatically generates debate topics based on the lesson content and students' interests. The system according to feature 1.

3. The system according to claim 1, characterized in that the progress unit has a timer function and fairly manages the speaking time of each student.

4. The aforementioned editing unit, We will use natural language processing technology to analyze students' statements and extract key points. The system according to feature 1.

5. The aforementioned feedback unit is Evaluate the logic and persuasiveness of the statements and provide feedback. The system according to feature 1.

6. The evaluation unit, The logical and persuasive nature of the statements will be used as evaluation criteria, and a score will be calculated. The system according to feature 1.

7. The generating unit is The system estimates students' emotions and adjusts the difficulty level of the debate topic based on those estimated emotions. The system according to feature 1.

8. The generating unit is We analyze the history of past debate topics and select the most suitable topic. The system according to feature 1.

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