system

Abstract

The system according to this embodiment aims to provide appropriate technical guidance to students even when there is a shortage of advisors to provide technical guidance in cultural club activities. [Solution] The system according to the embodiment comprises a reception unit, an evaluation unit, an example presentation unit, and an advice unit. The reception unit receives deliverables submitted by students. The evaluation unit evaluates the deliverables received by the reception unit. The example presentation unit presents examples based on the deliverables evaluated by the evaluation unit. The advice unit provides methods for technical improvement based on the examples presented by the example presentation unit.

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 and includes 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 shortage of advisors who provide technical guidance in cultural club activities, and there is a risk that the creative activities of students will be impaired.

[0005] The system according to the embodiment aims to provide appropriate technical guidance to students even when there is a shortage of advisors who provide technical guidance in cultural club activities.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, an example presentation unit, and an advice unit. The reception unit receives deliverables submitted by students. The evaluation unit evaluates the deliverables received by the reception unit. The example presentation unit presents examples based on the deliverables evaluated by the evaluation unit. The advice unit provides methods for technical improvement based on the examples presented by the example presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate technical guidance to students even when there is a shortage of advisors to provide technical guidance in cultural club activities. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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). <000009​​The smart device 14 comprises a computer 36, a reception 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 reception 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, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The technical guidance system according to an embodiment of the present invention is a system in which an AI agent is responsible for providing technical guidance to junior and senior high school students in cultural club activities such as illustration, music composition, video production, and programming. This technical guidance system provides opportunities for students' creative activities by having AI take over technical guidance for schools and club activities that lack advisors to provide technical guidance. Specifically, it consists of the following steps. First, students chat with the AI ​​agent using an application that runs on a PC or tablet and submit their work. The AI ​​agent provides technical guidance to the work submitted by the student in the following three steps: evaluation, example presentation, and advice. Through these three steps, technical guidance is provided, reducing the burden on teachers, broadening the scope of students' creative activities, and realizing the development of promising talent for the content industry. For example, the AI ​​agent evaluates the work submitted by the student. Specifically, it points out differences from AI-generated works of similar style as technical immaturities. For example, in an anime-style illustration, it points out line inconsistencies or incorrect shading. Next, in order to understand the student's intentions, the AI ​​agent presents high-quality examples of similar styles. This allows students to identify the style they aspire to create. For example, if they aim for a specific musical style, examples of that style are presented. Finally, the AI ​​agent explains, using text and diagrams, how to improve their technical weaknesses in order to achieve their chosen style. This allows students to learn concrete methods for improvement. In this way, the technical guidance system supports students' creative activities and reduces the burden on teachers.

[0029] The technical guidance system according to this embodiment comprises a reception unit, an evaluation unit, an example presentation unit, and an advice unit. The reception unit receives deliverables submitted by students. Deliverables submitted by students include, but are not limited to, reports, projects, and works. The reception unit receives deliverables, for example, through an online platform. The reception unit can also digitize and accept deliverables submitted offline. For example, the reception unit allows students to upload deliverables using an online form. The reception unit can also scan deliverables submitted by mail and accept them as digital data. The evaluation unit evaluates the deliverables received by the reception unit. The evaluation is performed by, for example, score evaluation, comment evaluation, rubric evaluation, etc., but is not limited to these methods. For example, the evaluation unit uses AI to automatically point out technical shortcomings in the deliverables. The evaluation unit can also have AI perform evaluations with reference to expert evaluations. For example, the evaluation unit has experts review the evaluation results generated by the AI ​​and make a final evaluation. The example presentation unit presents examples based on the deliverables evaluated by the evaluation unit. Examples may include, but are not limited to, successful examples, unsuccessful examples, and reference materials. For example, the example presentation unit may use AI to understand the student's intentions and present high-quality examples with a similar style. The example presentation unit may also present examples by referencing excellent past deliverables. For example, the example presentation unit may search a database for excellent past deliverables and present them to the student. The advice unit provides technical improvement methods based on the examples presented by the example presentation unit. Technical improvement methods may be provided in, but are not limited to, text, diagrams, or videos. For example, the advice unit may use AI to automatically generate technical improvement methods for areas of weakness. The advice unit may also use expert advice to enable AI to provide improvement methods. For example, the advice unit may have an expert review the improvement methods generated by the AI ​​and provide final advice. This enables the technical guidance system according to the embodiment to efficiently evaluate students' deliverables, present examples, and provide technical improvement methods.

[0030] The reception department receives deliverables submitted by students. These deliverables include, but are not limited to, reports, projects, and artwork. The reception department accepts deliverables, for example, through an online platform. Specifically, the online platform features a user-friendly interface that is easily accessible to students and guides them through the process of uploading deliverables. The reception department can also digitize and accept deliverables submitted offline. For example, the reception department allows students to upload deliverables using an online form. The online form clearly specifies file format and size restrictions and displays the upload progress in real time. The reception department can also scan deliverables submitted by mail and accept them as digital data. Mailed deliverables are digitized at high resolution using a dedicated scanning device and stored in a database. This allows the reception department to accept deliverables both online and offline, enhancing student convenience. Furthermore, the reception department uses a database to classify and search for submitted deliverables in order to streamline the management of these deliverables. For example, metadata such as submission date and time, submitter information, and type of deliverable can be added to enable quick access as needed. This allows the receiving department to efficiently manage submitted deliverables and facilitate smooth collaboration with the evaluation department and other departments.

[0031] The evaluation department evaluates the deliverables received by the reception department. Evaluation is carried out using methods such as score evaluation, comment evaluation, and rubric evaluation, but is not limited to these examples. Specifically, score evaluation is a method of assigning numerical values ​​to each item of the deliverable and calculating an overall score. Comment evaluation is a method of providing feedback to students by specifically describing the good points and areas for improvement of the deliverable. Rubric evaluation is a method of evaluating deliverables based on predetermined evaluation criteria and indicating the degree of achievement of each criterion. For example, the evaluation department can use AI to automatically point out technical shortcomings in deliverables. The AI ​​analyzes the content of reports using natural language processing technology and detects grammatical and logical errors. It also uses image recognition technology to evaluate the visual elements of projects and works and point out problems with design and composition. Furthermore, the evaluation department can have AI perform evaluations while referring to expert evaluations. For example, the evaluation department can have experts review the evaluation results generated by the AI ​​and make a final evaluation. Based on the AI ​​evaluation results, experts can provide more detailed feedback and deepen the students' understanding. This allows the assessment department, through collaboration between AI and experts, to conduct rapid and accurate assessments and support students' skill development. Furthermore, by storing assessment results in a database and referring to past assessment history, the assessment department can continuously track students' progress. This enables the assessment department to understand students' technical advancements and develop individualized instruction plans.

[0032] The example presentation section presents examples based on deliverables evaluated by the evaluation section. Examples include, but are not limited to, successful examples, unsuccessful examples, and reference materials. Specifically, successful examples showcase deliverables that have received high evaluations in the past, clarifying the standards that students should aim for. Unsuccessful examples highlight common errors and areas for improvement, preventing students from repeating the same mistakes. Reference materials provide technical background information and relevant knowledge, deepening students' understanding. For example, the example presentation section uses AI to understand students' intentions and present high-quality examples with similar styles. The AI ​​analyzes the content and style of students' deliverables and selects the most suitable examples from the database. The example presentation section can also present examples by referencing past excellent deliverables. For example, it can search the database for past excellent deliverables and present them to students. The database contains examples from various fields and levels, allowing it to provide appropriate examples according to students' needs. This enables the example presentation section to support students in learning with concrete goals and promotes skill improvement. Furthermore, the example presentation unit can collect student feedback on the presented examples and continuously improve their quality. For example, it can collect information in the form of questionnaires about how students felt about the examples and which parts they found helpful, and incorporate this into future example presentations. This allows the example presentation unit to provide effective examples that meet students' needs and maximize learning effectiveness.

[0033] The advice section provides technical improvement methods based on the examples presented by the example presentation section. These technical improvement methods are provided in various formats, including, but are not limited to, text, diagrams, and videos. Specifically, text-based advice describes detailed procedures and points to note, showing students exactly how to improve. Diagram-based advice uses diagrams and illustrations for easy visual understanding. Video-based advice demonstrates actual work procedures in video format, allowing students to learn visually. For example, the advice section can use AI to automatically generate technical improvement methods for areas of weakness. The AI ​​analyzes the content of students' work using natural language processing technology and extracts specific areas for improvement. It also evaluates the visual elements of the work using image recognition technology and suggests improvements to the design and composition. Furthermore, the advice section can also have AI provide improvement methods based on expert advice. For example, the advice section can have experts review the improvement methods generated by the AI ​​and provide final advice. Based on the AI's suggestions, experts can add more specific and practical advice to deepen students' understanding. This allows the advice department, through collaboration between AI and experts, to provide effective technical improvement methods and support students' skill development. Furthermore, the advice department can evaluate the effectiveness of the advice provided and continuously improve it. For example, it can collect feedback on the results of students implementing the advice and improve the quality of the advice. In this way, the advice department can continuously support students' skill development and maximize the effectiveness of the entire system.

[0034] The evaluation unit can identify differences between a student's work and AI-generated works with similar styles as areas of technical immaturity. For example, the evaluation unit can use AI to compare a student's work with AI-generated works with similar styles and identify areas of technical immaturity. For instance, in an anime-style illustration, the evaluation unit might point out issues such as shaky lines or incorrect shading. The evaluation unit can also use AI to automatically detect areas of technical immaturity in a student's work. For example, the evaluation unit can provide the student with a list of technical immaturities generated by the AI. Furthermore, the evaluation unit can use AI to suggest ways to improve these technical immaturities. For example, the evaluation unit can provide the student with methods for improving technical immaturities generated by the AI. This allows the evaluation unit to support students' technical improvement by clearly identifying areas of technical immaturity.

[0035] The example presentation section can understand the student's intentions and present high-quality examples in a similar style. For example, the example presentation section can use AI to understand the student's intentions and present high-quality examples in a similar style. For example, if a student aims for a specific musical style, the example presentation section will present examples in that style. The example presentation section can also use AI to grasp the student's intentions and present the most suitable examples. For example, the example presentation section can provide the student with a list of examples generated by AI. Furthermore, the example presentation section can use AI to adjust the method of presenting examples. For example, the example presentation section can provide the student with a method for presenting examples generated by AI. This helps clarify the style that students aim for and supports their skill improvement.

[0036] The Advice Department can explain, using text and diagrams, how to technically improve areas of weakness in order to achieve the target style. For example, the Advice Department can automatically generate technical improvement methods using AI. For instance, it can provide students with AI-generated improvement methods in text and diagrams. Furthermore, the Advice Department can also explain technical improvement methods using videos with AI. For example, it can provide students with AI-generated improvement methods in videos. In addition, the Advice Department can use AI to adjust how technical improvement methods are provided. For example, it can provide students with AI-generated methods for providing technical improvement. This allows the Advice Department to support students' skill improvement by providing concrete improvement methods.

[0037] The reception desk can analyze a student's past submission history and select the most suitable submission method. For example, the reception desk can use AI to analyze a student's past submission history. For example, the reception desk can analyze the time slots when students have previously submitted work and process submissions during those times. The reception desk can also prioritize suggesting submission methods (online, offline, etc.) that students have used in the past. For example, the reception desk can select the most suitable submission method based on the methods students have used in the past. Furthermore, the reception desk can consider the student's past submission frequency and set appropriate submission reminders. For example, the reception desk can set submission reminders based on the student's past submission frequency. This allows for more efficient instruction by selecting the most suitable submission method based on the student's past submission history.

[0038] The reception desk can filter submitted deliverables based on students' current projects and areas of interest. For example, it can use AI to analyze students' current projects and areas of interest. For instance, it can only accept deliverables related to projects students are currently working on. The reception desk can also prioritize the acceptance of highly relevant deliverables based on students' areas of interest. For example, it can select the most suitable deliverables based on students' areas of interest. Furthermore, the reception desk can accept deliverables based on themes students have shown interest in the past. For example, it can accept deliverables based on themes students have shown interest in the past. This allows for more efficient instruction by filtering deliverables based on students' current projects and areas of interest.

[0039] The reception desk can prioritize receiving deliverables that are highly relevant, taking into account the student's geographical location. For example, the reception desk can obtain the student's geographical location using AI. For example, the reception desk can obtain the student's location using GPS data. The reception desk can also obtain the student's location using address information. For example, the reception desk can obtain location information based on the student's address information. Furthermore, the reception desk can prioritize receiving deliverables that are highly relevant, taking into account the student's geographical location. For example, if the student is at school, the reception desk will prioritize receiving school-related deliverables. Also, if the student is at home, the reception desk can prioritize receiving deliverables that were worked on at home. For example, if the student is at home, the reception desk will prioritize receiving deliverables that were worked on at home. This allows for more efficient instruction by prioritizing the receipt of highly relevant deliverables, taking into account the student's geographical location.

[0040] The reception desk can analyze students' social media activity when receiving deliverables and accept relevant deliverables. For example, the reception desk can use AI to analyze students' social media activity. For example, the reception desk can automatically accept works that students have shared on social media. The reception desk can also prioritize accepting deliverables related to themes that students have shown interest in on social media. For example, the reception desk can accept deliverables based on themes that students have shown interest in on social media. Furthermore, the reception desk can accept highly relevant deliverables based on students' social media activity history. For example, the reception desk can select highly relevant deliverables based on students' social media activity history. This enables more efficient instruction by analyzing students' social media activity and accepting relevant deliverables.

[0041] The evaluation department can adjust the level of detail in its evaluations based on the importance of the deliverables. For example, the evaluation department can use AI to analyze the importance of deliverables. For example, the evaluation department can assess the importance of deliverables based on the scale and impact of the project. The evaluation department can also adjust the level of detail in its evaluations based on the completeness of the deliverables. For example, the evaluation department will conduct a detailed evaluation for important projects. The evaluation department can also conduct a concise evaluation for simple tasks. For example, the evaluation department will conduct a concise evaluation for simple tasks. By adjusting the level of detail in the evaluations based on the importance of the deliverables, more efficient guidance becomes possible.

[0042] The evaluation unit can apply different evaluation algorithms depending on the category of the deliverable during the evaluation process. For example, the evaluation unit can use AI to classify the categories of deliverables. For instance, the evaluation unit can classify deliverables into categories such as technology, art, and business. The evaluation unit can also apply different evaluation algorithms depending on the category of the deliverable. For example, in the case of illustrations, the evaluation unit can apply an algorithm that points out line inconsistencies and shading errors. Similarly, in the case of musical compositions, the evaluation unit can apply an algorithm that points out inconsistencies in melody and rhythm. By applying different evaluation algorithms depending on the category of the deliverable, more efficient instruction becomes possible.

[0043] The evaluation department can determine evaluation priorities based on the submission timing of deliverables. For example, the evaluation department can use AI to analyze the submission timing of deliverables. For example, the evaluation department can determine evaluation priorities based on submission deadlines and submission order. The evaluation department can also determine evaluation priorities by considering the frequency of submission. For example, the evaluation department can prioritize evaluating deliverables that are submitted early. The evaluation department can also postpone the evaluation of deliverables that are submitted late. For example, the evaluation department can postpone the evaluation of deliverables that are submitted late. By determining evaluation priorities based on the submission timing of deliverables, more efficient guidance becomes possible.

[0044] The evaluation unit can adjust the order of evaluation based on the relevance of deliverables during the evaluation process. For example, the evaluation unit can use AI to analyze the relevance of deliverables. For instance, the evaluation unit can prioritize evaluating deliverables related to a student's current project. It can also prioritize evaluating deliverables related to a student's area of ​​interest. For example, the evaluation unit can select highly relevant deliverables based on a student's area of ​​interest. Furthermore, the evaluation unit can prioritize evaluating highly relevant deliverables based on a student's past submission history. For example, the evaluation unit can select highly relevant deliverables based on a student's past submission history. By adjusting the order of evaluation based on the relevance of deliverables, more efficient instruction becomes possible.

[0045] The example presentation section can adjust the level of detail of examples based on the importance of the deliverables. For example, the example presentation section can use AI to analyze the importance of deliverables. For example, the example presentation section can evaluate the importance of deliverables based on the scale and impact of the project. The example presentation section can also adjust the level of detail of examples based on the completeness of the deliverables. For example, the example presentation section will present detailed examples for important projects. For example, the example presentation section can present concise examples for simple tasks. For example, the example presentation section will present concise examples for simple tasks. By adjusting the level of detail of examples based on the importance of the deliverables, more efficient instruction becomes possible.

[0046] The example presentation unit can apply different example presentation algorithms depending on the category of the deliverable when presenting examples. For example, the example presentation unit can classify deliverables into categories such as technology, art, and business. The example presentation unit can also apply different example presentation algorithms depending on the category of the deliverable. For example, in the case of illustrations, the example presentation unit can present examples that point out line inconsistencies or shading errors. In the case of musical compositions, the example presentation unit can also present examples that point out inconsistencies in melody and rhythm. For example, in the case of musical compositions, the example presentation unit can present examples that point out inconsistencies in melody and rhythm. This allows for more efficient instruction by applying different example presentation algorithms depending on the category of the deliverable.

[0047] The example presentation unit can prioritize examples based on the submission timing of deliverables. For example, the example presentation unit can use AI to analyze the submission timing of deliverables. For example, the example presentation unit can prioritize examples based on submission deadlines and submission order. The example presentation unit can also prioritize examples by considering submission frequency. For example, the example presentation unit can prioritize examples related to deliverables that have been submitted early. The example presentation unit can also postpone examples related to deliverables that have been submitted late. For example, the example presentation unit will postpone examples related to deliverables that have been submitted late. By prioritizing examples based on the submission timing of deliverables, more efficient instruction becomes possible.

[0048] The example presentation unit can adjust the order of examples based on the relevance of the deliverables. For example, the example presentation unit can use AI to analyze the relevance of deliverables. For example, the example presentation unit can prioritize presenting examples related to the student's current project. The example presentation unit can also prioritize presenting examples related to the student's areas of interest. For example, the example presentation unit can select highly relevant examples based on the student's areas of interest. Furthermore, the example presentation unit can prioritize presenting highly relevant examples based on the student's past submission history. For example, the example presentation unit can select highly relevant examples based on the student's past submission history. This allows for more efficient instruction by adjusting the order of examples based on the relevance of the deliverables.

[0049] The advisory department can adjust the level of detail in its advice based on the importance of the deliverables. For example, the advisory department can use AI to analyze the importance of deliverables. For example, the advisory department can evaluate the importance of deliverables based on the scale and impact of the project. The advisory department can also adjust the level of detail in its advice based on the completeness of the deliverables. For example, the advisory department will provide detailed advice for important projects. For example, the advisory department can provide concise advice for simple tasks. For example, the advisory department will provide concise advice for simple tasks. By adjusting the level of detail in advice based on the importance of the deliverables, efficient guidance becomes possible.

[0050] The advice unit can apply different advice algorithms depending on the category of the deliverable when providing advice. For example, the advice unit can use AI to classify deliverables into categories such as technology, art, and business. The advice unit can also apply different advice algorithms depending on the category of the deliverable. For example, in the case of illustrations, the advice unit can provide advice to correct line shading errors and other shading mistakes. In the case of musical compositions, the advice unit can also provide advice to correct inconsistencies in melody and rhythm. By applying different advice algorithms depending on the category of the deliverable, efficient guidance becomes possible.

[0051] The advisory department can prioritize advice based on the submission timing of deliverables. For example, the advisory department can use AI to analyze the submission timing of deliverables. For example, the advisory department can prioritize advice based on submission deadlines and submission order. The advisory department can also prioritize advice by considering the frequency of submission. For example, the advisory department can prioritize advice for deliverables that are submitted early. The advisory department can also postpone advice for deliverables that are submitted late. For example, the advisory department will postpone advice for deliverables that are submitted late. By prioritizing advice based on the submission timing of deliverables, efficient guidance becomes possible.

[0052] The advice department can adjust the order of advice based on the relevance of the deliverables. For example, the advice department can use AI to analyze the relevance of deliverables. For instance, it can prioritize advice on deliverables related to the student's current project. It can also prioritize advice on deliverables related to the student's areas of interest. For example, it can select highly relevant deliverables based on the student's areas of interest. Furthermore, the advice department can prioritize advice on highly relevant deliverables based on the student's past submission history. For example, it can select highly relevant deliverables based on the student's past submission history. This allows for more efficient instruction by adjusting the order of advice based on the relevance of deliverables.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The technical instruction system can also include a data analysis department. This department is responsible for analyzing student submission history and evaluation results to assess the effectiveness of instruction. For example, it can analyze which instruction methods were effective based on student submission history. Specifically, it can aggregate evaluation results of student deliverables and quantify the effectiveness of instruction. Furthermore, the data analysis department can generate reports to visualize the effectiveness of instruction. For instance, it can display student progress and evaluation results in graphs and charts, allowing for a quick overview of the instruction's effectiveness. Additionally, the data analysis department can suggest improvements to enhance the effectiveness of instruction. For example, it can analyze past data to determine which instructional methods were effective and incorporate this into future instruction. This allows for evaluation of instructional effectiveness and enables more efficient instruction.

[0055] The technical instruction system can also include a resource management unit. This unit manages the resources available to students and provides them at the appropriate time. For example, it can provide necessary resources according to a student's progress. Specifically, it can provide reference materials and tools needed when students are working on a particular assignment. Furthermore, it can estimate a student's emotions and adjust how resources are provided based on those estimates. For example, if a student is stressed, it can provide resources to help them relax; if a student is focused, it can provide resources to maintain that focus. Additionally, the resource management unit can customize resources based on a student's interests. For example, if a student is interested in a particular topic, it can provide resources related to that topic. This enables efficient instruction by providing students with the resources they need at the right time.

[0056] The technical instruction system can also include a collaboration section. The collaboration section plays a role in promoting cooperation among students and supporting collaborative work. For example, the collaboration section can support students when working on projects together. Specifically, the collaboration section provides tools to facilitate communication among students. Furthermore, the collaboration section can estimate students' emotions and adjust the collaboration method based on those estimates. For example, if a student is nervous, it can provide a relaxing environment; if a student is excited, it can provide support to maintain that excitement. Additionally, the collaboration section can select collaborative work themes based on students' interests. For example, it can set up projects based on themes that students share a common interest in. This promotes cooperation among students and enables efficient instruction.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The reception desk receives deliverables submitted by students. Deliverables submitted by students include reports, projects, and artwork. The reception desk can receive deliverables through an online platform, and can also digitize deliverables submitted offline. For example, students can upload deliverables using an online form, and deliverables submitted by mail can be scanned and accepted as digital data. Step 2: The evaluation department evaluates the deliverables received by the reception department. The evaluation is carried out using methods such as score evaluation, comment evaluation, and rubric evaluation. For example, the evaluation department can use AI to automatically point out technical shortcomings in the deliverables. AI can also perform evaluations while referring to expert evaluations. Experts can also review the evaluation results generated by the AI ​​and make a final evaluation. Step 3: The example presentation unit presents examples based on the deliverables evaluated by the evaluation unit. Examples include successful examples, unsuccessful examples, and reference materials. For example, the example presentation unit can use AI to understand the student's intentions and present high-quality examples with a similar style. It can also present examples by referencing past excellent deliverables. Past excellent deliverables can be searched from a database and presented to the students. Step 4: The advice section provides technical improvement methods based on the examples presented by the example presentation section. These technical improvement methods are provided in various formats, such as text, diagrams, and videos. For example, the advice section can use AI to automatically generate technical improvement methods for areas of weakness. It is also possible for the AI ​​to provide improvement methods based on expert advice. Experts can then review the improvement methods generated by the AI ​​and provide final advice.

[0059] (Example of form 2) The technical guidance system according to an embodiment of the present invention is a system in which an AI agent is responsible for providing technical guidance to junior and senior high school students in cultural club activities such as illustration, music composition, video production, and programming. This technical guidance system provides opportunities for students' creative activities by having AI take over technical guidance for schools and club activities that lack advisors to provide technical guidance. Specifically, it consists of the following steps. First, students chat with the AI ​​agent using an application that runs on a PC or tablet and submit their work. The AI ​​agent provides technical guidance to the work submitted by the student in the following three steps: evaluation, example presentation, and advice. Through these three steps, technical guidance is provided, reducing the burden on teachers, broadening the scope of students' creative activities, and realizing the development of promising talent for the content industry. For example, the AI ​​agent evaluates the work submitted by the student. Specifically, it points out differences from AI-generated works of similar style as technical immaturities. For example, in an anime-style illustration, it points out line inconsistencies or incorrect shading. Next, in order to understand the student's intentions, the AI ​​agent presents high-quality examples of similar styles. This allows students to identify the style they aspire to create. For example, if they aim for a specific musical style, examples of that style are presented. Finally, the AI ​​agent explains, using text and diagrams, how to improve their technical weaknesses in order to achieve their chosen style. This allows students to learn concrete methods for improvement. In this way, the technical guidance system supports students' creative activities and reduces the burden on teachers.

[0060] The technical guidance system according to this embodiment comprises a reception unit, an evaluation unit, an example presentation unit, and an advice unit. The reception unit receives deliverables submitted by students. Deliverables submitted by students include, but are not limited to, reports, projects, and works. The reception unit receives deliverables, for example, through an online platform. The reception unit can also digitize and accept deliverables submitted offline. For example, the reception unit allows students to upload deliverables using an online form. The reception unit can also scan deliverables submitted by mail and accept them as digital data. The evaluation unit evaluates the deliverables received by the reception unit. The evaluation is performed by, for example, score evaluation, comment evaluation, rubric evaluation, etc., but is not limited to these methods. For example, the evaluation unit uses AI to automatically point out technical shortcomings in the deliverables. The evaluation unit can also have AI perform evaluations with reference to expert evaluations. For example, the evaluation unit has experts review the evaluation results generated by the AI ​​and make a final evaluation. The example presentation unit presents examples based on the deliverables evaluated by the evaluation unit. Examples may include, but are not limited to, successful examples, unsuccessful examples, and reference materials. For example, the example presentation unit may use AI to understand the student's intentions and present high-quality examples with a similar style. The example presentation unit may also present examples by referencing excellent past deliverables. For example, the example presentation unit may search a database for excellent past deliverables and present them to the student. The advice unit provides technical improvement methods based on the examples presented by the example presentation unit. Technical improvement methods may be provided in, but are not limited to, text, diagrams, or videos. For example, the advice unit may use AI to automatically generate technical improvement methods for areas of weakness. The advice unit may also use expert advice to enable AI to provide improvement methods. For example, the advice unit may have an expert review the improvement methods generated by the AI ​​and provide final advice. This enables the technical guidance system according to the embodiment to efficiently evaluate students' deliverables, present examples, and provide technical improvement methods.

[0061] The reception department receives deliverables submitted by students. These deliverables include, but are not limited to, reports, projects, and artwork. The reception department accepts deliverables, for example, through an online platform. Specifically, the online platform features a user-friendly interface that is easily accessible to students and guides them through the process of uploading deliverables. The reception department can also digitize and accept deliverables submitted offline. For example, the reception department allows students to upload deliverables using an online form. The online form clearly specifies file format and size restrictions and displays the upload progress in real time. The reception department can also scan deliverables submitted by mail and accept them as digital data. Mailed deliverables are digitized at high resolution using a dedicated scanning device and stored in a database. This allows the reception department to accept deliverables both online and offline, enhancing student convenience. Furthermore, the reception department uses a database to classify and search for submitted deliverables in order to streamline the management of these deliverables. For example, metadata such as submission date and time, submitter information, and type of deliverable can be added to enable quick access as needed. This allows the receiving department to efficiently manage submitted deliverables and facilitate smooth collaboration with the evaluation department and other departments.

[0062] The evaluation department evaluates the deliverables received by the reception department. Evaluation is carried out using methods such as score evaluation, comment evaluation, and rubric evaluation, but is not limited to these examples. Specifically, score evaluation is a method of assigning numerical values ​​to each item of the deliverable and calculating an overall score. Comment evaluation is a method of providing feedback to students by specifically describing the good points and areas for improvement of the deliverable. Rubric evaluation is a method of evaluating deliverables based on predetermined evaluation criteria and indicating the degree of achievement of each criterion. For example, the evaluation department can use AI to automatically point out technical shortcomings in deliverables. The AI ​​analyzes the content of reports using natural language processing technology and detects grammatical and logical errors. It also uses image recognition technology to evaluate the visual elements of projects and works and point out problems with design and composition. Furthermore, the evaluation department can have AI perform evaluations while referring to expert evaluations. For example, the evaluation department can have experts review the evaluation results generated by the AI ​​and make a final evaluation. Based on the AI ​​evaluation results, experts can provide more detailed feedback and deepen the students' understanding. This allows the assessment department, through collaboration between AI and experts, to conduct rapid and accurate assessments and support students' skill development. Furthermore, by storing assessment results in a database and referring to past assessment history, the assessment department can continuously track students' progress. This enables the assessment department to understand students' technical advancements and develop individualized instruction plans.

[0063] The example presentation section presents examples based on deliverables evaluated by the evaluation section. Examples include, but are not limited to, successful examples, unsuccessful examples, and reference materials. Specifically, successful examples showcase deliverables that have received high evaluations in the past, clarifying the standards that students should aim for. Unsuccessful examples highlight common errors and areas for improvement, preventing students from repeating the same mistakes. Reference materials provide technical background information and relevant knowledge, deepening students' understanding. For example, the example presentation section uses AI to understand students' intentions and present high-quality examples with similar styles. The AI ​​analyzes the content and style of students' deliverables and selects the most suitable examples from the database. The example presentation section can also present examples by referencing past excellent deliverables. For example, it can search the database for past excellent deliverables and present them to students. The database contains examples from various fields and levels, allowing it to provide appropriate examples according to students' needs. This enables the example presentation section to support students in learning with concrete goals and promotes skill improvement. Furthermore, the example presentation unit can collect student feedback on the presented examples and continuously improve their quality. For example, it can collect information in the form of questionnaires about how students felt about the examples and which parts they found helpful, and incorporate this into future example presentations. This allows the example presentation unit to provide effective examples that meet students' needs and maximize learning effectiveness.

[0064] The advice section provides technical improvement methods based on the examples presented by the example presentation section. These technical improvement methods are provided in various formats, including, but are not limited to, text, diagrams, and videos. Specifically, text-based advice describes detailed procedures and points to note, showing students exactly how to improve. Diagram-based advice uses diagrams and illustrations for easy visual understanding. Video-based advice demonstrates actual work procedures in video format, allowing students to learn visually. For example, the advice section can use AI to automatically generate technical improvement methods for areas of weakness. The AI ​​analyzes the content of students' work using natural language processing technology and extracts specific areas for improvement. It also evaluates the visual elements of the work using image recognition technology and suggests improvements to the design and composition. Furthermore, the advice section can also have AI provide improvement methods based on expert advice. For example, the advice section can have experts review the improvement methods generated by the AI ​​and provide final advice. Based on the AI's suggestions, experts can add more specific and practical advice to deepen students' understanding. This allows the advice department, through collaboration between AI and experts, to provide effective technical improvement methods and support students' skill development. Furthermore, the advice department can evaluate the effectiveness of the advice provided and continuously improve it. For example, it can collect feedback on the results of students implementing the advice and improve the quality of the advice. In this way, the advice department can continuously support students' skill development and maximize the effectiveness of the entire system.

[0065] The evaluation unit can identify differences between a student's work and AI-generated works with similar styles as areas of technical immaturity. For example, the evaluation unit can use AI to compare a student's work with AI-generated works with similar styles and identify areas of technical immaturity. For instance, in an anime-style illustration, the evaluation unit might point out issues such as shaky lines or incorrect shading. The evaluation unit can also use AI to automatically detect areas of technical immaturity in a student's work. For example, the evaluation unit can provide the student with a list of technical immaturities generated by the AI. Furthermore, the evaluation unit can use AI to suggest ways to improve these technical immaturities. For example, the evaluation unit can provide the student with methods for improving technical immaturities generated by the AI. This allows the evaluation unit to support students' technical improvement by clearly identifying areas of technical immaturity.

[0066] The example presentation section can understand the student's intentions and present high-quality examples in a similar style. For example, the example presentation section can use AI to understand the student's intentions and present high-quality examples in a similar style. For example, if a student aims for a specific musical style, the example presentation section will present examples in that style. The example presentation section can also use AI to grasp the student's intentions and present the most suitable examples. For example, the example presentation section can provide the student with a list of examples generated by AI. Furthermore, the example presentation section can use AI to adjust the method of presenting examples. For example, the example presentation section can provide the student with a method for presenting examples generated by AI. This helps clarify the style that students aim for and supports their skill improvement.

[0067] The Advice Department can explain, using text and diagrams, how to technically improve areas of weakness in order to achieve the target style. For example, the Advice Department can automatically generate technical improvement methods using AI. For instance, it can provide students with AI-generated improvement methods in text and diagrams. Furthermore, the Advice Department can also explain technical improvement methods using videos with AI. For example, it can provide students with AI-generated improvement methods in videos. In addition, the Advice Department can use AI to adjust how technical improvement methods are provided. For example, it can provide students with AI-generated methods for providing technical improvement. This allows the Advice Department to support students' skill improvement by providing concrete improvement methods.

[0068] The reception desk can estimate students' emotions and adjust the timing of submitting deliverables based on those estimated emotions. The reception desk can estimate students' emotions using, for example, an emotion engine or generative AI. For instance, it can use facial recognition technology to estimate students' emotions. It can also use voice analysis technology to estimate students' emotions. For example, it can analyze the tone and speed of students' voices to estimate their emotions. Furthermore, the reception desk can adjust the timing of submitting deliverables based on students' emotions. For example, if a student is feeling stressed, it will submit their deliverables during a time when they can relax. It can also encourage students to submit their deliverables when they are concentrating. For example, it will submit their deliverables during times when students are concentrating. This allows for more efficient instruction by submitting deliverables at the appropriate time according to students' emotions.

[0069] The reception desk can analyze a student's past submission history and select the most suitable submission method. For example, the reception desk can use AI to analyze a student's past submission history. For example, the reception desk can analyze the time slots when students have previously submitted work and process submissions during those times. The reception desk can also prioritize suggesting submission methods (online, offline, etc.) that students have used in the past. For example, the reception desk can select the most suitable submission method based on the methods students have used in the past. Furthermore, the reception desk can consider the student's past submission frequency and set appropriate submission reminders. For example, the reception desk can set submission reminders based on the student's past submission frequency. This allows for more efficient instruction by selecting the most suitable submission method based on the student's past submission history.

[0070] The reception desk can filter submitted deliverables based on students' current projects and areas of interest. For example, it can use AI to analyze students' current projects and areas of interest. For instance, it can only accept deliverables related to projects students are currently working on. The reception desk can also prioritize the acceptance of highly relevant deliverables based on students' areas of interest. For example, it can select the most suitable deliverables based on students' areas of interest. Furthermore, the reception desk can accept deliverables based on themes students have shown interest in the past. For example, it can accept deliverables based on themes students have shown interest in the past. This allows for more efficient instruction by filtering deliverables based on students' current projects and areas of interest.

[0071] The reception desk can estimate a student's emotions and prioritize the deliverables to be received based on those emotions. The reception desk can estimate a student's emotions using, for example, an emotion engine or generative AI. For instance, it can use facial recognition technology to estimate a student's emotions. It can also use voice analysis technology to estimate a student's emotions. For example, it can analyze the tone and speed of a student's voice to estimate their emotions. Furthermore, the reception desk can prioritize the deliverables to be received based on the student's emotions. For example, if a student is excited, the reception desk will process the submission immediately to maintain their motivation. If a student is depressed, the reception desk can process the submission along with an encouraging message. For example, if a student is depressed, the reception desk will send an encouraging message and process the submission. This allows for more efficient instruction by prioritizing deliverables according to the student's emotions.

[0072] The reception desk can prioritize receiving deliverables that are highly relevant, taking into account the student's geographical location. For example, the reception desk can obtain the student's geographical location using AI. For example, the reception desk can obtain the student's location using GPS data. The reception desk can also obtain the student's location using address information. For example, the reception desk can obtain location information based on the student's address information. Furthermore, the reception desk can prioritize receiving deliverables that are highly relevant, taking into account the student's geographical location. For example, if the student is at school, the reception desk will prioritize receiving school-related deliverables. Also, if the student is at home, the reception desk can prioritize receiving deliverables that were worked on at home. For example, if the student is at home, the reception desk will prioritize receiving deliverables that were worked on at home. This allows for more efficient instruction by prioritizing the receipt of highly relevant deliverables, taking into account the student's geographical location.

[0073] The reception desk can analyze students' social media activity when receiving deliverables and accept relevant deliverables. For example, the reception desk can use AI to analyze students' social media activity. For example, the reception desk can automatically accept works that students have shared on social media. The reception desk can also prioritize accepting deliverables related to themes that students have shown interest in on social media. For example, the reception desk can accept deliverables based on themes that students have shown interest in on social media. Furthermore, the reception desk can accept highly relevant deliverables based on students' social media activity history. For example, the reception desk can select highly relevant deliverables based on students' social media activity history. This enables more efficient instruction by analyzing students' social media activity and accepting relevant deliverables.

[0074] The evaluation unit can estimate a student's emotions and adjust the way it expresses the evaluation based on those emotions. For example, the evaluation unit might use an emotion engine or generative AI to estimate a student's emotions. For instance, it might use facial recognition technology to estimate a student's emotions. It can also use voice analysis technology to estimate a student's emotions. For example, it might analyze the tone and speed of a student's voice to estimate their emotions. Furthermore, the evaluation unit can adjust the way it expresses the evaluation based on the student's emotions. For example, if a student is nervous, it might use gentle language in its evaluation. It can also provide detailed feedback if a student is relaxed. This allows for more efficient instruction by adjusting the evaluation expression according to the student's emotions.

[0075] The evaluation department can adjust the level of detail in its evaluations based on the importance of the deliverables. For example, the evaluation department can use AI to analyze the importance of deliverables. For example, the evaluation department can assess the importance of deliverables based on the scale and impact of the project. The evaluation department can also adjust the level of detail in its evaluations based on the completeness of the deliverables. For example, the evaluation department will conduct a detailed evaluation for important projects. The evaluation department can also conduct a concise evaluation for simple tasks. For example, the evaluation department will conduct a concise evaluation for simple tasks. By adjusting the level of detail in the evaluations based on the importance of the deliverables, more efficient guidance becomes possible.

[0076] The evaluation unit can apply different evaluation algorithms depending on the category of the deliverable during the evaluation process. For example, the evaluation unit can use AI to classify the categories of deliverables. For instance, the evaluation unit can classify deliverables into categories such as technology, art, and business. The evaluation unit can also apply different evaluation algorithms depending on the category of the deliverable. For example, in the case of illustrations, the evaluation unit can apply an algorithm that points out line inconsistencies and shading errors. Similarly, in the case of musical compositions, the evaluation unit can apply an algorithm that points out inconsistencies in melody and rhythm. By applying different evaluation algorithms depending on the category of the deliverable, more efficient instruction becomes possible.

[0077] The evaluation unit can estimate a student's emotions and adjust the length of the evaluation based on the estimated emotions. The evaluation unit can estimate a student's emotions using, for example, an emotion engine or generative AI. For instance, it can use facial recognition technology to estimate a student's emotions. It can also use voice analysis technology to estimate a student's emotions. For example, it can analyze the tone and speed of a student's voice to estimate their emotions. Furthermore, the evaluation unit can adjust the length of the evaluation based on the student's emotions. For example, if the student is nervous, it will provide a short, concise evaluation. Conversely, if the student is relaxed, it can provide a more detailed evaluation. This allows for more efficient instruction by adjusting the length of the evaluation according to the student's emotions.

[0078] The evaluation department can determine evaluation priorities based on the submission timing of deliverables. For example, the evaluation department can use AI to analyze the submission timing of deliverables. For example, the evaluation department can determine evaluation priorities based on submission deadlines and submission order. The evaluation department can also determine evaluation priorities by considering the frequency of submission. For example, the evaluation department can prioritize evaluating deliverables that are submitted early. The evaluation department can also postpone the evaluation of deliverables that are submitted late. For example, the evaluation department can postpone the evaluation of deliverables that are submitted late. By determining evaluation priorities based on the submission timing of deliverables, more efficient guidance becomes possible.

[0079] The evaluation unit can adjust the order of evaluation based on the relevance of deliverables during the evaluation process. For example, the evaluation unit can use AI to analyze the relevance of deliverables. For instance, the evaluation unit can prioritize evaluating deliverables related to a student's current project. It can also prioritize evaluating deliverables related to a student's area of ​​interest. For example, the evaluation unit can select highly relevant deliverables based on a student's area of ​​interest. Furthermore, the evaluation unit can prioritize evaluating highly relevant deliverables based on a student's past submission history. For example, the evaluation unit can select highly relevant deliverables based on a student's past submission history. By adjusting the order of evaluation based on the relevance of deliverables, more efficient instruction becomes possible.

[0080] The example presentation unit can estimate the student's emotions and adjust the presentation method of the examples based on the estimated emotions. The example presentation unit estimates the student's emotions using, for example, an emotion engine or generative AI. For example, it can estimate the student's emotions using facial recognition technology. It can also estimate the student's emotions using voice analysis technology. For example, it can analyze the tone and speed of the student's voice to estimate their emotions. Furthermore, the example presentation unit can adjust the presentation method of the examples based on the student's emotions. For example, if the student is nervous, it will present simple and highly visual examples. Conversely, if the student is relaxed, it can present more detailed examples. This allows for more efficient instruction by adjusting the presentation method of examples according to the student's emotions.

[0081] The example presentation section can adjust the level of detail of examples based on the importance of the deliverables. For example, the example presentation section can use AI to analyze the importance of deliverables. For example, the example presentation section can evaluate the importance of deliverables based on the scale and impact of the project. The example presentation section can also adjust the level of detail of examples based on the completeness of the deliverables. For example, the example presentation section will present detailed examples for important projects. For example, the example presentation section can present concise examples for simple tasks. For example, the example presentation section will present concise examples for simple tasks. By adjusting the level of detail of examples based on the importance of the deliverables, more efficient instruction becomes possible.

[0082] The example presentation unit can apply different example presentation algorithms depending on the category of the deliverable when presenting examples. For example, the example presentation unit can classify deliverables into categories such as technology, art, and business. The example presentation unit can also apply different example presentation algorithms depending on the category of the deliverable. For example, in the case of illustrations, the example presentation unit can present examples that point out line inconsistencies or shading errors. In the case of musical compositions, the example presentation unit can also present examples that point out inconsistencies in melody and rhythm. For example, in the case of musical compositions, the example presentation unit can present examples that point out inconsistencies in melody and rhythm. This allows for more efficient instruction by applying different example presentation algorithms depending on the category of the deliverable.

[0083] The example presentation unit can estimate the student's emotions and adjust the length of the example based on those emotions. The example presentation unit estimates the student's emotions using, for example, an emotion engine or generative AI. For instance, it can use facial recognition technology to estimate the student's emotions. It can also use voice analysis technology to estimate the student's emotions. For example, it analyzes the tone and speed of the student's voice to estimate their emotions. Furthermore, the example presentation unit can adjust the length of the example based on the student's emotions. For example, if the student is nervous, it will present a short, concise example. Conversely, if the student is relaxed, it can present a more detailed example. This allows for more efficient instruction by adjusting the length of the example according to the student's emotions.

[0084] The example presentation unit can prioritize examples based on the submission timing of deliverables. For example, the example presentation unit can use AI to analyze the submission timing of deliverables. For example, the example presentation unit can prioritize examples based on submission deadlines and submission order. The example presentation unit can also prioritize examples by considering submission frequency. For example, the example presentation unit can prioritize examples related to deliverables that have been submitted early. The example presentation unit can also postpone examples related to deliverables that have been submitted late. For example, the example presentation unit will postpone examples related to deliverables that have been submitted late. By prioritizing examples based on the submission timing of deliverables, more efficient instruction becomes possible.

[0085] The example presentation unit can adjust the order of examples based on the relevance of the deliverables. For example, the example presentation unit can use AI to analyze the relevance of deliverables. For example, the example presentation unit can prioritize presenting examples related to the student's current project. The example presentation unit can also prioritize presenting examples related to the student's areas of interest. For example, the example presentation unit can select highly relevant examples based on the student's areas of interest. Furthermore, the example presentation unit can prioritize presenting highly relevant examples based on the student's past submission history. For example, the example presentation unit can select highly relevant examples based on the student's past submission history. This allows for more efficient instruction by adjusting the order of examples based on the relevance of the deliverables.

[0086] The advice unit can estimate a student's emotions and adjust the way it delivers advice based on those emotions. For example, the advice unit can estimate a student's emotions using an emotion engine or generative AI. For instance, it can use facial recognition technology to estimate a student's emotions. It can also use voice analysis technology to estimate a student's emotions. For example, it can analyze the tone and speed of a student's voice to estimate their emotions. Furthermore, the advice unit can adjust the way it delivers advice based on the student's emotions. For example, if a student is nervous, it will give advice in gentle terms. Conversely, if a student is relaxed, it can provide more detailed advice. This allows for more efficient instruction by adjusting the way advice is delivered according to the student's emotions.

[0087] The advisory department can adjust the level of detail in its advice based on the importance of the deliverables. For example, the advisory department can use AI to analyze the importance of deliverables. For example, the advisory department can evaluate the importance of deliverables based on the scale and impact of the project. The advisory department can also adjust the level of detail in its advice based on the completeness of the deliverables. For example, the advisory department will provide detailed advice for important projects. For example, the advisory department can provide concise advice for simple tasks. For example, the advisory department will provide concise advice for simple tasks. By adjusting the level of detail in advice based on the importance of the deliverables, efficient guidance becomes possible.

[0088] The advice unit can apply different advice algorithms depending on the category of the deliverable when providing advice. For example, the advice unit can use AI to classify deliverables into categories such as technology, art, and business. The advice unit can also apply different advice algorithms depending on the category of the deliverable. For example, in the case of illustrations, the advice unit can provide advice to correct line shading errors and other shading mistakes. In the case of musical compositions, the advice unit can also provide advice to correct inconsistencies in melody and rhythm. By applying different advice algorithms depending on the category of the deliverable, efficient guidance becomes possible.

[0089] The advice unit can estimate a student's emotions and adjust the length of the advice based on those emotions. The advice unit can estimate a student's emotions using, for example, an emotion engine or generative AI. For instance, it can use facial recognition technology to estimate a student's emotions. It can also use voice analysis technology to estimate a student's emotions. For example, it can analyze the tone and speed of a student's voice to estimate their emotions. Furthermore, the advice unit can adjust the length of the advice based on the student's emotions. For example, if the student is nervous, it will provide short, concise advice. Conversely, if the student is relaxed, it can provide more detailed advice. This allows for more efficient instruction by adjusting the length of advice according to the student's emotions.

[0090] The advisory department can prioritize advice based on the submission timing of deliverables. For example, the advisory department can use AI to analyze the submission timing of deliverables. For example, the advisory department can prioritize advice based on submission deadlines and submission order. The advisory department can also prioritize advice by considering the frequency of submission. For example, the advisory department can prioritize advice for deliverables that are submitted early. The advisory department can also postpone advice for deliverables that are submitted late. For example, the advisory department will postpone advice for deliverables that are submitted late. By prioritizing advice based on the submission timing of deliverables, efficient guidance becomes possible.

[0091] The advice department can adjust the order of advice based on the relevance of the deliverables. For example, the advice department can use AI to analyze the relevance of deliverables. For instance, it can prioritize advice on deliverables related to the student's current project. It can also prioritize advice on deliverables related to the student's areas of interest. For example, it can select highly relevant deliverables based on the student's areas of interest. Furthermore, the advice department can prioritize advice on highly relevant deliverables based on the student's past submission history. For example, it can select highly relevant deliverables based on the student's past submission history. This allows for more efficient instruction by adjusting the order of advice based on the relevance of deliverables.

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

[0093] The technical instruction system can also include a feedback section. This section provides feedback on student-submitted work. For example, the feedback section can use AI to generate detailed feedback on student work. Specifically, it can point out technical shortcomings in the student's work and suggest ways to improve. It can also highlight the strengths of the student's work and provide positive feedback to boost motivation. Furthermore, the feedback section can estimate the student's emotions and adjust the content and expression of the feedback based on those emotions. For example, if the student is nervous, it might provide gentle feedback; if the student is relaxed, it might provide detailed feedback. This enables more efficient instruction by providing appropriate feedback tailored to the student's emotions.

[0094] The technical instruction system can also include a progress management unit. This unit manages the progress of students' creative activities and provides instruction at the appropriate time. For example, it can analyze students' submission history and activity logs to understand their progress. Specifically, it can identify where students are struggling and provide appropriate advice. It can also adjust instruction based on the student's progress. For instance, if a student is progressing well, it provides advice on moving to the next step; if a student is falling behind, it provides supplementary instruction. Furthermore, the progress management unit can estimate the student's emotions and adjust its progress management methods based on those emotions. For example, if a student is stressed, it provides instruction to help them relax; if a student is focused, it provides instruction at that time. This enables efficient instruction by providing appropriate guidance according to the student's progress.

[0095] The technical instruction system can also include a communication department. This department plays a role in facilitating communication between students and the AI ​​agent. For example, the communication department can provide appropriate answers to students' questions. Specifically, it analyzes the content of students' questions and provides relevant information. It can also estimate students' emotions and adjust its communication style based on those estimates. For instance, if a student is feeling anxious, it provides reassuring answers; if a student is excited, it provides answers that maintain that excitement. Furthermore, the communication department can collect student feedback and use it to improve the technical instruction system. For example, it can collect student opinions and requests to improve the system's functions and instructional content. This enables smoother communication with students and more efficient instruction.

[0096] The technical instruction system can also include a motivation management department. This department provides functions to maintain and improve student motivation. For example, it can provide positive feedback on student work. Specifically, it can praise students' efforts and progress and offer words of encouragement to move on to the next step. It can also estimate students' emotions and adjust methods to boost motivation based on those estimates. For example, if a student is discouraged, it can send encouraging messages; if a student is highly motivated, it can provide advice to maintain that motivation. Furthermore, the motivation management department can assist students in setting goals and provide rewards based on their achievement. For example, if a student achieves a set goal, it can award badges or points to increase their motivation towards the next goal. This allows for more efficient instruction by maintaining and improving student motivation.

[0097] The technical instruction system can also include a personalization component. This component is responsible for providing instruction tailored to each individual student. For example, it can analyze a student's past submission and learning history to provide optimal instruction for each student. Specifically, it can identify a student's strengths and weaknesses and provide instruction accordingly. Furthermore, it can estimate a student's emotions and adjust the instruction based on those emotions. For example, if a student is tired, it can provide easy tasks; if a student is focused, it can provide more challenging tasks. Additionally, the personalization component can customize instruction based on a student's interests. For example, if a student is interested in a particular topic, it can provide tasks related to that topic. This allows for more efficient instruction by providing instruction tailored to each student.

[0098] The technical instruction system can also include a data analysis department. This department is responsible for analyzing student submission history and evaluation results to assess the effectiveness of instruction. For example, it can analyze which instruction methods were effective based on student submission history. Specifically, it can aggregate evaluation results of student deliverables and quantify the effectiveness of instruction. Furthermore, the data analysis department can generate reports to visualize the effectiveness of instruction. For instance, it can display student progress and evaluation results in graphs and charts, allowing for a quick overview of the instruction's effectiveness. Additionally, the data analysis department can suggest improvements to enhance the effectiveness of instruction. For example, it can analyze past data to determine which instructional methods were effective and incorporate this into future instruction. This allows for evaluation of instructional effectiveness and enables more efficient instruction.

[0099] The technical instruction system can also include a resource management unit. This unit manages the resources available to students and provides them at the appropriate time. For example, it can provide necessary resources according to a student's progress. Specifically, it can provide reference materials and tools needed when students are working on a particular assignment. Furthermore, it can estimate a student's emotions and adjust how resources are provided based on those estimates. For example, if a student is stressed, it can provide resources to help them relax; if a student is focused, it can provide resources to maintain that focus. Additionally, the resource management unit can customize resources based on a student's interests. For example, if a student is interested in a particular topic, it can provide resources related to that topic. This enables efficient instruction by providing students with the resources they need at the right time.

[0100] The technical instruction system can also include a collaboration section. The collaboration section plays a role in promoting cooperation among students and supporting collaborative work. For example, the collaboration section can support students when working on projects together. Specifically, the collaboration section provides tools to facilitate communication among students. Furthermore, the collaboration section can estimate students' emotions and adjust the collaboration method based on those estimates. For example, if a student is nervous, it can provide a relaxing environment; if a student is excited, it can provide support to maintain that excitement. Additionally, the collaboration section can select collaborative work themes based on students' interests. For example, it can set up projects based on themes that students share a common interest in. This promotes cooperation among students and enables efficient instruction.

[0101] The technical instruction system can also include an evaluation and feedback unit. This unit is responsible for providing feedback on the evaluation results of students' work. For example, the evaluation and feedback unit can use AI to generate detailed evaluations of students' work. Specifically, it can point out technical shortcomings in students' work and suggest ways to improve. It can also highlight the strengths of students' work and provide positive feedback to boost motivation. Furthermore, the evaluation and feedback unit can estimate students' emotions and adjust the content and expression of feedback based on those estimates. For example, if a student is nervous, it might provide feedback in gentle terms; if a student is relaxed, it might provide detailed feedback. This enables more efficient instruction by providing appropriate feedback tailored to the student's emotions.

[0102] The technical instruction system can also include a progress tracking unit. This unit tracks the progress of students' creative activities and provides guidance at the appropriate time. For example, it can analyze students' submission history and activity logs to understand their progress. Specifically, it can identify where students are struggling and provide appropriate advice. It can also adjust instruction based on the student's progress. For instance, if a student is progressing well, it provides advice on moving to the next step; if a student is falling behind, it provides supplementary instruction. Furthermore, the progress tracking unit can estimate a student's emotions and adjust progress management based on those emotions. For example, if a student is stressed, it provides guidance to help them relax; if a student is focused, it provides guidance at that time. This enables efficient instruction by providing appropriate guidance according to the student's progress.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The reception desk receives deliverables submitted by students. Deliverables submitted by students include reports, projects, and artwork. The reception desk can receive deliverables through an online platform, and can also digitize deliverables submitted offline. For example, students can upload deliverables using an online form, and deliverables submitted by mail can be scanned and accepted as digital data. Step 2: The evaluation department evaluates the deliverables received by the reception department. The evaluation is carried out using methods such as score evaluation, comment evaluation, and rubric evaluation. For example, the evaluation department can use AI to automatically point out technical shortcomings in the deliverables. AI can also perform evaluations while referring to expert evaluations. Experts can also review the evaluation results generated by the AI ​​and make a final evaluation. Step 3: The example presentation unit presents examples based on the deliverables evaluated by the evaluation unit. Examples include successful examples, unsuccessful examples, and reference materials. For example, the example presentation unit can use AI to understand the student's intentions and present high-quality examples with a similar style. It can also present examples by referencing past excellent deliverables. Past excellent deliverables can be searched from a database and presented to the students. Step 4: The advice section provides technical improvement methods based on the examples presented by the example presentation section. These technical improvement methods are provided in various formats, such as text, diagrams, and videos. For example, the advice section can use AI to automatically generate technical improvement methods for areas of weakness. It is also possible for the AI ​​to provide improvement methods based on expert advice. Experts can then review the improvement methods generated by the AI ​​and provide final advice.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the reception unit, evaluation unit, example presentation unit, and advice unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, enabling students to upload their work using an online form. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12, automatically pointing out technical shortcomings in the work using AI. The example presentation unit is implemented by the control unit 46A of the smart device 14, understanding the student's intentions and presenting high-quality examples in a similar style. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12, automatically generating methods for technically improving the shortcomings. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0122] 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.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0124] Each of the multiple elements described above, including the reception unit, evaluation unit, example presentation unit, and advice unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, enabling students to upload their work using an online form. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to automatically point out technical shortcomings in the work. The example presentation unit is implemented by, for example, the control unit 46A of the smart glasses 214, which understands the student's intentions and presents high-quality examples with a similar style. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates methods for technically improving the shortcomings. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0140] Each of the multiple elements described above, including the reception unit, evaluation unit, example presentation unit, and advice unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, enabling students to upload their work using an online form. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to automatically point out technical shortcomings in the work. The example presentation unit is implemented by, for example, the control unit 46A of the headset terminal 314, which understands the student's intentions and presents high-quality examples with a similar style. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates methods for technically improving the shortcomings. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0155] 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.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0157] Each of the multiple elements described above, including the reception unit, evaluation unit, example presentation unit, and advice unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, enabling students to upload their work using an online form. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to automatically point out technical shortcomings in the work. The example presentation unit is implemented by, for example, the control unit 46A of the robot 414, which understands the student's intentions and presents high-quality examples in a similar style. The advice unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates methods for technically improving the shortcomings. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) The reception desk receives the deliverables submitted by the students, An evaluation unit that evaluates the deliverables received by the aforementioned reception unit, An example presentation unit presents examples based on the deliverables evaluated by the evaluation unit, The system includes an advice unit that provides technical improvement methods based on the examples presented by the example presentation unit. A system characterized by the following features. (Note 2) The evaluation unit, The differences between this AI-generated work and similar AI-generated works are pointed out as technical immaturities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned example presentation unit is, Understand the student's intentions and present high-quality examples in a similar style. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advice section, To achieve the desired style, I will explain, using text and diagrams, how to improve the technical aspects that are still lacking. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates students' emotions and adjusts the timing of submitting deliverables based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze students' past submission history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving deliverables, filtering will be performed based on the students' current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates students' emotions and prioritizes the deliverables to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving deliverables, the system prioritizes accepting deliverables that are highly relevant, taking into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving deliverables, the system analyzes students' social media activity and accepts relevant deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit, The system estimates students' emotions and adjusts the way evaluations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, During evaluation, adjust the level of detail based on the importance of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, During evaluation, different evaluation algorithms are applied depending on the category of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 14) The evaluation unit, The system estimates the students' emotions and adjusts the length of the assessment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The evaluation unit, During the evaluation process, the priority of evaluation will be determined based on the submission timing of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 16) The evaluation unit, During evaluation, the order of evaluation will be adjusted based on the relevance of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned example presentation unit is, The system estimates the students' emotions and adjusts the presentation method of the examples based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned example presentation unit is, When presenting examples, adjust the level of detail in the examples based on the importance of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned example presentation unit is, When presenting examples, different example presentation algorithms are applied depending on the category of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned example presentation unit is, The system estimates the students' emotions and adjusts the length of the example based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned example presentation unit is, When presenting examples, prioritize the examples based on the submission deadline for the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned example presentation unit is, When presenting examples, adjust the order of the examples based on the relevance of the deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, The system estimates the student's emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing advice, adjust the level of detail based on the importance of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of the deliverable. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice section, The system estimates the student's emotions and adjusts the length of the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, When providing advice, prioritize the advice based on the deadline for submitting deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, adjust the order of advice based on the relevance of the deliverables. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk receives the deliverables submitted by the students, An evaluation unit that evaluates the deliverables received by the aforementioned reception unit, An example presentation unit presents examples based on the deliverables evaluated by the evaluation unit, The system includes an advice unit that provides technical improvement methods based on the examples presented by the example presentation unit. A system characterized by the following features.

2. The evaluation unit described above, The differences between this AI-generated work and similar AI-generated works are pointed out as technical immaturities. The system according to feature 1.

3. The aforementioned example presentation unit is, Understand the student's intentions and present high-quality examples in a similar style. The system according to feature 1.

4. The aforementioned advice section, To achieve the desired style, I will explain, using text and diagrams, how to improve the technical aspects that are still lacking. The system according to feature 1.

5. The aforementioned reception unit is The system estimates students' emotions and adjusts the timing of submitting deliverables based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze students' past submission history to select the most suitable submission method. The system according to feature 1.

7. The aforementioned reception unit is When receiving deliverables, filtering will be performed based on the students' current projects and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is The system estimates students' emotions and prioritizes the deliverables to be accepted based on those estimated emotions. The system according to feature 1.

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