system

Abstract

The system according to this embodiment aims to reduce the burden on educational institutions by automating the creation and scoring of test questions. [Solution] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a scoring unit. The reception unit receives input of the content and scope of the test. The generation unit analyzes the information received by the reception unit and generates test questions. The provision unit provides the test questions generated by the generation unit. The scoring unit scores the test questions provided by the provision 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 prior art, there is a problem that creating and grading test questions takes a lot of time and effort, imposing a heavy burden on the educational field.

[0005] The system according to the embodiment aims to reduce the burden on the educational field by automating the creation and grading of test questions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a scoring unit. The reception unit receives input regarding the content and scope of the test. The generation unit analyzes the information received by the reception unit and generates test questions. The provision unit provides the test questions generated by the generation unit. The scoring unit scores the test questions provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the burden on educational institutions by automating the creation and scoring of test questions. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards 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. <​​​​​​The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, 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 automated test generation system according to an embodiment of the present invention is a mechanism that eliminates overtime in educational settings by automatically generating various tests. In this automated test generation system, teachers input the content and scope of the test, and the AI ​​analyzes the input information to automatically generate test questions. The generated test questions are provided to the teacher, who can make revisions as needed. Finally, the revised test questions are distributed to students, the AI ​​automatically grades them, and the results are provided to the teacher. This mechanism significantly reduces overtime in educational settings, allowing teachers to concentrate on other educational activities. Furthermore, because the AI ​​automatically generates the test questions, the quality of the questions is kept consistent, enabling fair evaluation. For example, when a teacher inputs the content and scope of the test, the reception unit receives that information. Next, the generation unit analyzes the information received by the reception unit and generates test questions. The generated test questions are provided to the teacher by the provision unit, and the teacher can make revisions as needed. Finally, the revised test questions are distributed to students, the grading unit automatically grades them, and the results are provided to the teacher. This significantly reduces overtime in educational settings, allowing teachers to concentrate on other educational activities. Furthermore, because the AI ​​automatically generates test questions, the quality of the questions remains consistent, enabling fair evaluation. As a result, the automated test generation system reduces overtime in schools, allowing teachers to focus on other educational activities.

[0029] The automated test generation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a scoring unit. The reception unit receives input of the test content and scope. The reception unit provides, for example, an interface for teachers to input the test content and scope. The reception unit stores the information entered by the teacher in a database and passes it to the generation unit. The generation unit analyzes the information received by the reception unit and generates test questions. The generation unit analyzes the input information and generates appropriate test questions, for example, using natural language processing technology. The generation unit can also generate test questions based on past test data or textbook content. The generation unit passes the generated test questions to the provision unit. The provision unit provides the test questions generated by the generation unit to teachers. The provision unit provides the test questions, for example, through an online platform. The provision unit can also provide an interface for teachers to correct the test questions. The provision unit passes the corrected test questions to the scoring unit. The scoring unit scores the test questions provided by the provision unit. The scoring unit scores the test questions, for example, based on example answers. The scoring unit can perform accurate scoring using AI. The scoring unit provides the scoring results to the teacher. As a result, the automated test generation system according to this embodiment can reduce overtime in educational settings by automating everything from inputting the test content and scope to generation, provision, and scoring.

[0030] The reception department accepts input regarding the content and scope of the test. For example, the reception department provides an interface for teachers to input the test content and scope. Specifically, the reception department allows teachers to easily input the test content and scope through web-based forms or dedicated applications. The forms include items such as the test subject, target grade level, question format (multiple choice, written, etc.), scope, and difficulty level. Teachers can provide detailed test information by selecting or entering these items. Furthermore, the reception department has a function to verify the entered information in real time and provide immediate feedback if there is missing information or inappropriate input. For example, if the scope is too broad or the difficulty level is unbalanced, a warning message will be displayed to prompt correction. The reception department stores the information entered by teachers in a database and passes it to the generation department. The database is secure and protected to prevent the leakage of entered information. This allows the reception department to provide an environment where teachers can easily and accurately input the test content and scope, and to ensure smooth information transmission to the generation department.

[0031] The generation unit analyzes the information received by the reception unit and generates test questions. For example, the generation unit uses natural language processing technology to analyze the input information and generate appropriate test questions. Specifically, the generation unit extracts relevant keywords and topics based on the test content and scope entered by the teacher, and generates question texts based on these. The generation unit can also generate test questions based on past test data and textbook content. For example, it searches for similar questions in the past test database and creates new questions based on them. It also analyzes textbook content, extracts important points and frequently appearing topics, and generates question texts. Before handing over the generated test questions to the delivery unit, the generation unit performs internal quality checks. For example, it checks the grammar and expression of the question texts, and checks for inappropriate expressions or typos. It also evaluates the difficulty and balance of the questions and adjusts them to achieve an appropriate overall difficulty level. This allows the generation unit to automatically generate high-quality, appropriate test questions based on the information entered by the teacher.

[0032] The provider unit provides teachers with test questions generated by the generation unit. The provider unit provides test questions, for example, through an online platform. Specifically, the provider unit provides an environment where teachers can review, modify, and download generated test questions through a web-based dashboard or a dedicated application. The provider unit can also provide an interface for teachers to modify test questions. For example, it provides editing tools for teachers to modify question text and answer choices, or add new questions. The editing tools are intuitive and designed to allow teachers to easily make corrections. Before submitting the modified test questions to the grading unit, the provider unit performs another quality check to ensure the modifications are appropriate. Furthermore, the provider unit provides a function to download generated test questions in PDF or Word format, allowing teachers to print and use them. If the test is administered online, the provider unit publishes the test questions on an online platform for student access. This allows the provider unit to quickly and appropriately provide teachers with generated test questions and an environment where teachers can modify and customize them as needed.

[0033] The grading department grades the test questions provided by the test provider. For example, the grading department grades the test questions based on example answers. Specifically, the grading department can use AI to perform accurate grading. The AI ​​uses natural language processing and image recognition technologies to analyze students' answers and grade them by comparing them to example answers. For example, in written response questions, it analyzes students' answers and evaluates whether keywords and important points are included. In multiple-choice questions, it compares the correct answer with the student's answer to determine correctness. Furthermore, the grading department can perform flexible grading, taking into account the distribution of partial credit and the diversity of answers. For example, in written response questions, partial credit is given to answers that are partially correct, even if they are not completely correct. Also, if there are multiple correct answers, appropriate points are assigned to each correct answer. The grading department provides the grading results to teachers. Teachers can access the grading results through a web-based dashboard or a dedicated application. Based on the grading results, teachers can evaluate students' understanding and learning progress and provide feedback as needed. This allows the grading department to accurately and efficiently grade test questions and provide teachers with the grading results quickly.

[0034] The generation unit can generate test questions based on past test data and textbook content. For example, the generation unit can analyze past test data and generate tests that include frequently appearing questions. The generation unit can also generate tests that include important concepts based on textbook content. The generation unit can combine past test data and textbook content to generate well-balanced tests. This allows for the generation of high-quality test questions based on past test data and textbook content. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past test data and textbook content into a generation AI, which can then generate test questions.

[0035] The scoring unit can score test questions based on example answers. For example, the scoring unit can learn the patterns of correct and incorrect answers based on model answers and optimize its scoring algorithm. The scoring unit can also optimize an algorithm for appropriately distributing partial credit based on example answers. The scoring unit can also optimize a flexible scoring algorithm that allows for different answer methods based on example answers. This makes accurate scoring possible by using example answers. Some or all of the above processes in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input example answers into AI, and the AI ​​can perform the scoring.

[0036] The distribution unit provides the generated test questions to the teacher and can make corrections as needed. The distribution unit provides the test questions, for example, through an online platform. The distribution unit can also provide an interface for teachers to correct the test questions. The distribution unit passes the corrected test questions to the grading unit. This allows for flexibility as teachers can correct the test questions. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the generated test questions into an AI, which can then suggest corrections.

[0037] The reception desk can analyze a teacher's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the content and scope of tests that the teacher has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the teacher has used in the past. The reception desk can also predict and suggest the content and scope of tests to be used at a specific time period based on the teacher's past input history. This improves the efficiency of input work by suggesting the optimal input method based on past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the teacher's past input history into AI, and the AI ​​can suggest the optimal input method.

[0038] The reception system can filter the input of test content and scope based on the teacher's current curriculum progress. For example, the reception system can automatically suggest relevant test content and scope based on the teacher's current curriculum progress. The reception system can also filter appropriate test content and scope according to the teacher's curriculum progress. The reception system can also prioritize displaying test content and scope related to a specific unit based on the teacher's curriculum progress. This allows for the suggestion of appropriate test content by filtering based on curriculum progress. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the teacher's curriculum progress into AI, which can then suggest appropriate test content.

[0039] The reception system can prioritize inputting test content and scope based on the teacher's geographical location. For example, if a teacher is in a specific region, the reception system will prioritize displaying test content and scope relevant to that region. If a teacher is in a specific school, the reception system can also prioritize displaying test content and scope relevant to that school's curriculum. The reception system can also prioritize displaying test content and scope relevant to a teacher based on the educational policies of a specific region. This allows for the provision of appropriate test content by prioritizing input of highly relevant content based on geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the teacher's geographical location information into the AI, which can then suggest highly relevant content.

[0040] The reception desk can analyze teachers' social media activity and suggest relevant content when inputting test content and scope. For example, the reception desk can suggest relevant test content and scope based on educational resources shared by teachers on social media. It can also suggest relevant test content and scope based on posts from educational experts that teachers follow on social media. It can also suggest relevant test content and scope based on topics in educational communities that teachers participate in on social media. This allows for the provision of appropriate test content by suggesting relevant content based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input teachers' social media activity data into AI, which can then suggest relevant content.

[0041] The generation unit can optimize its generation algorithm based on past test data and textbook content during the generation process. For example, the generation unit can analyze past test data and generate tests containing frequently appearing questions. The generation unit can also generate tests containing important concepts based on textbook content. The generation unit can combine past test data and textbook content to generate well-balanced tests. This allows for the generation of high-quality test questions by optimizing the generation algorithm based on past test data and textbook content. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past test data and textbook content into AI, which can then optimize the generation algorithm.

[0042] The generation unit can apply different generation algorithms during generation according to specific educational objectives. For example, the generation unit can apply an algorithm to generate tests aimed at confirming basic knowledge. It can also apply an algorithm to generate tests to assess applied skills. It can also apply an algorithm to generate tests to evaluate creativity. In this way, by applying a generation algorithm according to specific educational objectives, it is possible to generate test questions that are appropriate for the purpose. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input specific educational objectives into AI, and the AI ​​can apply an appropriate generation algorithm.

[0043] The generation unit can determine the priority of questions to generate based on the timing of the test. For example, during final exam periods, the generation unit can generate tests containing many comprehensive questions. During midterm exam periods, the generation unit can also generate tests containing many questions focused on specific units. During quiz periods, the generation unit can also generate tests containing many questions that can be solved in a short time. By prioritizing questions based on the timing of the test, appropriate test questions can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of the test into the AI, and the AI ​​can determine the priority of questions.

[0044] The generation unit can adjust the order in which it generates questions based on the relevance of the test during the generation process. For example, it can generate questions in order from basic to applied levels. It can also generate questions related to a specific unit consecutively. It can also generate questions in order from easiest to hardest level. By adjusting the order of questions based on the relevance of the test, it is possible to generate appropriate test questions. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the relevance of the test into the AI, which can then adjust the order of the questions.

[0045] The delivery unit can select the optimal delivery method by referring to the teacher's past revision history at the time of delivery. For example, the delivery unit can suggest the optimal delivery method based on the content the teacher has previously revised. The delivery unit can also prioritize displaying items that are frequently revised from the teacher's revision history. The delivery unit can also analyze the teacher's revision history and prioritize displaying items that are revised infrequently. This enables efficient revision by selecting the optimal delivery method based on past revision history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the teacher's past revision history into AI, and the AI ​​can select the optimal delivery method.

[0046] The providing unit can apply different providing algorithms depending on the category of the test questions at the time of provision. For example, the providing unit can apply an algorithm to provide test questions aimed at confirming basic knowledge. The providing unit can also apply an algorithm to provide test questions to test applied skills. The providing unit can also apply an algorithm to provide test questions to evaluate creativity. In this way, appropriate test questions can be provided by applying a providing algorithm according to the category of the test questions. Some or all of the above processing in the providing unit may be performed using AI, for example, or without using AI. For example, the providing unit can input the category of the test questions into the AI, and the AI ​​can apply an appropriate providing algorithm.

[0047] The distribution unit can determine the priority of test question distribution based on the submission timing. For example, during final exam periods, the distribution unit may prioritize providing comprehensive test questions. During midterm exam periods, the distribution unit may also prioritize providing test questions focused on specific units. During quiz periods, the distribution unit may also prioritize providing test questions that can be solved in a short amount of time. By determining the priority of distribution based on submission timing, test questions can be provided at the appropriate time. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input the submission timing of test questions into the AI, and the AI ​​can determine the priority of distribution.

[0048] The delivery unit can adjust the order in which test questions are delivered based on their relevance. For example, the delivery unit can deliver test questions sequentially from basic to advanced levels. The delivery unit can also deliver test questions related to a specific unit consecutively. The delivery unit can also deliver test questions sequentially from easier to harder levels. By adjusting the order of delivery based on the relevance of the test questions, the test questions can be delivered in an appropriate order. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of the test questions into the AI, and the AI ​​can adjust the order of delivery.

[0049] The scoring unit can optimize its scoring algorithm based on example answers during scoring. For example, the scoring unit can learn patterns of correct and incorrect answers based on example answers and optimize its scoring algorithm. The scoring unit can also optimize an algorithm for appropriately distributing partial credit based on example answers. The scoring unit can also optimize a flexible scoring algorithm that allows for different answer methods based on example answers. By optimizing the scoring algorithm based on example answers, accurate scoring becomes possible. Some or all of the above processes in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input example answers into AI, and the AI ​​can optimize the scoring algorithm.

[0050] The scoring unit can apply different scoring algorithms during scoring, depending on the specific educational objectives. For example, the scoring unit can apply a scoring algorithm to a test aimed at confirming basic knowledge. It can also apply a scoring algorithm to a test aimed at testing applied skills. It can also apply a scoring algorithm to a test aimed at evaluating creativity. This allows for scoring that is appropriate to the purpose by applying a scoring algorithm that is tailored to the specific educational objectives. Some or all of the above processes in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input specific educational objectives into AI, and the AI ​​can apply an appropriate scoring algorithm.

[0051] The grading department can determine the grading priority based on when the tests were administered. For example, the grading department might prioritize grading final exams. It could also prioritize grading midterm exams. It could also prioritize grading quizzes. By determining the grading priority based on when the tests were administered, grading can be done at the appropriate time. Some or all of the above processes in the grading department may be performed using AI, for example, or not. For example, the grading department could input the test administration dates into the AI, which could then determine the grading priority.

[0052] The scoring unit can adjust the scoring order based on the relevance of the test questions during the scoring process. For example, the scoring unit may score questions sequentially from basic to applied problems. The scoring unit may also score questions related to a specific unit consecutively. The scoring unit may also score questions sequentially from easier to harder problems. By adjusting the scoring order based on the relevance of the test questions, efficient scoring becomes possible. Some or all of the above processes in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit may input the relevance of the test questions into the AI, which can then adjust the scoring order.

[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 reception desk can analyze a teacher's past input history and suggest the most suitable input method. For example, it can automatically display as suggestions the content and scope of tests that the teacher has frequently entered in the past. Furthermore, it can prioritize suggesting input methods that the teacher has used in the past (voice, text, etc.). In addition, it can predict and suggest the content and scope of tests to be used at specific time slots based on the teacher's past input history. This improves the efficiency of input work by suggesting the most suitable input method based on past input history.

[0055] The reception system can filter test content and scope input based on the teacher's current curriculum progress. For example, it can automatically suggest relevant test content and scope based on the teacher's current curriculum progress. Furthermore, teachers can filter appropriate test content and scope according to their curriculum progress. In addition, teachers can prioritize displaying test content and scope related to specific units based on their curriculum progress. This allows for the suggestion of appropriate test content by filtering based on curriculum progress.

[0056] The reception system can prioritize inputting test content and scope based on the teacher's geographical location. For example, if a teacher is in a specific region, test content and scope related to that region can be displayed preferentially. Furthermore, if a teacher is at a specific school, test content and scope related to that school's curriculum can be displayed preferentially. In addition, test content and scope relevant to the teacher's specific region's educational policies can be displayed preferentially. This allows for the provision of appropriate test content by prioritizing input of highly relevant content based on geographical location.

[0057] The delivery unit can select the optimal delivery method by referring to the teacher's past revision history at the time of delivery. For example, it can suggest the optimal delivery method based on the content the teacher has previously revised. Furthermore, it can prioritize displaying items that are frequently revised based on the teacher's revision history. It can also analyze the teacher's revision history and prioritize displaying items that are revised infrequently. This enables efficient revisions by selecting the optimal delivery method based on past revision history.

[0058] The delivery unit can apply different delivery algorithms depending on the category of the test questions at the time of delivery. For example, it can apply an algorithm to provide test questions aimed at confirming basic knowledge. Furthermore, it can apply an algorithm to provide test questions to test applied skills. Furthermore, it can apply an algorithm to provide test questions to evaluate creativity. In this way, by applying a delivery algorithm according to the category of the test questions, appropriate test questions can be provided.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception unit receives input regarding the test content and scope. For example, it provides an interface for teachers to input the test content and scope, saves the entered information in a database, and passes it to the generation unit. Step 2: The generation unit analyzes the information received by the reception unit and generates test questions. For example, it analyzes the input information using natural language processing technology and generates appropriate test questions based on past test data and textbook content. Step 3: The supply unit provides the test questions generated by the generation unit. For example, the test questions can be provided through an online platform, and an interface can be provided for teachers to correct the test questions. The corrected test questions are then passed to the grading unit. Step 4: The grading department grades the test questions provided by the provider department. For example, it grades the test questions based on sample answers and uses AI to ensure accurate grading. The grading results are provided to the teacher.

[0061] (Example of form 2) The automated test generation system according to an embodiment of the present invention is a mechanism that eliminates overtime in educational settings by automatically generating various tests. In this automated test generation system, teachers input the content and scope of the test, and the AI ​​analyzes the input information to automatically generate test questions. The generated test questions are provided to the teacher, who can make revisions as needed. Finally, the revised test questions are distributed to students, the AI ​​automatically grades them, and the results are provided to the teacher. This mechanism significantly reduces overtime in educational settings, allowing teachers to concentrate on other educational activities. Furthermore, because the AI ​​automatically generates the test questions, the quality of the questions is kept consistent, enabling fair evaluation. For example, when a teacher inputs the content and scope of the test, the reception unit receives that information. Next, the generation unit analyzes the information received by the reception unit and generates test questions. The generated test questions are provided to the teacher by the provision unit, and the teacher can make revisions as needed. Finally, the revised test questions are distributed to students, the grading unit automatically grades them, and the results are provided to the teacher. This significantly reduces overtime in educational settings, allowing teachers to concentrate on other educational activities. Furthermore, because the AI ​​automatically generates test questions, the quality of the questions remains consistent, enabling fair evaluation. As a result, the automated test generation system reduces overtime in schools, allowing teachers to focus on other educational activities.

[0062] The automated test generation system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a scoring unit. The reception unit receives input of the test content and scope. The reception unit provides, for example, an interface for teachers to input the test content and scope. The reception unit stores the information entered by the teacher in a database and passes it to the generation unit. The generation unit analyzes the information received by the reception unit and generates test questions. The generation unit analyzes the input information and generates appropriate test questions, for example, using natural language processing technology. The generation unit can also generate test questions based on past test data or textbook content. The generation unit passes the generated test questions to the provision unit. The provision unit provides the test questions generated by the generation unit to teachers. The provision unit provides the test questions, for example, through an online platform. The provision unit can also provide an interface for teachers to correct the test questions. The provision unit passes the corrected test questions to the scoring unit. The scoring unit scores the test questions provided by the provision unit. The scoring unit scores the test questions, for example, based on example answers. The scoring unit can perform accurate scoring using AI. The scoring unit provides the scoring results to the teacher. As a result, the automated test generation system according to this embodiment can reduce overtime in educational settings by automating everything from inputting the test content and scope to generation, provision, and scoring.

[0063] The reception department accepts input regarding the content and scope of the test. For example, the reception department provides an interface for teachers to input the test content and scope. Specifically, the reception department allows teachers to easily input the test content and scope through web-based forms or dedicated applications. The forms include items such as the test subject, target grade level, question format (multiple choice, written, etc.), scope, and difficulty level. Teachers can provide detailed test information by selecting or entering these items. Furthermore, the reception department has a function to verify the entered information in real time and provide immediate feedback if there is missing information or inappropriate input. For example, if the scope is too broad or the difficulty level is unbalanced, a warning message will be displayed to prompt correction. The reception department stores the information entered by teachers in a database and passes it to the generation department. The database is secure and protected to prevent the leakage of entered information. This allows the reception department to provide an environment where teachers can easily and accurately input the test content and scope, and to ensure smooth information transmission to the generation department.

[0064] The generation unit analyzes the information received by the reception unit and generates test questions. For example, the generation unit uses natural language processing technology to analyze the input information and generate appropriate test questions. Specifically, the generation unit extracts relevant keywords and topics based on the test content and scope entered by the teacher, and generates question texts based on these. The generation unit can also generate test questions based on past test data and textbook content. For example, it searches for similar questions in the past test database and creates new questions based on them. It also analyzes textbook content, extracts important points and frequently appearing topics, and generates question texts. Before handing over the generated test questions to the delivery unit, the generation unit performs internal quality checks. For example, it checks the grammar and expression of the question texts, and checks for inappropriate expressions or typos. It also evaluates the difficulty and balance of the questions and adjusts them to achieve an appropriate overall difficulty level. This allows the generation unit to automatically generate high-quality, appropriate test questions based on the information entered by the teacher.

[0065] The provider unit provides teachers with test questions generated by the generation unit. The provider unit provides test questions, for example, through an online platform. Specifically, the provider unit provides an environment where teachers can review, modify, and download generated test questions through a web-based dashboard or a dedicated application. The provider unit can also provide an interface for teachers to modify test questions. For example, it provides editing tools for teachers to modify question text and answer choices, or add new questions. The editing tools are intuitive and designed to allow teachers to easily make corrections. Before submitting the modified test questions to the grading unit, the provider unit performs another quality check to ensure the modifications are appropriate. Furthermore, the provider unit provides a function to download generated test questions in PDF or Word format, allowing teachers to print and use them. If the test is administered online, the provider unit publishes the test questions on an online platform for student access. This allows the provider unit to quickly and appropriately provide teachers with generated test questions and an environment where teachers can modify and customize them as needed.

[0066] The grading department grades the test questions provided by the test provider. For example, the grading department grades the test questions based on example answers. Specifically, the grading department can use AI to perform accurate grading. The AI ​​uses natural language processing and image recognition technologies to analyze students' answers and grade them by comparing them to example answers. For example, in written response questions, it analyzes students' answers and evaluates whether keywords and important points are included. In multiple-choice questions, it compares the correct answer with the student's answer to determine correctness. Furthermore, the grading department can perform flexible grading, taking into account the distribution of partial credit and the diversity of answers. For example, in written response questions, partial credit is given to answers that are partially correct, even if they are not completely correct. Also, if there are multiple correct answers, appropriate points are assigned to each correct answer. The grading department provides the grading results to teachers. Teachers can access the grading results through a web-based dashboard or a dedicated application. Based on the grading results, teachers can evaluate students' understanding and learning progress and provide feedback as needed. This allows the grading department to accurately and efficiently grade test questions and provide teachers with the grading results quickly.

[0067] The generation unit can generate test questions based on past test data and textbook content. For example, the generation unit can analyze past test data and generate tests that include frequently appearing questions. The generation unit can also generate tests that include important concepts based on textbook content. The generation unit can combine past test data and textbook content to generate well-balanced tests. This allows for the generation of high-quality test questions based on past test data and textbook content. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input past test data and textbook content into a generation AI, which can then generate test questions.

[0068] The scoring unit can score test questions based on example answers. For example, the scoring unit can learn the patterns of correct and incorrect answers based on model answers and optimize its scoring algorithm. The scoring unit can also optimize an algorithm for appropriately distributing partial credit based on example answers. The scoring unit can also optimize a flexible scoring algorithm that allows for different answer methods based on example answers. This makes accurate scoring possible by using example answers. Some or all of the above processes in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input example answers into AI, and the AI ​​can perform the scoring.

[0069] The distribution unit provides the generated test questions to the teacher and can make corrections as needed. The distribution unit provides the test questions, for example, through an online platform. The distribution unit can also provide an interface for teachers to correct the test questions. The distribution unit passes the corrected test questions to the grading unit. This allows for flexibility as teachers can correct the test questions. Some or all of the above processes in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input the generated test questions into an AI, which can then suggest corrections.

[0070] The reception desk can estimate the teacher's emotions and adjust the display of the input interface based on the estimated emotions. For example, if the teacher is stressed, the reception desk can provide a simple interface and minimize the input steps. If the teacher is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the teacher is in a hurry, the reception desk can prioritize voice input, allowing for quick input of test content and scope. This reduces the burden of input work by providing an interface that responds to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input teacher emotion data into an AI, which can estimate the emotions and adjust the interface.

[0071] The reception desk can analyze a teacher's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the content and scope of tests that the teacher has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the teacher has used in the past. The reception desk can also predict and suggest the content and scope of tests to be used at a specific time period based on the teacher's past input history. This improves the efficiency of input work by suggesting the optimal input method based on past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the teacher's past input history into AI, and the AI ​​can suggest the optimal input method.

[0072] The reception system can filter the input of test content and scope based on the teacher's current curriculum progress. For example, the reception system can automatically suggest relevant test content and scope based on the teacher's current curriculum progress. The reception system can also filter appropriate test content and scope according to the teacher's curriculum progress. The reception system can also prioritize displaying test content and scope related to a specific unit based on the teacher's curriculum progress. This allows for the suggestion of appropriate test content by filtering based on curriculum progress. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the teacher's curriculum progress into AI, which can then suggest appropriate test content.

[0073] The reception desk can estimate the teacher's emotions and prioritize input content based on the estimated emotions. For example, if the teacher is stressed, the reception desk may prioritize displaying important input items and simplify the input process. If the teacher is relaxed, the reception desk may also provide detailed input options and suggest customizable input methods. If the teacher is in a hurry, the reception desk may prioritize voice input, allowing for quick input of test content and scope. This enables efficient input by prioritizing input content according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input teacher emotion data into an AI, which can estimate emotions and determine the priority of input content.

[0074] The reception system can prioritize inputting test content and scope based on the teacher's geographical location. For example, if a teacher is in a specific region, the reception system will prioritize displaying test content and scope relevant to that region. If a teacher is in a specific school, the reception system can also prioritize displaying test content and scope relevant to that school's curriculum. The reception system can also prioritize displaying test content and scope relevant to a teacher based on the educational policies of a specific region. This allows for the provision of appropriate test content by prioritizing input of highly relevant content based on geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the teacher's geographical location information into the AI, which can then suggest highly relevant content.

[0075] The reception desk can analyze teachers' social media activity and suggest relevant content when inputting test content and scope. For example, the reception desk can suggest relevant test content and scope based on educational resources shared by teachers on social media. It can also suggest relevant test content and scope based on posts from educational experts that teachers follow on social media. It can also suggest relevant test content and scope based on topics in educational communities that teachers participate in on social media. This allows for the provision of appropriate test content by suggesting relevant content based on social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input teachers' social media activity data into AI, which can then suggest relevant content.

[0076] The generation unit can estimate the teacher's emotions and adjust the difficulty level of the test questions it generates based on the estimated emotions. For example, if the teacher is stressed, the generation unit can generate a test with many easy questions. If the teacher is relaxed, the generation unit can also generate a test with many difficult questions. If the teacher is in a hurry, the generation unit can also generate a test with many questions that can be solved in a short time. In this way, appropriate test questions can be generated by adjusting the difficulty level of the test questions according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input teacher emotion data into an AI, the AI ​​can estimate the emotions, and adjust the difficulty level of the test questions.

[0077] The generation unit can optimize its generation algorithm based on past test data and textbook content during the generation process. For example, the generation unit can analyze past test data and generate tests containing frequently appearing questions. The generation unit can also generate tests containing important concepts based on textbook content. The generation unit can combine past test data and textbook content to generate well-balanced tests. This allows for the generation of high-quality test questions by optimizing the generation algorithm based on past test data and textbook content. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past test data and textbook content into AI, which can then optimize the generation algorithm.

[0078] The generation unit can apply different generation algorithms during generation according to specific educational objectives. For example, the generation unit can apply an algorithm to generate tests aimed at confirming basic knowledge. It can also apply an algorithm to generate tests to assess applied skills. It can also apply an algorithm to generate tests to evaluate creativity. In this way, by applying a generation algorithm according to specific educational objectives, it is possible to generate test questions that are appropriate for the purpose. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input specific educational objectives into AI, and the AI ​​can apply an appropriate generation algorithm.

[0079] The generation unit can estimate the teacher's emotions and adjust the format of the test questions it generates based on the estimated emotions. For example, if the teacher is stressed, the generation unit can generate a test with many multiple-choice questions. If the teacher is relaxed, the generation unit can also generate a test with many open-ended questions. If the teacher is in a hurry, the generation unit can also generate a test with many questions that can be solved in a short amount of time. In this way, appropriate test questions can be generated by adjusting the format of the test questions according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input teacher emotion data into an AI, which can estimate the emotions and adjust the format of the test questions.

[0080] The generation unit can determine the priority of questions to generate based on the timing of the test. For example, during final exam periods, the generation unit can generate tests containing many comprehensive questions. During midterm exam periods, the generation unit can also generate tests containing many questions focused on specific units. During quiz periods, the generation unit can also generate tests containing many questions that can be solved in a short time. By prioritizing questions based on the timing of the test, appropriate test questions can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the timing of the test into the AI, and the AI ​​can determine the priority of questions.

[0081] The generation unit can adjust the order in which it generates questions based on the relevance of the test during the generation process. For example, it can generate questions in order from basic to applied levels. It can also generate questions related to a specific unit consecutively. It can also generate questions in order from easiest to hardest level. By adjusting the order of questions based on the relevance of the test, it is possible to generate appropriate test questions. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the relevance of the test into the AI, which can then adjust the order of the questions.

[0082] The service provider can estimate the teacher's emotions and adjust the display method of the test questions based on the estimated emotions. For example, if the teacher is stressed, the service provider can provide a simple display method to reduce visual burden. If the teacher is relaxed, the service provider can also provide a display method that includes detailed information. If the teacher is in a hurry, the service provider can also provide a concise display method. This reduces visual burden by providing a display method that is appropriate to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input teacher emotion data into an AI, which can estimate the emotions and adjust the display method.

[0083] The delivery unit can select the optimal delivery method by referring to the teacher's past revision history at the time of delivery. For example, the delivery unit can suggest the optimal delivery method based on the content the teacher has previously revised. The delivery unit can also prioritize displaying items that are frequently revised from the teacher's revision history. The delivery unit can also analyze the teacher's revision history and prioritize displaying items that are revised infrequently. This enables efficient revision by selecting the optimal delivery method based on past revision history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the teacher's past revision history into AI, and the AI ​​can select the optimal delivery method.

[0084] The providing unit can apply different providing algorithms depending on the category of the test questions at the time of provision. For example, the providing unit can apply an algorithm to provide test questions aimed at confirming basic knowledge. The providing unit can also apply an algorithm to provide test questions to test applied skills. The providing unit can also apply an algorithm to provide test questions to evaluate creativity. In this way, appropriate test questions can be provided by applying a providing algorithm according to the category of the test questions. Some or all of the above processing in the providing unit may be performed using AI, for example, or without using AI. For example, the providing unit can input the category of the test questions into the AI, and the AI ​​can apply an appropriate providing algorithm.

[0085] The delivery unit can estimate the teacher's emotions and determine the priority of test questions to deliver based on the estimated emotions. For example, if the teacher is stressed, the delivery unit will prioritize displaying important test questions. If the teacher is relaxed, the delivery unit may also prioritize displaying test questions containing detailed information. If the teacher is in a hurry, the delivery unit may also prioritize displaying test questions that get straight to the point. This enables efficient delivery by prioritizing test questions according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input teacher emotion data into an AI, which can estimate the emotions and determine the priority of test questions.

[0086] The distribution unit can determine the priority of test question distribution based on the submission timing. For example, during final exam periods, the distribution unit may prioritize providing comprehensive test questions. During midterm exam periods, the distribution unit may also prioritize providing test questions focused on specific units. During quiz periods, the distribution unit may also prioritize providing test questions that can be solved in a short amount of time. By determining the priority of distribution based on submission timing, test questions can be provided at the appropriate time. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input the submission timing of test questions into the AI, and the AI ​​can determine the priority of distribution.

[0087] The delivery unit can adjust the order in which test questions are delivered based on their relevance. For example, the delivery unit can deliver test questions sequentially from basic to advanced levels. The delivery unit can also deliver test questions related to a specific unit consecutively. The delivery unit can also deliver test questions sequentially from easier to harder levels. By adjusting the order of delivery based on the relevance of the test questions, the test questions can be delivered in an appropriate order. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the relevance of the test questions into the AI, and the AI ​​can adjust the order of delivery.

[0088] The grading unit can estimate the teacher's emotions and adjust the grading criteria based on the estimated emotions. For example, if the teacher is stressed, the grading unit may relax strict grading criteria. If the teacher is relaxed, the grading unit may also apply strict grading criteria. If the teacher is in a hurry, the grading unit may also apply criteria that allow for quick grading. This allows for appropriate grading by adjusting the grading criteria according to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the grading unit may be performed using AI or not using AI. For example, the grading unit can input teacher emotion data into an AI, which can estimate the emotions and adjust the grading criteria.

[0089] The scoring unit can optimize its scoring algorithm based on example answers during scoring. For example, the scoring unit can learn patterns of correct and incorrect answers based on example answers and optimize its scoring algorithm. The scoring unit can also optimize an algorithm for appropriately distributing partial credit based on example answers. The scoring unit can also optimize a flexible scoring algorithm that allows for different answer methods based on example answers. By optimizing the scoring algorithm based on example answers, accurate scoring becomes possible. Some or all of the above processes in the scoring unit may be performed using AI, for example, or without AI. For example, the scoring unit can input example answers into AI, and the AI ​​can optimize the scoring algorithm.

[0090] The scoring unit can apply different scoring algorithms during scoring, depending on the specific educational objectives. For example, the scoring unit can apply a scoring algorithm to a test aimed at confirming basic knowledge. It can also apply a scoring algorithm to a test aimed at testing applied skills. It can also apply a scoring algorithm to a test aimed at evaluating creativity. This allows for scoring that is appropriate to the purpose by applying a scoring algorithm that is tailored to the specific educational objectives. Some or all of the above processes in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input specific educational objectives into AI, and the AI ​​can apply an appropriate scoring algorithm.

[0091] The scoring unit can estimate the teacher's emotions and adjust the display method of the scoring results based on the estimated emotions. For example, if the teacher is stressed, the scoring unit can provide a simple and easy-to-read display method. If the teacher is relaxed, the scoring unit can also provide a display method that includes detailed information. If the teacher is in a hurry, the scoring unit can also provide a concise display method. This allows for the provision of highly readable scoring results by providing a display method that is appropriate to the teacher's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit can input teacher emotion data into an AI, which can estimate the emotions and adjust the display method.

[0092] The grading department can determine the grading priority based on when the tests were administered. For example, the grading department might prioritize grading final exams. It could also prioritize grading midterm exams. It could also prioritize grading quizzes. By determining the grading priority based on when the tests were administered, grading can be done at the appropriate time. Some or all of the above processes in the grading department may be performed using AI, for example, or not. For example, the grading department could input the test administration dates into the AI, which could then determine the grading priority.

[0093] The scoring unit can adjust the scoring order based on the relevance of the test questions during the scoring process. For example, the scoring unit may score questions sequentially from basic to applied problems. The scoring unit may also score questions related to a specific unit consecutively. The scoring unit may also score questions sequentially from easier to harder problems. By adjusting the scoring order based on the relevance of the test questions, efficient scoring becomes possible. Some or all of the above processes in the scoring unit may be performed using AI, for example, or not using AI. For example, the scoring unit may input the relevance of the test questions into the AI, which can then adjust the scoring order.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception system can estimate the teacher's emotions and adjust the display of the input interface based on that estimation. For example, if the teacher is stressed, a simple interface can be provided, minimizing the input steps. If the teacher is relaxed, detailed input options can be provided, and customizable input methods can be suggested. Furthermore, if the teacher is in a hurry, voice input can be prioritized, allowing for quick input of test content and scope. This reduces the burden of input work by providing an interface that responds to the teacher's emotions.

[0096] The generation unit can adjust the difficulty level of the test questions it generates based on the teacher's mood. For example, if the teacher is stressed, it can generate a test with many easy questions. If the teacher is relaxed, it can generate a test with many difficult questions. Furthermore, if the teacher is in a hurry, it can generate a test with many questions that can be solved in a short time. In this way, by adjusting the difficulty level of the test questions according to the teacher's mood, appropriate test questions can be generated.

[0097] The system can estimate the teacher's emotions and adjust the display method of the test questions based on those emotions. For example, if the teacher is stressed, a simpler display method can be provided to reduce visual burden. If the teacher is relaxed, a display method including detailed information can be provided. Furthermore, if the teacher is in a hurry, a concise display method can be provided. In this way, by providing a display method that matches the teacher's emotions, visual burden can be reduced.

[0098] The grading system can estimate the teacher's emotions and adjust the grading criteria based on that estimation. For example, if a teacher is stressed, strict grading criteria can be relaxed. If the teacher is relaxed, strict grading criteria can be applied. Furthermore, if the teacher is in a hurry, criteria that allow for quick grading can be applied. This allows for appropriate grading by adjusting the grading criteria according to the teacher's emotions.

[0099] The scoring system can estimate the teacher's emotions and adjust the display method of the scoring results based on the estimated emotions. For example, if the teacher is stressed, a simple and easy-to-read display method can be provided. If the teacher is relaxed, a display method including detailed information can be provided. Furthermore, if the teacher is in a hurry, a display method that focuses on the key points can be provided. In this way, by providing a display method that is tailored to the teacher's emotions, highly visible scoring results can be provided.

[0100] The reception desk can analyze a teacher's past input history and suggest the most suitable input method. For example, it can automatically display as suggestions the content and scope of tests that the teacher has frequently entered in the past. Furthermore, it can prioritize suggesting input methods that the teacher has used in the past (voice, text, etc.). In addition, it can predict and suggest the content and scope of tests to be used at specific time slots based on the teacher's past input history. This improves the efficiency of input work by suggesting the most suitable input method based on past input history.

[0101] The reception system can filter test content and scope input based on the teacher's current curriculum progress. For example, it can automatically suggest relevant test content and scope based on the teacher's current curriculum progress. Furthermore, teachers can filter appropriate test content and scope according to their curriculum progress. In addition, teachers can prioritize displaying test content and scope related to specific units based on their curriculum progress. This allows for the suggestion of appropriate test content by filtering based on curriculum progress.

[0102] The reception system can prioritize inputting test content and scope based on the teacher's geographical location. For example, if a teacher is in a specific region, test content and scope related to that region can be displayed preferentially. Furthermore, if a teacher is at a specific school, test content and scope related to that school's curriculum can be displayed preferentially. In addition, test content and scope relevant to the teacher's specific region's educational policies can be displayed preferentially. This allows for the provision of appropriate test content by prioritizing input of highly relevant content based on geographical location.

[0103] The delivery unit can select the optimal delivery method by referring to the teacher's past revision history at the time of delivery. For example, it can suggest the optimal delivery method based on the content the teacher has previously revised. Furthermore, it can prioritize displaying items that are frequently revised based on the teacher's revision history. It can also analyze the teacher's revision history and prioritize displaying items that are revised infrequently. This enables efficient revisions by selecting the optimal delivery method based on past revision history.

[0104] The delivery unit can apply different delivery algorithms depending on the category of the test questions at the time of delivery. For example, it can apply an algorithm to provide test questions aimed at confirming basic knowledge. Furthermore, it can apply an algorithm to provide test questions to test applied skills. Furthermore, it can apply an algorithm to provide test questions to evaluate creativity. In this way, by applying a delivery algorithm according to the category of the test questions, appropriate test questions can be provided.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception unit receives input regarding the test content and scope. For example, it provides an interface for teachers to input the test content and scope, saves the entered information in a database, and passes it to the generation unit. Step 2: The generation unit analyzes the information received by the reception unit and generates test questions. For example, it analyzes the input information using natural language processing technology and generates appropriate test questions based on past test data and textbook content. Step 3: The supply unit provides the test questions generated by the generation unit. For example, the test questions can be provided through an online platform, and an interface can be provided for teachers to correct the test questions. The corrected test questions are then passed to the grading unit. Step 4: The grading department grades the test questions provided by the provider department. For example, it grades the test questions based on sample answers and uses AI to ensure accurate grading. The grading results are provided to the teacher.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and scoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the teacher to input the content and scope of the test. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates test questions by analyzing the input information using natural language processing technology. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated test questions to the teacher. The scoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and scores the test questions using AI and provides the results to the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and scoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the teacher to input the content and scope of the test by voice. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates test questions by analyzing the input information using natural language processing technology. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated test questions to the teacher. The scoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and scores the test questions using AI and provides the results to the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and scoring 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 microphone 238 of the headset terminal 314 and provides an interface for the teacher to input the content and scope of the test by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates test questions by analyzing the input information using natural language processing technology. The provision unit is implemented by, for example, the display 343 of the headset terminal 314 and provides the generated test questions to the teacher. The scoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and scores the test questions using AI and provides the results to the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and scoring 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 microphone 238 of the robot 414 and provides an interface for the teacher to input the content and scope of the test by voice. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates test questions by analyzing the input information using natural language processing technology. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated test questions to the teacher. The scoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and scores the test questions using AI and provides the results to the teacher. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception desk that accepts input regarding the content and scope of the test, A generation unit analyzes the information received by the reception unit and generates test questions, A providing unit that provides test questions generated by the generation unit, The system includes a scoring unit that scores the test questions provided by the aforementioned supply unit. A system characterized by the following features. (Note 2) The generating unit is Test questions are generated based on past test data and textbook content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The scoring unit is, The test questions will be graded based on the provided answer key. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, The generated test questions can be provided to teachers and modified as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the teacher's emotions and adjusts how the input interface is displayed based on the estimated teacher's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the teacher's past input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering the content and scope of the test, filtering is performed based on the teacher's current curriculum progress. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the teacher's emotions and prioritizes input 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 entering the content and scope of a test, the system prioritizes inputting highly relevant content based on the teacher's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering test content and scope, the system analyzes the teacher's social media activity and suggests relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the teacher's emotions and adjusts the difficulty level of the test questions generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During generation, the generation algorithm is optimized based on past test data and textbook content. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, different generation algorithms are applied according to specific educational objectives. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the teacher's emotions and adjusts the format of the test questions generated based on the estimated teacher's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the priority of the issues to be generated is determined based on when the tests were performed. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, adjust the order of questions generated based on the relevance of the tests. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the teacher's emotions and adjusts how test questions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the materials, the optimal delivery method will be selected by referring to the teacher's past revision history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the test questions, different distribution algorithms are applied depending on the category of the test questions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates the teacher's emotions and prioritizes the test questions to be provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing the materials, we will prioritize their distribution based on when the test questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the test questions, we adjust the order in which they are provided based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The scoring unit is, The system estimates the teacher's emotions and adjusts the grading criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The scoring unit is, During grading, the grading algorithm is optimized based on the example answers. The system described in Appendix 1, characterized by the features described herein. (Note 25) The scoring unit is, When grading, different grading algorithms are applied according to specific educational objectives. The system described in Appendix 1, characterized by the features described herein. (Note 26) The scoring unit is, The system estimates the teacher's emotions and adjusts how the grading results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The scoring unit is, When grading, the grading priority is determined based on when the test was administered. The system described in Appendix 1, characterized by the features described herein. (Note 28) The scoring unit is, During grading, the order of grading will be adjusted based on the relevance of the tests. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input regarding the content and scope of the test, A generation unit analyzes the information received by the reception unit and generates test questions, A providing unit that provides test questions generated by the generation unit, The system includes a scoring unit that scores the test questions provided by the aforementioned supply unit. A system characterized by the following features.

2. The generating unit is Test questions are generated based on past test data and textbook content. The system according to feature 1.

3. The scoring unit is, The test questions will be graded based on the provided answer key. The system according to feature 1.

4. The aforementioned supply unit is, The generated test questions can be provided to teachers and modified as needed. The system according to feature 1.

5. The aforementioned reception unit is It estimates the teacher's emotions and adjusts how the input interface is displayed based on the estimated teacher's emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the teacher's past input history and suggest the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When entering the content and scope of the test, filtering is performed based on the teacher's current curriculum progress. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the teacher's emotions and prioritizes input based on those estimated emotions. The system according to feature 1.

Images