system

Abstract

The system according to this embodiment aims to streamline the creation of examination questions and provide questions of appropriate difficulty levels. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, an adjustment unit, and a provision unit. The collection unit collects information necessary for creating test questions. The generation unit generates test questions based on the information collected by the collection unit. The adjustment unit adjusts the difficulty level of the test questions generated by the generation unit. The provision unit provides the test questions adjusted by the adjustment unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that creating test questions is a heavy burden for teachers and instructors, and it is difficult to create questions with appropriate difficulty levels.

[0005] The system according to the embodiment aims to streamline the creation of test questions and provide questions with appropriate difficulty levels.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, an adjustment unit, and a provision unit. The collection unit collects information necessary for creating test questions. The generation unit generates test questions based on the information collected by the collection unit. The adjustment unit adjusts the difficulty level of the test questions generated by the generation unit. The provision unit provides the test questions adjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the creation of test questions and provide questions of appropriate difficulty levels. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, 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 examination question creation system according to an embodiment of the present invention is a system that uses a generation AI to create examination questions for schools, cram schools, and various qualification examination schools. Creating examination questions is an extremely burdensome task for teachers and instructors. Even when referring to past questions, if the questions are too easy, the average score will be high, and if they are too difficult, many students will fail. By using a generation AI, it is possible to create original examination questions by inputting detailed conditions. For example, by specifying the subject and question format (multiple-choice questions, essay questions, etc.), questions of appropriate difficulty can be generated. It is also possible to extract questions based on the correct answer rate of past questions. This reduces the burden on teachers and instructors and enables the provision of effective examination questions. First, a collection unit is required to collect the information necessary for creating examination questions. Next, a generation unit is required to generate questions based on the collected information. Furthermore, an adjustment unit is required to adjust the difficulty level of the generated questions. The collection unit is responsible for collecting information such as past questions and correct answer rates. The generation unit generates questions based on the collected information. The adjustment unit adjusts the difficulty level of the generated questions. Finally, a provision unit is required to provide the generated questions. The provision unit is responsible for providing the generated questions to teachers and instructors. This allows the exam question creation system to reduce the burden on teachers and instructors and provide effective exam questions.

[0029] The examination question creation system according to the embodiment comprises a collection unit, a generation unit, an adjustment unit, and a provision unit. The collection unit collects information necessary for creating examination questions. The collection unit can collect information such as past examination questions and correct answer rates. For example, the collection unit can retrieve past examination questions from a database and calculate the correct answer rate. The collection unit can also collect feedback from teachers and instructors. For example, the collection unit collects evaluations and suggestions for improvement of examination questions provided by teachers and instructors and reflects them in the creation of the next examination questions. The generation unit generates examination questions based on the information collected by the collection unit. The generation unit can generate examination questions based on the collected information, for example, using a generation AI. For example, the generation unit can specify the subject and question format to generate questions of appropriate difficulty. The generation unit can also extract questions based on the correct answer rates of past questions. For example, the generation unit can prioritize extracting questions with low correct answer rates and generate questions of high difficulty. The adjustment unit adjusts the difficulty level of the examination questions generated by the generation unit. For example, the adjustment unit can use AI to adjust the difficulty level of the generated examination questions. The adjustment unit adjusts the difficulty level of the questions, for example, based on the correct answer rate. The adjustment unit can also adjust the difficulty level by considering the complexity of the questions and the time allotted for answering. The provisioning unit provides the exam questions adjusted by the adjustment unit to teachers and instructors. The provisioning unit can, for example, use AI to provide the generated exam questions to teachers and instructors. The provisioning unit can, for example, provide the generated exam questions through an online platform. The provisioning unit can also print and provide the generated exam questions. This allows the exam question creation system according to this embodiment to efficiently create, adjust the difficulty level of, and provide exam questions.

[0030] The data collection unit collects information necessary for creating exam questions. For example, it can collect information such as past exam questions and correct answer rates. Specifically, the unit accesses the databases of educational institutions to obtain the content of past exam questions and their correct answer rates. This allows for an understanding of the difficulty level of each question. The unit can also collect feedback from teachers and instructors. For example, it collects evaluations and suggestions for improvement from teachers and instructors regarding exam questions and incorporates them into the creation of future exam questions. This improves the quality of exam questions. Furthermore, the unit can collect student learning history and performance data. This provides foundational data for creating questions tailored to students' understanding and learning progress. The unit centrally manages this data and can collaborate with other departments as needed. For example, collected data is stored on a cloud server, making it accessible to the generation and adjustment units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The generation unit generates exam questions based on information collected by the collection unit. For example, the generation unit can use a generation AI to generate exam questions based on the collected information. Specifically, the generation AI uses natural language processing technology to analyze past exam questions and feedback data to generate new exam questions. The generation AI can generate questions of appropriate difficulty by specifying the subject and question format. For example, when generating mathematics exam questions, the generation AI generates questions corresponding to each field, such as algebra, geometry, and calculus. The generation unit can also extract questions based on the correct answer rate of past questions. For example, it can prioritize extracting questions with low correct answer rates and generate more difficult questions. This allows for a more accurate assessment of students' understanding. Furthermore, to increase the variety of questions, the generation unit can generate questions in different formats for the same theme. For example, it can generate not only multiple-choice questions but also written and fill-in-the-blank questions. This ensures diversity in exam questions and allows for the assessment of students' multifaceted understanding. The generation unit centrally manages the generated exam questions and can collaborate with other departments as needed. This allows the generation unit to generate test questions efficiently and effectively, improving the overall performance of the system.

[0032] The adjustment unit adjusts the difficulty level of the test questions generated by the generation unit. For example, the adjustment unit can use AI to adjust the difficulty level of the generated test questions. Specifically, the adjustment AI analyzes data such as the correct answer rate, answer time, and question complexity of the generated test questions and adjusts the difficulty level accordingly. For example, questions with a low correct answer rate are judged to be too difficult and adjusted to an appropriate level. Questions with long answer times can also be adjusted considering their complexity. This allows the adjustment unit to appropriately adjust the difficulty level of the test questions and provide questions that match students' understanding. Furthermore, the adjustment unit can also adjust the overall difficulty level considering the balance of the test questions. For example, it can adjust the difficulty level of each question to maintain balance in the overall test, ensuring that the difficulty level of the test is not uneven. The adjustment unit can also readjust the difficulty level of the test questions based on feedback from teachers and instructors. This allows the adjustment unit to appropriately adjust the difficulty level of the test questions and accurately assess students' understanding. The adjustment unit centrally manages the adjusted test questions and can collaborate with other departments as needed. This allows the adjustment unit to efficiently and effectively adjust the difficulty level of the test questions and improve the overall system performance.

[0033] The provisioning department provides teachers and instructors with exam questions that have been adjusted by the adjustment department. The provisioning department can use AI, for example, to provide teachers and instructors with generated exam questions. Specifically, the provisioning AI provides teachers and instructors with generated exam questions through an online platform. Teachers and instructors can access the online platform to review the generated exam questions and make corrections or additions as needed. The provisioning department can also print and provide the generated exam questions. This allows teachers and instructors to review the exam questions in paper form and distribute them to students. Furthermore, the provisioning department can manage the status of exam question distribution and collaborate with other departments as needed. For example, it can collect usage data and feedback on the provided exam questions and incorporate this into the creation of future exam questions. The provisioning department can also adjust the frequency and format of exam question distribution, enabling flexible responses to specific situations and conditions. This allows the provisioning department to provide exam questions efficiently and effectively, improving the overall performance of the system.

[0034] The data collection unit can collect information such as past exam questions and correct answer rates. For example, the data collection unit can retrieve past exam questions from a database and calculate the correct answer rate. The data collection unit can also collect feedback from teachers and instructors. For example, the data collection unit can collect evaluations and suggestions for improvement of exam questions provided by teachers and instructors and reflect them in the creation of the next exam questions. This helps in creating exam questions by collecting information on past exam questions and correct answer rates. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have AI perform the process of retrieving past exam questions from a database and calculating the correct answer rate.

[0035] The generation unit can generate exam questions based on the collected information. For example, the generation unit uses a generation AI to generate exam questions based on the collected information. The generation unit can, for example, specify subjects and question formats to generate questions of appropriate difficulty. The generation unit can also extract questions based on the correct answer rate of past questions. For example, the generation unit can prioritize extracting questions with low correct answer rates and generate questions of higher difficulty. In this way, by generating exam questions based on the collected information, appropriate questions can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input collected information into the generation AI and have the generation AI execute the generation of exam questions.

[0036] The adjustment unit can adjust the difficulty level of the generated test questions. For example, the adjustment unit can use AI to adjust the difficulty level of the generated test questions. For example, the adjustment unit adjusts the difficulty level of the questions based on the correct answer rate. The adjustment unit can also adjust the difficulty level by considering the complexity of the questions and the time it takes to answer them. In this way, by adjusting the difficulty level of the generated test questions, it is possible to provide questions of an appropriate difficulty level. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can have AI perform the process of adjusting the difficulty level of the generated test questions.

[0037] The service provider can provide the generated test questions to teachers and instructors. For example, the service provider can use AI to provide the generated test questions to teachers and instructors. For example, the service provider can provide the generated test questions through an online platform. Alternatively, the service provider can provide the generated test questions in print. This streamlines test preparation by providing the generated test questions to teachers and instructors. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can have AI perform the process of providing the generated test questions through an online platform.

[0038] The data collection unit can analyze the frequency of past exam questions and prioritize collecting the most frequently asked questions. For example, the data collection unit can analyze exam questions from the past five years and list the most frequently asked questions. For example, the data collection unit can analyze the frequency of questions for each subject and prioritize collecting the most frequently asked questions. For example, the data collection unit can balance the exam questions by prioritizing the collection of frequently asked questions. This allows for the priority collection of frequently asked questions by analyzing past question frequencies. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of analyzing the frequency of past exam questions and prioritizing the collection of frequently asked questions.

[0039] The data collection unit can analyze students' learning histories and collect problems best suited to each individual student. For example, the data collection unit can analyze students' past performance and prioritize collecting problems in areas where students struggle. For example, the data collection unit can collect problems in areas where students excel based on their learning history. For example, the data collection unit can analyze students' learning progress in real time and collect the most suitable problems. In this way, by analyzing students' learning histories, the data collection unit can collect problems best suited to each individual student. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of analyzing students' learning histories and collecting the most suitable problems.

[0040] The data collection unit can collect feedback from teachers and instructors and improve its algorithm. For example, the data collection unit can collect feedback from teachers and instructors and improve its algorithm. For example, the data collection unit can adjust the scope of information collection based on the feedback. For example, the data collection unit can analyze the feedback and optimize its algorithm. In this way, the data collection unit's algorithm can be improved by collecting feedback. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect feedback from teachers and instructors and have AI perform the algorithm improvement.

[0041] The data collection unit can analyze students' learning styles and collect information tailored to those styles. For example, the data collection unit can analyze students' learning styles and collect problems containing many diagrams and charts for visual learners. For example, the data collection unit can analyze students' learning styles and collect audio problems for auditory learners. For example, the data collection unit can analyze students' learning styles and collect problems including experiments and practical exercises for tactile learners. In this way, by analyzing students' learning styles, information tailored to their styles can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of analyzing students' learning styles and collecting information tailored to their styles.

[0042] The generation unit can analyze the question trends for each subject and generate questions based on those trends. For example, the generation unit can analyze past question trends for each subject and generate questions based on those trends. For example, the generation unit can generate well-balanced questions based on the question trends for each subject. For example, the generation unit can analyze question trends and generate questions so as not to be biased towards any particular field. In this way, by analyzing the question trends for each subject, it is possible to generate questions based on those trends. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can have the generation AI perform the process of analyzing the question trends for each subject and generating questions based on those trends.

[0043] The generation unit can evaluate students' understanding and generate problems appropriate to that level. For example, the generation unit can generate problems appropriate to a student's understanding based on their past performance. For example, the generation unit can evaluate students' understanding in real time and generate appropriate problems. For example, the generation unit can analyze students' understanding and generate problems in areas where they struggle. In this way, by evaluating students' understanding, problems appropriate to their level can be generated. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can have the generation AI perform the process of evaluating students' understanding and generating problems appropriate to that understanding.

[0044] The generation unit can generate questions on a specific theme based on instructions from teachers or instructors. For example, the generation unit generates questions on a specific theme based on instructions from teachers or instructors. For example, the generation unit generates related questions based on the instructed theme. For example, the generation unit generates questions aligned with the theme, reflecting the instructions from teachers or instructors. In this way, by generating questions on a specific theme based on the instructions from teachers or instructors, questions aligned with the theme can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can have the generation AI perform the process of generating questions on a specific theme based on instructions from teachers or instructors.

[0045] The generation unit can analyze students' learning progress in real time and generate problems appropriate to that progress. For example, the generation unit can analyze students' learning progress in real time and generate appropriate problems. For example, the generation unit can generate problems of varying difficulty levels according to learning progress. For example, the generation unit can generate well-balanced problems based on students' progress. In this way, by analyzing students' learning progress in real time, problems appropriate to their progress can be generated. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can have the generation AI perform the process of analyzing students' learning progress in real time and generating problems appropriate to that progress.

[0046] The adjustment unit can analyze past test results and adjust the difficulty level of the questions based on those results. For example, the adjustment unit can analyze past test results and increase the difficulty level if the average score is high. For example, the adjustment unit can analyze past test results and decrease the difficulty level if the average score is low. For example, the adjustment unit can analyze the test results and adjust the difficulty level to a balanced level. In this way, by analyzing past test results, the difficulty level of the questions can be adjusted based on those results. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can have AI perform the process of analyzing past test results and adjusting the difficulty level of the questions based on those results.

[0047] The adjustment unit can consider a student's learning history and adjust the difficulty level of problems based on that history. For example, the adjustment unit can adjust the difficulty level of problems in areas where the student struggles, based on the student's learning history. For example, the adjustment unit can consider the student's learning history and adjust the difficulty level of problems in areas where the student excels. For example, the adjustment unit can analyze the student's learning history and adjust the difficulty level to a balanced level. In this way, by considering the student's learning history, the difficulty level of problems can be adjusted based on that history. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can have AI perform the process of considering the student's learning history and adjusting the difficulty level of problems based on that history.

[0048] The adjustment unit can adjust the difficulty level of the problems by reflecting feedback from teachers and instructors. For example, the adjustment unit adjusts the difficulty level of the problems based on feedback from teachers and instructors. For example, the adjustment unit adjusts the difficulty level to a balanced level by reflecting the feedback. For example, the adjustment unit optimizes the difficulty level of the problems by incorporating the opinions of teachers and instructors. In this way, the difficulty level of the problems can be appropriately adjusted by reflecting the feedback from teachers and instructors. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can have AI perform the process of reflecting feedback from teachers and instructors and adjusting the difficulty level of the problems.

[0049] The adjustment unit can take into account students' learning styles and adjust the difficulty level of problems accordingly. For example, the adjustment unit can analyze students' learning styles and provide easy-to-understand diagram problems to visual learners. For example, the adjustment unit can take students' learning styles into account and provide easy-to-understand audio problems to auditory learners. For example, the adjustment unit can provide easy-to-understand experimental problems to tactile learners based on students' learning styles. In this way, by taking students' learning styles into account, the difficulty level of problems can be adjusted according to their style. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can have AI perform the process of taking students' learning styles into account and adjusting the difficulty level of problems accordingly.

[0050] The distribution unit can provide problems at the optimal time, taking into account the schedules of teachers and instructors. For example, the distribution unit can provide problems at the optimal time based on the teacher's or instructor's class schedule. For example, the distribution unit can provide problems before or after class, taking into account the teacher's or instructor's schedule. For example, the distribution unit can analyze the teacher's or instructor's schedule and provide problems during their free time. In this way, by taking into account the teacher's or instructor's schedule, problems can be provided at the optimal time. Some or all of the above processes in the distribution unit may be performed using AI or not. For example, the distribution unit can have AI perform the process of considering the teacher's or instructor's schedule and providing problems at the optimal time.

[0051] The distribution unit can monitor students' learning progress in real time and provide problems according to their progress. For example, the distribution unit can monitor students' learning progress in real time and provide problems at the appropriate time. For example, the distribution unit can provide problems of varying difficulty levels according to learning progress. For example, the distribution unit can provide balanced problems based on students' progress. In this way, by monitoring students' learning progress in real time, problems can be provided according to their progress. Some or all of the above processes in the distribution unit may be performed using AI or not. For example, the distribution unit can have AI perform the process of monitoring students' learning progress in real time and providing problems according to their progress.

[0052] The delivery unit can improve the way problems are delivered by incorporating feedback from teachers and instructors. For example, the delivery unit improves the way problems are delivered based on feedback from teachers and instructors. For example, the delivery unit can improve the way problems are delivered by incorporating feedback. For example, the delivery unit optimizes the delivery method by incorporating the opinions of teachers and instructors. In this way, the way problems are delivered can be improved by incorporating feedback from teachers and instructors. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can have AI perform the process of improving the way problems are delivered by incorporating feedback from teachers and instructors.

[0053] The service provider can consider students' learning styles and provide problems tailored to those styles. For example, the service provider can analyze students' learning styles and provide problems with many diagrams and charts to visual learners. For example, the service provider can consider students' learning styles and provide audio problems to auditory learners. For example, based on students' learning styles, the service provider can provide problems that include experiments and practical exercises to tactile learners. In this way, by considering students' learning styles, problems tailored to their styles can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can have AI perform the process of considering students' learning styles and providing problems tailored to those styles.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The exam question creation system may also include an answer generation unit that generates example answers to the questions. The answer generation unit generates appropriate example answers for the generated exam questions. For example, the answer generation unit can generate a model answer for a generated text-based question. It can also show the correct answer option for multiple-choice questions. Furthermore, the answer generation unit can generate detailed example answers that include the reasoning and explanations for the answers. This makes exam preparation even more efficient for teachers and instructors, as they are provided not only with exam questions but also with example answers.

[0056] The exam question creation system may also include a frequency adjustment unit that adjusts the frequency of questions asked. The frequency adjustment unit analyzes the frequency of past exam questions and adjusts them to prevent certain questions from appearing excessively. For example, the frequency adjustment unit analyzes exam questions from the past five years and lists the most frequently asked questions. Furthermore, the frequency adjustment unit can analyze the frequency of questions for each subject and provide a balanced set of questions. In addition, the frequency adjustment unit can prioritize the collection of frequently asked questions to balance the exam questions. This allows for the prioritization of high-frequency questions by analyzing past question frequencies.

[0057] The test question creation system may also include a sequence adjustment unit that adjusts the order in which the questions are presented. The sequence adjustment unit adjusts the order in which the generated test questions are presented, reducing the burden on the test-taker. For example, the sequence adjustment unit can adjust the order from easy questions to difficult questions. It can also present questions in a balanced order to maintain the test-taker's concentration. Furthermore, the sequence adjustment unit can adjust the order of questions based on a specific theme. This reduces the burden on the test-taker and enables the implementation of an effective examination.

[0058] The test question creation system may further include a question time estimation unit that estimates the time required to answer each question. The question time estimation unit estimates an appropriate time for each generated test question. For example, the unit can estimate the time based on the difficulty and complexity of the question. It can also estimate the time based on past test data. Furthermore, the unit can adjust the time according to the test-taker's answering speed. This ensures that test-takers are given an appropriate time to answer each question and guarantees fairness in the examination.

[0059] The exam question creation system may further include an answer method provider unit that provides methods for answering the questions. This unit provides appropriate answer methods for the generated exam questions. For example, it can provide methods for selecting the correct option in multiple-choice questions. It can also provide effective ways of writing answers to essay questions. Furthermore, it can provide the knowledge and skills necessary to answer the questions. This allows test-takers to learn appropriate answer methods and prepare effectively for the exam.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit gathers the information necessary to create the exam questions. For example, it retrieves information such as past exam questions and correct answer rates from a database and calculates the correct answer rate. It also collects feedback from teachers and instructors and incorporates evaluations and areas for improvement of the exam questions into the creation of the next exam questions. Step 2: The generation unit generates exam questions based on the information collected by the collection unit. For example, it uses a generation AI to specify subjects and question formats and generates questions of appropriate difficulty. It also extracts questions based on the correct answer rate of past questions, prioritizing questions with low correct answer rates to generate more difficult questions. Step 3: The adjustment unit adjusts the difficulty level of the test questions generated by the generation unit. For example, it may use AI to adjust the difficulty level based on the correct answer rate, or it may adjust the difficulty level considering the complexity of the questions and the time allotted for answering. Step 4: The provisioning department provides the exam questions, which have been adjusted by the adjustment department, to teachers and instructors. For example, the generated exam questions may be provided through an online platform or in printed form.

[0062] (Example of form 2) The examination question creation system according to an embodiment of the present invention is a system that uses a generation AI to create examination questions for schools, cram schools, and various qualification examination schools. Creating examination questions is an extremely burdensome task for teachers and instructors. Even when referring to past questions, if the questions are too easy, the average score will be high, and if they are too difficult, many students will fail. By using a generation AI, it is possible to create original examination questions by inputting detailed conditions. For example, by specifying the subject and question format (multiple-choice questions, essay questions, etc.), questions of appropriate difficulty can be generated. It is also possible to extract questions based on the correct answer rate of past questions. This reduces the burden on teachers and instructors and enables the provision of effective examination questions. First, a collection unit is required to collect the information necessary for creating examination questions. Next, a generation unit is required to generate questions based on the collected information. Furthermore, an adjustment unit is required to adjust the difficulty level of the generated questions. The collection unit is responsible for collecting information such as past questions and correct answer rates. The generation unit generates questions based on the collected information. The adjustment unit adjusts the difficulty level of the generated questions. Finally, a provision unit is required to provide the generated questions. The provision unit is responsible for providing the generated questions to teachers and instructors. This allows the exam question creation system to reduce the burden on teachers and instructors and provide effective exam questions.

[0063] The examination question creation system according to the embodiment comprises a collection unit, a generation unit, an adjustment unit, and a provision unit. The collection unit collects information necessary for creating examination questions. The collection unit can collect information such as past examination questions and correct answer rates. For example, the collection unit can retrieve past examination questions from a database and calculate the correct answer rate. The collection unit can also collect feedback from teachers and instructors. For example, the collection unit collects evaluations and suggestions for improvement of examination questions provided by teachers and instructors and reflects them in the creation of the next examination questions. The generation unit generates examination questions based on the information collected by the collection unit. The generation unit can generate examination questions based on the collected information, for example, using a generation AI. For example, the generation unit can specify the subject and question format to generate questions of appropriate difficulty. The generation unit can also extract questions based on the correct answer rates of past questions. For example, the generation unit can prioritize extracting questions with low correct answer rates and generate questions of high difficulty. The adjustment unit adjusts the difficulty level of the examination questions generated by the generation unit. For example, the adjustment unit can use AI to adjust the difficulty level of the generated examination questions. The adjustment unit adjusts the difficulty level of the questions, for example, based on the correct answer rate. The adjustment unit can also adjust the difficulty level by considering the complexity of the questions and the time allotted for answering. The provisioning unit provides the exam questions adjusted by the adjustment unit to teachers and instructors. The provisioning unit can, for example, use AI to provide the generated exam questions to teachers and instructors. The provisioning unit can, for example, provide the generated exam questions through an online platform. The provisioning unit can also print and provide the generated exam questions. This allows the exam question creation system according to this embodiment to efficiently create, adjust the difficulty level of, and provide exam questions.

[0064] The data collection unit collects information necessary for creating exam questions. For example, it can collect information such as past exam questions and correct answer rates. Specifically, the unit accesses the databases of educational institutions to obtain the content of past exam questions and their correct answer rates. This allows for an understanding of the difficulty level of each question. The unit can also collect feedback from teachers and instructors. For example, it collects evaluations and suggestions for improvement from teachers and instructors regarding exam questions and incorporates them into the creation of future exam questions. This improves the quality of exam questions. Furthermore, the unit can collect student learning history and performance data. This provides foundational data for creating questions tailored to students' understanding and learning progress. The unit centrally manages this data and can collaborate with other departments as needed. For example, collected data is stored on a cloud server, making it accessible to the generation and adjustment units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The generation unit generates exam questions based on information collected by the collection unit. For example, the generation unit can use a generation AI to generate exam questions based on the collected information. Specifically, the generation AI uses natural language processing technology to analyze past exam questions and feedback data to generate new exam questions. The generation AI can generate questions of appropriate difficulty by specifying the subject and question format. For example, when generating mathematics exam questions, the generation AI generates questions corresponding to each field, such as algebra, geometry, and calculus. The generation unit can also extract questions based on the correct answer rate of past questions. For example, it can prioritize extracting questions with low correct answer rates and generate more difficult questions. This allows for a more accurate assessment of students' understanding. Furthermore, to increase the variety of questions, the generation unit can generate questions in different formats for the same theme. For example, it can generate not only multiple-choice questions but also written and fill-in-the-blank questions. This ensures diversity in exam questions and allows for the assessment of students' multifaceted understanding. The generation unit centrally manages the generated exam questions and can collaborate with other departments as needed. This allows the generation unit to generate test questions efficiently and effectively, improving the overall performance of the system.

[0066] The adjustment unit adjusts the difficulty level of the test questions generated by the generation unit. For example, the adjustment unit can use AI to adjust the difficulty level of the generated test questions. Specifically, the adjustment AI analyzes data such as the correct answer rate, answer time, and question complexity of the generated test questions and adjusts the difficulty level accordingly. For example, questions with a low correct answer rate are judged to be too difficult and adjusted to an appropriate level. Questions with long answer times can also be adjusted considering their complexity. This allows the adjustment unit to appropriately adjust the difficulty level of the test questions and provide questions that match students' understanding. Furthermore, the adjustment unit can also adjust the overall difficulty level considering the balance of the test questions. For example, it can adjust the difficulty level of each question to maintain balance in the overall test, ensuring that the difficulty level of the test is not uneven. The adjustment unit can also readjust the difficulty level of the test questions based on feedback from teachers and instructors. This allows the adjustment unit to appropriately adjust the difficulty level of the test questions and accurately assess students' understanding. The adjustment unit centrally manages the adjusted test questions and can collaborate with other departments as needed. This allows the adjustment unit to efficiently and effectively adjust the difficulty level of the test questions and improve the overall system performance.

[0067] The provisioning department provides teachers and instructors with exam questions that have been adjusted by the adjustment department. The provisioning department can use AI, for example, to provide teachers and instructors with generated exam questions. Specifically, the provisioning AI provides teachers and instructors with generated exam questions through an online platform. Teachers and instructors can access the online platform to review the generated exam questions and make corrections or additions as needed. The provisioning department can also print and provide the generated exam questions. This allows teachers and instructors to review the exam questions in paper form and distribute them to students. Furthermore, the provisioning department can manage the status of exam question distribution and collaborate with other departments as needed. For example, it can collect usage data and feedback on the provided exam questions and incorporate this into the creation of future exam questions. The provisioning department can also adjust the frequency and format of exam question distribution, enabling flexible responses to specific situations and conditions. This allows the provisioning department to provide exam questions efficiently and effectively, improving the overall performance of the system.

[0068] The data collection unit can collect information such as past exam questions and correct answer rates. For example, the data collection unit can retrieve past exam questions from a database and calculate the correct answer rate. The data collection unit can also collect feedback from teachers and instructors. For example, the data collection unit can collect evaluations and suggestions for improvement of exam questions provided by teachers and instructors and reflect them in the creation of the next exam questions. This helps in creating exam questions by collecting information on past exam questions and correct answer rates. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can have AI perform the process of retrieving past exam questions from a database and calculating the correct answer rate.

[0069] The generation unit can generate exam questions based on the collected information. For example, the generation unit uses a generation AI to generate exam questions based on the collected information. The generation unit can, for example, specify subjects and question formats to generate questions of appropriate difficulty. The generation unit can also extract questions based on the correct answer rate of past questions. For example, the generation unit can prioritize extracting questions with low correct answer rates and generate questions of higher difficulty. In this way, by generating exam questions based on the collected information, appropriate questions can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input collected information into the generation AI and have the generation AI execute the generation of exam questions.

[0070] The adjustment unit can adjust the difficulty level of the generated test questions. For example, the adjustment unit can use AI to adjust the difficulty level of the generated test questions. For example, the adjustment unit adjusts the difficulty level of the questions based on the correct answer rate. The adjustment unit can also adjust the difficulty level by considering the complexity of the questions and the time it takes to answer them. In this way, by adjusting the difficulty level of the generated test questions, it is possible to provide questions of an appropriate difficulty level. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can have AI perform the process of adjusting the difficulty level of the generated test questions.

[0071] The service provider can provide the generated test questions to teachers and instructors. For example, the service provider can use AI to provide the generated test questions to teachers and instructors. For example, the service provider can provide the generated test questions through an online platform. Alternatively, the service provider can provide the generated test questions in print. This streamlines test preparation by providing the generated test questions to teachers and instructors. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can have AI perform the process of providing the generated test questions through an online platform.

[0072] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting easier questions from past questions. For example, if the user is relaxed, the data collection unit will prioritize collecting more difficult questions. For example, if the user is focused, the data collection unit will prioritize collecting balanced questions. By prioritizing information based on the user's emotions, more appropriate information can be collected. 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 data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of estimating the user's emotions and determining the priority of information based on the estimated emotions.

[0073] The data collection unit can analyze the frequency of past exam questions and prioritize collecting the most frequently asked questions. For example, the data collection unit can analyze exam questions from the past five years and list the most frequently asked questions. For example, the data collection unit can analyze the frequency of questions for each subject and prioritize collecting the most frequently asked questions. For example, the data collection unit can balance the exam questions by prioritizing the collection of frequently asked questions. This allows for the priority collection of frequently asked questions by analyzing past question frequencies. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of analyzing the frequency of past exam questions and prioritizing the collection of frequently asked questions.

[0074] The data collection unit can analyze students' learning histories and collect problems best suited to each individual student. For example, the data collection unit can analyze students' past performance and prioritize collecting problems in areas where students struggle. For example, the data collection unit can collect problems in areas where students excel based on their learning history. For example, the data collection unit can analyze students' learning progress in real time and collect the most suitable problems. In this way, by analyzing students' learning histories, the data collection unit can collect problems best suited to each individual student. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of analyzing students' learning histories and collecting the most suitable problems.

[0075] The data collection unit can estimate the user's emotions and adjust the scope of information collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will focus on collecting easy questions. If the user is relaxed, the data collection unit will also collect more difficult questions. If the user is focused, the data collection unit will collect a balanced set of questions. By adjusting the scope of information based on the user's emotions, more appropriate information can be collected. Emotion estimation is achieved using an emotion estimation function, such as 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 data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of estimating the user's emotions and adjusting the scope of information based on the estimated emotions.

[0076] The data collection unit can collect feedback from teachers and instructors and improve its algorithm. For example, the data collection unit can collect feedback from teachers and instructors and improve its algorithm. For example, the data collection unit can adjust the scope of information collection based on the feedback. For example, the data collection unit can analyze the feedback and optimize its algorithm. In this way, the data collection unit's algorithm can be improved by collecting feedback. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can collect feedback from teachers and instructors and have AI perform the algorithm improvement.

[0077] The data collection unit can analyze students' learning styles and collect information tailored to those styles. For example, the data collection unit can analyze students' learning styles and collect problems containing many diagrams and charts for visual learners. For example, the data collection unit can analyze students' learning styles and collect audio problems for auditory learners. For example, the data collection unit can analyze students' learning styles and collect problems including experiments and practical exercises for tactile learners. In this way, by analyzing students' learning styles, information tailored to their styles can be collected. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can have AI perform the process of analyzing students' learning styles and collecting information tailored to their styles.

[0078] The generation unit can estimate the user's emotions and adjust the format of the questions it generates based on those emotions. For example, if the user is stressed, the generation unit will generate easy questions. If the user is relaxed, the generation unit will generate difficult questions. If the user is focused, the generation unit will generate balanced questions. By adjusting the question format based on the user's emotions, more appropriate questions can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit is performed using the generation AI. For example, the generation unit can have the generation AI perform the process of estimating the user's emotions and adjusting the question format based on those emotions.

[0079] The generation unit can analyze the question trends for each subject and generate questions based on those trends. For example, the generation unit can analyze past question trends for each subject and generate questions based on those trends. For example, the generation unit can generate well-balanced questions based on the question trends for each subject. For example, the generation unit can analyze question trends and generate questions so as not to be biased towards any particular field. In this way, by analyzing the question trends for each subject, it is possible to generate questions based on those trends. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can have the generation AI perform the process of analyzing the question trends for each subject and generating questions based on those trends.

[0080] The generation unit can evaluate students' understanding and generate problems appropriate to that level. For example, the generation unit can generate problems appropriate to a student's understanding based on their past performance. For example, the generation unit can evaluate students' understanding in real time and generate appropriate problems. For example, the generation unit can analyze students' understanding and generate problems in areas where they struggle. In this way, by evaluating students' understanding, problems appropriate to their level can be generated. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can have the generation AI perform the process of evaluating students' understanding and generating problems appropriate to that understanding.

[0081] The generation unit can estimate the user's emotions and adjust the difficulty level of the problems it generates based on those emotions. For example, if the user is stressed, the generation unit will generate easy problems. If the user is relaxed, the generation unit will generate difficult problems. If the user is focused, the generation unit will generate problems of balanced difficulty. By adjusting the difficulty level of problems based on the user's emotions, more appropriate problems can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can have the generation AI perform the process of estimating the user's emotions and adjusting the difficulty level of the problems based on those emotions.

[0082] The generation unit can generate questions on a specific theme based on instructions from teachers or instructors. For example, the generation unit generates questions on a specific theme based on instructions from teachers or instructors. For example, the generation unit generates related questions based on the instructed theme. For example, the generation unit generates questions aligned with the theme, reflecting the instructions from teachers or instructors. In this way, by generating questions on a specific theme based on the instructions from teachers or instructors, questions aligned with the theme can be provided. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can have the generation AI perform the process of generating questions on a specific theme based on instructions from teachers or instructors.

[0083] The generation unit can analyze students' learning progress in real time and generate problems appropriate to that progress. For example, the generation unit can analyze students' learning progress in real time and generate appropriate problems. For example, the generation unit can generate problems of varying difficulty levels according to learning progress. For example, the generation unit can generate well-balanced problems based on students' progress. In this way, by analyzing students' learning progress in real time, problems appropriate to their progress can be generated. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit can have the generation AI perform the process of analyzing students' learning progress in real time and generating problems appropriate to that progress.

[0084] The adjustment unit can estimate the user's emotions and adjust the difficulty of the problem based on the estimated emotions. For example, if the user is stressed, the adjustment unit will lower the difficulty of the problem. For example, if the user is relaxed, the adjustment unit will raise the difficulty of the problem. For example, if the user is focused, the adjustment unit will adjust the difficulty to a balanced level. In this way, by adjusting the difficulty of the problem based on the user's emotions, it is possible to provide problems of a more appropriate difficulty level. 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 adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can have an AI perform the process of estimating the user's emotions and adjusting the difficulty of the problem based on the estimated emotions.

[0085] The adjustment unit can analyze past test results and adjust the difficulty level of the questions based on those results. For example, the adjustment unit can analyze past test results and increase the difficulty level if the average score is high. For example, the adjustment unit can analyze past test results and decrease the difficulty level if the average score is low. For example, the adjustment unit can analyze the test results and adjust the difficulty level to a balanced level. In this way, by analyzing past test results, the difficulty level of the questions can be adjusted based on those results. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can have AI perform the process of analyzing past test results and adjusting the difficulty level of the questions based on those results.

[0086] The adjustment unit can consider a student's learning history and adjust the difficulty level of problems based on that history. For example, the adjustment unit can adjust the difficulty level of problems in areas where the student struggles, based on the student's learning history. For example, the adjustment unit can consider the student's learning history and adjust the difficulty level of problems in areas where the student excels. For example, the adjustment unit can analyze the student's learning history and adjust the difficulty level to a balanced level. In this way, by considering the student's learning history, the difficulty level of problems can be adjusted based on that history. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can have AI perform the process of considering the student's learning history and adjusting the difficulty level of problems based on that history.

[0087] The adjustment unit can estimate the user's emotions and adjust the order in which questions are presented based on the estimated emotions. For example, if the user is stressed, the adjustment unit will present easier questions first. If the user is relaxed, the adjustment unit will present more difficult questions first. If the user is focused, the adjustment unit will present questions in a balanced order. By adjusting the order of questions based on the user's emotions, questions can be provided in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, such as 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 adjustment unit may be performed using AI or not. For example, the adjustment unit can have an AI perform the process of estimating the user's emotions and adjusting the order in which questions are presented based on the estimated emotions.

[0088] The adjustment unit can adjust the difficulty level of the problems by reflecting feedback from teachers and instructors. For example, the adjustment unit adjusts the difficulty level of the problems based on feedback from teachers and instructors. For example, the adjustment unit adjusts the difficulty level to a balanced level by reflecting the feedback. For example, the adjustment unit optimizes the difficulty level of the problems by incorporating the opinions of teachers and instructors. In this way, the difficulty level of the problems can be appropriately adjusted by reflecting the feedback from teachers and instructors. Some or all of the above processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can have AI perform the process of reflecting feedback from teachers and instructors and adjusting the difficulty level of the problems.

[0089] The adjustment unit can take into account students' learning styles and adjust the difficulty level of problems accordingly. For example, the adjustment unit can analyze students' learning styles and provide easy-to-understand diagram problems to visual learners. For example, the adjustment unit can take students' learning styles into account and provide easy-to-understand audio problems to auditory learners. For example, the adjustment unit can provide easy-to-understand experimental problems to tactile learners based on students' learning styles. In this way, by taking students' learning styles into account, the difficulty level of problems can be adjusted according to their style. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can have AI perform the process of taking students' learning styles into account and adjusting the difficulty level of problems accordingly.

[0090] The service provider can estimate the user's emotions and adjust how the problem is presented based on those emotions. For example, if the user is stressed, the service provider might present the problem with a simple interface. If the user is relaxed, the service provider might present the problem with an interface that includes detailed explanations. If the user is focused, the service provider might present the problem with a balanced interface. This allows the service provider to present the problem in a more appropriate way by adjusting how it is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can have AI perform the process of estimating the user's emotions and adjusting how the problem is presented based on those estimated emotions.

[0091] The distribution unit can provide problems at the optimal time, taking into account the schedules of teachers and instructors. For example, the distribution unit can provide problems at the optimal time based on the teacher's or instructor's class schedule. For example, the distribution unit can provide problems before or after class, taking into account the teacher's or instructor's schedule. For example, the distribution unit can analyze the teacher's or instructor's schedule and provide problems during their free time. In this way, by taking into account the teacher's or instructor's schedule, problems can be provided at the optimal time. Some or all of the above processes in the distribution unit may be performed using AI or not. For example, the distribution unit can have AI perform the process of considering the teacher's or instructor's schedule and providing problems at the optimal time.

[0092] The distribution unit can monitor students' learning progress in real time and provide problems according to their progress. For example, the distribution unit can monitor students' learning progress in real time and provide problems at the appropriate time. For example, the distribution unit can provide problems of varying difficulty levels according to learning progress. For example, the distribution unit can provide balanced problems based on students' progress. In this way, by monitoring students' learning progress in real time, problems can be provided according to their progress. Some or all of the above processes in the distribution unit may be performed using AI or not. For example, the distribution unit can have AI perform the process of monitoring students' learning progress in real time and providing problems according to their progress.

[0093] The service provider can estimate the user's emotions and adjust the order in which problems are presented based on the estimated emotions. For example, if the user is stressed, the service provider will present easier problems first. If the user is relaxed, the service provider will present more difficult problems first. If the user is focused, the service provider will present problems in a balanced order. By adjusting the order in which problems are presented based on the user's emotions, problems can be presented in a more appropriate order. 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. For example, the service provider can have AI perform the process of estimating the user's emotions and adjusting the order in which problems are presented based on the estimated emotions.

[0094] The delivery unit can improve the way problems are delivered by incorporating feedback from teachers and instructors. For example, the delivery unit improves the way problems are delivered based on feedback from teachers and instructors. For example, the delivery unit can improve the way problems are delivered by incorporating feedback. For example, the delivery unit optimizes the delivery method by incorporating the opinions of teachers and instructors. In this way, the way problems are delivered can be improved by incorporating feedback from teachers and instructors. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can have AI perform the process of improving the way problems are delivered by incorporating feedback from teachers and instructors.

[0095] The service provider can consider students' learning styles and provide problems tailored to those styles. For example, the service provider can analyze students' learning styles and provide problems with many diagrams and charts to visual learners. For example, the service provider can consider students' learning styles and provide audio problems to auditory learners. For example, based on students' learning styles, the service provider can provide problems that include experiments and practical exercises to tactile learners. In this way, by considering students' learning styles, problems tailored to their styles can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can have AI perform the process of considering students' learning styles and providing problems tailored to those styles.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The exam question creation system may also include an answer generation unit that generates example answers to the questions. The answer generation unit generates appropriate example answers for the generated exam questions. For example, the answer generation unit can generate a model answer for a generated text-based question. It can also show the correct answer option for multiple-choice questions. Furthermore, the answer generation unit can generate detailed example answers that include the reasoning and explanations for the answers. This makes exam preparation even more efficient for teachers and instructors, as they are provided not only with exam questions but also with example answers.

[0098] The exam question creation system may also include a frequency adjustment unit that adjusts the frequency of questions asked. The frequency adjustment unit analyzes the frequency of past exam questions and adjusts them to prevent certain questions from appearing excessively. For example, the frequency adjustment unit analyzes exam questions from the past five years and lists the most frequently asked questions. Furthermore, the frequency adjustment unit can analyze the frequency of questions for each subject and provide a balanced set of questions. In addition, the frequency adjustment unit can prioritize the collection of frequently asked questions to balance the exam questions. This allows for the prioritization of high-frequency questions by analyzing past question frequencies.

[0099] The test question creation system may also include a sequence adjustment unit that adjusts the order in which the questions are presented. The sequence adjustment unit adjusts the order in which the generated test questions are presented, reducing the burden on the test-taker. For example, the sequence adjustment unit can adjust the order from easy questions to difficult questions. It can also present questions in a balanced order to maintain the test-taker's concentration. Furthermore, the sequence adjustment unit can adjust the order of questions based on a specific theme. This reduces the burden on the test-taker and enables the implementation of an effective examination.

[0100] The test question creation system may further include a question time estimation unit that estimates the time required to answer each question. The question time estimation unit estimates an appropriate time for each generated test question. For example, the unit can estimate the time based on the difficulty and complexity of the question. It can also estimate the time based on past test data. Furthermore, the unit can adjust the time according to the test-taker's answering speed. This ensures that test-takers are given an appropriate time to answer each question and guarantees fairness in the examination.

[0101] The exam question creation system may further include an answer method provider unit that provides methods for answering the questions. This unit provides appropriate answer methods for the generated exam questions. For example, it can provide methods for selecting the correct option in multiple-choice questions. It can also provide effective ways of writing answers to essay questions. Furthermore, it can provide the knowledge and skills necessary to answer the questions. This allows test-takers to learn appropriate answer methods and prepare effectively for the exam.

[0102] The test question creation system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the difficulty level of the questions based on those emotions. For example, if the user is stressed, the emotion adjustment unit may provide easy questions. For example, if the user is relaxed, the emotion adjustment unit may provide difficult questions. For example, if the user is focused, the emotion adjustment unit may provide questions of balanced difficulty. This allows for the provision of more appropriate questions by adjusting the difficulty level based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0103] The exam question creation system may further include an emotion order adjustment unit that estimates the user's emotions and adjusts the order in which questions are presented based on those emotions. For example, if the user is feeling stressed, the emotion order adjustment unit will present easier questions first. If the user is relaxed, the emotion order adjustment unit will present more difficult questions first. If the user is focused, the emotion order adjustment unit will present questions in a balanced order. This allows for the provision of questions in a more appropriate order by adjusting the order based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0104] The test question creation system may further include an emotion delivery unit that estimates the user's emotions and adjusts the way questions are presented based on those emotions. For example, if the user is stressed, the emotion delivery unit might present the questions with a simple interface. If the user is relaxed, for example, the emotion delivery unit might present the questions with an interface that includes detailed explanations. If the user is focused, for example, the emotion delivery unit might present the questions with a balanced interface. This allows for the presentation of questions in a more appropriate way by adjusting the method of presentation based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, for example.

[0105] The test question creation system may further include an emotion format adjustment unit that estimates the user's emotions and adjusts the question format based on the estimated emotions. For example, if the user is stressed, the emotion format adjustment unit may provide easy questions. For example, if the user is relaxed, the emotion format adjustment unit may provide difficult questions. For example, if the user is focused, the emotion format adjustment unit may provide balanced questions. This allows for the provision of more appropriate questions by adjusting the question format based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0106] The test question creation system may further include an emotion response time adjustment unit that estimates the user's emotions and adjusts the response time based on the estimated emotions. For example, if the user is feeling stressed, the emotion response time adjustment unit will extend the response time. For example, if the user is relaxed, the emotion response time adjustment unit will provide a standard response time. For example, if the user is focused, the emotion response time adjustment unit will provide a shorter response time. In this way, by adjusting the response time based on the user's emotions, a more appropriate response time can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit gathers the information necessary to create the exam questions. For example, it retrieves information such as past exam questions and correct answer rates from a database and calculates the correct answer rate. It also collects feedback from teachers and instructors and incorporates evaluations and areas for improvement of the exam questions into the creation of the next exam questions. Step 2: The generation unit generates exam questions based on the information collected by the collection unit. For example, it uses a generation AI to specify subjects and question formats and generates questions of appropriate difficulty. It also extracts questions based on the correct answer rate of past questions, prioritizing questions with low correct answer rates to generate more difficult questions. Step 3: The adjustment unit adjusts the difficulty level of the test questions generated by the generation unit. For example, it may use AI to adjust the difficulty level based on the correct answer rate, or it may adjust the difficulty level considering the complexity of the questions and the time allotted for answering. Step 4: The provisioning department provides the exam questions, which have been adjusted by the adjustment department, to teachers and instructors. For example, the generated exam questions may be provided through an online platform or in printed form.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, generation unit, adjustment unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect information such as past exam questions and correct answer rates, and the control unit 46A collects feedback from teachers and instructors. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates exam questions using a generation AI based on the collected information. The adjustment unit is implemented in the specific processing unit 290 of the data processing unit 12 and adjusts the difficulty level of the generated exam questions. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the generated exam questions to teachers and instructors through an online platform. 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.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the collection unit, generation unit, adjustment unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect information such as past exam questions and correct answer rates, and the control unit 46A collects feedback from teachers and instructors. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates exam questions using a generation AI based on the collected information. The adjustment unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and adjusts the difficulty level of the generated exam questions. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides the generated exam questions to teachers and instructors through an online platform. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, generation unit, adjustment unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information such as past exam questions and correct answer rates, and the control unit 46A collects feedback from teachers and instructors. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates exam questions using a generation AI based on the collected information. The adjustment unit is implemented in the specific processing unit 290 of the data processing unit 12 and adjusts the difficulty level of the generated exam questions. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the generated exam questions to teachers and instructors through an online platform. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, generation unit, adjustment unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information such as past exam questions and correct answer rates, and the control unit 46A collects feedback from teachers and instructors. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates exam questions using a generation AI based on the collected information. The adjustment unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and adjusts the difficulty level of the generated exam questions. The provision unit is implemented, for example, in the control unit 46A of the robot 414, and provides the generated exam questions to teachers and instructors through an online platform. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A collection unit that collects information necessary for creating exam questions, A generation unit that generates test questions based on the information collected by the collection unit, An adjustment unit for adjusting the difficulty level of the test questions generated by the generation unit, The system includes a providing unit that provides test questions adjusted by the adjustment unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect information such as past exam questions and correct answer rates. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Test questions are generated based on the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, Adjust the difficulty level of the generated test questions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated test questions are provided to teachers and instructors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We analyze the frequency of questions from past exams and prioritize collecting the most frequently asked questions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze students' learning histories and collect problems best suited to each individual student. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the scope of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We collect feedback from teachers and instructors to improve the algorithms used in the data collection department. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze students' learning styles and collect information tailored to those styles. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the format of the questions generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We analyze the question trends for each subject and generate questions based on those trends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is The system evaluates students' understanding and generates problems tailored to their level of comprehension. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the difficulty of the generated problems based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Generate questions on a specific topic based on instructions from teachers and instructors. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Analyzes students' learning progress in real time and generates problems tailored to their progress. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, The system estimates the user's emotions and adjusts the difficulty of the problem based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The adjustment unit is, We analyze past test results and adjust the difficulty level of the questions based on those results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The adjustment unit is, We take into account students' learning history and adjust the difficulty level of the questions based on that history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The adjustment unit is, The system estimates the user's emotions and adjusts the order in which questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, We adjust the difficulty level of the questions based on feedback from teachers and instructors. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, We take into account each student's learning style and adjust the difficulty level of the problems accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the problem is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We take into account the schedules of teachers and instructors and provide problems at the optimal time. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system monitors students' learning progress in real time and provides them with problems based on their progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which problems are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, We will incorporate feedback from teachers and instructors to improve how we present problems. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, We take into account students' learning styles and provide problems tailored to their individual styles. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 collection unit that collects information necessary for creating exam questions, A generation unit that generates test questions based on the information collected by the collection unit, An adjustment unit for adjusting the difficulty level of the test questions generated by the generation unit, The system includes a providing unit that provides test questions adjusted by the adjustment unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect information such as past exam questions and correct answer rates. The system according to feature 1.

3. The generating unit is Test questions are generated based on the collected information. The system according to feature 1.

4. The adjustment unit is, Adjust the difficulty level of the generated test questions. The system according to feature 1.

5. The aforementioned supply unit is, The generated test questions are provided to teachers and instructors. The system according to feature 1.

6. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is We analyze the frequency of questions from past exams and prioritize collecting the most frequently asked questions. The system according to feature 1.

8. The aforementioned collection unit is Analyze students' learning histories and collect problems best suited to each individual student. The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the scope of information collected based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is We collect feedback from teachers and instructors and improve the algorithm of the aforementioned data collection unit. The system according to feature 1.

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