system

Abstract

The system according to the embodiment aims to create and appropriately adjust a curriculum according to the learning style and interests of each child. [Solution] A system according to an embodiment includes an initial assessment unit, a curriculum generation unit, a feedback provision unit, and an adjustment unit. The initial assessment unit assesses a child's learning style and interests. The curriculum generation unit creates a learning curriculum based on the results of the assessment by the initial assessment unit. The feedback provision unit provides immediate feedback based on the curriculum created by the curriculum generation unit. The adjustment unit adjusts the learning content based on the feedback provided by the feedback provision unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately create and tailor curriculum to each child's individual learning style and interests, and there is room for improvement.

[0005] The system according to the embodiment aims to create and appropriately adjust a curriculum according to the learning style and interests of each child. [Means for solving the problem]

[0006] The system according to the embodiment includes an initial assessment unit, a curriculum generation unit, a feedback provision unit, and an adjustment unit. The initial assessment unit assesses a child's learning style and interests. The curriculum generation unit creates a learning curriculum based on the results of the assessment by the initial assessment unit. The feedback provision unit provides immediate feedback based on the curriculum created by the curriculum generation unit. The adjustment unit adjusts the learning content based on the feedback provided by the feedback provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can create and appropriately adjust a curriculum according to a child's learning style and interests. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A learning support system according to an embodiment of the present invention assesses a child's learning style and interests and creates a highly efficient learning curriculum based on the assessment. The learning support system performs an initial assessment of the child, creates a learning curriculum based on the assessment results, and adaptively adjusts the learning content through real-time feedback. For example, the learning support system performs an initial assessment to evaluate a child's learning style and interests. For example, the learning support system performs an initial assessment including questions and tests to understand the child's learning history and interests. Next, the learning support system creates a learning curriculum optimized for each child based on the results of the initial assessment. For example, the learning support system uses an algorithm that dynamically changes the learning content based on the child's level of understanding and interests. Furthermore, the learning support system adjusts the learning content based on the child's level of understanding and interests through real-time feedback. For example, the learning support system monitors the child's learning progress and comprehension in real time and provides supplementary or additional learning materials as needed. This allows the learning support system to create a highly efficient learning curriculum based on the child's learning style and interests and adaptively adjust the learning content through real-time feedback. This allows the learning support system to provide a highly efficient and effective learning environment even for students who are not suited to traditional teaching styles. For example, providing an environment where students can learn at their own pace and providing ongoing support to reduce barriers to learning allows students to learn at their own pace and improves their learning outcomes.

[0029] A learning support system according to an embodiment includes an initial assessment unit, a curriculum generation unit, a feedback providing unit, and an adjustment unit. The initial assessment unit assesses a child's learning style and interests. For example, the initial assessment unit includes questions and tests for understanding the child's learning history and interests. The initial assessment unit assesses the child's interests using, for example, multiple-choice questions. The initial assessment unit can also assess the child's learning style using a written test. The initial assessment unit can also assess the child's learning ability using a practical test. The curriculum generation unit creates a learning curriculum based on the results of the assessment by the initial assessment unit. For example, the curriculum generation unit includes an algorithm that dynamically changes the learning content according to the child's level of understanding and interests. The curriculum generation unit adjusts the learning content using, for example, an adaptive learning algorithm. The curriculum generation unit can also optimize the learning content using a machine learning algorithm. The curriculum generation unit can also customize the learning content based on the child's learning history. The feedback providing unit provides real-time feedback based on the curriculum created by the curriculum generation unit. For example, the feedback providing unit may monitor a child's learning progress and level of understanding in real time and provide supplementary or additional learning materials as needed. The feedback providing unit may track a child's learning progress using, for example, real-time data collection. The feedback providing unit may also evaluate a child's level of understanding using progress tracking. The feedback providing unit may also provide video learning materials and practice problems. The adjustment unit may adjust learning content based on the feedback provided by the feedback providing unit. For example, the adjustment unit may provide special support to a child with a learning disability to support their learning progress. The adjustment unit may provide, for example, special learning materials. The adjustment unit may also provide individualized instruction. The adjustment unit may also provide assistance tools. As a result, the learning support system according to the embodiment can create a highly efficient learning curriculum based on a child's learning style and interests and adaptively adjust learning content through real-time feedback.

[0030] The initial assessment unit may include specific questions or tests to understand the child's learning history and interests. Specific questions or tests include, but are not limited to, multiple-choice questions, written tests, and practical tests. For example, the initial assessment unit may use multiple-choice questions to assess the child's interests. The initial assessment unit may also use written tests to assess the child's learning style. The initial assessment unit may also use practical tests to assess the child's learning ability. This allows the child's learning history and interests to be understood in detail.

[0031] The curriculum generation unit may include an algorithm that adaptively changes the learning content according to the child's level of understanding and interests. Examples of the algorithm include, but are not limited to, an adaptive learning algorithm and a machine learning algorithm. The curriculum generation unit adjusts the learning content using, for example, an adaptive learning algorithm. The curriculum generation unit may also optimize the learning content using a machine learning algorithm. The curriculum generation unit may also customize the learning content based on the child's learning history. This allows the learning content to be dynamically changed according to the child's level of understanding and interests.

[0032] The feedback providing unit can monitor the student's learning progress and level of understanding in real time and provide supplementary or additional learning materials as needed. Real-time monitoring includes, but is not limited to, real-time data collection, progress tracking, and the like. For example, the feedback providing unit tracks the student's learning progress using real-time data collection. The feedback providing unit can also evaluate the student's level of understanding using progress tracking. The feedback providing unit can also provide video learning materials and practice questions. This makes it possible to provide appropriate supplementary or additional learning materials according to the student's learning progress and level of understanding.

[0033] The adjustment unit can provide individualized support to children with learning disabilities and support their learning progress. Examples of individualized support include, but are not limited to, special teaching materials, individualized instruction, and support tools. For example, the adjustment unit can provide special teaching materials. The adjustment unit can also provide individualized instruction. The adjustment unit can also provide support tools. This makes it possible to provide special support to children with learning disabilities and support their learning progress.

[0034] The adjustment unit can use the online learning platform to provide an environment in which the child can progress through learning at their own pace. Examples of self-paced learning include, but are not limited to, a self-learning module and a progress management tool. The adjustment unit can provide, for example, a self-learning module. The adjustment unit can also provide a progress management tool. This makes it possible to provide an environment in which the child can progress through learning at their own pace.

[0035] The initial assessment unit can analyze the child's past learning history and select the optimal assessment method. For example, the initial assessment unit can pose more difficult questions for subjects in which the child was good at in the past. The initial assessment unit can also pose more basic questions for subjects in which the child was weak in the past. The initial assessment unit can also select a specific assessment method (test, project, etc.) from the child's past learning history. This makes it possible to select the optimal assessment method based on the child's past learning history.

[0036] The initial assessment unit can customize questions and tests based on the child's current interests and areas of interest during the initial assessment. For example, if the child is interested in science, the initial assessment unit can ask science-related questions. If the child is interested in history, the initial assessment unit can also administer a history-related test. If the child is interested in art, the initial assessment unit can also include an art-related project in the assessment. This makes it possible to customize questions and tests based on the child's current interests and areas of interest.

[0037] The initial evaluation unit can select the optimal evaluation means depending on the child's input method during the initial evaluation. For example, if the child prefers voice input, the initial evaluation unit provides a test that can be answered by voice. Furthermore, if the child prefers text input, the initial evaluation unit can also provide questions that can be answered by text. Furthermore, if the child prefers image input, the initial evaluation unit can also provide an evaluation means that uses images. This makes it possible to select the optimal evaluation means depending on the child's input method.

[0038] During the initial assessment, the initial assessment unit can prioritize highly relevant assessment items taking into account the child's geographical location information. For example, if the child lives in an urban area, the initial assessment unit can prioritize urban-related assessment items. If the child lives in a rural area, the initial assessment unit can prioritize agriculture-related assessment items. If the child lives by the sea, the initial assessment unit can prioritize ocean-related assessment items. This allows highly relevant assessment items to be prioritized based on the child's geographical location information.

[0039] The initial assessment unit can analyze the child's social media activities during the initial assessment and add related assessment items. For example, the initial assessment unit can add assessment items based on the content the child frequently shares on social media. The initial assessment unit can also analyze the child's interests on social media and add related assessment items. The initial assessment unit can also add assessment items based on the activities of the child's friends on social media. In this way, related assessment items can be added based on the child's social media activities.

[0040] The initial evaluation unit can customize the evaluation method by reflecting the student's past feedback during the initial evaluation. For example, the initial evaluation unit adjusts the evaluation method based on feedback the student has received in the past. The initial evaluation unit can also select a specific evaluation method (test, project, etc.) based on the student's past feedback. The initial evaluation unit can also add evaluation items by referring to the student's past feedback. This makes it possible to customize the evaluation method based on the student's past feedback.

[0041] The curriculum generation unit can adjust the level of detail of the curriculum based on the child's level of understanding when generating the curriculum. For example, if the child shows a high level of understanding, the curriculum generation unit can add detailed content to the curriculum. Also, if the child shows a low level of understanding, the curriculum generation unit can add basic content to the curriculum. Also, the curriculum generation unit can adjust the progress speed of the curriculum according to the child's level of understanding. In this way, the level of detail of the curriculum can be adjusted based on the child's level of understanding.

[0042] The curriculum generation unit can apply different curriculum algorithms depending on the interests of the child when generating a curriculum. For example, if the child is interested in science, the curriculum generation unit can apply a curriculum algorithm specialized for science. Also, if the child is interested in history, the curriculum generation unit can apply a curriculum algorithm specialized for history. Also, if the child is interested in art, the curriculum generation unit can apply a curriculum algorithm specialized for art. In this way, different curriculum algorithms can be applied depending on the interests of the child.

[0043] The curriculum generation unit can improve the accuracy of the curriculum by referring to the child's past learning results when generating the curriculum. The curriculum generation unit adjusts the content of the curriculum based on, for example, the child's past test results. The curriculum generation unit can also adjust the content of the curriculum based on the child's past project results. The curriculum generation unit can also adjust the content of the curriculum based on the child's past feedback. In this way, the accuracy of the curriculum can be improved based on the child's past learning results.

[0044] The curriculum generation unit can determine the priority of the curriculum based on the child's learning history when generating the curriculum. For example, the curriculum generation unit preferentially incorporates into the curriculum subjects that the child was good at in the past. The curriculum generation unit can also preferentially incorporate into the curriculum subjects that the child was weak at in the past. The curriculum generation unit can also preferentially incorporate into the curriculum specific subjects based on the child's learning history. In this way, the priority of the curriculum can be determined based on the child's learning history.

[0045] The curriculum generation unit can adjust the order of the curriculum based on the relevance of the child when generating the curriculum. For example, the curriculum generation unit first incorporates subjects that the child is interested in into the curriculum. The curriculum generation unit can also incorporate subjects that the child is weak in later into the curriculum. The curriculum generation unit can also adjust the order of the curriculum based on the interests of the child. In this way, the order of the curriculum can be adjusted based on the relevance of the child.

[0046] The curriculum generation unit may adjust the use of technical terms in the curriculum according to the child's level of expertise when generating the curriculum. For example, if the child has high level of expertise, the curriculum generation unit may provide a curriculum that uses a lot of technical terms. Also, if the child has low level of expertise, the curriculum generation unit may provide a curriculum that uses less technical terms. Also, the curriculum generation unit may adjust the use of technical terms in the curriculum according to the child's level of expertise. In this way, the use of technical terms in the curriculum can be adjusted according to the child's level of expertise.

[0047] When providing feedback, the feedback providing unit can adjust the level of detail of the feedback based on the child's learning progress. For example, if the child shows high progress, the feedback providing unit can provide detailed feedback. Also, if the child shows low progress, the feedback providing unit can provide brief feedback. Also, the feedback providing unit can adjust the level of detail of the feedback according to the child's learning progress. In this way, the level of detail of the feedback can be adjusted based on the child's learning progress.

[0048] The feedback providing unit may apply different feedback algorithms depending on the child's level of understanding when providing feedback. For example, if the child shows a high level of understanding, the feedback providing unit may apply a detailed feedback algorithm. Alternatively, if the child shows a low level of understanding, the feedback providing unit may apply a simple feedback algorithm. Alternatively, the feedback providing unit may adjust the feedback algorithm depending on the child's level of understanding. This allows different feedback algorithms to be applied depending on the child's level of understanding.

[0049] When providing feedback, the feedback providing unit can improve the accuracy of the feedback by referring to the child's past feedback results. The feedback providing unit can, for example, adjust the content of the feedback based on the child's past feedback results. The feedback providing unit can also select a specific feedback method based on the child's past feedback results. The feedback providing unit can also improve the accuracy of the feedback by referring to the child's past feedback results. This makes it possible to improve the accuracy of the feedback based on the child's past feedback results.

[0050] When providing feedback, the feedback providing unit can determine the priority of feedback based on the child's learning history. For example, the feedback providing unit can provide feedback preferentially to subjects in which the child was weak in the past. The feedback providing unit can also provide feedback preferentially to subjects in which the child was good in the past. The feedback providing unit can also provide feedback preferentially to specific subjects based on the child's learning history. This makes it possible to determine the priority of feedback based on the child's learning history.

[0051] The feedback providing unit can adjust the order of feedback based on the relevance of the child when providing feedback. For example, the feedback providing unit can provide feedback first for subjects in which the child is interested. The feedback providing unit can also provide feedback later for subjects in which the child is weak. The feedback providing unit can also adjust the order of feedback based on the interests of the child. This makes it possible to adjust the order of feedback based on the relevance of the child.

[0052] The feedback providing unit may adjust the use of technical terms in the feedback depending on the child's level of expertise when providing the feedback. For example, if the child has high level of expertise, the feedback providing unit may provide feedback that uses a lot of technical terms. Also, if the child has low level of expertise, the feedback providing unit may provide feedback that uses less technical terms. Also, the feedback providing unit may adjust the use of technical terms in the feedback depending on the child's level of expertise. In this way, the use of technical terms in the feedback can be adjusted depending on the child's level of expertise.

[0053] When making adjustments, the adjustment unit can analyze the child's past learning history and select the optimal adjustment method. For example, the adjustment unit can add more difficult content to subjects in which the child was good at in the past. The adjustment unit can also add basic content to subjects in which the child was weak in the past. The adjustment unit can also select a specific adjustment method (such as a test or project) from the child's past learning history. This makes it possible to select the optimal adjustment method based on the child's past learning history.

[0054] During adjustment, the adjustment unit can customize the learning content based on the child's current living situation. For example, if the child is busy, the adjustment unit can provide content that can be learned in a short time. Also, if the child has time, the adjustment unit can provide detailed content. The adjustment unit can also customize the learning content according to the child's living situation. In this way, the learning content can be customized based on the child's current living situation.

[0055] The adjustment unit can improve the adjustment method of the learning content by reflecting the student's feedback during adjustment. The adjustment unit adjusts the learning content based on, for example, the student's feedback. The adjustment unit can also select a specific adjustment method (such as a test or a project) from the student's feedback. The adjustment unit can also improve the adjustment method of the learning content by referring to the student's feedback. In this way, the adjustment method of the learning content can be improved based on the student's feedback.

[0056] During adjustment, the adjustment unit can select optimal learning content taking into consideration the child's geographical location information. For example, if the child lives in an urban area, the adjustment unit can provide urban-related learning content. Also, if the child lives in a rural area, the adjustment unit can provide agriculture-related learning content. Also, if the child lives by the sea, the adjustment unit can provide ocean-related learning content. In this way, optimal learning content can be selected based on the child's geographical location information.

[0057] During adjustment, the adjustment unit can suggest learning content by analyzing the child's social media activities. For example, the adjustment unit can suggest learning content based on content frequently shared by the child on social media. The adjustment unit can also analyze the child's interests on social media and suggest related learning content. The adjustment unit can also suggest learning content by taking into account the activities of the child's friends on social media. In this way, learning content can be suggested based on the child's social media activities.

[0058] During adjustment, the adjustment unit can customize the learning content by reflecting the child's past feedback. The adjustment unit adjusts the learning content based on, for example, the child's past feedback. The adjustment unit can also select specific learning content from the child's past feedback. The adjustment unit can also customize the learning content by referring to the child's past feedback. In this way, the learning content can be customized based on the child's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The learning support system can further include a gamification unit. The gamification unit can award points and badges according to the child's learning progress, thereby increasing motivation to learn. For example, the gamification unit can award points when the child completes a specific task. The gamification unit can also award bonus points when the child continues to study. The gamification unit can also award badges when the child achieves a specific goal. This can increase the child's motivation to learn and encourage continuation of learning.

[0061] The learning support system may further include a social interaction section. The social interaction section enables students to discuss learning content with other students and work on projects together. For example, the social interaction section may provide an online forum where students can exchange questions and opinions. The social interaction section may also provide a group chat function where students can communicate in real time. The social interaction section may also provide tools to support the progress of collaborative projects. This allows students to work together with other students while learning, improving learning effectiveness.

[0062] The learning support system can further include a virtual reality (VR) unit. The VR unit allows children to have learning experiences in a virtual space. For example, the VR unit provides a virtual space that recreates historical events, allowing children to learn within it. The VR unit also allows scientific experiments to be conducted in a virtual space, allowing children to safely experience the experiments. The VR unit also allows children to visit geographical locations in a virtual space, allowing them to learn about those locations. This allows children to learn in a way that is closer to real-life experiences, improving learning effectiveness.

[0063] The learning support system may further include a speech recognition unit. The speech recognition unit may analyze the speech input of the child and reflect the results in the learning content. For example, the speech recognition unit may enable the child to input a question by speech and provide an answer to the question. The speech recognition unit may also enable the child to input an answer by speech and evaluate the answer. The speech recognition unit may also analyze the child's pronunciation and provide feedback on areas for improvement in pronunciation. This allows the child to progress with their learning using speech, improving the convenience of learning.

[0064] The learning support system may further include a personalized reminder unit. The personalized reminder unit may provide reminders based on the child's learning schedule to support continuation of learning. For example, the personalized reminder unit may send a reminder to the child to start learning at a specific time. The personalized reminder unit may also send periodic reminders to prevent the child from interrupting their learning. The personalized reminder unit may also provide reminders to help the child achieve their learning goals. This makes it easier for the child to continue their learning, improving learning effectiveness.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The initial assessment portion assesses the student's learning style and interests. For example, the initial assessment portion may include questions and tests to understand the student's learning history and interests. Multiple-choice questions may be used to assess the student's interests, and written or practical tests may be used to assess the student's learning style and learning abilities. Step 2: The curriculum generation unit creates a learning curriculum based on the results of the evaluation by the initial evaluation unit. For example, the curriculum generation unit includes an algorithm that dynamically changes the learning content according to the child's level of understanding and interests, and optimizes the learning content using adaptive learning algorithms and machine learning algorithms. It also customizes the learning content based on the child's learning history. Step 3: The feedback provider provides real-time feedback based on the curriculum created by the curriculum generator. For example, the feedback provider monitors the student's learning progress and understanding in real time and provides supplementary or additional learning materials as needed. Real-time data collection is used to track the student's learning progress and provide video learning materials and practice problems. Step 4: The coordinator adjusts the learning content based on the feedback provided by the feedback provider. For example, the coordinator may provide special support to children with learning disabilities and support their learning progress, providing special learning materials, individualized instruction, and assistive tools.

[0067] (Example 2) A learning support system according to an embodiment of the present invention assesses a child's learning style and interests and creates a highly efficient learning curriculum based on the assessment. The learning support system performs an initial assessment of the child, creates a learning curriculum based on the assessment results, and adaptively adjusts the learning content through real-time feedback. For example, the learning support system performs an initial assessment to evaluate a child's learning style and interests. For example, the learning support system performs an initial assessment including questions and tests to understand the child's learning history and interests. Next, the learning support system creates a learning curriculum optimized for each child based on the results of the initial assessment. For example, the learning support system uses an algorithm that dynamically changes the learning content based on the child's level of understanding and interests. Furthermore, the learning support system adjusts the learning content based on the child's level of understanding and interests through real-time feedback. For example, the learning support system monitors the child's learning progress and comprehension in real time and provides supplementary or additional learning materials as needed. This allows the learning support system to create a highly efficient learning curriculum based on the child's learning style and interests and adaptively adjust the learning content through real-time feedback. This allows the learning support system to provide a highly efficient and effective learning environment even for students who are not suited to traditional teaching styles. For example, providing an environment where students can learn at their own pace and providing ongoing support to reduce barriers to learning allows students to learn at their own pace and improves their learning outcomes.

[0068] A learning support system according to an embodiment includes an initial assessment unit, a curriculum generation unit, a feedback providing unit, and an adjustment unit. The initial assessment unit assesses a child's learning style and interests. For example, the initial assessment unit includes questions and tests for understanding the child's learning history and interests. The initial assessment unit assesses the child's interests using, for example, multiple-choice questions. The initial assessment unit can also assess the child's learning style using a written test. The initial assessment unit can also assess the child's learning ability using a practical test. The curriculum generation unit creates a learning curriculum based on the results of the assessment by the initial assessment unit. For example, the curriculum generation unit includes an algorithm that dynamically changes the learning content according to the child's level of understanding and interests. The curriculum generation unit adjusts the learning content using, for example, an adaptive learning algorithm. The curriculum generation unit can also optimize the learning content using a machine learning algorithm. The curriculum generation unit can also customize the learning content based on the child's learning history. The feedback providing unit provides real-time feedback based on the curriculum created by the curriculum generation unit. For example, the feedback providing unit may monitor a child's learning progress and level of understanding in real time and provide supplementary or additional learning materials as needed. The feedback providing unit may track a child's learning progress using, for example, real-time data collection. The feedback providing unit may also evaluate a child's level of understanding using progress tracking. The feedback providing unit may also provide video learning materials and practice problems. The adjustment unit may adjust learning content based on the feedback provided by the feedback providing unit. For example, the adjustment unit may provide special support to a child with a learning disability to support their learning progress. The adjustment unit may provide, for example, special learning materials. The adjustment unit may also provide individualized instruction. The adjustment unit may also provide assistance tools. As a result, the learning support system according to the embodiment can create a highly efficient learning curriculum based on a child's learning style and interests and adaptively adjust learning content through real-time feedback.

[0069] The initial assessment unit may include specific questions or tests to understand the child's learning history and interests. Specific questions or tests include, but are not limited to, multiple-choice questions, written tests, and practical tests. For example, the initial assessment unit may use multiple-choice questions to assess the child's interests. The initial assessment unit may also use written tests to assess the child's learning style. The initial assessment unit may also use practical tests to assess the child's learning ability. This allows the child's learning history and interests to be understood in detail.

[0070] The curriculum generation unit may include an algorithm that adaptively changes the learning content according to the child's level of understanding and interests. Examples of the algorithm include, but are not limited to, an adaptive learning algorithm and a machine learning algorithm. The curriculum generation unit adjusts the learning content using, for example, an adaptive learning algorithm. The curriculum generation unit may also optimize the learning content using a machine learning algorithm. The curriculum generation unit may also customize the learning content based on the child's learning history. This allows the learning content to be dynamically changed according to the child's level of understanding and interests.

[0071] The feedback providing unit can monitor the student's learning progress and level of understanding in real time and provide supplementary or additional learning materials as needed. Real-time monitoring includes, but is not limited to, real-time data collection, progress tracking, and the like. For example, the feedback providing unit tracks the student's learning progress using real-time data collection. The feedback providing unit can also evaluate the student's level of understanding using progress tracking. The feedback providing unit can also provide video learning materials and practice questions. This makes it possible to provide appropriate supplementary or additional learning materials according to the student's learning progress and level of understanding.

[0072] The adjustment unit can provide individualized support to children with learning disabilities and support their learning progress. Examples of individualized support include, but are not limited to, special teaching materials, individualized instruction, and support tools. For example, the adjustment unit can provide special teaching materials. The adjustment unit can also provide individualized instruction. The adjustment unit can also provide support tools. This makes it possible to provide special support to children with learning disabilities and support their learning progress.

[0073] The adjustment unit can use the online learning platform to provide an environment in which the child can progress through learning at their own pace. Examples of self-paced learning include, but are not limited to, a self-learning module and a progress management tool. The adjustment unit can provide, for example, a self-learning module. The adjustment unit can also provide a progress management tool. This makes it possible to provide an environment in which the child can progress through learning at their own pace.

[0074] The initial evaluation unit can estimate the child's emotions and adjust the timing of evaluation based on the estimated child's emotions. For example, if the child is tense, the initial evaluation unit delays the timing of evaluation so that the child can relax. Furthermore, if the child is concentrating, the initial evaluation unit can immediately evaluate the child to make the most of that concentration. Furthermore, if the child is tired, the initial evaluation unit can evaluate the child after a break. This makes it possible to adjust the timing of evaluation according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The initial assessment unit can analyze the child's past learning history and select the optimal assessment method. For example, the initial assessment unit can pose more difficult questions for subjects in which the child was good at in the past. The initial assessment unit can also pose more basic questions for subjects in which the child was weak in the past. The initial assessment unit can also select a specific assessment method (test, project, etc.) from the child's past learning history. This makes it possible to select the optimal assessment method based on the child's past learning history.

[0076] The initial assessment unit can customize questions and tests based on the child's current interests and areas of interest during the initial assessment. For example, if the child is interested in science, the initial assessment unit can ask science-related questions. If the child is interested in history, the initial assessment unit can also administer a history-related test. If the child is interested in art, the initial assessment unit can also include an art-related project in the assessment. This makes it possible to customize questions and tests based on the child's current interests and areas of interest.

[0077] The initial evaluation unit can select the optimal evaluation means depending on the child's input method during the initial evaluation. For example, if the child prefers voice input, the initial evaluation unit provides a test that can be answered by voice. Furthermore, if the child prefers text input, the initial evaluation unit can also provide questions that can be answered by text. Furthermore, if the child prefers image input, the initial evaluation unit can also provide an evaluation means that uses images. This makes it possible to select the optimal evaluation means depending on the child's input method.

[0078] The initial evaluation unit can estimate the child's emotions and determine the priority of items to be evaluated based on the estimated child's emotions. For example, if the child is excited, the initial evaluation unit can prioritize evaluating items that attract the child's attention. Furthermore, if the child is calm, the initial evaluation unit can prioritize evaluating items that require concentration. Furthermore, if the child is tired, the initial evaluation unit can prioritize evaluating easy items. This makes it possible to determine the priority of items to be evaluated according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] During the initial assessment, the initial assessment unit can prioritize highly relevant assessment items taking into account the child's geographical location information. For example, if the child lives in an urban area, the initial assessment unit can prioritize urban-related assessment items. If the child lives in a rural area, the initial assessment unit can prioritize agriculture-related assessment items. If the child lives by the sea, the initial assessment unit can prioritize ocean-related assessment items. This allows highly relevant assessment items to be prioritized based on the child's geographical location information.

[0080] The initial assessment unit can analyze the child's social media activities during the initial assessment and add related assessment items. For example, the initial assessment unit can add assessment items based on the content the child frequently shares on social media. The initial assessment unit can also analyze the child's interests on social media and add related assessment items. The initial assessment unit can also add assessment items based on the activities of the child's friends on social media. In this way, related assessment items can be added based on the child's social media activities.

[0081] The initial evaluation unit can customize the evaluation method by reflecting the student's past feedback during the initial evaluation. For example, the initial evaluation unit adjusts the evaluation method based on feedback the student has received in the past. The initial evaluation unit can also select a specific evaluation method (test, project, etc.) based on the student's past feedback. The initial evaluation unit can also add evaluation items by referring to the student's past feedback. This makes it possible to customize the evaluation method based on the student's past feedback.

[0082] The curriculum generation unit can estimate the child's emotions and adjust the curriculum content based on the estimated child's emotions. For example, if the child is excited, the curriculum generation unit can add interesting content to the curriculum. Also, if the child is calm, the curriculum generation unit can add content that requires concentration to the curriculum. Also, if the child is tired, the curriculum generation unit can add easy content to the curriculum. In this way, the curriculum content can be adjusted according to the child's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0083] The curriculum generation unit can adjust the level of detail of the curriculum based on the child's level of understanding when generating the curriculum. For example, if the child shows a high level of understanding, the curriculum generation unit can add detailed content to the curriculum. Also, if the child shows a low level of understanding, the curriculum generation unit can add basic content to the curriculum. Also, the curriculum generation unit can adjust the progress speed of the curriculum according to the child's level of understanding. In this way, the level of detail of the curriculum can be adjusted based on the child's level of understanding.

[0084] The curriculum generation unit can apply different curriculum algorithms depending on the interests of the child when generating a curriculum. For example, if the child is interested in science, the curriculum generation unit can apply a curriculum algorithm specialized for science. Also, if the child is interested in history, the curriculum generation unit can apply a curriculum algorithm specialized for history. Also, if the child is interested in art, the curriculum generation unit can apply a curriculum algorithm specialized for art. In this way, different curriculum algorithms can be applied depending on the interests of the child.

[0085] The curriculum generation unit can improve the accuracy of the curriculum by referring to the child's past learning results when generating the curriculum. The curriculum generation unit adjusts the content of the curriculum based on, for example, the child's past test results. The curriculum generation unit can also adjust the content of the curriculum based on the child's past project results. The curriculum generation unit can also adjust the content of the curriculum based on the child's past feedback. In this way, the accuracy of the curriculum can be improved based on the child's past learning results.

[0086] The curriculum generation unit can estimate the child's emotions and adjust the length of the curriculum based on the estimated child's emotions. For example, the curriculum generation unit can provide a short curriculum when the child is excited. The curriculum generation unit can also provide a long curriculum when the child is calm. The curriculum generation unit can also provide a short curriculum when the child is tired. This makes it possible to adjust the length of the curriculum according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0087] The curriculum generation unit can determine the priority of the curriculum based on the child's learning history when generating the curriculum. For example, the curriculum generation unit preferentially incorporates into the curriculum subjects that the child was good at in the past. The curriculum generation unit can also preferentially incorporate into the curriculum subjects that the child was weak at in the past. The curriculum generation unit can also preferentially incorporate into the curriculum specific subjects based on the child's learning history. In this way, the priority of the curriculum can be determined based on the child's learning history.

[0088] The curriculum generation unit can adjust the order of the curriculum based on the relevance of the child when generating the curriculum. For example, the curriculum generation unit first incorporates subjects that the child is interested in into the curriculum. The curriculum generation unit can also incorporate subjects that the child is weak in later into the curriculum. The curriculum generation unit can also adjust the order of the curriculum based on the interests of the child. In this way, the order of the curriculum can be adjusted based on the relevance of the child.

[0089] The curriculum generation unit may adjust the use of technical terms in the curriculum according to the child's level of expertise when generating the curriculum. For example, if the child has high level of expertise, the curriculum generation unit may provide a curriculum that uses a lot of technical terms. Also, if the child has low level of expertise, the curriculum generation unit may provide a curriculum that uses less technical terms. Also, the curriculum generation unit may adjust the use of technical terms in the curriculum according to the child's level of expertise. In this way, the use of technical terms in the curriculum can be adjusted according to the child's level of expertise.

[0090] The feedback providing unit can estimate the child's emotions and adjust the way in which feedback is expressed based on the estimated child's emotions. For example, if the child is nervous, the feedback providing unit can provide feedback in gentle words. Furthermore, if the child is relaxed, the feedback providing unit can provide detailed feedback. Furthermore, if the child is excited, the feedback providing unit can provide feedback that includes many encouraging words. This makes it possible to adjust the way in which feedback is expressed depending on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] When providing feedback, the feedback providing unit can adjust the level of detail of the feedback based on the child's learning progress. For example, if the child shows high progress, the feedback providing unit can provide detailed feedback. Also, if the child shows low progress, the feedback providing unit can provide brief feedback. Also, the feedback providing unit can adjust the level of detail of the feedback according to the child's learning progress. In this way, the level of detail of the feedback can be adjusted based on the child's learning progress.

[0092] The feedback providing unit may apply different feedback algorithms depending on the child's level of understanding when providing feedback. For example, if the child shows a high level of understanding, the feedback providing unit may apply a detailed feedback algorithm. Alternatively, if the child shows a low level of understanding, the feedback providing unit may apply a simple feedback algorithm. Alternatively, the feedback providing unit may adjust the feedback algorithm depending on the child's level of understanding. This allows different feedback algorithms to be applied depending on the child's level of understanding.

[0093] When providing feedback, the feedback providing unit can improve the accuracy of the feedback by referring to the child's past feedback results. The feedback providing unit can, for example, adjust the content of the feedback based on the child's past feedback results. The feedback providing unit can also select a specific feedback method based on the child's past feedback results. The feedback providing unit can also improve the accuracy of the feedback by referring to the child's past feedback results. This makes it possible to improve the accuracy of the feedback based on the child's past feedback results.

[0094] The feedback providing unit can estimate the child's emotions and adjust the length of the feedback based on the estimated child's emotions. For example, if the child is nervous, the feedback providing unit can provide short feedback. Also, if the child is relaxed, the feedback providing unit can provide long feedback. Also, if the child is excited, the feedback providing unit can provide feedback that includes many encouraging words. This makes it possible to adjust the length of the feedback according to the child's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] When providing feedback, the feedback providing unit can determine the priority of feedback based on the child's learning history. For example, the feedback providing unit can provide feedback preferentially to subjects in which the child was weak in the past. The feedback providing unit can also provide feedback preferentially to subjects in which the child was good in the past. The feedback providing unit can also provide feedback preferentially to specific subjects based on the child's learning history. This makes it possible to determine the priority of feedback based on the child's learning history.

[0096] The feedback providing unit can adjust the order of feedback based on the relevance of the child when providing feedback. For example, the feedback providing unit can provide feedback first for subjects in which the child is interested. The feedback providing unit can also provide feedback later for subjects in which the child is weak. The feedback providing unit can also adjust the order of feedback based on the interests of the child. This makes it possible to adjust the order of feedback based on the relevance of the child.

[0097] The feedback providing unit may adjust the use of technical terms in the feedback depending on the child's level of expertise when providing the feedback. For example, if the child has high level of expertise, the feedback providing unit may provide feedback that uses a lot of technical terms. Also, if the child has low level of expertise, the feedback providing unit may provide feedback that uses less technical terms. Also, the feedback providing unit may adjust the use of technical terms in the feedback depending on the child's level of expertise. In this way, the use of technical terms in the feedback can be adjusted depending on the child's level of expertise.

[0098] The adjustment unit can estimate the child's emotions and determine how to adjust the learning content based on the estimated child's emotions. For example, if the child is excited, the adjustment unit can add interesting content to the learning content. If the child is calm, the adjustment unit can also add content that requires concentration to the learning content. If the child is tired, the adjustment unit can also add easy content to the learning content. This makes it possible to determine how to adjust the learning content according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] When making adjustments, the adjustment unit can analyze the child's past learning history and select the optimal adjustment method. For example, the adjustment unit can add more difficult content to subjects in which the child was good at in the past. The adjustment unit can also add basic content to subjects in which the child was weak in the past. The adjustment unit can also select a specific adjustment method (such as a test or project) from the child's past learning history. This makes it possible to select the optimal adjustment method based on the child's past learning history.

[0100] During adjustment, the adjustment unit can customize the learning content based on the child's current living situation. For example, if the child is busy, the adjustment unit can provide content that can be learned in a short time. Also, if the child has time, the adjustment unit can provide detailed content. The adjustment unit can also customize the learning content according to the child's living situation. In this way, the learning content can be customized based on the child's current living situation.

[0101] The adjustment unit can improve the adjustment method of the learning content by reflecting the student's feedback during adjustment. The adjustment unit adjusts the learning content based on, for example, the student's feedback. The adjustment unit can also select a specific adjustment method (such as a test or a project) from the student's feedback. The adjustment unit can also improve the adjustment method of the learning content by referring to the student's feedback. In this way, the adjustment method of the learning content can be improved based on the student's feedback.

[0102] The adjustment unit can estimate the emotions of the child and determine the priority of learning content based on the estimated emotions of the child. For example, if the child is excited, the adjustment unit can prioritize adding interesting content to the learning content. Furthermore, if the child is calm, the adjustment unit can prioritize adding content that requires concentration to the learning content. Furthermore, if the child is tired, the adjustment unit can prioritize adding easy content to the learning content. In this way, the priority of learning content can be determined according to the emotions of the child. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] During adjustment, the adjustment unit can select optimal learning content taking into consideration the child's geographical location information. For example, if the child lives in an urban area, the adjustment unit can provide urban-related learning content. Also, if the child lives in a rural area, the adjustment unit can provide agriculture-related learning content. Also, if the child lives by the sea, the adjustment unit can provide ocean-related learning content. In this way, optimal learning content can be selected based on the child's geographical location information.

[0104] During adjustment, the adjustment unit can suggest learning content by analyzing the child's social media activities. For example, the adjustment unit can suggest learning content based on content frequently shared by the child on social media. The adjustment unit can also analyze the child's interests on social media and suggest related learning content. The adjustment unit can also suggest learning content by taking into account the activities of the child's friends on social media. In this way, learning content can be suggested based on the child's social media activities.

[0105] During adjustment, the adjustment unit can customize the learning content by reflecting the child's past feedback. The adjustment unit adjusts the learning content based on, for example, the child's past feedback. The adjustment unit can also select specific learning content from the child's past feedback. The adjustment unit can also customize the learning content by referring to the child's past feedback. In this way, the learning content can be customized based on the child's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned initial assessment unit, curriculum generation unit, feedback provision unit, and adjustment unit, is implemented, for example, in at least one of the smart device 14 and the data processing device 12. For example, the initial assessment unit assesses a child's learning style and interests using the camera 42 and microphone 38B of the smart device 14 and transmits the assessment results to the data processing device 12 via the control unit 46A. The curriculum generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, creates a learning curriculum based on the results of the initial assessment unit. The feedback provision unit, implemented, for example, by the control unit 46A of the smart device 14, monitors the child's learning progress in real time and provides supplementary or additional learning materials as needed. The adjustment unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, adjusts the learning content based on information from the feedback provision unit. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned initial assessment unit, curriculum generation unit, feedback provision unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the initial assessment unit assesses the child's learning style and interests using the camera 42 and microphone 238 of the smart glasses 214 and transmits the assessment results to the data processing device 12 via the control unit 46A. The curriculum generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, creates a learning curriculum based on the results of the initial assessment unit. The feedback provision unit, implemented, for example, by the control unit 46A of the smart glasses 214, monitors the child's learning progress in real time and provides supplementary or additional learning materials as needed. The adjustment unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, adjusts the learning content based on information from the feedback provision unit. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned initial assessment unit, curriculum generation unit, feedback provision unit, and adjustment unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the initial assessment unit assesses the child's learning style and interests using the camera 42 and microphone 238 of the headset terminal 314 and transmits the assessment results to the data processing device 12 via the control unit 46A. The curriculum generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a learning curriculum based on the results of the initial assessment unit. The feedback provision unit, realized, for example, by the control unit 46A of the headset terminal 314, monitors the child's learning progress in real time and provides supplementary or additional learning materials as needed. The adjustment unit, realized, for example, by the specific processing unit 290 of the data processing device 12, adjusts the learning content based on information from the feedback provision unit. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned initial assessment unit, curriculum generation unit, feedback provision unit, and adjustment unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the initial assessment unit assesses the child's learning style and interests using the camera 42 and microphone 238 of the robot 414 and transmits the assessment results to the data processing device 12 via the control unit 46A. The curriculum generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, creates a learning curriculum based on the results of the initial assessment unit. The feedback provision unit, realized, for example, by the control unit 46A of the robot 414, monitors the child's learning progress in real time and provides supplementary or additional learning materials as needed. The adjustment unit, realized, for example, by the specific processing unit 290 of the data processing device 12, adjusts the learning content based on information from the feedback provision unit.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The learning support system can further include a gamification unit. The gamification unit can award points and badges according to the child's learning progress, thereby increasing motivation to learn. For example, the gamification unit can award points when the child completes a specific task. The gamification unit can also award bonus points when the child continues to study. The gamification unit can also award badges when the child achieves a specific goal. This can increase the child's motivation to learn and encourage continuation of learning.

[0108] The learning support system may further include a social interaction section. The social interaction section enables students to discuss learning content with other students and work on projects together. For example, the social interaction section may provide an online forum where students can exchange questions and opinions. The social interaction section may also provide a group chat function where students can communicate in real time. The social interaction section may also provide tools to support the progress of collaborative projects. This allows students to work together with other students while learning, improving learning effectiveness.

[0109] The learning support system can further include a virtual reality (VR) unit. The VR unit allows children to have learning experiences in a virtual space. For example, the VR unit provides a virtual space that recreates historical events, allowing children to learn within it. The VR unit also allows scientific experiments to be conducted in a virtual space, allowing children to safely experience the experiments. The VR unit also allows children to visit geographical locations in a virtual space, allowing them to learn about those locations. This allows children to learn in a way that is closer to real-life experiences, improving learning effectiveness.

[0110] The learning support system may further include a speech recognition unit. The speech recognition unit may analyze the speech input of the child and reflect the results in the learning content. For example, the speech recognition unit may enable the child to input a question by speech and provide an answer to the question. The speech recognition unit may also enable the child to input an answer by speech and evaluate the answer. The speech recognition unit may also analyze the child's pronunciation and provide feedback on areas for improvement in pronunciation. This allows the child to progress with their learning using speech, improving the convenience of learning.

[0111] The learning support system may further include an emotion estimation unit. The emotion estimation unit may estimate a child's emotion from their facial expressions and voice, and adjust the learning content based on the emotion. For example, if the child is tired, the emotion estimation unit may suggest taking a break. If the child is excited, the emotion estimation unit may also provide content that piques the child's interest. If the child is calm, the emotion estimation unit may also provide content that requires concentration. This allows the learning content to be adjusted according to the child's emotion, maximizing the learning effect.

[0112] The learning support system may further include a biometrics unit. The biometrics unit may collect biological information of the child and adjust the learning content based on that information. For example, the biometrics unit may monitor the child's heart rate and evaluate the child's stress level. The biometrics unit may also measure the child's skin temperature and evaluate the child's concentration level. The biometrics unit may also analyze the child's brain waves and evaluate the effectiveness of the learning. This allows the learning content to be adjusted based on the child's biological information, maximizing the learning effect.

[0113] The learning support system may further include a personalized reminder unit. The personalized reminder unit may provide reminders based on the child's learning schedule to support continuation of learning. For example, the personalized reminder unit may send a reminder to the child to start learning at a specific time. The personalized reminder unit may also send periodic reminders to prevent the child from interrupting their learning. The personalized reminder unit may also provide reminders to help the child achieve their learning goals. This makes it easier for the child to continue their learning, improving learning effectiveness.

[0114] The learning support system may further include an emotion estimation unit. The emotion estimation unit may estimate the emotion of the child and adjust the progress of the learning based on the emotion. For example, if the child is feeling anxious, the emotion estimation unit may provide content that helps the child relax. If the child is excited, the emotion estimation unit may also provide content that helps the child to improve concentration. If the child is tired, the emotion estimation unit may also suggest taking a break. This allows the progress of the learning to be adjusted according to the child's emotion, maximizing the learning effect.

[0115] The learning support system may further include an emotion estimation unit. The emotion estimation unit may estimate the emotion of the child and adjust the content of the feedback based on the emotion. For example, if the child is feeling down, the emotion estimation unit may provide feedback that includes many encouraging words. If the child is feeling confident, the emotion estimation unit may provide a challenging task. If the child is feeling nervous, the emotion estimation unit may provide feedback that helps the child to relax. In this way, the content of the feedback may be adjusted according to the child's emotion, maximizing the learning effect.

[0116] The learning support system may further include an emotion estimation unit. The emotion estimation unit may estimate the emotion of the child and adjust the progress speed of the learning curriculum based on the emotion. For example, the emotion estimation unit may slow down the progress speed of the learning curriculum when the child is impatient. The emotion estimation unit may also speed up the progress speed of the learning curriculum when the child is relaxed. The emotion estimation unit may also optimize the progress speed of the learning curriculum when the child is concentrating. This allows the progress speed of the learning curriculum to be adjusted according to the emotion of the child, maximizing the learning effect.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The initial assessment portion assesses the student's learning style and interests. For example, the initial assessment portion may include questions and tests to understand the student's learning history and interests. Multiple-choice questions may be used to assess the student's interests, and written or practical tests may be used to assess the student's learning style and learning abilities. Step 2: The curriculum generation unit creates a learning curriculum based on the results of the evaluation by the initial evaluation unit. For example, the curriculum generation unit includes an algorithm that dynamically changes the learning content according to the child's level of understanding and interests, and optimizes the learning content using adaptive learning algorithms and machine learning algorithms. It also customizes the learning content based on the child's learning history. Step 3: The feedback provider provides real-time feedback based on the curriculum created by the curriculum generator. For example, the feedback provider monitors the student's learning progress and understanding in real time and provides supplementary or additional learning materials as needed. Real-time data collection is used to track the student's learning progress and provide video learning materials and practice problems. Step 4: The coordinator adjusts the learning content based on the feedback provided by the feedback provider. For example, the coordinator may provide special support to children with learning disabilities and support their learning progress, providing special learning materials, individualized instruction, and assistive tools.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: an initial assessment unit that assesses a child's learning style and interests; a curriculum generation unit that creates a learning curriculum based on the results of the assessment by the initial assessment unit; a feedback provision unit that provides immediate feedback based on the curriculum created by the curriculum generation unit; and an adjustment unit that adjusts the learning content based on the feedback provided by the feedback provision unit.

2. 2. The system according to claim 1, wherein the initial assessment section includes specific questions and tests for grasping the child's learning history and interests.

3. 2. The system according to claim 1, wherein the curriculum generation unit includes an algorithm for adaptively changing learning content according to the child's level of understanding and interests.

4. The system according to claim 1, wherein the feedback providing unit monitors the student's learning progress and understanding in real time and provides supplementary or additional learning materials as needed.

5. 10. The system of claim 1, wherein the coordinating unit provides individualized assistance to children with learning disabilities to support their learning progress.

6. The adjustment unit Use online learning platforms to provide an environment where students can learn at their own pace.

2. The system of claim 1.

7. The initial evaluation unit Estimate the child's emotions and adjust the timing of evaluation based on the estimated emotions.

2. The system of claim 1.

8. The initial evaluation unit Analyze students' past learning history and select the most appropriate assessment method 2. The system of claim 1.

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