system

Abstract

The system according to this embodiment aims to provide individually optimized learning tailored to each student's learning style and interests. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, a proposal unit, and a feedback unit. The analysis unit analyzes the student's learning style and interests. The generation unit creates a virtual companion based on the information analyzed by the analysis unit. The proposal unit suggests and supports quest-based learning using the virtual companion created by the generation unit. The feedback unit provides real-time feedback on learning progress based on the learning content suggested by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, individualized optimal learning according to the learning style and interest of each student has not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to provide individualized optimal learning according to the learning style and interest of each student.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, a proposal unit, and a feedback unit. The analysis unit analyzes the student's learning style and interests. The generation unit creates a virtual companion based on the information analyzed by the analysis unit. The proposal unit suggests and supports quest-based learning using the virtual companion created by the generation unit. The feedback unit provides real-time feedback on learning progress based on the learning content suggested by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide individually optimized learning tailored to each student's learning style and interests. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The individualized learning system according to an embodiment of the present invention is a system that creates a virtual companion tailored to each student. This system evolves according to the student's learning style, interests, and progress, and proposes and supports quest-based learning. Specifically, it generates learning tasks based on the student's interests and presents the next step according to the level of achievement. This allows students to learn at their own pace while maintaining motivation. For example, the generating AI analyzes the student's learning style and interests and creates a virtual companion based on that. This virtual companion analyzes the student's learning progress in real time and provides optimal learning content. For example, if a student is interested in mathematics, the generating AI proposes a mathematics quest to that student and presents the next step according to the level of achievement. Next, through quest-based learning, students can learn in a fun, game-like way. For example, in a certain quest, students can earn points by solving math problems and advance to the next level. In this way, students can learn at their own pace while maintaining motivation. Furthermore, the generating AI provides real-time feedback on the student's learning progress, providing immediate confirmation of understanding and suggesting the next step. For example, if a student is struggling with a particular problem, the generating AI will provide additional support and advice to improve their understanding. Progress reports for parents are also provided, enabling effective support at home. For instance, parents can monitor their child's learning progress and provide additional support as needed. This system addresses individual learning styles and interests that cannot be accommodated by standardized education, providing personalized instruction for each student. This allows students to learn at their own pace, maintain motivation, and maximize their potential. In short, the personalized learning system provides learning tailored to each student, helping them progress while maintaining motivation.

[0029] The individualized learning system according to this embodiment comprises an analysis unit, a generation unit, a proposal unit, and a feedback unit. The analysis unit analyzes the student's learning style and interests. For example, the analysis unit analyzes the student's learning style based on the student's learning history and survey results. The analysis unit can also collect data on social media activities and hobbies to identify the student's interests. For example, the analysis unit analyzes subjects in which the student has previously achieved high scores and topics of interest. The generation unit creates a virtual companion based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate a virtual companion based on the student's learning style and interests. For example, the generation unit uses a generation AI to generate a virtual companion based on the student's learning style and interests. The proposal unit suggests and supports quest-based learning using the virtual companion created by the generation unit. For example, the proposal unit uses a generation AI to suggest quest-based learning based on the student's learning style and interests. The proposal unit uses generative AI to propose quest-based learning based on the student's learning style and interests. The proposal unit uses generative AI to propose quest-based learning based on the student's learning style and interests. The feedback unit provides real-time feedback on learning progress based on the learning content proposed by the proposal unit. The feedback unit uses generative AI to provide real-time feedback on the student's learning progress. The feedback unit uses generative AI to provide real-time feedback on the student's learning progress. As a result, the individualized learning system according to the embodiment provides individually optimized learning based on the student's learning style and interests, enabling them to progress in their learning while maintaining motivation.

[0030] The analytics department analyzes students' learning styles and interests. For example, it analyzes learning styles based on students' learning history and survey results. Specifically, it collects data such as which subjects students have scored highly in the past, what learning methods they prefer, and what times of day they most often study, and uses this data to analyze students' learning styles in detail. The analytics department can also collect data on social media activity and hobbies to identify students' interests. For example, it collects information on what topics students are interested in on social media, what kind of posts they make, and what groups they participate in, and uses this data to identify students' interests. Furthermore, the analytics department integrates data on students' learning styles and interests to create individual learning profiles. These profiles contain detailed information on students' learning styles, interests, strengths, and weaknesses, and serve as foundational data for other departments to provide individually optimized learning content. The analytics department uses AI to analyze this data and understand students' learning styles and interests in real time. This allows the analytics department to play a crucial role in providing the most suitable learning methods and content for each student.

[0031] The generation unit creates virtual companions based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate virtual companions based on students' learning styles and interests. Specifically, the generation AI designs a virtual companion that is the optimal learning partner for each student, based on their learning profile. This virtual companion can provide learning advice at the appropriate time and introduce interesting topics, tailored to the student's learning style. For example, it can provide explanations using diagrams and graphs for students who prefer visual learning, and audio explanations for students who prefer auditory learning. The virtual companion can also suggest relevant learning topics and tasks based on the student's interests. For example, it can introduce the latest science news and experiment videos to students interested in science, and provide quizzes about historical events and figures for students interested in history. The generation unit can also customize the appearance and personality of the virtual companion using the generation AI. This allows students to learn with a learning partner that suits them, increasing their motivation to learn. The generation unit can continuously improve the virtual companion generation process and provide more effective learning support based on student feedback.

[0032] The suggestion department, created by the generation department, proposes and supports quest-based learning through virtual companions. For example, the suggestion department uses generative AI to propose quest-based learning based on the student's learning style and interests. Specifically, based on the student's learning profile, the suggestion department transforms learning content into a game-like quest format and presents it to the student. Quest-based learning is designed to allow students to learn while having fun, with each quest having clear goals and rewards. For example, in math quests, students earn points by solving specific problems, and can advance to the next level once they collect a certain number of points. In history quests, students can earn virtual items and titles by answering quizzes about historical events and figures. The suggestion department can use generative AI to adjust the difficulty and content of each quest to match the student's learning progress. This allows students to learn at their own pace and achieve their learning goals without difficulty. Furthermore, the suggestion department provides students with timely learning advice and support through the virtual companion. For example, if a student gets stuck on a quest, the virtual companion can provide hints or introduce additional resources. This allows the proposal department to provide support to help students learn in an enjoyable and effective way.

[0033] The Feedback Department provides real-time feedback on learning progress based on the learning content proposed by the Proposal Department. For example, the Feedback Department uses generative AI to provide real-time feedback on students' learning progress. Specifically, the Feedback Department evaluates the achievements and challenges students encounter as they progress through quests in real time and provides appropriate feedback. For instance, when a student solves a particular problem, the Feedback Department evaluates the accuracy of the answer and the time taken to solve it, providing immediate feedback. Furthermore, when a student completes a quest, it provides feedback on their level of achievement and advice for the next step. The Feedback Department can also use generative AI to analyze students' learning data and provide personalized feedback. For example, if a student struggles in a particular area, it suggests additional practice problems or resources related to that area. Conversely, if a student excels in a particular area, it suggests advanced challenges or projects related to that area. In addition, the Feedback Department can continuously monitor students' learning progress and adjust the learning plan as needed. This ensures that students are always provided with the most suitable learning content, allowing them to learn effectively. The Feedback Department can play a crucial role in maintaining student motivation and maximizing learning outcomes.

[0034] The feedback unit can immediately check students' understanding and suggest the next steps. For example, the feedback unit can use generative AI to check students' understanding in real time. The feedback unit can also use generative AI to check students' understanding in real time. Furthermore, the feedback unit can suggest the next steps. For example, the feedback unit can use generative AI to suggest the next steps according to the student's understanding. This enables appropriate feedback and suggestions for the next steps tailored to the student's understanding.

[0035] The proposal department can propose a quest-based learning format that makes learning fun and engaging, like a game. For example, the proposal department can use generative AI to suggest quest-based learning tailored to each student's learning style and interests. This allows students to enjoy learning and makes it easier to maintain their motivation.

[0036] The generation unit can generate virtual companions based on students' learning styles and interests. For example, the generation unit uses a generation AI to generate virtual companions based on students' learning styles and interests. This allows for individually optimized learning by generating virtual companions tailored to each student.

[0037] The analysis unit can analyze students' learning styles and interests. For example, the analysis unit uses generative AI to analyze students' learning styles and interests. This allows for accurate analysis of students' learning styles and interests, enabling the provision of individually optimized learning.

[0038] The proposal unit may include a reporting unit that provides progress reports for parents. The proposal unit may, for example, use a generative AI to provide progress reports for parents. The proposal unit may, for example, use a generative AI to provide progress reports for parents. This allows parents to understand their child's learning progress and provide effective support at home.

[0039] The analysis unit can analyze a student's past learning history and select the optimal analysis algorithm. For example, the analysis unit uses generative AI to analyze a student's past learning history. Furthermore, the analysis unit can select the optimal analysis algorithm. For example, the analysis unit prioritizes analyzing the learning style of subjects in which the student has previously achieved high scores. The analysis unit performs analysis to improve the learning style of subjects in which the student has previously struggled. The analysis unit identifies the most effective learning method from the student's past learning history and selects an analysis algorithm. This allows for more effective analysis of learning styles and interests by selecting the optimal analysis algorithm based on past learning history.

[0040] The analysis unit can filter learning styles and interests based on students' current living situations and areas of interest. For example, the analysis unit uses generative AI to analyze students' current living situations and areas of interest. The analysis unit uses generative AI to analyze students' current living situations and areas of interest. The analysis unit uses generative AI to analyze students' current living situations and areas of interest. Furthermore, the analysis unit can perform filtering. For example, the analysis unit prioritizes analyzing learning styles related to topics that students are currently interested in. The analysis unit filters learning styles that are constrained by time and place, according to students' living situations. The analysis unit analyzes relevant learning styles based on students' areas of interest. This allows for the analysis of more relevant learning styles and interests by filtering based on students' current living situations and areas of interest.

[0041] The analysis unit can prioritize the analysis of highly relevant information by considering the student's geographical location when analyzing learning styles and interests. For example, the analysis unit uses generative AI to analyze the student's geographical location. The analysis unit uses generative AI to analyze the student's geographical location. For example, the analysis unit uses generative AI to analyze the student's geographical location. Furthermore, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit analyzes learning styles based on the educational curriculum of the area where the student lives. Based on the student's geographical location, the analysis unit prioritizes the analysis of learning styles related to the local culture and history. The analysis unit analyzes learning styles that utilize local educational resources, taking the student's geographical location into consideration. This makes it possible to analyze learning styles and interests related to the region by considering the student's geographical location.

[0042] The analysis unit can analyze students' social media activity and related information when analyzing learning styles and interests. For example, the analysis unit uses generative AI to analyze students' social media activity. Furthermore, the analysis unit can analyze related information. For example, the analysis unit analyzes learning styles based on topics students show interest in on social media. The analysis unit identifies areas of interest from students' social media activity and incorporates this into the analysis. The analysis unit analyzes the content of students' social media interactions and analyzes related learning styles. This allows for a more relevant analysis of learning styles and interests by analyzing students' social media activity.

[0043] The generation unit can select the optimal generation algorithm based on the student's learning history when generating virtual companions. The generation unit analyzes the student's learning history using, for example, a generation AI. The generation unit analyzes the student's learning history using, for example, a generation AI. Furthermore, the generation unit can select the optimal generation algorithm. The generation unit generates a virtual companion that reflects the learning style of subjects in which the student has previously achieved high scores. The generation unit generates a virtual companion to improve the learning style of subjects in which the student has previously struggled. The generation unit identifies the most effective learning method from the student's learning history and generates a virtual companion based on it. In this way, by selecting the optimal generation algorithm based on the student's learning history, a more effective virtual companion can be generated.

[0044] The generation unit can customize virtual companions based on the student's current living situation and areas of interest. For example, the generation unit uses a generation AI to analyze the student's current living situation and areas of interest. The generation unit uses a generation AI to analyze the student's current living situation and areas of interest. For example, the generation unit uses a generation AI to analyze the student's current living situation and areas of interest. Furthermore, the generation unit can customize the virtual companions. For example, the generation unit can generate virtual companions related to topics the student is currently interested in. The generation unit can generate virtual companions that reflect learning styles constrained by time and place, depending on the student's living situation. The generation unit can generate relevant virtual companions based on the student's areas of interest. In this way, by customizing based on the student's current living situation and areas of interest, it is possible to generate more relevant virtual companions.

[0045] The generation unit can generate the optimal virtual companion by considering the student's geographical location information. For example, the generation unit uses a generation AI to analyze the student's geographical location information. Furthermore, the generation unit can generate the optimal companion. For example, the generation unit can generate a virtual companion related to the culture and history of the area where the student lives. Based on the student's geographical location information, the generation unit generates a virtual companion that utilizes local educational resources. The generation unit considers the student's geographical location information to generate a virtual companion based on the local educational curriculum. This allows for the generation of region-related virtual companions by considering the student's geographical location information.

[0046] The generation unit can analyze students' social media activity and reflect relevant information when generating virtual companions. For example, the generation unit uses a generation AI to analyze students' social media activity. Furthermore, the generation unit can reflect relevant information. For example, the generation unit generates virtual companions based on topics students show interest in on social media. The generation unit identifies areas of interest from students' social media activity and generates virtual companions based on those areas. The generation unit analyzes students' social media interactions and generates relevant virtual companions. This allows for the generation of more relevant virtual companions by analyzing students' social media activity.

[0047] The suggestion function can select the optimal suggestion algorithm based on the student's learning history when suggesting quest-based learning. The suggestion function can analyze the student's learning history using, for example, generative AI. The suggestion function can analyze the student's learning history using, for example, generative AI. Furthermore, the suggestion function can select the optimal suggestion algorithm. For example, the suggestion function can prioritize suggesting quests in subjects in which the student has previously achieved high scores. The suggestion function can make suggestions to improve quests in subjects in which the student has previously struggled. The suggestion function identifies the most effective quests from the student's learning history and makes suggestions based on them. This makes it possible to make more effective learning suggestions by selecting the optimal suggestion algorithm based on the student's learning history.

[0048] The suggestion function can customize quest-based learning suggestions based on the student's current living situation and areas of interest. For example, the suggestion function uses generative AI to analyze the student's current living situation and areas of interest. The suggestion function uses generative AI to analyze the student's current living situation and areas of interest. For example, the suggestion function uses generative AI to analyze the student's current living situation and areas of interest. Furthermore, the suggestion function can customize the suggestions. For example, the suggestion function can suggest quests related to topics the student is currently interested in. The suggestion function can suggest quests with time and location constraints depending on the student's living situation. The suggestion function can suggest relevant quests based on the student's areas of interest. This allows for more relevant learning suggestions by customizing them based on the student's current living situation and areas of interest.

[0049] The suggestion function can propose optimal quests when suggesting quest-based learning, taking into account the student's geographical location. For example, the suggestion function uses generative AI to analyze the student's geographical location. Furthermore, the suggestion function can propose optimal quests. For example, the suggestion function can propose quests related to the culture and history of the area where the student lives. Based on the student's geographical location, the suggestion function can propose quests that utilize local educational resources. The suggestion function can propose quests based on the local educational curriculum, taking the student's geographical location into consideration. This makes it possible to propose quests relevant to the region by considering the student's geographical location.

[0050] The suggestion function can analyze students' social media activity and reflect relevant information when proposing quest-based learning. For example, the suggestion function can use generative AI to analyze students' social media activity. The suggestion function can use generative AI to analyze students' social media activity. For example, the suggestion function can use generative AI to analyze students' social media activity. Furthermore, the suggestion function can reflect relevant information. For example, the suggestion function can propose quests based on topics that students show interest in on social media. The suggestion function can identify areas of interest from students' social media activity and propose quests based on those areas. The suggestion function can analyze the content of students' social media interactions and propose relevant quests. This makes it possible to propose more relevant quests by analyzing students' social media activity.

[0051] The feedback unit can select the optimal feedback algorithm based on the student's learning history when providing feedback. For example, the feedback unit analyzes the student's learning history using generative AI. Furthermore, the feedback unit can select the optimal feedback algorithm. For example, the feedback unit prioritizes providing feedback on subjects where the student has previously achieved high scores. The feedback unit selects an algorithm to improve feedback on subjects where the student has previously struggled. The feedback unit identifies the most effective feedback method from the student's learning history and provides feedback based on that. This allows for more effective feedback by selecting the optimal feedback algorithm based on the student's learning history.

[0052] The feedback unit can customize feedback based on the student's current living situation and areas of interest. For example, the feedback unit uses generative AI to analyze the student's current living situation and areas of interest. Furthermore, the feedback unit can be customized. For example, the feedback unit can provide feedback related to topics the student is currently interested in. The feedback unit can provide feedback with time and location constraints depending on the student's living situation. The feedback unit can provide relevant feedback based on the student's areas of interest. This allows for more relevant feedback by customizing it based on the student's current living situation and areas of interest.

[0053] The feedback unit can provide optimal feedback by considering the student's geographical location. For example, the feedback unit analyzes the student's geographical location using generative AI. Furthermore, the feedback unit can provide optimal feedback. For example, the feedback unit provides feedback based on the educational curriculum of the area where the student lives. Based on the student's geographical location, the feedback unit provides feedback related to the local culture and history. The feedback unit considers the student's geographical location and provides feedback utilizing local educational resources. This makes it possible to provide region-related feedback by considering the student's geographical location.

[0054] The feedback department can analyze students' social media activity and reflect relevant information when providing feedback. For example, the feedback department can use generative AI to analyze students' social media activity. Furthermore, the feedback department can reflect relevant information. For example, the feedback department can provide feedback based on topics students show interest in on social media. The feedback department can identify areas of interest from students' social media activity and provide feedback based on those areas. The feedback department can analyze students' social media interactions and provide relevant feedback. This allows for more relevant feedback by analyzing students' social media activity.

[0055] The reporting department can select the optimal reporting algorithm based on the student's learning history when creating progress reports. For example, the reporting department uses generative AI to analyze the student's learning history. Furthermore, the reporting department can select the optimal reporting algorithm. For example, the reporting department prioritizes providing progress reports for subjects in which the student has previously achieved high scores. The reporting department selects an algorithm to improve progress reports for subjects in which the student has previously struggled. The reporting department identifies the most effective progress reporting method from the student's learning history and provides progress reports based on it. This allows for the provision of more effective progress reports by selecting the optimal reporting algorithm based on the student's learning history.

[0056] The reporting department can customize progress reports based on students' current living situations and areas of interest. For example, the reporting department uses generative AI to analyze students' current living situations and areas of interest. Furthermore, the reporting department can customize the reports. For example, the reporting department can provide progress reports related to topics students are currently interested in. The reporting department can provide progress reports with time and location constraints depending on the student's living situation. The reporting department can provide relevant progress reports based on students' areas of interest. This allows for the provision of more relevant progress reports by customizing them based on students' current living situations and areas of interest.

[0057] The reporting department can provide optimal reports by considering students' geographical location information when creating progress reports. For example, the reporting department uses generative AI to analyze students' geographical location information. Furthermore, the reporting department can provide optimal reports. For example, the reporting department provides progress reports based on the educational curriculum of the area where the student lives. The reporting department provides progress reports related to local culture and history based on students' geographical location information. The reporting department provides progress reports that utilize local educational resources, taking students' geographical location information into consideration. This allows for the provision of region-related progress reports by considering students' geographical location information.

[0058] The reporting department can analyze students' social media activities and reflect relevant information when creating progress reports. For example, the reporting department can use generative AI to analyze students' social media activities. Furthermore, the reporting department can reflect relevant information. For example, the reporting department can provide progress reports based on topics students show interest in on social media. The reporting department can identify areas of interest from students' social media activities and provide progress reports based on those areas. The reporting department can analyze students' social media interactions and provide relevant progress reports. This allows for the provision of more relevant progress reports by analyzing students' social media activities.

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

[0060] The analytics department can analyze not only students' learning styles and interests, but also their daily routines and activity patterns. For example, it can use data acquired from students' smartwatches and smartphones to analyze their daily activity levels and sleep patterns. This allows it to identify the times when students are most focused and when they need breaks. Furthermore, the analytics department can analyze students' eating and exercise habits to provide learning suggestions based on their health status. For instance, it can analyze that students' concentration levels increase after exercise and suggest more challenging tasks at that time. This enables the provision of individually optimized learning based on each student's routine and health status.

[0061] The feedback department can provide motivational feedback to enhance students' learning motivation, in addition to checking their understanding and suggesting the next steps. For example, the feedback department can send praise and encouraging messages when students achieve their goals. It can also suggest appropriate breaks and provide advice for refreshing themselves when students are working on difficult tasks. Furthermore, the feedback department can implement a reward system that allows students to feel a sense of accomplishment as they progress. For example, the feedback department can award virtual badges or points each time a student achieves a certain goal, maintaining their motivation towards the next goal. This allows for effective feedback while increasing students' motivation to learn.

[0062] In addition to quest-based learning suggestions, the suggestion department can propose customized learning environments tailored to students' learning styles. For example, if a student prefers visual learning, the suggestion department can suggest learning materials that make extensive use of visual aids and infographics. If a student prefers auditory learning, the suggestion department can suggest learning content in the form of audiobooks or podcasts. Furthermore, if a student prefers hands-on learning, the suggestion department can suggest experiments or project-based learning activities. For example, if a student is interested in science, the suggestion department can suggest a simple experiment kit that can be done at home, providing an opportunity for hands-on learning. This allows for the provision of a customized learning environment tailored to students' learning styles.

[0063] The generation unit can generate virtual companions not only based on students' learning styles and interests, but also tailored to each student's personality and character. For example, if a student is introverted, the generation unit can generate a virtual companion with a calm and gentle personality. If a student is extroverted, the generation unit can generate a virtual companion with a lively and energetic personality. Furthermore, if a student enjoys humor, the generation unit can generate a virtual companion with a sense of humor. For instance, the generation unit can create a virtual companion that occasionally includes jokes and funny anecdotes in conversation to help students relax while studying. This allows for the provision of virtual companions tailored to each student's personality and character.

[0064] The analysis unit can analyze not only students' learning styles and interests, but also their learning environment and the influences of their surroundings. For example, the analysis unit can analyze students' home and school environments to identify factors that influence their learning. It can also analyze students' friendships and relationships with teachers to understand the support systems available for their learning. Furthermore, the analysis unit can analyze physical factors in students' learning environments. For instance, it can analyze lighting and noise levels in students' study spaces to suggest an optimal learning environment. This allows for the provision of individually optimized learning that takes into account students' learning environments and surrounding influences.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The analysis unit analyzes students' learning styles and interests. Based on students' learning history and survey results, the analysis unit analyzes their learning styles and identifies their interests by collecting data on their social media activities and hobbies. For example, it analyzes subjects in which students have scored highly in the past and topics they are interested in. Step 2: The generation unit creates a virtual companion based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a virtual companion based on the student's learning style and interests. Step 3: The suggestion unit uses a virtual companion created by the generation unit to suggest and support quest-based learning. The suggestion unit uses a generation AI to suggest quest-based learning based on the student's learning style and interests. Step 4: The feedback unit provides real-time feedback on learning progress based on the learning content proposed by the proposal unit. The feedback unit uses generative AI to provide real-time feedback on the student's learning progress.

[0067] (Example of form 2) The individualized learning system according to an embodiment of the present invention is a system that creates a virtual companion tailored to each student. This system evolves according to the student's learning style, interests, and progress, and proposes and supports quest-based learning. Specifically, it generates learning tasks based on the student's interests and presents the next step according to the level of achievement. This allows students to learn at their own pace while maintaining motivation. For example, the generating AI analyzes the student's learning style and interests and creates a virtual companion based on that. This virtual companion analyzes the student's learning progress in real time and provides optimal learning content. For example, if a student is interested in mathematics, the generating AI proposes a mathematics quest to that student and presents the next step according to the level of achievement. Next, through quest-based learning, students can learn in a fun, game-like way. For example, in a certain quest, students can earn points by solving math problems and advance to the next level. In this way, students can learn at their own pace while maintaining motivation. Furthermore, the generating AI provides real-time feedback on the student's learning progress, providing immediate confirmation of understanding and suggesting the next step. For example, if a student is struggling with a particular problem, the generating AI will provide additional support and advice to improve their understanding. Progress reports for parents are also provided, enabling effective support at home. For instance, parents can monitor their child's learning progress and provide additional support as needed. This system addresses individual learning styles and interests that cannot be accommodated by standardized education, providing personalized instruction for each student. This allows students to learn at their own pace, maintain motivation, and maximize their potential. In short, the personalized learning system provides learning tailored to each student, helping them progress while maintaining motivation.

[0068] The individualized learning system according to this embodiment comprises an analysis unit, a generation unit, a proposal unit, and a feedback unit. The analysis unit analyzes the student's learning style and interests. For example, the analysis unit analyzes the student's learning style based on the student's learning history and survey results. The analysis unit can also collect data on social media activities and hobbies to identify the student's interests. For example, the analysis unit analyzes subjects in which the student has previously achieved high scores and topics of interest. The generation unit creates a virtual companion based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate a virtual companion based on the student's learning style and interests. For example, the generation unit uses a generation AI to generate a virtual companion based on the student's learning style and interests. The proposal unit suggests and supports quest-based learning using the virtual companion created by the generation unit. For example, the proposal unit uses a generation AI to suggest quest-based learning based on the student's learning style and interests. The proposal unit uses generative AI to propose quest-based learning based on the student's learning style and interests. The proposal unit uses generative AI to propose quest-based learning based on the student's learning style and interests. The feedback unit provides real-time feedback on learning progress based on the learning content proposed by the proposal unit. The feedback unit uses generative AI to provide real-time feedback on the student's learning progress. The feedback unit uses generative AI to provide real-time feedback on the student's learning progress. As a result, the individualized learning system according to the embodiment provides individually optimized learning based on the student's learning style and interests, enabling them to progress in their learning while maintaining motivation.

[0069] The analytics department analyzes students' learning styles and interests. For example, it analyzes learning styles based on students' learning history and survey results. Specifically, it collects data such as which subjects students have scored highly in the past, what learning methods they prefer, and what times of day they most often study, and uses this data to analyze students' learning styles in detail. The analytics department can also collect data on social media activity and hobbies to identify students' interests. For example, it collects information on what topics students are interested in on social media, what kind of posts they make, and what groups they participate in, and uses this data to identify students' interests. Furthermore, the analytics department integrates data on students' learning styles and interests to create individual learning profiles. These profiles contain detailed information on students' learning styles, interests, strengths, and weaknesses, and serve as foundational data for other departments to provide individually optimized learning content. The analytics department uses AI to analyze this data and understand students' learning styles and interests in real time. This allows the analytics department to play a crucial role in providing the most suitable learning methods and content for each student.

[0070] The generation unit creates virtual companions based on the information analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate virtual companions based on students' learning styles and interests. Specifically, the generation AI designs a virtual companion that is the optimal learning partner for each student, based on their learning profile. This virtual companion can provide learning advice at the appropriate time and introduce interesting topics, tailored to the student's learning style. For example, it can provide explanations using diagrams and graphs for students who prefer visual learning, and audio explanations for students who prefer auditory learning. The virtual companion can also suggest relevant learning topics and tasks based on the student's interests. For example, it can introduce the latest science news and experiment videos to students interested in science, and provide quizzes about historical events and figures for students interested in history. The generation unit can also customize the appearance and personality of the virtual companion using the generation AI. This allows students to learn with a learning partner that suits them, increasing their motivation to learn. The generation unit can continuously improve the virtual companion generation process and provide more effective learning support based on student feedback.

[0071] The suggestion department, created by the generation department, proposes and supports quest-based learning through virtual companions. For example, the suggestion department uses generative AI to propose quest-based learning based on the student's learning style and interests. Specifically, based on the student's learning profile, the suggestion department transforms learning content into a game-like quest format and presents it to the student. Quest-based learning is designed to allow students to learn while having fun, with each quest having clear goals and rewards. For example, in math quests, students earn points by solving specific problems, and can advance to the next level once they collect a certain number of points. In history quests, students can earn virtual items and titles by answering quizzes about historical events and figures. The suggestion department can use generative AI to adjust the difficulty and content of each quest to match the student's learning progress. This allows students to learn at their own pace and achieve their learning goals without difficulty. Furthermore, the suggestion department provides students with timely learning advice and support through the virtual companion. For example, if a student gets stuck on a quest, the virtual companion can provide hints or introduce additional resources. This allows the proposal department to provide support to help students learn in an enjoyable and effective way.

[0072] The Feedback Department provides real-time feedback on learning progress based on the learning content proposed by the Proposal Department. For example, the Feedback Department uses generative AI to provide real-time feedback on students' learning progress. Specifically, the Feedback Department evaluates the achievements and challenges students encounter as they progress through quests in real time and provides appropriate feedback. For instance, when a student solves a particular problem, the Feedback Department evaluates the accuracy of the answer and the time taken to solve it, providing immediate feedback. Furthermore, when a student completes a quest, it provides feedback on their level of achievement and advice for the next step. The Feedback Department can also use generative AI to analyze students' learning data and provide personalized feedback. For example, if a student struggles in a particular area, it suggests additional practice problems or resources related to that area. Conversely, if a student excels in a particular area, it suggests advanced challenges or projects related to that area. In addition, the Feedback Department can continuously monitor students' learning progress and adjust the learning plan as needed. This ensures that students are always provided with the most suitable learning content, allowing them to learn effectively. The Feedback Department can play a crucial role in maintaining student motivation and maximizing learning outcomes.

[0073] The feedback unit can immediately check students' understanding and suggest the next steps. For example, the feedback unit can use generative AI to check students' understanding in real time. The feedback unit can also use generative AI to check students' understanding in real time. Furthermore, the feedback unit can suggest the next steps. For example, the feedback unit can use generative AI to suggest the next steps according to the student's understanding. This enables appropriate feedback and suggestions for the next steps tailored to the student's understanding.

[0074] The proposal department can propose a quest-based learning format that makes learning fun and engaging, like a game. For example, the proposal department can use generative AI to suggest quest-based learning tailored to each student's learning style and interests. This allows students to enjoy learning and makes it easier to maintain their motivation.

[0075] The generation unit can generate virtual companions based on students' learning styles and interests. For example, the generation unit uses a generation AI to generate virtual companions based on students' learning styles and interests. This allows for individually optimized learning by generating virtual companions tailored to each student.

[0076] The analysis unit can analyze students' learning styles and interests. For example, the analysis unit uses generative AI to analyze students' learning styles and interests. This allows for accurate analysis of students' learning styles and interests, enabling the provision of individually optimized learning.

[0077] The proposal unit may include a reporting unit that provides progress reports for parents. The proposal unit may, for example, use a generative AI to provide progress reports for parents. The proposal unit may, for example, use a generative AI to provide progress reports for parents. This allows parents to understand their child's learning progress and provide effective support at home.

[0078] The analysis unit can estimate students' emotions and adjust the analysis method for learning styles and interests based on the estimated emotions. For example, the analysis unit uses generative AI to estimate students' emotions. Furthermore, the analysis unit can adjust the analysis method for learning styles and interests based on the estimated emotions. For example, if a student is stressed, the analysis unit prioritizes and analyzes learning styles that promote relaxation. If a student is excited, the analysis unit analyzes learning styles that enhance concentration. If a student is tired, the analysis unit analyzes learning styles that are effective in a short amount of time. This allows for a more appropriate analysis of learning styles and interests by adjusting the analysis method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0079] The analysis unit can analyze a student's past learning history and select the optimal analysis algorithm. For example, the analysis unit uses generative AI to analyze a student's past learning history. Furthermore, the analysis unit can select the optimal analysis algorithm. For example, the analysis unit prioritizes analyzing the learning style of subjects in which the student has previously achieved high scores. The analysis unit performs analysis to improve the learning style of subjects in which the student has previously struggled. The analysis unit identifies the most effective learning method from the student's past learning history and selects an analysis algorithm. This allows for more effective analysis of learning styles and interests by selecting the optimal analysis algorithm based on past learning history.

[0080] The analysis unit can filter learning styles and interests based on students' current living situations and areas of interest. For example, the analysis unit uses generative AI to analyze students' current living situations and areas of interest. The analysis unit uses generative AI to analyze students' current living situations and areas of interest. The analysis unit uses generative AI to analyze students' current living situations and areas of interest. Furthermore, the analysis unit can perform filtering. For example, the analysis unit prioritizes analyzing learning styles related to topics that students are currently interested in. The analysis unit filters learning styles that are constrained by time and place, according to students' living situations. The analysis unit analyzes relevant learning styles based on students' areas of interest. This allows for the analysis of more relevant learning styles and interests by filtering based on students' current living situations and areas of interest.

[0081] The analysis unit can estimate the student's emotions and determine the priority of the analysis results based on the estimated emotions. The analysis unit estimates the student's emotions using, for example, generative AI. The analysis unit estimates the student's emotions using, for example, generative AI. The analysis unit estimates the student's emotions using, for example, generative AI. Furthermore, the analysis unit can determine the priority of the analysis results based on the estimated emotions. For example, if the student is relaxed, the analysis unit will prioritize providing detailed analysis results. If the student is in a hurry, the analysis unit will prioritize providing concise analysis results. If the student is excited, the analysis unit will prioritize providing interesting analysis results. In this way, by determining the priority of analysis results according to the student's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can prioritize the analysis of highly relevant information by considering the student's geographical location when analyzing learning styles and interests. For example, the analysis unit uses generative AI to analyze the student's geographical location. The analysis unit uses generative AI to analyze the student's geographical location. For example, the analysis unit uses generative AI to analyze the student's geographical location. Furthermore, the analysis unit can prioritize the analysis of highly relevant information. For example, the analysis unit analyzes learning styles based on the educational curriculum of the area where the student lives. Based on the student's geographical location, the analysis unit prioritizes the analysis of learning styles related to the local culture and history. The analysis unit analyzes learning styles that utilize local educational resources, taking the student's geographical location into consideration. This makes it possible to analyze learning styles and interests related to the region by considering the student's geographical location.

[0083] The analysis unit can analyze students' social media activity and related information when analyzing learning styles and interests. For example, the analysis unit uses generative AI to analyze students' social media activity. Furthermore, the analysis unit can analyze related information. For example, the analysis unit analyzes learning styles based on topics students show interest in on social media. The analysis unit identifies areas of interest from students' social media activity and incorporates this into the analysis. The analysis unit analyzes the content of students' social media interactions and analyzes related learning styles. This allows for a more relevant analysis of learning styles and interests by analyzing students' social media activity.

[0084] The generation unit can estimate the student's emotions and adjust the method of generating the virtual companion based on the estimated student's emotions. The generation unit estimates the student's emotions using, for example, a generation AI. The generation unit estimates the student's emotions using, for example, a generation AI. The generation unit estimates the student's emotions using, for example, a generation AI. Furthermore, the generation unit can adjust the method of generating the virtual companion based on the estimated student's emotions. For example, if the student is relaxed, the generation unit will generate a virtual companion with a calm personality. If the student is excited, the generation unit will generate a lively and energetic virtual companion. If the student is stressed, the generation unit will generate a soothing virtual companion. In this way, by adjusting the method of generating the virtual companion according to the student's emotions, a more appropriate virtual companion can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The generation unit can select the optimal generation algorithm based on the student's learning history when generating virtual companions. The generation unit analyzes the student's learning history using, for example, a generation AI. The generation unit analyzes the student's learning history using, for example, a generation AI. Furthermore, the generation unit can select the optimal generation algorithm. The generation unit generates a virtual companion that reflects the learning style of subjects in which the student has previously achieved high scores. The generation unit generates a virtual companion to improve the learning style of subjects in which the student has previously struggled. The generation unit identifies the most effective learning method from the student's learning history and generates a virtual companion based on it. In this way, by selecting the optimal generation algorithm based on the student's learning history, a more effective virtual companion can be generated.

[0086] The generation unit can customize virtual companions based on the student's current living situation and areas of interest. For example, the generation unit uses a generation AI to analyze the student's current living situation and areas of interest. The generation unit uses a generation AI to analyze the student's current living situation and areas of interest. For example, the generation unit uses a generation AI to analyze the student's current living situation and areas of interest. Furthermore, the generation unit can customize the virtual companions. For example, the generation unit can generate virtual companions related to topics the student is currently interested in. The generation unit can generate virtual companions that reflect learning styles constrained by time and place, depending on the student's living situation. The generation unit can generate relevant virtual companions based on the student's areas of interest. In this way, by customizing based on the student's current living situation and areas of interest, it is possible to generate more relevant virtual companions.

[0087] The generation unit can estimate the student's emotions and determine the priority of virtual companions to generate based on the estimated emotions. The generation unit estimates the student's emotions using, for example, a generation AI. The generation unit estimates the student's emotions using, for example, a generation AI. The generation unit estimates the student's emotions using, for example, a generation AI. Furthermore, the generation unit can determine the priority of virtual companions to generate based on the estimated emotions. For example, if the student is relaxed, the generation unit will prioritize generating a virtual companion with a calm personality. If the student is excited, the generation unit will prioritize generating an active and energetic virtual companion. If the student is stressed, the generation unit will prioritize generating a soothing virtual companion. In this way, by determining the priority of virtual companions according to the student's emotions, a more appropriate virtual companion can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0088] The generation unit can generate the optimal virtual companion by considering the student's geographical location information. For example, the generation unit uses a generation AI to analyze the student's geographical location information. Furthermore, the generation unit can generate the optimal companion. For example, the generation unit can generate a virtual companion related to the culture and history of the area where the student lives. Based on the student's geographical location information, the generation unit generates a virtual companion that utilizes local educational resources. The generation unit considers the student's geographical location information to generate a virtual companion based on the local educational curriculum. This allows for the generation of region-related virtual companions by considering the student's geographical location information.

[0089] The generation unit can analyze students' social media activity and reflect relevant information when generating virtual companions. For example, the generation unit uses a generation AI to analyze students' social media activity. Furthermore, the generation unit can reflect relevant information. For example, the generation unit generates virtual companions based on topics students show interest in on social media. The generation unit identifies areas of interest from students' social media activity and generates virtual companions based on those areas. The generation unit analyzes students' social media interactions and generates relevant virtual companions. This allows for the generation of more relevant virtual companions by analyzing students' social media activity.

[0090] The suggestion unit can estimate a student's emotions and adjust the quest-based learning suggestion method based on the estimated emotions. The suggestion unit estimates a student's emotions using, for example, generative AI. The suggestion unit estimates a student's emotions using, for example, generative AI. The suggestion unit estimates a student's emotions using, for example, generative AI. Furthermore, the suggestion unit can adjust the quest-based learning suggestion method based on the estimated emotions. For example, if a student is relaxed, the suggestion unit suggests a calm quest. If a student is excited, the suggestion unit suggests a challenging quest. If a student is stressed, the suggestion unit suggests a relaxing quest. This allows for more appropriate learning suggestions by adjusting the quest-based learning suggestion method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The suggestion function can select the optimal suggestion algorithm based on the student's learning history when suggesting quest-based learning. The suggestion function can analyze the student's learning history using, for example, generative AI. The suggestion function can analyze the student's learning history using, for example, generative AI. Furthermore, the suggestion function can select the optimal suggestion algorithm. For example, the suggestion function can prioritize suggesting quests in subjects in which the student has previously achieved high scores. The suggestion function can make suggestions to improve quests in subjects in which the student has previously struggled. The suggestion function identifies the most effective quests from the student's learning history and makes suggestions based on them. This makes it possible to make more effective learning suggestions by selecting the optimal suggestion algorithm based on the student's learning history.

[0092] The suggestion function can customize quest-based learning suggestions based on the student's current living situation and areas of interest. For example, the suggestion function uses generative AI to analyze the student's current living situation and areas of interest. The suggestion function uses generative AI to analyze the student's current living situation and areas of interest. For example, the suggestion function uses generative AI to analyze the student's current living situation and areas of interest. Furthermore, the suggestion function can customize the suggestions. For example, the suggestion function can suggest quests related to topics the student is currently interested in. The suggestion function can suggest quests with time and location constraints depending on the student's living situation. The suggestion function can suggest relevant quests based on the student's areas of interest. This allows for more relevant learning suggestions by customizing them based on the student's current living situation and areas of interest.

[0093] The suggestion unit can estimate a student's emotions and determine the priority of suggested quests based on the estimated emotions. The suggestion unit can estimate a student's emotions using, for example, generative AI. The suggestion unit can estimate a student's emotions using, for example, generative AI. The suggestion unit can estimate a student's emotions using, for example, generative AI. Furthermore, the suggestion unit can determine the priority of suggested quests based on the estimated emotions. For example, if a student is relaxed, the suggestion unit will prioritize suggesting calm quests. If a student is excited, the suggestion unit will prioritize suggesting challenging quests. If a student is stressed, the suggestion unit will prioritize suggesting relaxing quests. This allows for more appropriate quest suggestions by prioritizing quests according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The suggestion function can propose optimal quests when suggesting quest-based learning, taking into account the student's geographical location. For example, the suggestion function uses generative AI to analyze the student's geographical location. Furthermore, the suggestion function can propose optimal quests. For example, the suggestion function can propose quests related to the culture and history of the area where the student lives. Based on the student's geographical location, the suggestion function can propose quests that utilize local educational resources. The suggestion function can propose quests based on the local educational curriculum, taking the student's geographical location into consideration. This makes it possible to propose quests relevant to the region by considering the student's geographical location.

[0095] The suggestion function can analyze students' social media activity and reflect relevant information when proposing quest-based learning. For example, the suggestion function can use generative AI to analyze students' social media activity. The suggestion function can use generative AI to analyze students' social media activity. For example, the suggestion function can use generative AI to analyze students' social media activity. Furthermore, the suggestion function can reflect relevant information. For example, the suggestion function can propose quests based on topics that students show interest in on social media. The suggestion function can identify areas of interest from students' social media activity and propose quests based on those areas. The suggestion function can analyze the content of students' social media interactions and propose relevant quests. This makes it possible to propose more relevant quests by analyzing students' social media activity.

[0096] The feedback unit can estimate a student's emotions and adjust the feedback method based on the estimated emotions. The feedback unit estimates a student's emotions using, for example, generative AI. Furthermore, the feedback unit can adjust the feedback method based on the estimated emotions. For example, if a student is relaxed, the feedback unit provides detailed feedback. If a student is excited, the feedback unit provides concise feedback. If a student is stressed, the feedback unit provides feedback that includes words of encouragement. This allows for more appropriate feedback by adjusting the feedback method according to the student's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The feedback unit can select the optimal feedback algorithm based on the student's learning history when providing feedback. For example, the feedback unit analyzes the student's learning history using generative AI. Furthermore, the feedback unit can select the optimal feedback algorithm. For example, the feedback unit prioritizes providing feedback on subjects where the student has previously achieved high scores. The feedback unit selects an algorithm to improve feedback on subjects where the student has previously struggled. The feedback unit identifies the most effective feedback method from the student's learning history and provides feedback based on that. This allows for more effective feedback by selecting the optimal feedback algorithm based on the student's learning history.

[0098] The feedback unit can customize feedback based on the student's current living situation and areas of interest. For example, the feedback unit uses generative AI to analyze the student's current living situation and areas of interest. Furthermore, the feedback unit can be customized. For example, the feedback unit can provide feedback related to topics the student is currently interested in. The feedback unit can provide feedback with time and location constraints depending on the student's living situation. The feedback unit can provide relevant feedback based on the student's areas of interest. This allows for more relevant feedback by customizing it based on the student's current living situation and areas of interest.

[0099] The feedback unit can estimate a student's emotions and prioritize feedback based on the estimated emotions. The feedback unit estimates a student's emotions using, for example, generative AI. Furthermore, the feedback unit can prioritize feedback based on the estimated emotions. For example, if a student is relaxed, the feedback unit prioritizes detailed feedback. If a student is excited, the feedback unit prioritizes concise feedback. If a student is stressed, the feedback unit prioritizes feedback that includes words of encouragement. This allows for more appropriate feedback by prioritizing feedback according to the student's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The feedback unit can provide optimal feedback by considering the student's geographical location. For example, the feedback unit analyzes the student's geographical location using generative AI. Furthermore, the feedback unit can provide optimal feedback. For example, the feedback unit provides feedback based on the educational curriculum of the area where the student lives. Based on the student's geographical location, the feedback unit provides feedback related to the local culture and history. The feedback unit considers the student's geographical location and provides feedback utilizing local educational resources. This makes it possible to provide region-related feedback by considering the student's geographical location.

[0101] The feedback department can analyze students' social media activity and reflect relevant information when providing feedback. For example, the feedback department can use generative AI to analyze students' social media activity. Furthermore, the feedback department can reflect relevant information. For example, the feedback department can provide feedback based on topics students show interest in on social media. The feedback department can identify areas of interest from students' social media activity and provide feedback based on those areas. The feedback department can analyze students' social media interactions and provide relevant feedback. This allows for more relevant feedback by analyzing students' social media activity.

[0102] The reporting system can estimate students' emotions and adjust the content of progress reports based on those estimated emotions. For example, the reporting system can use generative AI to estimate students' emotions. Furthermore, the reporting system can adjust the content of progress reports based on those estimated emotions. For example, if a student is relaxed, the reporting system provides a detailed progress report. If a student is excited, the reporting system provides a concise progress report. If a student is stressed, the reporting system provides a progress report that includes words of encouragement. This allows for the provision of more appropriate progress reports by adjusting the content according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The reporting department can select the optimal reporting algorithm based on the student's learning history when creating progress reports. For example, the reporting department uses generative AI to analyze the student's learning history. Furthermore, the reporting department can select the optimal reporting algorithm. For example, the reporting department prioritizes providing progress reports for subjects in which the student has previously achieved high scores. The reporting department selects an algorithm to improve progress reports for subjects in which the student has previously struggled. The reporting department identifies the most effective progress reporting method from the student's learning history and provides progress reports based on it. This allows for the provision of more effective progress reports by selecting the optimal reporting algorithm based on the student's learning history.

[0104] The reporting department can customize progress reports based on students' current living situations and areas of interest. For example, the reporting department uses generative AI to analyze students' current living situations and areas of interest. Furthermore, the reporting department can customize the reports. For example, the reporting department can provide progress reports related to topics students are currently interested in. The reporting department can provide progress reports with time and location constraints depending on the student's living situation. The reporting department can provide relevant progress reports based on students' areas of interest. This allows for the provision of more relevant progress reports by customizing them based on students' current living situations and areas of interest.

[0105] The reporting system can estimate students' emotions and prioritize progress reports based on those estimated emotions. The reporting system can estimate students' emotions using, for example, generative AI. Furthermore, the reporting system can prioritize progress reports based on those estimated emotions. For example, if a student is relaxed, the reporting system will prioritize providing a detailed progress report. If a student is excited, the reporting system will prioritize providing a concise progress report. If a student is stressed, the reporting system will prioritize providing a progress report that includes words of encouragement. This allows for the provision of more appropriate progress reports by prioritizing them according to students' emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The reporting department can provide optimal reports by considering students' geographical location information when creating progress reports. For example, the reporting department uses generative AI to analyze students' geographical location information. Furthermore, the reporting department can provide optimal reports. For example, the reporting department provides progress reports based on the educational curriculum of the area where the student lives. The reporting department provides progress reports related to local culture and history based on students' geographical location information. The reporting department provides progress reports that utilize local educational resources, taking students' geographical location information into consideration. This allows for the provision of region-related progress reports by considering students' geographical location information.

[0107] The reporting department can analyze students' social media activities and reflect relevant information when creating progress reports. For example, the reporting department can use generative AI to analyze students' social media activities. Furthermore, the reporting department can reflect relevant information. For example, the reporting department can provide progress reports based on topics students show interest in on social media. The reporting department can identify areas of interest from students' social media activities and provide progress reports based on those areas. The reporting department can analyze students' social media interactions and provide relevant progress reports. This allows for the provision of more relevant progress reports by analyzing students' social media activities.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analytics department can analyze not only students' learning styles and interests, but also their daily routines and activity patterns. For example, it can use data acquired from students' smartwatches and smartphones to analyze their daily activity levels and sleep patterns. This allows it to identify the times when students are most focused and when they need breaks. Furthermore, the analytics department can analyze students' eating and exercise habits to provide learning suggestions based on their health status. For instance, it can analyze that students' concentration levels increase after exercise and suggest more challenging tasks at that time. This enables the provision of individually optimized learning based on each student's routine and health status.

[0110] The feedback department can provide motivational feedback to enhance students' learning motivation, in addition to checking their understanding and suggesting the next steps. For example, the feedback department can send praise and encouraging messages when students achieve their goals. It can also suggest appropriate breaks and provide advice for refreshing themselves when students are working on difficult tasks. Furthermore, the feedback department can implement a reward system that allows students to feel a sense of accomplishment as they progress. For example, the feedback department can award virtual badges or points each time a student achieves a certain goal, maintaining their motivation towards the next goal. This allows for effective feedback while increasing students' motivation to learn.

[0111] In addition to quest-based learning suggestions, the suggestion department can propose customized learning environments tailored to students' learning styles. For example, if a student prefers visual learning, the suggestion department can suggest learning materials that make extensive use of visual aids and infographics. If a student prefers auditory learning, the suggestion department can suggest learning content in the form of audiobooks or podcasts. Furthermore, if a student prefers hands-on learning, the suggestion department can suggest experiments or project-based learning activities. For example, if a student is interested in science, the suggestion department can suggest a simple experiment kit that can be done at home, providing an opportunity for hands-on learning. This allows for the provision of a customized learning environment tailored to students' learning styles.

[0112] The generation unit can generate virtual companions not only based on students' learning styles and interests, but also tailored to each student's personality and character. For example, if a student is introverted, the generation unit can generate a virtual companion with a calm and gentle personality. If a student is extroverted, the generation unit can generate a virtual companion with a lively and energetic personality. Furthermore, if a student enjoys humor, the generation unit can generate a virtual companion with a sense of humor. For instance, the generation unit can create a virtual companion that occasionally includes jokes and funny anecdotes in conversation to help students relax while studying. This allows for the provision of virtual companions tailored to each student's personality and character.

[0113] The analysis unit can analyze not only students' learning styles and interests, but also their learning environment and the influences of their surroundings. For example, the analysis unit can analyze students' home and school environments to identify factors that influence their learning. It can also analyze students' friendships and relationships with teachers to understand the support systems available for their learning. Furthermore, the analysis unit can analyze physical factors in students' learning environments. For instance, it can analyze lighting and noise levels in students' study spaces to suggest an optimal learning environment. This allows for the provision of individually optimized learning that takes into account students' learning environments and surrounding influences.

[0114] The analysis unit can estimate students' emotions and adjust the analysis method for learning styles and interests based on the estimated emotions. For example, if a student is stressed, the analysis unit prioritizes analyzing learning styles that promote relaxation. If a student is excited, the analysis unit analyzes learning styles that enhance concentration. If a student is tired, the analysis unit analyzes learning styles that are effective in a short amount of time. By adjusting the analysis method according to the student's emotions, it becomes possible to analyze learning styles and interests more appropriately. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The generation unit can estimate the student's emotions and adjust the method of generating the virtual companion based on the estimated emotions. For example, if the student is relaxed, the generation unit will generate a virtual companion with a calm personality. If the student is excited, the generation unit will generate a lively and energetic virtual companion. If the student is stressed, the generation unit will generate a soothing virtual companion. In this way, by adjusting the method of generating the virtual companion according to the student's emotions, a more appropriate virtual companion can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The suggestion function can estimate a student's emotions and adjust the quest-based learning suggestion method based on the estimated emotions. For example, if the student is relaxed, the suggestion function will suggest a calm quest. If the student is excited, the suggestion function will suggest a challenging quest. If the student is stressed, the suggestion function will suggest a relaxing quest. By adjusting the quest-based learning suggestion method according to the student's emotions, more appropriate learning suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0117] The feedback unit can estimate a student's emotions and adjust the feedback method based on the estimated emotions. For example, if the student is relaxed, the feedback unit will provide detailed feedback. If the student is excited, the feedback unit will provide concise feedback. If the student is stressed, the feedback unit will provide feedback that includes words of encouragement. This allows for more appropriate feedback by adjusting the feedback method according to the student's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The reporting system can estimate students' emotions and adjust the content of progress reports based on those estimates. For example, if a student is relaxed, the reporting system provides a detailed progress report. If a student is excited, it provides a concise progress report. If a student is stressed, it provides a progress report that includes words of encouragement. By adjusting the content of progress reports according to students' emotions, more appropriate progress reports can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The analysis unit analyzes students' learning styles and interests. Based on students' learning history and survey results, the analysis unit analyzes their learning styles and identifies their interests by collecting data on their social media activities and hobbies. For example, it analyzes subjects in which students have scored highly in the past and topics they are interested in. Step 2: The generation unit creates a virtual companion based on the information analyzed by the analysis unit. The generation unit uses a generation AI to generate a virtual companion based on the student's learning style and interests. Step 3: The suggestion unit uses a virtual companion created by the generation unit to suggest and support quest-based learning. The suggestion unit uses a generation AI to suggest quest-based learning based on the student's learning style and interests. Step 4: The feedback unit provides real-time feedback on learning progress based on the learning content proposed by the proposal unit. The feedback unit uses generative AI to provide real-time feedback on the student's learning progress.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0123] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the analysis unit, generation unit, proposal unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the student's learning style based on their learning history and survey results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a virtual companion using a generation AI. The proposal unit is implemented by the control unit 46A of the smart device 14 and proposes quest-based learning. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time feedback on the student's learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the analysis unit, generation unit, proposal unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the student's learning style based on their learning history and questionnaire results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a virtual companion using a generation AI. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and proposes quest-based learning. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time feedback on the student's learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] Each of the multiple elements described above, including the analysis unit, generation unit, proposal unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the student's learning style based on their learning history and questionnaire results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a virtual companion using a generation AI. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and proposes quest-based learning. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time feedback on the student's learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0165] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the analysis unit, generation unit, proposal unit, and feedback unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the student's learning style based on their learning history and questionnaire results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a virtual companion using a generation AI. The proposal unit is implemented by the control unit 46A of the robot 414 and proposes quest-based learning. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time feedback on the student's learning progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0174] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0175] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0176] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0177] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0178] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0179] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0180] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0181] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0182] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0183] 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.

[0184] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0185] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0186] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0187] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0188] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0189] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0190] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0191] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0192] (Note 1) The analysis department analyzes students' learning styles and interests, A generation unit creates a virtual companion based on the information analyzed by the analysis unit, The virtual companion created by the generation unit proposes and supports quest-based learning in the proposal unit, The system includes a feedback unit that provides real-time feedback on learning progress based on the learning content proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned feedback unit is We will immediately check your understanding and suggest the next steps. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose a quest-based learning format to make learning fun and engaging, like a game. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate virtual companions based on students' learning styles and interests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Analyze students' learning styles and interests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, It includes a reporting section that provides progress reports for parents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate students' emotions and adjust the analysis of their learning styles and interests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, Analyze students' past learning history and select the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing learning styles and interests, filtering is performed based on students' current living situations and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates students' emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing learning styles and interests, the system prioritizes analyzing highly relevant information by considering students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing learning styles and interests, we analyze students' social media activity and related information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the students' emotions and adjusts the virtual companion generation method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating virtual companions, the optimal generation algorithm is selected based on the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When creating a virtual companion, it is customized based on the student's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates students' emotions and determines the priority of virtual companions to generate based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating virtual companions, the system considers the students' geographical location information to create the most suitable companion. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When creating virtual companions, analyze students' social media activity and reflect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, The system estimates students' emotions and adjusts the quest-based learning suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When suggesting quest-based learning activities, the system selects the optimal suggestion algorithm based on the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When proposing quest-based learning activities, customize them based on the student's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, The system estimates students' emotions and determines the priority of suggested quests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When proposing quest-based learning activities, the system takes students' geographical location into consideration to suggest the most suitable quests. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When creating quest-based learning suggestions, analyze students' social media activity and incorporate relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is Estimate students' emotions and adjust the feedback method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is During feedback, the optimal feedback algorithm is selected based on the student's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is Feedback is customized based on the student's current life situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is The system estimates students' emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, consider the student's geographical location to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, analyze students' social media activity and incorporate relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned report section is, The system estimates students' emotions and adjusts the content of progress reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned report section is, When creating progress reports, the optimal reporting algorithm is selected based on the students' learning history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned report section is, When creating progress reports, customize them based on the students' current living situations and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned report section is, The system estimates students' emotions and prioritizes progress reports based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned report section is, When creating progress reports, we provide optimal reports that take into account students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned report section is, When creating progress reports, analyze students' social media activity and reflect relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes students' learning styles and interests, A generation unit creates a virtual companion based on the information analyzed by the analysis unit, The virtual companion created by the generation unit proposes and supports quest-based learning in the proposal unit, The system includes a feedback unit that provides real-time feedback on learning progress based on the learning content proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned feedback unit is We will immediately check your understanding and suggest the next steps. The system according to feature 1.

3. The aforementioned proposal section is, We propose a quest-based learning format to make learning fun and engaging, like a game. The system according to feature 1.

4. The generating unit is Generate virtual companions based on students' learning styles and interests. The system according to feature 1.

5. The aforementioned analysis unit, Analyze students' learning styles and interests. The system according to feature 1.

6. The aforementioned proposal section is, It includes a reporting section that provides progress reports for parents. The system according to feature 1.

7. The aforementioned analysis unit, We estimate students' emotions and adjust the analysis of their learning styles and interests based on those estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, Analyze students' past learning history and select the optimal analysis algorithm. The system according to feature 1.

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