system

Abstract

The system according to this embodiment aims to grasp students' learning progress and understanding in real time and provide individually optimized curricula and feedback. [Solution] The system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes students' learning progress and understanding in real time. The generation unit generates a curriculum and feedback optimized for each individual based on the data analyzed by the analysis unit. The provision unit provides the curriculum and feedback generated by the generation 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, the method including 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, there is a problem that it is difficult to grasp the learning progress and comprehension level of students in real time and provide an individually optimized curriculum and feedback.

[0005] The system according to the embodiment aims to grasp the learning progress and comprehension level of students in real time and provide an individually optimized curriculum and feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes students' learning progress and understanding in real time. The generation unit generates personalized curricula and feedback based on the data analyzed by the analysis unit. The provision unit provides the curricula and feedback generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp students' learning progress and understanding in real time and provide individually optimized curricula and feedback. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The global AI education platform according to an embodiment of the present invention is an innovative learning platform that utilizes generative AI technology to solve global challenges such as educational disparities and resource shortages. This global AI education platform provides high-quality education, particularly to students in developing countries, regions with limited educational resources, and developed countries where individualized instruction is required. The global AI education platform features AI teachers that analyze students' learning progress and comprehension in real time and automatically generate personalized curricula and feedback. This solves problems such as teacher shortages, uniform educational curricula, and unequal access to education. Furthermore, multilingual AI teachers provide lessons tailored to each student's language and learning style, regardless of location. It aims to equalize educational opportunities, enabling all children to learn at their own pace and develop the skills to shape the future. By eliminating disparities in educational resources and nurturing the next generation of leaders and innovators, it builds a learning foundation that supports the future of the entire world. For example, the AI ​​teacher analyzes students' learning progress and comprehension in real time. Next, the generative AI automatically generates personalized curricula and feedback. For example, if a student struggles with a math problem, the AI ​​teacher analyzes the student's level of understanding and provides appropriate supplementary explanations or additional problems. Furthermore, multilingual AI teachers provide lessons tailored to each student's language and learning style. For instance, students whose native language is not English can receive instruction in their native language to deepen their understanding. This ensures equal educational opportunities and allows all children to learn at their own pace. Moreover, generative AI can deliver high-quality education even in areas with limited educational resources. For example, schools in developing countries face challenges such as teacher and material shortages; generative AI can address these issues and provide high-quality education. In this way, the global AI education platform leverages generative AI technology to solve global challenges such as educational inequality and resource shortages, providing an innovative learning foundation for nurturing the next generation of leaders and innovators. Thus, the global AI education platform can solve global challenges such as educational inequality and resource shortages and deliver high-quality education.

[0029] The global AI education platform according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes students' learning progress and comprehension in real time. For example, the analysis unit collects student learning data and analyzes that data using AI. For example, the analysis unit evaluates learning progress based on students' response time and correct answer rate. The analysis unit can also analyze test results and assignment submission status to evaluate students' comprehension. For example, the analysis unit analyzes the distribution of test scores to evaluate students' comprehension. The generation unit uses a generation AI to generate personalized curricula and feedback based on the data analyzed by the analysis unit. For example, the generation AI receives student learning data as input and generates an optimal curriculum. For example, the generation AI generates additional problems to address students' weaknesses. The generation unit can also generate feedback tailored to students' learning styles. For example, the generation AI generates diagrammatic feedback for visually-oriented students and audio feedback for auditory-oriented students. The delivery unit provides the curriculum and feedback generated by the generation unit. The delivery unit, for example, delivers the generated curriculum to the students' learning platform. For example, the delivery unit provides the curriculum through a web application or a mobile application. The delivery unit can also notify students of the generated feedback. For example, the delivery unit provides feedback using email or push notifications. In this way, the global AI education platform according to the embodiment realizes high-quality education by analyzing students' learning progress and understanding in real time and providing optimized curriculum and feedback.

[0030] The analytics department analyzes students' learning progress and comprehension in real time. Specifically, it collects student learning data and analyzes it using AI. For example, it evaluates learning progress based on the time taken to answer questions and the accuracy rate of answers submitted online. If the answer time is short and the accuracy rate is high, the student is judged to have a sufficient understanding of the topic. On the other hand, if the answer time is long and the accuracy rate is low, the student is judged to have an insufficient understanding of the topic. The analytics department can also analyze test results and assignment submission status to evaluate students' comprehension. For example, it can analyze the score distribution of regularly administered tests to evaluate comprehension of specific topics. Furthermore, by analyzing assignment submission status and the content of submitted assignments, it can comprehensively evaluate students' learning attitudes and comprehension. This allows the analytics department to grasp the learning situation of each student in detail and provide data to address individual learning needs. In addition, by comparing past learning data with data from other students, the analytics department can understand the learning patterns and trends of specific students and predict their future learning progress. This allows teachers and educational institutions to monitor students' learning progress in real time and provide appropriate guidance and support.

[0031] The generation unit uses a generation AI to generate personalized curricula and feedback based on data analyzed by the analysis unit. Specifically, the generation AI receives student learning data as input and generates an optimal curriculum. For example, the generation AI generates additional problems to address a student's weaknesses. If a student shows a low accuracy rate on a particular topic, the generation AI generates and provides additional practice problems related to that topic. The generation AI can also generate feedback tailored to the student's learning style. For example, it generates diagrammatic feedback for visually-oriented students and audio feedback for auditory-oriented students. Furthermore, the generation AI can suggest future learning plans based on the student's learning history and performance data. For example, it suggests topics the student should study next and topics that need review, supporting efficient learning. This allows the generation unit to provide a personalized learning experience for each student, maximizing learning effectiveness. In addition, the generation unit can continuously update the generated curriculum and feedback, flexibly adapting to the student's learning progress and understanding. This allows the generation unit to provide optimal learning support based on the latest information at all times, thereby improving students' learning outcomes.

[0032] The delivery unit provides the curriculum and feedback generated by the generation unit. Specifically, it delivers the generated curriculum to the students' learning platform. For example, the delivery unit provides the curriculum through web applications or mobile applications. Students can access the generated curriculum and progress through their studies by logging into their accounts. The delivery unit can also notify students of the generated feedback. For example, the delivery unit provides feedback using email or push notifications. By receiving notifications, students can understand their learning progress and areas for improvement and move on to the next learning step. Furthermore, the delivery unit can report on students' learning progress to teachers and parents. For example, it can send regularly generated reports via email to share students' learning progress and understanding. This allows teachers and parents to understand students' learning progress and provide appropriate support. In addition, the delivery unit optimizes the user interface of the learning platform to make it intuitive for students to use. For example, it devises ways to display the curriculum and feedback so that students can quickly access the information they need. In this way, the delivery unit can improve the students' learning experience and support efficient learning.

[0033] The service provider can offer multilingual lessons. For example, it can use generative AI to translate in real time and provide multilingual lessons. For instance, it can teach students whose native language is not English in their native language. Furthermore, the service provider can use generative AI to improve translation accuracy in order to support multiple languages. For example, the service provider can use generative AI to translate between the student's native language and the language of instruction, providing accurate lesson content. This enables education tailored to each student's language by providing multilingual lessons.

[0034] The service provider can be equipped with a style adaptation unit that provides lessons tailored to students' language and learning styles. The style adaptation unit, for example, uses generative AI to analyze students' learning styles and provide the optimal lesson style. For instance, it might provide lessons with extensive use of diagrams for visually-oriented students and lessons that emphasize audio for auditory-oriented students. Furthermore, the style adaptation unit can also provide lessons tailored to students' language. For example, it might use generative AI to conduct lessons in the student's native language to deepen their understanding. This enables individualized instruction by providing lessons tailored to each student's language and learning style.

[0035] The provision department can include a resource provision department that provides high-quality education even in areas with insufficient educational resources. For example, the resource provision department could generate and provide educational resources using generative AI. For instance, the resource provision department could use generative AI to automatically generate teaching materials and lesson content and provide them to areas lacking educational resources. Furthermore, the resource provision department could also use generative AI to compensate for teacher shortages. For example, the resource provision department could have generative AI conduct lessons in place of teachers, providing high-quality education to students. This would eliminate educational disparities by providing high-quality education even in areas lacking educational resources.

[0036] The generation unit can generate personalized curricula and feedback using a generation AI. For example, the generation unit can use the generation AI to receive student learning data as input and generate an optimal curriculum. For example, the generation unit can use the generation AI to generate additional problems to address the student's weaknesses. The generation unit can also use the generation AI to generate feedback tailored to the student's learning style. For example, the generation unit can use the generation AI to generate diagrammatic feedback for visually-oriented students and audio feedback for auditory-oriented students. In this way, the generation AI enables the creation of personalized curricula and feedback.

[0037] The analytics department can analyze students' learning progress and comprehension in real time. For example, the analytics department collects student learning data and analyzes that data using AI. For instance, the analytics department evaluates learning progress based on students' response time and accuracy rates. The analytics department can also analyze test results and assignment submission status to assess students' comprehension. For example, the analytics department analyzes the distribution of test scores to assess students' comprehension. By analyzing students' learning progress and comprehension in real time, the department can provide appropriate curricula and feedback.

[0038] The analysis department can analyze students' past learning history and select the optimal analysis algorithm. For example, the analysis department collects students' past learning data and analyzes that data using AI. For instance, the analysis department can select an algorithm that performs a detailed analysis of subjects that students have struggled with in the past. It can also select an algorithm that performs a simplified analysis of subjects that students excel at. Furthermore, the analysis department can analyze the tendency for students to have higher concentration levels at specific times of day based on their learning history and select the optimal algorithm for those times. In this way, the optimal analysis algorithm is selected by analyzing students' past learning history.

[0039] The analysis unit can filter data based on students' current learning environment and circumstances when analyzing learning progress and comprehension. For example, the analysis unit collects data on students' learning environment and analyzes it using AI. For instance, if a student is in a noisy environment, the analysis unit can slow down the learning pace and perform a more detailed analysis of comprehension. Conversely, if a student is in a quiet environment, the analysis unit can maintain a normal learning pace and perform a standard analysis of comprehension. Furthermore, if a student is on the move, the analysis unit can accelerate the learning pace and simplify the analysis of comprehension. This allows for more accurate analysis by filtering data based on students' current learning environment and circumstances.

[0040] The analytics department can prioritize analyzing highly relevant data based on students' geographical location when analyzing learning progress and comprehension levels. For example, the analytics department collects students' geographical location information and analyzes that data using AI. For instance, if a student is in an urban area, the analytics department will prioritize analyzing urban education data. Similarly, if a student is in a rural area, the analytics department can prioritize analyzing rural education data. Furthermore, if a student is overseas, the analytics department can prioritize analyzing education data from that country. This allows for more appropriate analysis by prioritizing the analysis of highly relevant data based on students' geographical location information.

[0041] The analytics department can analyze students' social media activity and obtain relevant data when analyzing learning progress and comprehension levels. For example, the analytics department collects data on students' social media activity and analyzes that data using AI. For instance, the analytics department analyzes relevant data based on the learning content that students share on social media. It can also analyze relevant data based on information from educational accounts that students follow on social media. Furthermore, the analytics department can analyze relevant data based on the activities of learning groups that students participate in on social media. This allows for more accurate analysis by obtaining relevant data through the analysis of students' social media activity.

[0042] The generation unit can adjust the level of detail based on the importance of the learning content when generating curricula and feedback. For example, the generation unit uses a generation AI to evaluate the importance of the learning content and adjusts the level of detail of the curriculum and feedback based on that evaluation. For instance, the generation unit generates a detailed curriculum for important learning content. It can also generate a simplified curriculum for less important learning content. Furthermore, the generation unit can adjust the level of detail of the feedback according to the importance of the learning content. This allows for the provision of more effective curricula and feedback by adjusting the level of detail based on the importance of the learning content.

[0043] The generation unit can apply different generation algorithms depending on the category of learning content when generating curricula and feedback. For example, the generation unit can classify the categories of learning content using a generation AI and apply the most suitable generation algorithm according to that category. For instance, the generation unit can generate a curriculum by applying a specific algorithm to mathematics learning content. It can also generate a curriculum by applying a different algorithm to language learning content. Furthermore, the generation unit can generate a curriculum by applying a different algorithm to science learning content. By applying different generation algorithms depending on the category of learning content, it can provide more appropriate curricula and feedback.

[0044] The generation unit can prioritize curriculum and feedback based on the submission deadlines for learning materials. For example, the generation unit uses a generation AI to evaluate the submission deadlines for learning materials and determines the priority of the curriculum and feedback based on that evaluation. For instance, the generation unit prioritizes generating curriculum for learning materials with approaching submission deadlines. It can also postpone generating curriculum for learning materials with later submission deadlines. Furthermore, the generation unit can also determine the priority of feedback based on the submission deadlines. This allows for the provision of effective curriculum and feedback tailored to submission deadlines by prioritizing based on the submission deadlines of learning materials.

[0045] The generation unit can adjust the order of learning content based on its relevance when generating curriculum and feedback. For example, the generation unit uses a generation AI to evaluate the relevance of learning content and adjusts the order of curriculum and feedback based on that evaluation. For instance, the generation unit prioritizes incorporating highly relevant learning content into the curriculum. It can also postpone incorporating less relevant learning content into the curriculum. Furthermore, the generation unit can adjust the order of feedback according to the relevance of the learning content. By adjusting the order based on the relevance of learning content, it provides a more effective learning experience.

[0046] The delivery department can select the optimal delivery method by referring to students' past learning history when providing curriculum and feedback. For example, the delivery department can collect students' past learning data and analyze that data using AI. For instance, the delivery department can prioritize delivery methods that students have preferred in the past. It can also select delivery methods that have been effective for students in the past. Furthermore, the delivery department can select the optimal delivery method based on students' past learning history. In this way, the optimal delivery method is selected by referring to students' past learning history.

[0047] The delivery department can customize the delivery methods based on the student's current learning environment when providing curriculum and feedback. For example, the delivery department can collect data on the student's learning environment and analyze that data using AI. For instance, if the student is in a noisy environment, the delivery department can select a delivery method suited to a quiet environment. Alternatively, if the student is in a quiet environment, the delivery department can select a standard delivery method. Furthermore, if the student is on the go, the delivery department can select a delivery method optimized for mobile devices. This allows for more appropriate delivery by customizing the delivery methods based on the student's current learning environment.

[0048] The service provider can select the optimal delivery method for curriculum and feedback based on students' geographical location information. For example, the service provider can collect students' geographical location information and analyze that data using AI. For instance, if a student is in an urban area, the service provider can select a delivery method based on urban education data. If a student is in a rural area, the service provider can also select a delivery method based on rural education data. Furthermore, if a student is overseas, the service provider can select a delivery method based on the education data of that country. This allows for more appropriate delivery by selecting the optimal delivery method based on students' geographical location information.

[0049] The service provider can analyze students' social media activity and suggest delivery methods when providing curriculum and feedback. For example, the service provider can collect data on students' social media activity and analyze that data using AI. For instance, the service provider can suggest relevant delivery methods based on the learning content that students share on social media. It can also suggest relevant delivery methods based on information about educational accounts that students follow on social media. Furthermore, the service provider can suggest relevant delivery methods based on the activities of learning groups that students participate in on social media. By analyzing students' social media activity, it becomes possible to suggest relevant delivery methods and provide more appropriate content.

[0050] The style adaptation unit can select the optimal style when adapting to a lesson style by referring to the student's past learning style history. For example, the style adaptation unit collects data on the student's past learning style and analyzes that data using AI. For instance, the style adaptation unit prioritizes selecting lesson styles that the student has preferred in the past. It can also select lesson styles that have been effective for the student in the past. Furthermore, the style adaptation unit can select the optimal lesson style based on the student's past learning style history. In this way, the optimal lesson style is selected by referring to the student's past learning style history.

[0051] The style adaptation unit can provide the optimal teaching style based on students' geographical location information when adapting to different teaching styles. For example, the style adaptation unit collects students' geographical location information and analyzes that data using AI. For instance, if a student is in an urban area, the style adaptation unit will provide a style based on urban education styles. It can also provide a style based on rural education styles if the student is in a rural area. Furthermore, if a student is overseas, the style adaptation unit can provide a style based on the education style of that country. By providing the optimal teaching style based on students' geographical location information, more appropriate lessons can be provided.

[0052] The resource provisioning unit can select the most suitable resources when providing educational resources by referring to the student's past resource usage history. For example, the resource provisioning unit collects data on the student's past resource usage and analyzes that data using AI. For instance, the resource provisioning unit can prioritize providing educational resources that the student has preferred in the past. It can also provide educational resources that the student found effective in the past. Furthermore, the resource provisioning unit can provide the most suitable educational resources based on the student's past resource usage history. In this way, the optimal educational resources are selected by referring to the student's past resource usage history.

[0053] The resource provision department can provide the most suitable educational resources based on the student's geographical location. For example, the resource provision department collects the student's geographical location information and analyzes that data using AI. For instance, if the student is in an urban area, the resource provision department will provide resources based on urban educational resources. Similarly, if the student is in a rural area, the resource provision department can provide resources based on rural educational resources. Furthermore, if the student is overseas, the resource provision department can provide resources based on educational resources in that country. This allows for the provision of more appropriate educational resources by providing the most suitable resources based on the student's geographical location.

[0054] The resource provision department can analyze students' social media activity to suggest the most suitable resources when providing educational resources. For example, the resource provision department collects data on students' social media activity and analyzes that data using AI. For instance, the resource provision department suggests relevant educational resources based on the learning content that students share on social media. It can also suggest relevant educational resources based on information about educational accounts that students follow on social media. Furthermore, the resource provision department can suggest relevant educational resources based on the activities of learning groups that students participate in on social media. In this way, by analyzing students' social media activity, relevant educational resources can be suggested, and more appropriate educational resources can be provided.

[0055] The resource provisioning unit can suggest the most suitable educational resources by referring to students' calendar information when providing educational resources. For example, the resource provisioning unit collects students' calendar information and analyzes that data using AI. For instance, the resource provisioning unit refers to the schedule registered in the student's calendar and provides the most suitable educational resources. The resource provisioning unit can also suggest educational resources related to specific events based on the student's calendar information. Furthermore, the resource provisioning unit can suggest the most suitable educational resources based on the student's calendar information and schedule. In this way, by referring to students' calendar information, the most suitable educational resources are suggested and more appropriate educational resources are provided.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analytics department can not only analyze students' learning progress and comprehension in real time, but also track students' learning patterns over the long term to understand learning trends. For example, the analytics department can detect when students tend to concentrate more at specific times and provide the most important learning content during those times. Furthermore, if a student has difficulty with a particular subject, the analytics department can provide supplementary materials for that subject. In addition, based on students' learning patterns, the analytics department can predict future learning progress and propose appropriate learning plans. This allows for more effective learning support by tracking students' learning patterns over the long term.

[0058] The service provider can not only offer multilingual lessons but also tailor lesson content to each student's cultural background. For example, it can use generative AI to create example problems and case studies based on students' cultural backgrounds. It can also provide lessons that incorporate cultural perspectives to deepen common understanding among students from different cultural backgrounds. Furthermore, it can provide culturally-based feedback, increasing students' motivation to learn by allowing them to study content related to their own culture. This allows the service provider to cater to a wider range of students by providing lessons tailored to their cultural backgrounds.

[0059] The system not only includes a style-adaptation unit that provides lessons tailored to students' language and learning styles, but can also provide lesson styles that suit students' learning environments. For example, if a student is studying at home, the style-adaptation unit can provide a lesson style suitable for the home environment. It can also provide a lesson style suitable for the classroom environment if the student is studying at school. Furthermore, if a student is studying while on the go, the style-adaptation unit can provide a lesson style optimized for mobile devices. This allows for more flexible learning by providing lesson styles tailored to students' learning environments.

[0060] The resource provision department not only provides high-quality education even in areas lacking educational resources, but can also provide educational resources tailored to the specific characteristics of each region. For example, the resource provision department can provide agricultural-related teaching materials to students in rural areas. It can also provide teaching materials related to urban planning and environmental issues to students in urban areas. Furthermore, the resource provision department can offer practical learning programs tailored to the specific characteristics of each region, enabling students to acquire the skills to solve local problems. This allows for more practical learning by providing educational resources tailored to the characteristics of each region.

[0061] The generation unit not only generates personalized curricula and feedback using generation AI, but can also generate curricula tailored to students' learning goals. For example, if a student aims to pass a specific exam, the generation unit will generate a curriculum specifically for that exam. Similarly, if a student aims to acquire a specific skill, the generation unit can generate a curriculum focused on that skill. Furthermore, if a student aims to pursue a specific profession in the future, the generation unit can generate a curriculum related to that profession. This allows for more effective learning by providing curricula tailored to students' learning goals.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The analysis department analyzes students' learning progress and comprehension in real time. Specifically, it collects student learning data and analyzes that data using AI. For example, it evaluates learning progress based on answer time and correct answer rate, and assesses comprehension by analyzing test results and assignment submission status. Step 2: The generation unit generates personalized curricula and feedback based on the data analyzed by the analysis unit. Specifically, it uses a generation AI to receive student learning data as input and generate optimal curricula and feedback. For example, the generation AI generates additional problems to address the student's weaknesses and feedback tailored to their learning style (diagrams for visual learners, audio feedback for auditory learners). Step 3: The delivery unit provides the curriculum and feedback generated by the generation unit. Specifically, it delivers the generated curriculum to students' learning platforms and provides it through web and mobile applications. It also notifies students of the generated feedback using email and push notifications.

[0064] (Example of form 2) The global AI education platform according to an embodiment of the present invention is an innovative learning platform that utilizes generative AI technology to solve global challenges such as educational disparities and resource shortages. This global AI education platform provides high-quality education, particularly to students in developing countries, regions with limited educational resources, and developed countries where individualized instruction is required. The global AI education platform features AI teachers that analyze students' learning progress and comprehension in real time and automatically generate personalized curricula and feedback. This solves problems such as teacher shortages, uniform educational curricula, and unequal access to education. Furthermore, multilingual AI teachers provide lessons tailored to each student's language and learning style, regardless of location. It aims to equalize educational opportunities, enabling all children to learn at their own pace and develop the skills to shape the future. By eliminating disparities in educational resources and nurturing the next generation of leaders and innovators, it builds a learning foundation that supports the future of the entire world. For example, the AI ​​teacher analyzes students' learning progress and comprehension in real time. Next, the generative AI automatically generates personalized curricula and feedback. For example, if a student struggles with a math problem, the AI ​​teacher analyzes the student's level of understanding and provides appropriate supplementary explanations or additional problems. Furthermore, multilingual AI teachers provide lessons tailored to each student's language and learning style. For instance, students whose native language is not English can receive instruction in their native language to deepen their understanding. This ensures equal educational opportunities and allows all children to learn at their own pace. Moreover, generative AI can deliver high-quality education even in areas with limited educational resources. For example, schools in developing countries face challenges such as teacher and material shortages; generative AI can address these issues and provide high-quality education. In this way, the global AI education platform leverages generative AI technology to solve global challenges such as educational inequality and resource shortages, providing an innovative learning foundation for nurturing the next generation of leaders and innovators. Thus, the global AI education platform can solve global challenges such as educational inequality and resource shortages and deliver high-quality education.

[0065] The global AI education platform according to this embodiment comprises an analysis unit, a generation unit, and a provision unit. The analysis unit analyzes students' learning progress and comprehension in real time. For example, the analysis unit collects student learning data and analyzes that data using AI. For example, the analysis unit evaluates learning progress based on students' response time and correct answer rate. The analysis unit can also analyze test results and assignment submission status to evaluate students' comprehension. For example, the analysis unit analyzes the distribution of test scores to evaluate students' comprehension. The generation unit uses a generation AI to generate personalized curricula and feedback based on the data analyzed by the analysis unit. For example, the generation AI receives student learning data as input and generates an optimal curriculum. For example, the generation AI generates additional problems to address students' weaknesses. The generation unit can also generate feedback tailored to students' learning styles. For example, the generation AI generates diagrammatic feedback for visually-oriented students and audio feedback for auditory-oriented students. The delivery unit provides the curriculum and feedback generated by the generation unit. The delivery unit, for example, delivers the generated curriculum to the students' learning platform. For example, the delivery unit provides the curriculum through a web application or a mobile application. The delivery unit can also notify students of the generated feedback. For example, the delivery unit provides feedback using email or push notifications. In this way, the global AI education platform according to the embodiment realizes high-quality education by analyzing students' learning progress and understanding in real time and providing optimized curriculum and feedback.

[0066] The analytics department analyzes students' learning progress and comprehension in real time. Specifically, it collects student learning data and analyzes it using AI. For example, it evaluates learning progress based on the time taken to answer questions and the accuracy rate of answers submitted online. If the answer time is short and the accuracy rate is high, the student is judged to have a sufficient understanding of the topic. On the other hand, if the answer time is long and the accuracy rate is low, the student is judged to have an insufficient understanding of the topic. The analytics department can also analyze test results and assignment submission status to evaluate students' comprehension. For example, it can analyze the score distribution of regularly administered tests to evaluate comprehension of specific topics. Furthermore, by analyzing assignment submission status and the content of submitted assignments, it can comprehensively evaluate students' learning attitudes and comprehension. This allows the analytics department to grasp the learning situation of each student in detail and provide data to address individual learning needs. In addition, by comparing past learning data with data from other students, the analytics department can understand the learning patterns and trends of specific students and predict their future learning progress. This allows teachers and educational institutions to monitor students' learning progress in real time and provide appropriate guidance and support.

[0067] The generation unit uses a generation AI to generate personalized curricula and feedback based on data analyzed by the analysis unit. Specifically, the generation AI receives student learning data as input and generates an optimal curriculum. For example, the generation AI generates additional problems to address a student's weaknesses. If a student shows a low accuracy rate on a particular topic, the generation AI generates and provides additional practice problems related to that topic. The generation AI can also generate feedback tailored to the student's learning style. For example, it generates diagrammatic feedback for visually-oriented students and audio feedback for auditory-oriented students. Furthermore, the generation AI can suggest future learning plans based on the student's learning history and performance data. For example, it suggests topics the student should study next and topics that need review, supporting efficient learning. This allows the generation unit to provide a personalized learning experience for each student, maximizing learning effectiveness. In addition, the generation unit can continuously update the generated curriculum and feedback, flexibly adapting to the student's learning progress and understanding. This allows the generation unit to provide optimal learning support based on the latest information at all times, thereby improving students' learning outcomes.

[0068] The delivery unit provides the curriculum and feedback generated by the generation unit. Specifically, it delivers the generated curriculum to the students' learning platform. For example, the delivery unit provides the curriculum through web applications or mobile applications. Students can access the generated curriculum and progress through their studies by logging into their accounts. The delivery unit can also notify students of the generated feedback. For example, the delivery unit provides feedback using email or push notifications. By receiving notifications, students can understand their learning progress and areas for improvement and move on to the next learning step. Furthermore, the delivery unit can report on students' learning progress to teachers and parents. For example, it can send regularly generated reports via email to share students' learning progress and understanding. This allows teachers and parents to understand students' learning progress and provide appropriate support. In addition, the delivery unit optimizes the user interface of the learning platform to make it intuitive for students to use. For example, it devises ways to display the curriculum and feedback so that students can quickly access the information they need. In this way, the delivery unit can improve the students' learning experience and support efficient learning.

[0069] The service provider can offer multilingual lessons. For example, it can use generative AI to translate in real time and provide multilingual lessons. For instance, it can teach students whose native language is not English in their native language. Furthermore, the service provider can use generative AI to improve translation accuracy in order to support multiple languages. For example, the service provider can use generative AI to translate between the student's native language and the language of instruction, providing accurate lesson content. This enables education tailored to each student's language by providing multilingual lessons.

[0070] The service provider can be equipped with a style adaptation unit that provides lessons tailored to students' language and learning styles. The style adaptation unit, for example, uses generative AI to analyze students' learning styles and provide the optimal lesson style. For instance, it might provide lessons with extensive use of diagrams for visually-oriented students and lessons that emphasize audio for auditory-oriented students. Furthermore, the style adaptation unit can also provide lessons tailored to students' language. For example, it might use generative AI to conduct lessons in the student's native language to deepen their understanding. This enables individualized instruction by providing lessons tailored to each student's language and learning style.

[0071] The provision department can include a resource provision department that provides high-quality education even in areas with insufficient educational resources. For example, the resource provision department could generate and provide educational resources using generative AI. For instance, the resource provision department could use generative AI to automatically generate teaching materials and lesson content and provide them to areas lacking educational resources. Furthermore, the resource provision department could also use generative AI to compensate for teacher shortages. For example, the resource provision department could have generative AI conduct lessons in place of teachers, providing high-quality education to students. This would eliminate educational disparities by providing high-quality education even in areas lacking educational resources.

[0072] The generation unit can generate personalized curricula and feedback using a generation AI. For example, the generation unit can use the generation AI to receive student learning data as input and generate an optimal curriculum. For example, the generation unit can use the generation AI to generate additional problems to address the student's weaknesses. The generation unit can also use the generation AI to generate feedback tailored to the student's learning style. For example, the generation unit can use the generation AI to generate diagrammatic feedback for visually-oriented students and audio feedback for auditory-oriented students. In this way, the generation AI enables the creation of personalized curricula and feedback.

[0073] The analytics department can analyze students' learning progress and comprehension in real time. For example, the analytics department collects student learning data and analyzes that data using AI. For instance, the analytics department evaluates learning progress based on students' response time and accuracy rates. The analytics department can also analyze test results and assignment submission status to assess students' comprehension. For example, the analytics department analyzes the distribution of test scores to assess students' comprehension. By analyzing students' learning progress and comprehension in real time, the department can provide appropriate curricula and feedback.

[0074] The analysis unit can estimate students' emotions and adjust the analysis of learning progress and comprehension based on the estimated emotions. For example, the analysis unit estimates students' emotions using an emotion engine or generative AI. For instance, if a student is stressed, the AI ​​can slow down the learning pace and perform a more detailed analysis of comprehension. If a student is relaxed, the AI ​​can maintain a normal learning pace and perform a standard analysis of comprehension. Furthermore, if a student is agitated, the AI ​​can speed up the learning pace and simplify the analysis of comprehension. This allows for more appropriate analysis by adjusting the analysis of learning progress and comprehension based on students' emotions.

[0075] The analysis department can analyze students' past learning history and select the optimal analysis algorithm. For example, the analysis department collects students' past learning data and analyzes that data using AI. For instance, the analysis department can select an algorithm that performs a detailed analysis of subjects that students have struggled with in the past. It can also select an algorithm that performs a simplified analysis of subjects that students excel at. Furthermore, the analysis department can analyze the tendency for students to have higher concentration levels at specific times of day based on their learning history and select the optimal algorithm for those times. In this way, the optimal analysis algorithm is selected by analyzing students' past learning history.

[0076] The analysis unit can filter data based on students' current learning environment and circumstances when analyzing learning progress and comprehension. For example, the analysis unit collects data on students' learning environment and analyzes it using AI. For instance, if a student is in a noisy environment, the analysis unit can slow down the learning pace and perform a more detailed analysis of comprehension. Conversely, if a student is in a quiet environment, the analysis unit can maintain a normal learning pace and perform a standard analysis of comprehension. Furthermore, if a student is on the move, the analysis unit can accelerate the learning pace and simplify the analysis of comprehension. This allows for more accurate analysis by filtering data based on students' current learning environment and circumstances.

[0077] The analysis unit can estimate students' emotions and prioritize analysis results based on those estimated emotions. For example, the analysis unit uses an emotion engine or generative AI to estimate students' emotions. For instance, if a student is stressed, the AI ​​will prioritize displaying important analysis results. If a student is relaxed, the AI ​​can display all analysis results equally. Furthermore, if a student is agitated, the AI ​​can prioritize displaying simpler analysis results. This prioritizes important information by determining the priority of analysis results based on the student's emotions.

[0078] The analytics department can prioritize analyzing highly relevant data based on students' geographical location when analyzing learning progress and comprehension levels. For example, the analytics department collects students' geographical location information and analyzes that data using AI. For instance, if a student is in an urban area, the analytics department will prioritize analyzing urban education data. Similarly, if a student is in a rural area, the analytics department can prioritize analyzing rural education data. Furthermore, if a student is overseas, the analytics department can prioritize analyzing education data from that country. This allows for more appropriate analysis by prioritizing the analysis of highly relevant data based on students' geographical location information.

[0079] The analytics department can analyze students' social media activity and obtain relevant data when analyzing learning progress and comprehension levels. For example, the analytics department collects data on students' social media activity and analyzes that data using AI. For instance, the analytics department analyzes relevant data based on the learning content that students share on social media. It can also analyze relevant data based on information from educational accounts that students follow on social media. Furthermore, the analytics department can analyze relevant data based on the activities of learning groups that students participate in on social media. This allows for more accurate analysis by obtaining relevant data through the analysis of students' social media activity.

[0080] The generation unit can estimate students' emotions and adjust the curriculum and feedback generation methods based on the estimated emotions. For example, the generation unit estimates students' emotions using an emotion engine or a generative AI. For instance, if a student is stressed, the generative AI can generate a simple curriculum. If the student is relaxed, the generative AI can generate a normal curriculum. Furthermore, if the student is excited, the generative AI can generate a challenging curriculum. This allows for the provision of more appropriate curriculum and feedback by adjusting the curriculum and feedback generation methods based on students' emotions.

[0081] The generation unit can adjust the level of detail based on the importance of the learning content when generating curricula and feedback. For example, the generation unit uses a generation AI to evaluate the importance of the learning content and adjusts the level of detail of the curriculum and feedback based on that evaluation. For instance, the generation unit generates a detailed curriculum for important learning content. It can also generate a simplified curriculum for less important learning content. Furthermore, the generation unit can adjust the level of detail of the feedback according to the importance of the learning content. This allows for the provision of more effective curricula and feedback by adjusting the level of detail based on the importance of the learning content.

[0082] The generation unit can apply different generation algorithms depending on the category of learning content when generating curricula and feedback. For example, the generation unit can classify the categories of learning content using a generation AI and apply the most suitable generation algorithm according to that category. For instance, the generation unit can generate a curriculum by applying a specific algorithm to mathematics learning content. It can also generate a curriculum by applying a different algorithm to language learning content. Furthermore, the generation unit can generate a curriculum by applying a different algorithm to science learning content. By applying different generation algorithms depending on the category of learning content, it can provide more appropriate curricula and feedback.

[0083] The generation unit can estimate a student's emotions and adjust the length of the curriculum and feedback based on those emotions. For example, the generation unit uses an emotion engine or generative AI to estimate a student's emotions. For instance, if a student is stressed, the generative AI can generate a shorter curriculum. Conversely, if a student is relaxed, the generative AI can generate a curriculum of normal length. Furthermore, if a student is agitated, the generative AI can generate a longer curriculum. This allows for a more appropriate learning experience by adjusting the length of the curriculum and feedback based on the student's emotions.

[0084] The generation unit can prioritize curriculum and feedback based on the submission deadlines for learning materials. For example, the generation unit uses a generation AI to evaluate the submission deadlines for learning materials and determines the priority of the curriculum and feedback based on that evaluation. For instance, the generation unit prioritizes generating curriculum for learning materials with approaching submission deadlines. It can also postpone generating curriculum for learning materials with later submission deadlines. Furthermore, the generation unit can also determine the priority of feedback based on the submission deadlines. This allows for the provision of effective curriculum and feedback tailored to submission deadlines by prioritizing based on the submission deadlines of learning materials.

[0085] The generation unit can adjust the order of learning content based on its relevance when generating curriculum and feedback. For example, the generation unit uses a generation AI to evaluate the relevance of learning content and adjusts the order of curriculum and feedback based on that evaluation. For instance, the generation unit prioritizes incorporating highly relevant learning content into the curriculum. It can also postpone incorporating less relevant learning content into the curriculum. Furthermore, the generation unit can adjust the order of feedback according to the relevance of the learning content. By adjusting the order based on the relevance of learning content, it provides a more effective learning experience.

[0086] The delivery unit can estimate students' emotions and adjust the curriculum and feedback delivery methods based on those estimated emotions. For example, the delivery unit can estimate students' emotions using an emotion engine or generative AI. For instance, if a student is stressed, the AI ​​might select a simple delivery method. If a student is relaxed, the AI ​​might select a standard delivery method. Furthermore, if a student is agitated, the AI ​​might select a more detailed delivery method. This allows for more appropriate delivery by adjusting the curriculum and feedback delivery methods based on students' emotions.

[0087] The delivery department can select the optimal delivery method by referring to students' past learning history when providing curriculum and feedback. For example, the delivery department can collect students' past learning data and analyze that data using AI. For instance, the delivery department can prioritize delivery methods that students have preferred in the past. It can also select delivery methods that have been effective for students in the past. Furthermore, the delivery department can select the optimal delivery method based on students' past learning history. In this way, the optimal delivery method is selected by referring to students' past learning history.

[0088] The delivery department can customize the delivery methods based on the student's current learning environment when providing curriculum and feedback. For example, the delivery department can collect data on the student's learning environment and analyze that data using AI. For instance, if the student is in a noisy environment, the delivery department can select a delivery method suited to a quiet environment. Alternatively, if the student is in a quiet environment, the delivery department can select a standard delivery method. Furthermore, if the student is on the go, the delivery department can select a delivery method optimized for mobile devices. This allows for more appropriate delivery by customizing the delivery methods based on the student's current learning environment.

[0089] The delivery system can estimate students' emotions and determine the order in which curriculum and feedback are delivered based on those estimated emotions. For example, the delivery system might use an emotion engine or generative AI to estimate students' emotions. For instance, if a student is stressed, the AI ​​might prioritize delivering important curriculum. If a student is relaxed, the AI ​​could deliver all curriculum equally. Furthermore, if a student is agitated, the AI ​​could prioritize delivering simpler curriculum. This ensures that important information is prioritized by determining the order in which curriculum and feedback are delivered based on students' emotions.

[0090] The service provider can select the optimal delivery method for curriculum and feedback based on students' geographical location information. For example, the service provider can collect students' geographical location information and analyze that data using AI. For instance, if a student is in an urban area, the service provider can select a delivery method based on urban education data. If a student is in a rural area, the service provider can also select a delivery method based on rural education data. Furthermore, if a student is overseas, the service provider can select a delivery method based on the education data of that country. This allows for more appropriate delivery by selecting the optimal delivery method based on students' geographical location information.

[0091] The service provider can analyze students' social media activity and suggest delivery methods when providing curriculum and feedback. For example, the service provider can collect data on students' social media activity and analyze that data using AI. For instance, the service provider can suggest relevant delivery methods based on the learning content that students share on social media. It can also suggest relevant delivery methods based on information about educational accounts that students follow on social media. Furthermore, the service provider can suggest relevant delivery methods based on the activities of learning groups that students participate in on social media. By analyzing students' social media activity, it becomes possible to suggest relevant delivery methods and provide more appropriate content.

[0092] The style adaptation unit can estimate students' emotions and adjust the teaching style based on those emotions. For example, the style adaptation unit estimates students' emotions using an emotion engine or generative AI. For instance, if a student is stressed, the AI ​​will select a relaxing teaching style. Alternatively, if a student is relaxed, the AI ​​can select a normal teaching style. Furthermore, if a student is excited, the AI ​​can select a stimulating teaching style. This allows for more appropriate lessons to be provided by adjusting the teaching style based on students' emotions.

[0093] The style adaptation unit can select the optimal style when adapting to a lesson style by referring to the student's past learning style history. For example, the style adaptation unit collects data on the student's past learning style and analyzes that data using AI. For instance, the style adaptation unit prioritizes selecting lesson styles that the student has preferred in the past. It can also select lesson styles that have been effective for the student in the past. Furthermore, the style adaptation unit can select the optimal lesson style based on the student's past learning style history. In this way, the optimal lesson style is selected by referring to the student's past learning style history.

[0094] The style adaptation unit can estimate students' emotions and prioritize lesson styles based on those emotions. For example, the style adaptation unit estimates students' emotions using an emotion engine or generative AI. For instance, if a student is stressed, the AI ​​will prioritize providing relaxing lesson styles. Alternatively, if a student is relaxed, the AI ​​can provide all lesson styles equally. Furthermore, if a student is excited, the AI ​​can prioritize providing stimulating lesson styles. This prioritizes important lesson styles by determining them based on students' emotions.

[0095] The style adaptation unit can provide the optimal teaching style based on students' geographical location information when adapting to different teaching styles. For example, the style adaptation unit collects students' geographical location information and analyzes that data using AI. For instance, if a student is in an urban area, the style adaptation unit will provide a style based on urban education styles. It can also provide a style based on rural education styles if the student is in a rural area. Furthermore, if a student is overseas, the style adaptation unit can provide a style based on the education style of that country. By providing the optimal teaching style based on students' geographical location information, more appropriate lessons can be provided.

[0096] The resource provider can estimate a student's emotions and adjust how educational resources are delivered based on those estimated emotions. For example, the resource provider might use an emotion engine or generative AI to estimate a student's emotions. For instance, if a student is stressed, the AI ​​might provide simple educational resources. If the student is relaxed, the AI ​​might provide standard educational resources. Furthermore, if the student is agitated, the AI ​​might provide more detailed educational resources. This allows for the delivery of more appropriate educational resources by adjusting the delivery method based on the student's emotions.

[0097] The resource provisioning unit can select the most suitable resources when providing educational resources by referring to the student's past resource usage history. For example, the resource provisioning unit collects data on the student's past resource usage and analyzes that data using AI. For instance, the resource provisioning unit can prioritize providing educational resources that the student has preferred in the past. It can also provide educational resources that the student found effective in the past. Furthermore, the resource provisioning unit can provide the most suitable educational resources based on the student's past resource usage history. In this way, the optimal educational resources are selected by referring to the student's past resource usage history.

[0098] The resource provisioning unit can estimate students' emotions and prioritize educational resources based on those estimated emotions. For example, the resource provisioning unit might use an emotion engine or generative AI to estimate students' emotions. For instance, if a student is stressed, the AI ​​might prioritize providing important educational resources. Alternatively, if a student is relaxed, the AI ​​could distribute all educational resources equally. Furthermore, if a student is agitated, the AI ​​could prioritize providing simpler educational resources. This prioritizes important educational resources by determining their importance based on students' emotions.

[0099] The resource provision department can provide the most suitable educational resources based on the student's geographical location. For example, the resource provision department collects the student's geographical location information and analyzes that data using AI. For instance, if the student is in an urban area, the resource provision department will provide resources based on urban educational resources. Similarly, if the student is in a rural area, the resource provision department can provide resources based on rural educational resources. Furthermore, if the student is overseas, the resource provision department can provide resources based on educational resources in that country. This allows for the provision of more appropriate educational resources by providing the most suitable resources based on the student's geographical location.

[0100] The resource provision department can analyze students' social media activity to suggest the most suitable resources when providing educational resources. For example, the resource provision department collects data on students' social media activity and analyzes that data using AI. For instance, the resource provision department suggests relevant educational resources based on the learning content that students share on social media. It can also suggest relevant educational resources based on information about educational accounts that students follow on social media. Furthermore, the resource provision department can suggest relevant educational resources based on the activities of learning groups that students participate in on social media. In this way, by analyzing students' social media activity, relevant educational resources can be suggested, and more appropriate educational resources can be provided.

[0101] The resource provisioning unit can suggest the most suitable educational resources by referring to students' calendar information when providing educational resources. For example, the resource provisioning unit collects students' calendar information and analyzes that data using AI. For instance, the resource provisioning unit refers to the schedule registered in the student's calendar and provides the most suitable educational resources. The resource provisioning unit can also suggest educational resources related to specific events based on the student's calendar information. Furthermore, the resource provisioning unit can suggest the most suitable educational resources based on the student's calendar information and schedule. In this way, by referring to students' calendar information, the most suitable educational resources are suggested and more appropriate educational resources are provided.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The analytics department can not only analyze students' learning progress and comprehension in real time, but also track students' learning patterns over the long term to understand learning trends. For example, the analytics department can detect when students tend to concentrate more at specific times and provide the most important learning content during those times. Furthermore, if a student has difficulty with a particular subject, the analytics department can provide supplementary materials for that subject. In addition, based on students' learning patterns, the analytics department can predict future learning progress and propose appropriate learning plans. This allows for more effective learning support by tracking students' learning patterns over the long term.

[0104] The service provider can not only offer multilingual lessons but also tailor lesson content to each student's cultural background. For example, it can use generative AI to create example problems and case studies based on students' cultural backgrounds. It can also provide lessons that incorporate cultural perspectives to deepen common understanding among students from different cultural backgrounds. Furthermore, it can provide culturally-based feedback, increasing students' motivation to learn by allowing them to study content related to their own culture. This allows the service provider to cater to a wider range of students by providing lessons tailored to their cultural backgrounds.

[0105] The system not only includes a style-adaptation unit that provides lessons tailored to students' language and learning styles, but can also provide lesson styles that suit students' learning environments. For example, if a student is studying at home, the style-adaptation unit can provide a lesson style suitable for the home environment. It can also provide a lesson style suitable for the classroom environment if the student is studying at school. Furthermore, if a student is studying while on the go, the style-adaptation unit can provide a lesson style optimized for mobile devices. This allows for more flexible learning by providing lesson styles tailored to students' learning environments.

[0106] The resource provision department not only provides high-quality education even in areas lacking educational resources, but can also provide educational resources tailored to the specific characteristics of each region. For example, the resource provision department can provide agricultural-related teaching materials to students in rural areas. It can also provide teaching materials related to urban planning and environmental issues to students in urban areas. Furthermore, the resource provision department can offer practical learning programs tailored to the specific characteristics of each region, enabling students to acquire the skills to solve local problems. This allows for more practical learning by providing educational resources tailored to the characteristics of each region.

[0107] The generation unit not only generates personalized curricula and feedback using generation AI, but can also generate curricula tailored to students' learning goals. For example, if a student aims to pass a specific exam, the generation unit will generate a curriculum specifically for that exam. Similarly, if a student aims to acquire a specific skill, the generation unit can generate a curriculum focused on that skill. Furthermore, if a student aims to pursue a specific profession in the future, the generation unit can generate a curriculum related to that profession. This allows for more effective learning by providing curricula tailored to students' learning goals.

[0108] The analytics department not only analyzes students' learning progress and comprehension in real time, but can also estimate students' emotions and adjust the analysis method of learning progress and comprehension based on the estimated emotions. For example, if a student is stressed, the AI ​​in the analytics department can slow down the learning pace and perform a more detailed analysis of comprehension. If a student is relaxed, the AI ​​in the analytics department can maintain a normal learning pace and perform a standard analysis of comprehension. Furthermore, if a student is agitated, the AI ​​in the analytics department can speed up the learning pace and simplify the analysis of comprehension. By adjusting the analysis method of learning progress and comprehension based on students' emotions, more appropriate analysis becomes possible.

[0109] The generation unit can estimate students' emotions and adjust the curriculum and feedback generation methods based on those estimated emotions. For example, if a student is stressed, the generation AI will generate a simple curriculum. If the student is relaxed, the generation AI can generate a standard curriculum. Furthermore, if the student is excited, the generation AI can generate a challenging curriculum. This allows for more appropriate curriculum and feedback by adjusting the generation methods based on students' emotions.

[0110] The delivery system can estimate students' emotions and adjust the curriculum and feedback delivery methods based on those estimates. For example, if a student is stressed, the AI ​​can select a simple delivery method. If a student is relaxed, the AI ​​can select a standard delivery method. Furthermore, if a student is agitated, the AI ​​can select a more detailed delivery method. This allows for more appropriate delivery by adjusting the curriculum and feedback delivery methods based on students' emotions.

[0111] The style adaptation unit can estimate students' emotions and adjust the teaching style based on those emotions. For example, if a student is feeling stressed, the AI ​​will select a relaxing teaching style. Conversely, if a student is relaxed, the AI ​​can select a normal teaching style. Furthermore, if a student is excited, the AI ​​can select a stimulating teaching style. This allows for more appropriate lessons to be provided by adjusting the teaching style based on students' emotions.

[0112] The resource delivery unit can estimate students' emotions and adjust how educational resources are delivered based on those estimates. For example, if a student is stressed, the AI ​​can provide simple educational resources. If a student is relaxed, the AI ​​can provide standard educational resources. Furthermore, if a student is agitated, the AI ​​can provide detailed educational resources. By adjusting how educational resources are delivered based on students' emotions, more appropriate resources are provided.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The analysis department analyzes students' learning progress and comprehension in real time. Specifically, it collects student learning data and analyzes that data using AI. For example, it evaluates learning progress based on answer time and correct answer rate, and assesses comprehension by analyzing test results and assignment submission status. Step 2: The generation unit generates personalized curricula and feedback based on the data analyzed by the analysis unit. Specifically, it uses a generation AI to receive student learning data as input and generate optimal curricula and feedback. For example, the generation AI generates additional problems to address the student's weaknesses and feedback tailored to their learning style (diagrams for visual learners, audio feedback for auditory learners). Step 3: The delivery unit provides the curriculum and feedback generated by the generation unit. Specifically, it delivers the generated curriculum to students' learning platforms and provides it through web and mobile applications. It also notifies students of the generated feedback using email and push notifications.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit collects student learning data using the camera 42 and microphone 38B of the smart device 14 and analyzes it in real time using the control unit 46A. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimized curriculum and feedback using a generation AI. The provision unit provides the curriculum and feedback generated by the control unit 46A of the smart device 14 to the students. 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.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit collects student learning data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it in real time using the control unit 46A. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimized curriculum and feedback using a generation AI. The provision unit provides the curriculum and feedback generated by the control unit 46A of the smart glasses 214 to the students. 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.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit collects student learning data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it in real time using the control unit 46A. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimized curriculum and feedback using a generation AI. The provision unit provides the curriculum and feedback generated by the control unit 46A of the headset terminal 314 to the students. 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.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit collects student learning data using the camera 42 and microphone 238 of the robot 414 and analyzes it in real time using the control unit 46A. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimized curriculum and feedback using a generation AI. The provision unit provides the curriculum and feedback generated by the control unit 46A of the robot 414 to the students. 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) The analysis department analyzes students' learning progress and understanding in real time, A generation unit generates a curriculum and feedback optimized for each individual based on the data analyzed by the aforementioned analysis unit, The system comprises a providing unit that provides the curriculum and feedback generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, We offer multilingual classes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, It features a style-adaptation section that provides lessons tailored to each student's language and learning style. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We have a resource provision department that provides high-quality education even in areas where educational resources are scarce. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The AI ​​generates personalized curricula and feedback tailored to each individual. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is Analyze students' learning progress and comprehension in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate students' emotions and adjust the analysis methods for learning progress and comprehension based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is 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 is When analyzing learning progress and comprehension, filtering is performed based on the student's current learning environment and situation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is 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 is When analyzing learning progress and comprehension levels, the system prioritizes analyzing data with high relevance based on students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing learning progress and comprehension levels, we analyze students' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate students' emotions and adjust the curriculum and feedback generation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating curricula and feedback, adjust the level of detail based on the importance of the learning content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating curricula and feedback, different generation algorithms are applied depending on the category of learning content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates students' emotions and adjusts the curriculum and feedback length 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 curricula and feedback, prioritize based on the submission deadline for learning materials. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating curricula and feedback, the order of learning content is adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate students' emotions and adjust the curriculum and feedback delivery methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing curriculum and feedback, refer to students' past learning history to select the most appropriate delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing curriculum and feedback, customize the delivery method based on the student's current learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates students' emotions and determines the curriculum and the order in which feedback is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing curriculum and feedback, the optimal delivery method will be selected based on students' geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing curriculum and feedback, we analyze students' social media activity and propose methods for delivering that information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned style adaptation section is The system estimates students' emotions and adjusts the teaching style based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 26) The aforementioned style adaptation section is When adapting to a new teaching style, the optimal style is selected by referring to the students' past learning style history. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned style adaptation section is The system estimates students' emotions and prioritizes teaching styles based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned style adaptation section is When adapting teaching styles, we provide the optimal style based on students' geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 29) The resource provisioning unit, Estimate students' emotions and adjust how educational resources are provided based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 30) The resource provisioning unit, When providing educational resources, the most suitable resources are selected by referring to students' past resource usage history. The system described in Appendix 4, characterized by the features described herein. (Note 31) The resource provisioning unit, The system estimates students' emotions and prioritizes educational resources based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 32) The resource provisioning unit, When providing educational resources, we will provide the most suitable resources based on students' geographical location information. The system described in Appendix 4, characterized by the features described herein. (Note 33) The resource provisioning unit, When providing educational resources, we analyze students' social media activity to suggest the most suitable resources. The system described in Appendix 4, characterized by the features described herein. (Note 34) The resource provisioning unit, When providing educational resources, we refer to students' calendar information to suggest the most suitable resources. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0187] 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 progress and understanding in real time, A generation unit generates a curriculum and feedback optimized for each individual based on the data analyzed by the aforementioned analysis unit, The system comprises a providing unit that provides the curriculum and feedback generated by the generation unit. A system characterized by the following features.

2. The aforementioned supply unit is, We offer multilingual classes. The system according to feature 1.

3. The aforementioned supply unit is, It features a style-adaptation section that provides lessons tailored to each student's language and learning style. The system according to feature 1.

4. The aforementioned supply unit is, We have a resource provision department that provides high-quality education even in areas where educational resources are scarce. The system according to feature 1.

5. The generating unit is Generative AI generates personalized curricula and feedback for each individual. The system according to feature 1.

6. The aforementioned analysis unit is Analyze students' learning progress and comprehension in real time. The system according to feature 1.

7. The aforementioned analysis unit is We estimate students' emotions and adjust the analysis methods for learning progress and comprehension based on the estimated emotions. The system according to feature 1.

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

9. The aforementioned analysis unit is When analyzing learning progress and comprehension, filtering is performed based on the student's current learning environment and situation. The system according to feature 1.

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