Method for generating guidance information, apparatus for generating guidance information, computer program, method for information processing, and information processing apparatus

Abstract

To provide a method for generating guidance information capable of creating guidance information suitable for characteristics of a child or student.SOLUTION: The method for generating guidance information causes a computer to execute a process including acquiring answer information indicating answers to a plurality of questions regarding characteristics of a child or student, and generating guidance information corresponding to the acquired answer information by using a language model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for generating guidance information, a device for generating guidance information, a computer program, an information processing method, and an information processing device. [Background technology]

[0002] Conventionally, technologies have been proposed to support instructors in teaching students in educational settings. For example, Patent Document 1 discloses an information processing device that can evaluate lesson plans by comparing the actual lesson content with the content planned in the lesson plan used in that lesson and determining the degree of agreement. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-173566 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 does not generate teaching information that matches the characteristics of students.

[0005] An object of one aspect of the present disclosure is to provide a method for generating guidance information that can generate guidance information that suits the characteristics of students. [Means for solving the problem]

[0006] (1) A method for generating instruction information according to one aspect of the present disclosure includes a computer executing a process of acquiring answer information indicating answers to a plurality of questions regarding characteristics of a student, and using a language model, generating instruction information according to the acquired answer information.

[0007] (2) In the method for generating guidance information described above in (1), each question may be classified into a plurality of areas, and guidance information may be generated according to the answer information indicating the answers to the questions classified into each area.

[0008] (3) In the method for generating guidance information described in (1) or (2) above, the guidance information may be generated by creating a prompt including the answer information and predetermined correspondence information between the answer information and the guidance information, and providing the created prompt to the language model.

[0009] (4) In the method for generating guidance information described in any one of (1) to (3) above, correspondence information between the answer information and the guidance information may be updated based on the answer information and the generated guidance information.

[0010] (5) In the method for generating instruction information described in any one of (1) to (4) above, the instruction information may be generated by acquiring a student type or area of ​​interest corresponding to the answer information, creating a prompt including the acquired student type or area of ​​interest and correspondence information between the predetermined student type or area of ​​interest and the instruction information, and providing the created prompt to the language model.

[0011] (6) In the method for generating instruction information described in (5) above, the correspondence information between a predetermined student type or area of ​​interest and instruction information may be correspondence information between a student type or area of ​​interest similar to the acquired student type or area of ​​interest and an instruction plan.

[0012] (7) In the method for generating instruction information described in (5) or (6) above, the student type corresponding to the answer information may be obtained using a language model that uses correspondence information between the answer information and the student type.

[0013] (8) In the method for generating instruction information described in any one of (5) to (7) above, an area of ​​interest within each area may be derived based on answers to questions classified into each area, a prompt may be created that includes the derived area of ​​interest and correspondence information between the predetermined area of ​​interest and a student type, and the created prompt may be provided to a language model to obtain the student type.

[0014] (9) In the method for generating instruction information described in any one of (5) to (8) above, correspondence information between the student type and the instruction information may be updated based on the student type and the generated instruction information.

[0015] (10) In the method for generating instruction information described in any one of (1) to (9) above, the instruction information may include a student instruction plan, and the student instruction plan may include a student instruction plan for each subject.

[0016] (11) A computer program according to one embodiment of the present disclosure acquires answer information indicating answers to a plurality of questions regarding characteristics of a student, and causes a computer to execute a process of generating instructional information corresponding to the acquired answer information using a language model.

[0017] (12) A teaching information generation device according to one embodiment of the present disclosure includes a control unit that acquires answer information indicating answers to a plurality of questions regarding characteristics of a student, and uses a language model to execute a process of generating teaching information according to the acquired answer information.

[0018] (13) A computer program according to one embodiment of the present disclosure causes a computer to execute a process of storing multiple pieces of instructional information corresponding to each piece of first answer information, the multiple pieces of instructional information being generated using a language model based on multiple pieces of first answer information indicating answers to multiple questions regarding the characteristics of children and students, acquiring second answer information indicating answers to multiple questions regarding the characteristics of children and students, and generating instructional information corresponding to the acquired second answer information based on the multiple pieces of instructional information stored.

[0019] (14) In the computer program described in (13) above, the child / student observations may be generated by acquiring the area of ​​interest or the type of student according to the second response information and information on the child / student's learning and / or lifestyle attitudes, creating a prompt including the area of ​​interest or the type of student according to the acquired second response information and information on the child / student's learning and / or lifestyle attitudes, and providing the created prompt to a language model.

[0020] (15) In the computer program described in (14) above, improvements to the student observations may be obtained, and the obtained improvements may be input into a language model to generate new student observations.

[0021] (16) In the computer program according to any one of (13) to (15) above, guidance information corresponding to the acquired second response information may be output using a language model that uses the second response information and correspondence information between the stored guidance information and the first response information.

[0022] (17) In the computer program according to any one of (13) to (16) above, a language model may be used to generate an explanatory sentence for the instruction information corresponding to the second response information, and the generated explanatory sentence may be output.

[0023] (18) In the computer program described in any one of (13) to (17) above, a language model may be used to generate an explanatory sentence based on a plurality of pieces of instruction information corresponding to the second response information, the explanatory sentence including a summary of the instruction information or instruction information summarized into a number less than the number of pieces of instruction information corresponding to the second response information, and the generated explanatory sentence may be output.

[0024] (19) In the computer program described in any one of (13) to (18) above, guidance information corresponding to the second response information may be provided to a user who provided the second response information, and if the second response information satisfies a predetermined requirement, the guidance information corresponding to the second response information may be provided to a user other than the user who provided the second response information.

[0025] (20) In the computer program described in (19) above, the predetermined requirement may be set according to the type of question related to the second answer information or the type of the different users.

[0026] (21) An information processing method according to one embodiment of the present disclosure includes a computer executing a process of acquiring a plurality of first answer information indicating answers to a plurality of questions regarding characteristics of a child or student, storing a plurality of pieces of instruction information generated by generating instruction information corresponding to each piece of first answer information acquired using a language model, acquiring second answer information indicating answers to a plurality of questions regarding characteristics of the child or student, and generating instruction information corresponding to the acquired second answer information based on the plurality of pieces of instruction information stored.

[0027] (22) An information processing device according to one embodiment of the present disclosure includes a control unit that acquires a plurality of first answer information indicating answers to a plurality of questions regarding characteristics of children and students, stores a plurality of pieces of instruction information generated by generating instruction information corresponding to each piece of first answer information acquired using a language model, acquires second answer information indicating answers to a plurality of questions regarding characteristics of children and students, and executes a process of generating instruction information corresponding to the acquired second answer information based on the plurality of pieces of instruction information stored.

[0028] (23) A method for generating guidance information according to one aspect of the present disclosure acquires answer information indicating answers to a plurality of questions regarding the characteristics of children and students, acquires a child / student type corresponding to the answer information using a language model that uses correspondence information between the answer information and the child / student type, and generates guidance information corresponding to the answer information using a language model that uses the acquired child / student type and correspondence information between predetermined child / student types and guidance information. [Effects of the Invention]

[0029] According to the present disclosure, it is possible to generate instruction information that matches the characteristics of students. [Brief explanation of the drawings]

[0030] [Figure 1] FIG. 1 is a schematic diagram of an information processing system. [Figure 2] FIG. 1 is a block diagram showing a configuration of an information processing device. [Figure 3] FIG. 2 is a block diagram showing the configuration of a terminal device. [Figure 4] FIG. 10 is a diagram showing an example of information stored in a question DB. [Figure 5] FIG. 10 is a diagram showing an example of the contents of information stored in a teaching DB. [Figure 6] 10 is a flowchart showing an example of a procedure for generating a teaching plan. [Figure 7] FIG. 10 is a diagram illustrating an overview of a process for generating student types using a large-scale language model. [Figure 8] FIG. 10 is a diagram showing an example of the content of a first prompt. [Figure 9] FIG. 1 is a diagram showing an overview of a process for generating a teaching plan using a large-scale language model. [Figure 10] FIG. 10 is a diagram showing an example of the content of a second prompt. [Figure 11] 10 is a flowchart showing an example of a procedure for providing a teaching plan. [Figure 12] 10 is a flowchart showing an example of a procedure for generating a teaching plan in the first modification. [Figure 13] 10 is a flowchart showing an example of a procedure for providing a teaching plan in Modification 2. [Figure 14] FIG. 10 is a block diagram showing the configuration of an information processing device according to a second embodiment. [Figure 15] FIG. 10 is a diagram showing an example of the contents of information stored in a findings DB. [Figure 16] 10 is a flowchart showing an example of a procedure for providing student findings. [Figure 17] FIG. 10 is a diagram illustrating an overview of a process for generating student findings using a large-scale language model. [Figure 18] FIG. 10 is a diagram showing an example of the content of a fourth prompt. [Figure 19]FIG. 10 is a diagram showing another example of an outline of a process for generating student findings using a large-scale language model. [Figure 20] 11 is a flowchart illustrating an example of a processing procedure executed by an information processing system according to a third embodiment. [Figure 21] FIG. 2 is a schematic diagram illustrating an example of a screen displayed on a display unit of a terminal device. [Figure 22] FIG. 10 is a diagram showing an example of setting information of a threshold value used to determine whether or not a teaching plan needs to be shared. [Figure 23] 10 is a flowchart illustrating an example of a processing procedure executed by an information processing system according to a fourth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0031] The present disclosure will be specifically described with reference to the drawings showing embodiments thereof.

[0032] (First embodiment) 1 is a schematic diagram of an information processing system 100. The information processing system 100 includes an information processing device 1 and a terminal device 2. The information processing device 1 and the terminal device 2 are connected to each other so as to be able to communicate with each other via a network N. The network N is a wired or wireless network including a public communication network such as the Internet, a carrier network, etc. The information processing system 100 is also capable of transmitting and receiving data to and from a language processing server 3 via the network N. The number of terminal devices 2 may be one or three or more.

[0033] The information processing system 100 disclosed herein is a system for supporting instructors who instruct children and students in educational settings in generating instruction information for individual children and students. Children and students include children (elementary school students) and students (junior high school students, high school students, university students, etc.). Children and students may also include kindergarteners. Children and students may be, for example, children and students who need support, children and students who need comprehensive education, etc. In this embodiment, a child and student instruction plan (hereinafter also simply referred to as an instruction plan) is generated for each child and student as an example of instruction information.

[0034] The information processing device 1 is an information processing device capable of various information processing and sending and receiving information, such as a server computer, a personal computer, a quantum computer, etc. The information processing device 1 acquires answer information to questions about students, generates a teaching plan according to the answer information using a large-scale language model (LLM) 4, and provides the teaching plan to an instructor via a terminal device 2. The information processing device 1 functions as a generating device that generates teaching information and a providing device that provides teaching information.

[0035] The terminal device 2 is an information processing terminal used by the instructor, such as a personal computer, a smartphone, or a tablet terminal. The terminal device 2 transmits answer information received from the instructor to the information processing device 1 and displays an instruction plan according to the answer information. The instructor is an example of a user who receives the instruction plan, such as a teacher at an elementary or junior high school.

[0036] The language processing server 3 is a server device equipped with a large-scale language model 4. The language processing server 3 provides an interactive AI (Artificial Intelligence) service using the large-scale language model 4. The large-scale language model 4 is a general-purpose natural language processing model constructed by performing unsupervised pre-learning using a large set of sentences. Examples of the large-scale language model 4 include Transformer, BERT (Bidirectional Encoder Representations from Transformers), GPT (Generative Pre-trained Transformer), and LaMDA (Language Models for Dialogue Applications). Note that the information processing device 1 may be configured to be equipped with the large-scale language model 4 and to provide an interactive AI service.

[0037] 2 is a block diagram showing the configuration of the information processing device 1. The information processing device 1 includes a control unit 11, a storage unit 12, and a communication unit 13. The information processing device 1 may be a multi-computer consisting of multiple computers, or may be a virtual machine virtually constructed by software.

[0038] The control unit 11 includes one or more arithmetic processing devices such as a central processing unit (CPU) or a graphics processing unit (GPU). The control unit 11 controls each component unit and executes processing using built-in memories such as a read-only memory (ROM) or a random access memory (RAM), a clock, a counter, etc. The functional units of the control unit 11 may be realized by software, or some or all of them may be realized by hardware such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

[0039] The storage unit 12 includes a nonvolatile memory such as a hard disk or a flash memory. The storage unit 12 stores computer programs and data referenced by the control unit 11. The storage unit 12 of this embodiment stores a program 1P for causing a computer to execute processes related to the generation and provision of guidance information, a question DB (Data Base) 121, and a guidance DB 122. The storage unit 12 may be separate from the information processing device 1 and may be one or more external storage devices externally connected thereto.

[0040] A computer program (program product) including program 1P may be provided by a non-transitory recording medium 1A on which the computer program is readably recorded. Storage unit 12 stores the computer program read from recording medium 1A by a reading device (not shown). Recording medium 1A may be, for example, a magnetic disk, optical disk, or semiconductor memory. Alternatively, the computer program may be downloaded from an external server connected to a communications network and stored in storage unit 12. Program 1P may be a single computer program or may be composed of multiple computer programs, and may be executed on a single computer or on multiple computers interconnected by a communications network.

[0041] The communication unit 13 includes a communication device that performs communication via the network N. The control unit 11 transmits and receives data to and from the terminal device 2 and the language processing server 3 via the communication unit 13.

[0042] The configuration of the information processing device 1 is not limited to the above example, and may include, for example, a display unit for displaying images, an operation unit for accepting user operations, and the like.

[0043] 3 is a block diagram showing the configuration of the terminal device 2. The terminal device 2 includes a control unit 21, a storage unit 22, a communication unit 23, a display unit 24, and an operation unit 25.

[0044] The control unit 21 includes one or more arithmetic processing units such as a CPU, a GPU, etc. The control unit 21 controls each component unit and executes processing using built-in memories such as ROM or RAM, clocks, counters, etc. The functional units of the control unit 21 may be realized by software, or some or all of them may be realized by hardware.

[0045] The storage unit 22 includes a non-volatile memory such as a hard disk or a flash memory. The storage unit 22 stores various computer programs and data referenced by the control unit 21. The storage unit 22 stores a program 2P for causing a computer to execute processing related to the acquisition of guidance information. A computer program (computer program product) including the program 2P may be provided by a non-transitory recording medium 2A on which the computer program is readably recorded. The computer program may also be downloaded from an external server connected to a communications network and stored in the storage unit 22.

[0046] The communication unit 23 includes a communication device that performs communication via the network N. The control unit 21 transmits and receives data to and from the information processing device 1 via the communication unit 23.

[0047] The display unit 24 includes a display device such as a liquid crystal display, an organic EL (Electro Luminescence) display, etc. The display unit 24 displays various information including the teaching plan in accordance with instructions from the control unit 21. The display unit 24 may also be an audio output unit including a speaker.

[0048] The operation unit 25 is an interface that accepts user operations. The operation unit 25 includes, for example, a keyboard, a mouse, etc. The operation unit 25 may be a touch panel built into the display unit 24. The operation unit 25 may be an audio input unit that includes a microphone. The operation unit 25 accepts operation input from the user and sends a control signal to the control unit 21 according to the operation content.

[0049] FIG. 4 is a diagram showing an example of the content of information stored in the question DB 121. The question DB 121 is a database that stores information about questions related to the characteristics of students. The question DB 121 stores records that link information such as domains, questions, and answers using, for example, a question ID as a key. The domain is information that indicates the domain to which the question belongs. The question is information that indicates the content of the question. The answer is information that indicates the answer to the question.

[0050] In this embodiment, questions about student characteristics are questions for obtaining response information used to determine student types and generate teaching plans, which will be described later, and include multiple, multifaceted questions about the student's status in school life. Questions about student characteristics are structured to enable evaluation of, for example, the educational needs that students exhibit in their daily lives and learning situations, and the behavioral characteristics that may be causing those needs, and can be classified into multiple areas depending on the content of the question.

[0051] In the example shown in Figure 4, the domains include physical, emotional, lifestyle, and learning. Physical domains include "physical condition" and "posture, movement, and behavior." Emotional domains include "inattention," "hyperactivity and impulsivity," "obsessiveness," and "self-esteem." Life domains include "social functioning" and "communication." Learning domains include "listening," "speaking," "reading," "writing," "calculating," and "reasoning." Physical and emotional domains represent the causes of a child's condition, while lifestyle and learning domains represent the results of the child's condition. In this way, each question is classified into 14 domains in four aspects.

[0052] Answers to questions are expressed, for example, by a five-level index value. A smaller index value means that the state of the target child / student is more likely to match the question, and a larger index value means that the state of the target child / student is less likely to match the question. Note that the areas, questions, and answers shown in FIG. 4 are merely examples. The contents stored in question DB 121 are not limited to the example shown in FIG. 4.

[0053] 5 is a diagram showing an example of the content of information stored in the instruction DB 122. The instruction DB 122 is a database that stores information related to instruction plans. The instruction DB 122 stores records that link together information such as an answer ID, answer information, an area of ​​interest, a type ID, student type information, an instruction plan ID, and instruction plan information.

[0054] The answer ID is identification information for identifying the answer information. The answer information is information indicating the answers from the instructor to multiple questions regarding the characteristics of the student. The answer information may include index values ​​representing the answers corresponding to each of the multiple questions, or may include a total value obtained by adding up the index values ​​for each area. The attention area is information indicating an area that should be paid attention to in the answer information. The attention area means an area where the student has issues. The attention area can be identified, for example, based on the index value of the answer information. Details of how to identify the attention area will be described later.

[0055] The type ID is identification information for identifying student type information. The student type information is information that indicates a student type. The student type represents a pattern of students when classifying students based on an area of ​​interest.

[0056] Student types are made up of combinations of areas of attention, for example. Specific examples of student types include a pattern in which a decline in "physical condition" causes "inattention" and affects "reasoning," a pattern in which problems in "posture, movement, and behavior" cause "hyperactivity and impulsivity," which creates difficulties in "communication," and affects the ability to "read" and "write," and a pattern in which a decline in "physical condition" causes a decline in "self-esteem," which affects "listening."

[0057] The lesson plan ID is identification information for identifying lesson plan information. The lesson plan information is information that indicates a lesson plan. A lesson plan, for example, represents specific teaching content for school life in general. Specific examples of lesson plans include, "It would be a good idea to circle dates, pages, and study questions in different colors to make them easier to understand," and "If writing on the board is difficult, it would be a good idea to create a printout that mimics the writing on the board and have students fill in the gaps."

[0058] The lesson plan may be a lesson plan for a specific field, or may be a subject-specific lesson plan representing educational content corresponding to various subjects.

[0059] The teaching DB 122 may be a vector database that vectorizes and stores some text. Examples of the data to be vectorized include areas of interest, student type information, and teaching plan information.

[0060] The information processing device 1 collects multiple sets of previously generated answer information, student type information, and teaching plan information, and stores them in advance in the teaching DB 122. Furthermore, when the information processing device 1 generates student type information and teaching plan information in response to new answer information, it additionally stores the generated information in the teaching DB 122. In this way, the information in the teaching DB 122 is updated as needed. Note that the student types and teaching plans shown in FIG. 5 are merely examples. The contents stored in the teaching DB 122 are not limited to the example shown in FIG. 5.

[0061] In the information processing system 100 configured as described above, a large number of teaching plans suited to individual students are generated based on response information relating to each student having a variety of characteristics. The processing executed in the information processing system 100 will now be described.

[0062] 6 is a flowchart showing an example of a processing procedure for generating a teaching plan. The processing in each of the following flowcharts is executed by the control unit 11 in accordance with a program 1P stored in the storage unit 12 of the information processing device 1, or by the control unit 21 in accordance with a program 2P stored in the storage unit 22 of the terminal device 2.

[0063] The control unit 11 of the information processing device 1 acquires answer information indicating answers to a plurality of questions related to characteristics of students stored in the question DB 121 (step S101). The control unit 11 may acquire the answer information by accepting input from a user, or may acquire the answer information through the terminal device 2 or another external device. The answer information may be answers corresponding to actual students or answers corresponding to virtual students.

[0064] The control unit 11 derives an attention area for the student based on the acquired answer information (step S102). In step S102, the control unit 11 obtains an answer for each area by integrating answers to multiple questions for each area, and derives an attention area based on the obtained answer for each area.

[0065] The method for deriving the region of interest is not particularly limited. For example, the total value of the index values ​​(answer scores) as answers to all questions belonging to the same region is calculated for each region, and the calculated total value is compared with a predetermined threshold value for each region. If the total value for a certain region is less than the predetermined threshold value, the region can be determined to be a region of interest. If the total value for a certain region is equal to or greater than the predetermined threshold value, the region can be determined not to be a region of interest. For example, if there are 10 questions belonging to the region "physical condition," the threshold for the region is 10 points, and the total value in the answer information is 20 points, the "physical condition" can be determined not to be a region of interest. The control unit 11 performs the above-mentioned comparison for each predetermined region and identifies one or more regions whose total value is less than the threshold value.

[0066] The attention area may be determined based on whether the number or percentage of answers whose index values ​​are less than a predetermined threshold among all answers in the same area is equal to or greater than a predetermined threshold. If the number or percentage of answers in a certain area is equal to or greater than a threshold, the area can be identified as an attention area. The control unit 11 associates the area with the corresponding threshold and stores them in the storage unit 12. The threshold may be stored in the question DB 121.

[0067] The control unit 11 creates a first prompt that includes the attention area derived from the answer information and first correspondence information indicating the correspondence between the attention area in the answer information and the student type, and requests output of the student type of the student having the specific attention area (step S103). The first correspondence information is related information used to generate the student type. The control unit 11 extracts the first correspondence information including the attention area and the student type information corresponding to the attention area based on the information stored in the teaching DB 122. The control unit 11 may further generate a role for the large-scale language model 4 (e.g., "elementary and junior high school teaching expert") and include the generated role in the first prompt.

[0068] The control unit 11 provides the generated first prompt to the large-scale language model 4 provided by the language processing server 3 or to the large-scale language model 4 that has been trained in the information processing device 1 (step S104). The control unit 11 acquires the student type output from the large-scale language model 4 (step S105).

[0069] Fig. 7 is a diagram showing an overview of the process of generating student types using the large-scale language model 4. Fig. 8 is a diagram showing an example of the content of the first prompt. Figs. 7 and 8 show an example in which the focus areas in the answer information are "physical condition," "self-esteem," and "listening."

[0070] The role of the first prompt is set as "You are an elementary and junior high school teaching expert. You are trying to classify students into patterns in order to provide appropriate instruction to students who need support and comprehensive education." The related information includes the content of each of the 14 attention areas and first correspondence information including the attention area and student type information extracted from the teaching DB 122. The first correspondence information includes student type information related to multiple attention areas.

[0071] The first prompt further includes a sentence such as, "Referring to the related information, please output the student type for which the following areas are areas of interest," along with the derived areas of interest (e.g., "physical condition," "self-esteem," and "listening").

[0072] An example of a student type output from the large-scale language model 4 based on the first prompt described above is shown below. Child and student type: A pattern in which a decline in "physical condition" causes a decline in "self-esteem" and affects "listening."

[0073] Returning to FIG. 6, the explanation will be continued. The control unit 11 creates a second prompt that includes the acquired student type and second correspondence information indicating the correspondence between the student type and the teaching plan, and requests output of a teaching plan for students classified into a specific student type (step S106). The second correspondence information is related information used to generate a teaching plan. The control unit 11 extracts the second correspondence information that includes student type information and teaching plan information corresponding to the student type information based on the information stored in the teaching DB 122. The control unit 11 may further create a role for the large-scale language model 4 and include the created role in the second prompt.

[0074] The control unit 11 provides the generated second prompt to the large-scale language model 4 provided by the language processing server 3 or to the large-scale language model 4 that has been trained in the information processing device 1 (step S107). The control unit 11 acquires the teaching plan output from the large-scale language model 4 (step S108).

[0075] The control unit 11 associates the obtained answer information, attention area, student type, and teaching plan and stores them in the teaching DB 122 (step S109), and ends the series of processes. It is preferable that the student type and teaching plan stored in the teaching DB 122 are supervised by an expert in teaching students and updated as appropriate.

[0076] Fig. 9 is a diagram showing an overview of the generation process of a teaching plan using the large-scale language model 4. Fig. 10 is a diagram showing an example of the content of the second prompt. Figs. 9 and 10 show an example in which the student type is one in which a decline in "physical condition" causes a decline in "self-esteem" and affects "listening."

[0077] The role of the second prompt is set as "You are an elementary and junior high school teaching expert. You are trying to create a teaching plan according to student types for students who need support and comprehensive education." The related information includes second correspondence information including student type information and teaching plan information extracted from the teaching DB 122. The second correspondence information includes teaching plan information related to multiple student types.

[0078] The second prompt further includes a sentence such as, "Referring to the relevant information, please output as many examples of appropriate teaching plans as possible for the following types of students," along with the acquired student types (for example, a pattern in which a decline in "physical condition" causes a decline in "self-esteem," which in turn affects "listening").

[0079] An example of relevant information included in the second prompt is: Child and student type 1: A pattern in which a decline in "physical condition" causes "inattention" and affects "reasoning." Lesson Plan 1: It is a good idea to use different colors to highlight dates, pages, and study questions to make them easier to understand. Lesson Plan 2: If writing on the board is difficult, it would be a good idea to create a printout that mimics the writing on the board and have students fill in the gaps. Teaching Plan 3: When students become agitated, it is okay to leave the classroom for a while and then teach them again when they have calmed down.

[0080] An example of a lesson plan output from large-scale language model 4 based on the second prompt is shown below. Lesson Plan: Proactively acknowledge students' efforts and progress and provide positive feedback, which helps to improve their self-esteem. Lesson Plan: Set learning goals together with your students and build their self-esteem by achieving small goals. A sense of accomplishment will help them feel more confident in their listening abilities.

[0081] In the above process, the information processing device 1 may extract some data sets from the information stored in the instruction DB 122 as the first correspondence information or the second correspondence information in accordance with preset extraction conditions. The extraction conditions may be to extract a predetermined number of randomly selected data sets. Alternatively, the extraction conditions may be set to take into consideration the similarity between the area of ​​interest derived in step S102 or the student type acquired in step S105 and the information stored in the instruction DB 122.

[0082] When similarity is taken into consideration, the information processing device 1 may extract, as first correspondence information, only those attention areas similar to the derived attention area and student types associated with the attention areas from among the attention areas stored in the instruction DB 122. The information processing device 1 may extract, as second correspondence information, only student type information similar to the acquired student type and instruction plan information associated with the student type information from among the student type information stored in the instruction DB 122. The determination of similarity may be performed using Euclidean distance, cosine similarity, or the like, based on the vectors of the attention area or student type. For example, the information processing device 1 extracts, as second correspondence information, student type information whose similarity to the acquired student type is equal to or greater than a predetermined value.

[0083] In the above process, the first correspondence information and the second correspondence information are provided as related information to the large-scale language model 4 to generate student types and teaching plans, but the method for generating student types and teaching plans is not limited to this. The large-scale language model 4 may be fine-tuned using at least one of the first correspondence information and the second correspondence information as training data. When using a large-scale language model 4 trained using at least one of the first correspondence information and the second correspondence information, the first prompt may not include the first correspondence information, or the second prompt may not include the second correspondence information.

[0084] The information processing device 1 executes the above-described process for multiple pieces of answer information to preset questions to generate student types and teaching plans. In this way, student types and teaching plans corresponding to various answer patterns are stored in the teaching DB 122. The above-described process for generating teaching plans may be executed at a stage prior to the operational stage in which teaching plans are provided to instructors.

[0085] The information processing device 1 acquires a teaching plan corresponding to the response information transmitted from the terminal device 2 based on the information stored in the teaching DB 122, and provides the teaching plan to the terminal device 2. Fig. 11 is a flowchart showing an example of a processing procedure for providing a teaching plan.

[0086] The control unit 21 of the terminal device 2 displays a reception screen on the display unit 24 (step S201). The reception screen may be provided by the information processing device 1 or may be displayed independently on the terminal device 2. The reception screen includes, for example, the contents of a plurality of questions regarding the characteristics of the student stored in the question DB 121, and answer reception fields for receiving answers to each question. The instructor can input an answer by using the operation unit 25 to select an answer option (index value) that applies to the state of the specific student for whom a teaching plan is to be generated from among the answer options (index values) associated with each question.

[0087] The control unit 21 acquires answer information indicating an answer to the question based on the instructor's operation (step S202), and transmits the acquired answer information to the information processing device 1 (step S203).

[0088] The control unit 11 of the information processing device 1 receives the answer information (step S204). Note that the method for obtaining the answer information is not limited to using the answer acceptance screen.

[0089] The control unit 11 generates a training plan according to the received answer information based on the information stored in the training DB 122 (step S205). Specifically, the control unit 11 identifies answer information that matches the received answer information from multiple pieces of answer information stored in the training DB 122, and extracts one or more training plans associated with the identified answer information. The control unit 11 may identify a problem area in the answer information based on the total points for each area in the received answer information, and extract data that matches the identified problem area from the training DB 122.

[0090] The control unit 11 transmits the obtained teaching plan to the terminal device 2 (step S206).

[0091] The control unit 21 of the terminal device 2 receives the teaching plan transmitted in response to the response information (step S207), and displays a screen showing the received teaching plan on the display unit 24 (step S208). One or more teaching plans for a specific student are displayed on the screen. The control unit 21 ends the process.

[0092] In the above-mentioned step S205, if no answer information matching the acquired answer information is stored in the instruction DB 122, the control unit 11 may identify the answer information that is most similar to the acquired answer information and extract the instruction plan associated with the identified most similar answer information.

[0093] After outputting the teaching plan, the information processing device 1 may receive, from the instructor, correction information indicating the content of corrections to the output teaching plan, and update the teaching plan information in the teaching DB 122 based on the received correction information.

[0094] According to this embodiment, student types and teaching plans can be efficiently generated using the large-scale language model 4. While preparing a large number of questions allows for an accurate understanding of student characteristics, the number of answer patterns also increases as the number of questions increases. Therefore, it is a heavy burden to comprehensively construct student types and teaching plans that correspond to the answer patterns of all students. By using the large-scale language model 4, the burden can be reduced.

[0095] By adding a relatively small number of pre-constructed examples of student types and teaching plans to the input information to the large-scale language model 4, it becomes possible to output more appropriate student types and teaching plans. Typically, pre-trained large-scale language models are pre-trained with a large amount of publicly available data, and therefore cannot output information that is not publicly available. By providing the large-scale language model 4 with data extracted from the teaching DB 122, which includes information that is not publicly available, it is possible to output more specialized information.

[0096] By comprehensively assessing the answers to multiple questions by area and generating teaching plans based on the student types indicated by the answer patterns for each area, teaching plans can be generated efficiently while appropriately preserving the characteristics of the answer information.

[0097] (Variation 1) The information processing device 1 of the first modification generates a teaching plan directly from the response information without performing a process of deriving a student type in the process of generating a teaching plan. Fig. 12 is a flowchart showing an example of a processing procedure for generating a teaching plan in the first modification.

[0098] Similar to step S101, the control unit 11 of the information processing device 1 acquires answer information indicating answers to a plurality of questions regarding characteristics of students (step S301).

[0099] The control unit 11 creates a third prompt including the answer information and third correspondence information indicating a correspondence between the predetermined answer information and the teaching plan, and requests output of a teaching plan for the student of the specific answer information (step S302).The control unit 11 extracts the third correspondence information including the answer information and the teaching plan information corresponding to the answer information based on the information stored in the teaching DB 122.

[0100] The control unit 11 provides the generated third prompt to the large scale language model 4 (step S303). The control unit 11 acquires the teaching plan output from the large scale language model 4 (step S304).

[0101] The control unit 11 associates the obtained answer information with the teaching plan and stores them in the teaching DB 122 (step S305), and ends the series of processes.

[0102] In the above-described process, the control unit 11 may include, in the third prompt, an attention area identified based on the answer information, instead of or in addition to the answer information.

[0103] According to the above configuration, the step of deriving student types can be omitted, further simplifying the process of generating teaching plans.

[0104] (Variation 2) In the processing for providing a teaching plan, the information processing device 1 of the second modification generates a teaching plan using the large-scale language model 4. FIG.

[0105] The control unit 21 of the terminal device 2 executes the same processes as steps S201 to S203, displays a reception screen (step S401), receives answer information (step S402), and transmits the answer information (step S403).

[0106] The control unit 11 of the information processing device 1 receives the answer information (step S404). Thereafter, the control unit 11 executes the same processes as steps S102 to S108 to generate a teaching plan using the large-scale language model 4. Specifically, the control unit 11 derives an attention area based on the index value for each area in the answer information (step S405).

[0107] The control unit 11 creates a first prompt including the derived attention area and first correspondence information indicating the correspondence between the attention area in the answer information and the student type (step S406). In step S406, the control unit 11 performs the generation process shown in Fig. 6 to extract the first correspondence information by referring to the teaching DB 122 after the student types generated using the large-scale language model 4 have been added. In other words, the first prompt created in step S406 may include the attention area and student type information extracted from the teaching DB 122 after it has been updated by the process of step S109.

[0108] The control unit 11 provides the generated first prompt to the large-scale language model 4 (step S407), and acquires the student type output from the large-scale language model 4 (step S408).

[0109] The control unit 11 creates a second prompt including the acquired student type and second correspondence information indicating the correspondence between the student type and the teaching plan (step S409). In step S409, the control unit 11 performs the generation process shown in Fig. 6 to extract the second correspondence information by referring to the teaching DB 122 after the teaching plan generated using the large-scale language model 4 has been added. In other words, the second prompt created in step S409 may include student type information and teaching plan information extracted from the teaching DB 122 after it has been updated by the process of step S109.

[0110] The control unit 11 provides the generated second prompt to the large-scale language model 4 (step S410), and acquires the teaching plan output from the large-scale language model 4 (step S411). After that, the information processing system 100 executes the same processes as steps S206 to S208, and presents the acquired teaching plan to the instructor via the terminal device 2.

[0111] In the above-described processing, the control unit 11 may update the information in the teaching DB 122 by storing the newly acquired answer information, attention area, student type, and teaching plan in the teaching DB 122 in association with each other.

[0112] Note that the control unit 11 may generate a teaching plan directly from the answer information using the large-scale language model 4, as in Modification 1. In this case, the control unit 11 provides the large-scale language model 4 with a third prompt including the answer information and third correspondence information indicating the correspondence between the answer information and the teaching plan, and acquires the teaching plan output from the large-scale language model 4. The third prompt includes the answer information and teaching plan information extracted from the teaching DB 122 after being updated by the processing of step S109.

[0113] According to the above configuration, even in the operational stage, a teaching plan corresponding to new answer information can be generated using the large-scale language model 4. For example, even if matching or similar answer information is not stored in the teaching DB 122, a teaching plan can be generated with high accuracy in accordance with the new answer information. By providing the large-scale language model 4 with related information based on the updated teaching DB 122, a more appropriate teaching plan can be generated.

[0114] (Second embodiment) In the second embodiment, a configuration for generating student findings will be described. In the following embodiment, differences from the first embodiment will be mainly described, and components common to the first embodiment will be assigned the same reference numerals and detailed descriptions thereof will be omitted.

[0115] 14 is a block diagram showing the configuration of an information processing device 1 in the second embodiment. The storage unit 12 of the information processing device 1 in the second embodiment further stores a finding DB 123. The finding DB 123 is a database that stores information related to student findings. Student findings are findings about students. The storage unit 12 of the information processing device 1 in the second embodiment does not necessarily have to store the question DB 121 and / or the instruction DB 122.

[0116] FIG. 15 is a diagram illustrating an example of information stored in the observation DB 123. The observation DB 123 stores records linking information such as an observation ID and student observation information. The student observation information is information indicating student observations. Specific examples of student observations include, "He has firm opinions and can speak and act based on those opinions," and "Recently, it seems that changes in his physical condition are affecting his attention span. He seems to get tired easily, especially in situations that require long periods of concentration." The information processing device 1 acquires multiple pieces of student observation information generated in advance and stores them in the observation DB 123. The observation DB 123 may store the student observation information in association with, for example, answer information, student type information, etc.

[0117] 16 is a flowchart showing an example of a procedure for providing a student observation. After the flowchart shown in FIG. 11 is completed, the information processing system 100 starts the following process in response to receiving a request to generate a student observation via the terminal device 2.

[0118] The control unit 21 of the terminal device 2 acquires student information corresponding to the student for whom the student observation is to be generated, based on the instructor's operation (step S501). The student information includes information related to the student's learning and / or information related to the student's lifestyle. The student information may be data in text format. The control unit 21 transmits the acquired student information to the information processing device 1 (step S502).

[0119] The control unit 11 of the information processing device 1 receives student information (step S503). Based on the response information received in step S204, the control unit 11 derives a student type of the student for whom a student observation is to be generated (step S504). In step S504, the control unit 11, for example, refers to the instruction DB 122 to identify response information that matches the received response information, and extracts the student type associated with the identified response information. The control unit 11 may acquire the student type using the large-scale language model 4 by performing the same processes as in steps S102 to S105.

[0120] The control unit 11 generates a fourth prompt including the acquired student information, student type, and example student findings, requesting output of student findings for students classified into a specific student type (step S505). The example student findings are related information used to generate student findings. The control unit 11 extracts multiple example student findings based on information stored in the findings DB 123. The control unit 11 may further generate roles for the large-scale language model 4 and include the generated roles in the fourth prompt.

[0121] The control unit 11 provides the generated fourth prompt to the large-scale language model 4 provided by the language processing server 3 or to the large-scale language model 4 that has been trained in the information processing device 1 (step S506). The control unit 11 acquires the student observations output from the large-scale language model 4 (step S507). The control unit 11 transmits the acquired student observations to the terminal device 2 (step S508).

[0122] The control unit 21 of the terminal device 2 receives the student observations (step S509), and displays a proposal screen showing the received student observations on the display unit 24 (step S510).

[0123] The control unit 21 determines whether or not improvements to the student findings have been accepted (step S511). For example, if it is determined that no improvements have been accepted because no input of improvements has been accepted through a message input field provided on the proposal screen (S511: NO), the control unit 21 ends the series of processes.

[0124] For example, if it is determined that an improvement point has been received by receiving input of the improvement point through a message input field provided on the proposal screen (S511: YES), the control unit 21 transmits the input improvement point to the information processing device 1 (step S512).

[0125] The control unit 11 of the information processing device 1 acquires the improvements (step S513). The control unit 11 provides the acquired improvements to the large-scale language model 4 as a response to the output of the student observation (step S514). The control unit 11 acquires a new student observation output from the large-scale language model 4 (step S515). A student observation regenerated based on the improvements is output from the large-scale language model 4. The control unit 11 returns the process to step S508 and transmits the acquired new student observation to the terminal device 2.

[0126] Thereafter, the information processing system 100 repeatedly receives suggestions for improvements and acquires student comments until it has finished accepting suggestions for new improvements.

[0127] In the above process, acquisition of student information may be omitted. If student information is not acquired, the fourth prompt may not include student information. Also, the fourth prompt may not include examples of student findings.

[0128] Fig. 17 is a diagram showing an overview of the process for generating student observations using the large-scale language model 4. Fig. 18 is a diagram showing an example of the content of the fourth prompt. Figs. 17 and 18 show an example in which the student type is one in which a decline in "physical condition" causes a decline in "self-esteem" and affects "listening."

[0129] The role for the fourth prompt is set as "You are a system that supports elementary and junior high school teachers in creating draft student observations to inform parents of the results of their teaching and the current status of their students." The related information includes one or more pre-created example sentences for student observations. The fourth prompt further includes a sentence such as "Please refer to the related information to obtain the following student information and output draft student observations for students classified into student types," along with the acquired student information (e.g., active in class, low concentration, active in sports day, average grades, etc.) and student types (e.g., a pattern in which a decline in "physical condition" causes a decline in "self-esteem" and affects "listening").

[0130] The fourth prompt may include an attention area corresponding to the answer information instead of or in addition to the student type. In this case, in step S504, the information processing device 1 refers to the instruction DB 122 to identify answer information that matches the received answer information, and extracts the attention area associated with the identified answer information.

[0131] The related information included in the fourth prompt may include correspondence information that associates at least one of the answer information, attention area, and student type information with examples of student findings. In this case, the information processing device 1 extracts a predetermined number of pieces of correspondence information between at least one of the answer information, attention area, and student type information and examples of student findings based on the information stored in the finding DB 123, and generates a fourth prompt that includes the extracted correspondence information. Note that the information processing device 1 may extract correspondence information from the finding DB 123 that includes the student type derived in step S504, or answer information, attention area, student type information, etc. that match or are similar to the answer information or attention area corresponding to the student type.

[0132] An example of a student observation output from Large-Scale Language Model 4 based on the fourth prompt is shown below. This student is outstanding in that he actively speaks up and expresses his opinions in class, but he sometimes misses what is being taught due to his lack of concentration. Although students are seen participating energetically in physical activities such as athletic meets, physical fatigue may be affecting their ability to pay attention and concentrate in school. Your grades are average, and improving your concentration will help you improve your studies. Regular rest and good physical health will help improve your listening skills.

[0133] The information processing device 1 may add information indicating that improvements from an instructor are acceptable to the student observations output from the large-scale language model 4 and transmit the information to the terminal device 2. For example, the information processing device 1 may output a sentence including the fact that improvements are acceptable by providing the large-scale language model 4 with a prompt requesting that the student observations be improved while accepting improvements from the user.

[0134] FIG. 19 is a diagram showing another example of the outline of the process for generating a student observation using the large-scale language model 4. When the information processing device 1 acquires a student observation output from the large-scale language model 4, for example, by the process shown in FIG. 17 , the information processing device 1 generates a second fourth prompt. The second fourth prompt includes a draft of the acquired student observation and a sentence such as, "Please present this draft and complete the student observation while discussing with the student's teacher whether there is anything that needs to be added or corrected." The information processing device 1 provides the generated second fourth prompt to the large-scale language model 4.

[0135] The text output from the large-scale language model 4 based on the second fourth prompt includes, for example, the student observations that were included in the second fourth prompt and a sentence encouraging the acceptance of areas for improvement. The sentence encouraging the acceptance of areas for improvement may be, for example, "Here are proposed student observations. Is there anything you would like to add or revise? In particular, if you have information about the student's daily life or behavior at home, you can make your observations more specific and comprehensive based on that information. Also, if you have information about the student's areas of strength, special skills, and interests, incorporating that information will result in a more balanced evaluation."

[0136] The information processing device 1 generates a proposal screen including output information output from the large-scale language model 4, i.e., output information including student observations and sentences encouraging the acceptance of improvements. The information processing device 1 displays the generated proposal screen via the terminal device 2. The instructor can use the proposal screen to check the student observations and input improvements to the generated student observations.

[0137] When the information processing device 1 receives improvements from the terminal device 2, it provides the received improvements to the large-scale language model 4 as a response to the output information. This allows the large-scale language model 4 to output student observations that have been corrected based on the improvements.

[0138] The method of receiving improvements from an instructor and generating student observations in accordance with the improvements is not limited to the above example. For example, the information processing device 1 may generate student observations in accordance with the improvements by providing the large-scale language model 4 with a prompt requesting that the large-scale language model 4 correct the improvements received from the instructor and the output student observations in accordance with the improvements, in response to the output from the large-scale language model 4 shown in FIG.

[0139] FIG. 16 illustrates an example in which the generation of student findings and corrections according to areas for improvement are performed as a series of processes during the operational phase, but the timing of performing the processes in FIG. 16 can be changed as appropriate. For example, the information processing device 1 may store in advance in the storage unit 12 student findings obtained by performing the processes up to step S507. In this case, during the operational phase, the information processing device 1 reads out student findings corresponding to the target student from among the pre-stored student findings and transmits the read student findings to the terminal device 2. After transmitting the student findings, the processes from step S509 onward can be performed to improve the student findings. The process of generating student findings can be performed independently of the process of generating a teaching plan. The information processing device 1 may execute only the generation of student findings without generating a teaching plan.

[0140] According to this embodiment, student observations can be presented based on answer information to questions, improving the usability of the system. By accepting improvements from instructors and providing the accepted improvements to the large-scale language model, the characteristics of the interactive large-scale language model can be utilized to appropriately update student observations based on the improvements. Student observations that meet the instructor's wishes can be easily generated, improving convenience.

[0141] In the above example, a so-called large-scale language model is used as the language model that outputs student types, teaching plans, and student observations. However, regardless of whether the language model is large-scale or not, it may be a model that has been trained to be able to output student types and teaching information in response to input in natural language. The language model is not limited to one model, and may be multiple models.

[0142] (Third embodiment) In the third embodiment, a configuration will be described in which an explanatory text regarding the generated teaching plan is presented to the instructor.

[0143] The information processing device 1 of the third embodiment generates an explanatory text that explains the teaching plan generated in step S205, and provides the generated explanatory text to the instructor. The explanatory text is a text generated based on the teaching plan, and is a text that clearly explains the teaching plan using, for example, specific examples. The explanatory text may be a summary that summarizes the contents of the teaching plan. If there are multiple teaching plans corresponding to the student type, the explanatory text may include teaching plans that have been consolidated into a number fewer than the original number of plans. The information processing device 1 generates an explanatory text corresponding to the teaching plan, for example, using a large-scale language model 4.

[0144] 20 is a flowchart showing an example of a processing procedure executed by the information processing system in the third embodiment. After the end of the flowchart shown in FIG. 11, for example, the information processing system 100 starts the following processing.

[0145] Based on the instructor's operation, the control unit 21 of the terminal device 2 accepts the selection of a teaching plan for which an explanatory text is desired to be generated from among the plurality of teaching plans displayed on the display unit 24 in step S208 (step S601). The instructor selects a teaching plan using, for example, a display screen including text indicating the plurality of teaching plans and a selection accepting unit such as a check box corresponding to each teaching plan.

[0146] The control unit 21 transmits the training plan selected by the instructor to the information processing device 1 (step S602). The control unit 21 may transmit to the information processing device 1 information for identifying the training plan selected by the instructor.

[0147] The control unit 11 of the information processing device 1 receives the training plan selected by the instructor (step S603). Note that, if all of the training plans generated in step S205 are to be used as the target for generating commentary, the processes of steps S601 to S603 may be omitted.

[0148] The control unit 11 creates a fifth prompt that includes the received teaching plan selected by the instructor and the student type of the student for whom the teaching plan is to be generated, and requests that an explanatory text be output according to the teaching plan (step S604). The control unit 11 provides the created fifth prompt to the large-scale language model 4 (step S605). The control unit 11 acquires the explanatory text output from the large-scale language model 4 (step S606).

[0149] An example of the role of the fifth prompt to be given to the large-scale language model 4 is set as "You are an expert in teaching at an elementary or junior high school. You are trying to propose to teachers a teaching plan for students classified into a certain student type." The fifth prompt also includes a request for the generation of an explanatory text, conditions for the generation of the explanatory text (e.g., a request for a summary, a request for the integration of sentences within a specified number, etc.), and a teaching plan selected by the instructor.

[0150] An example of the fifth prompt is shown below. Please write a document explaining your teaching plan to teachers, using concrete examples, etc. If there are many teaching methods or many similar methods, summarize and organize them into about five pieces of advice. The output should be connected to the following sentence: "The following instructional plan would be effective for this student." Now, please write an explanation to teachers about the following student types and teaching plans. Student type: A pattern in which a decline in "physical condition" causes "inattention" and affects "listening" Teaching plan: If possible, when using the daily rhythm check card, it would be even better if parents could fill out information about the home situation. Teaching plan: If the teacher's explanation takes too long, students will lose concentration, so it is a good idea to actively incorporate pair and small group discussions and experiential activities. Teaching plan: When explaining the experimental method, it is a good idea for the teacher to demonstrate by actually operating the equipment. Teaching plan: When identifying problems, rather than asking vague questions like "Do you have any questions?", it is better to incorporate activities such as comparing one thing with another (a circuit with a lit light bulb and one without, or an area on a playground with puddles and an area without puddles, etc.), and then ask questions that will help students notice the difference between the two phenomena, such as "What's the difference?"

[0151] Although only four lesson plans are exemplified above, the fifth prompt may include many lesson plans, such as several dozen or more.

[0152] An example of an explanatory sentence output from the large-scale language model 4 based on the fifth prompt above is shown below. The following teaching plan would be effective for this student: - Check and manage your daily routine Example: Using a "Life Rhythm Check Card," parents can also fill out information about their students' home situations. This helps them understand their daily rhythms, especially things like the time they wake up in the morning and meal times, and shares with their families any areas that need improvement. Additionally, by giving students a "Life Rhythm Master Certificate" or a "Life Rhythm Hard Work Certificate," they can recognize their efforts and boost their motivation. -Explanatory ideas Example: Avoid long, one-sided explanations and incorporate discussions and hands-on activities in pairs or small groups. This will help students maintain their concentration. Also, when explaining experimental methods, it is a good idea to include a demonstration, such as the teacher actually operating the equipment. ·Division of roles for guidance and support Example: When giving warnings and instruction, separate teachers should be assigned the roles of "scolding" and "accepting," and each role should be clearly defined. The teacher in charge of scolding will explain "what went wrong," while the teacher in charge of accepting will check how the student reacts and provide instruction as necessary. If one teacher is handling the situation, the order of responses should be devised, with the teacher switching between scolding and accepting behavior. Specific and clear questions Example: When posing a problem, rather than asking vague questions like "Do you have any questions?", ask questions that include specific comparisons such as "What is the difference between a circuit where the light bulb is lit and one where it is not lit?", and ask questions that make students aware of the difference between the two phenomena, such as "What is the difference?" Visual and hearing support Example: When using the blackboard to explain something, write in a structured manner using diagrams and arrows, rather than just using words. Also, help students understand by calling out, "Mr. A, look at the blackboard," or by explaining with actions. For example, when explaining that the leaves of a plant have grown larger, it is effective to add visual aids, such as spreading your arms wide to indicate the size. Combining these teaching methods can reduce students' inattention, improve their listening skills, and provide a better learning environment.

[0153] When generating commentary, it is not necessary to request summarization or consolidation into a predetermined number of sentences. Also, if consolidation into a predetermined number of sentences is required, the number of sentences to be consolidated may be set by the service provider or the user (instructor).

[0154] The control unit 11 transmits the obtained comment to the terminal device 2 (step S607).

[0155] The control unit 21 of the terminal device 2 receives the comment (step S608). The control unit 21 displays a screen showing the received comment on the display unit 24 (step S609), and ends the series of processes.

[0156] FIG. 21 is a schematic diagram showing an example of a screen 240 displayed on the display unit 24 of the terminal device 2. By processing step S208, the terminal device 2 receives from the information processing device 1 screen information for displaying multiple teaching plans corresponding to the response information, and displays on the display unit 24 a screen 240a displaying the teaching plans based on the received screen information. As shown in FIG. 21, the screen 240a displaying the teaching plans includes a student type corresponding to the student to be displayed, text indicating multiple teaching plans, and check boxes associated with each teaching plan. The instructor can input the selection of a desired teaching plan by using the operation unit 25 to select (designate) a check box corresponding to one or more teaching plans.

[0157] The terminal device 2 transmits the instruction plan selection received from the instructor to the information processing device 1, and receives from the information processing device 1 screen information for displaying explanatory text corresponding to the transmitted instruction plan selection. Based on the received screen information, the terminal device 2 displays a screen 240b for displaying explanatory text on the display unit, as shown in the bottom of FIG. 21. The screen 240b for displaying explanatory text summarizes the instruction plan selected by the instructor and displays explanatory texts grouped into a predetermined number. In the explanatory texts, for example, multiple instruction plans with similar content are combined to reduce the overall number of instruction plans.

[0158] The screen 240 is configured to be switchable between a display screen 240a that displays a teaching plan and a display screen 240b that displays explanatory text, for example, by switching the tabs at the top. The tabs for switching screens may include a tab corresponding to a screen that displays the evaluation results of the student, a tab corresponding to a screen that displays the teaching plan that the instructor has finally selected as the implementation target, etc. When a tab for a screen that displays the evaluation results is selected via the terminal device 2, the information processing device 1 can generate a screen that shows the index values ​​for each question item and the scores for each area in table or graph format based on the response information corresponding to the student to be displayed, and display the generated screen. The instructor can check the evaluation results for the student, the teaching plan, the explanatory text for the teaching plan, etc. through the screen 240.

[0159] According to this embodiment, the teaching plan can be presented to the instructor in a more easily understandable manner. Even when a large number of teaching plans suitable for a student are identified, the teaching plans can be presented in a summarized form or in a small amount, making it easier for the instructor to understand the teaching plans.

[0160] (Fourth embodiment) In the fourth embodiment, a configuration will be described in which a teaching plan is provided to an instructor other than the instructor who requested the teaching plan.

[0161] The information processing device 1 of the fourth embodiment transmits the instruction plan to a person other than the student's instructor when predetermined requirements are met, thereby enabling the instructor and a person other than the instructor to share information about the student, including the instruction plan. Persons other than the instructor who share the instruction plan (hereinafter also referred to as "sharers") include persons other than the instructor who can support the student, such as other teachers at the same school as the student's teacher (e.g., head teachers, school nurses, etc.), external support staff, people involved in free schools, medical professionals, etc.

[0162] The predetermined conditions for sharing a teaching plan include, for example, that the index value of the response information is less than a preset threshold value, that a sharing request has been received from the instructor, and the like.

[0163] When the index value of the response information from the instructor relating to a specific student is less than a preset threshold, the information processing device 1 of this embodiment provides the response information, teaching plan, etc. of the specific student to the sharer.

[0164] Fig. 22 is a diagram showing an example of setting information of thresholds used to determine whether or not a teaching plan needs to be shared. In the example shown in Fig. 22, instructor identification information for identifying the instructor, sharer identification information for identifying the sharer, question ID, and threshold are stored in association with each other as setting information. The question ID is the same as the question ID shown in Fig. 4. The instructor identification information includes, for example, an instructor ID, a device ID of the instructor's terminal device 2, an email address of the terminal device 2, etc. The sharer identification information includes, for example, a sharer ID, a device ID of a sharer terminal device used by a sharer, an email address of the sharer terminal device, etc.

[0165] In the example shown in FIG. 22, a threshold is set for each question item identified by a question ID. By adjusting the threshold setting, it is possible to set whether or not a teaching plan needs to be shared, and to what extent the teaching plan must apply to the question before it can be shared. For example, by setting the threshold to a low value, a teaching plan can be shared with others if the question content applies well to a student. The threshold may be set, for example, by the student's instructor, the instructor's school, the local government that manages the school, etc.

[0166] The threshold may be set only for some of the question items selected from all the question items. In other words, the questions to be judged as to whether or not the teaching plan needs to be shared may be all the question items, or some of all the question items. A question item for which a threshold is not set means that information sharing is not necessary regardless of the answer. A flag indicating that the question item is not subject to judgment may be assigned to the question item for which a threshold is not set. A threshold may be set for the question item that is not subject to judgment such that it is judged that information sharing is not necessary for all preset index values.

[0167] If multiple recipients of the teaching plan are expected, the threshold may be set according to the type of question (attribute) and the type of recipient (attribute). For example, if the recipient is a free school official, the threshold is set so that information sharing of students who answer questions about school life is necessary, but information sharing of students who answer questions about medical conditions is unnecessary. On the other hand, if the recipient is a medical professional, the threshold is set so that information sharing of students who answer questions about school life is unnecessary, but information sharing of students who answer questions about medical conditions is necessary.

[0168] The threshold setting unit is not limited to each question item. For example, a threshold may be set for each area. The information processing device 1 collects threshold setting information in advance and stores it in the storage unit 12. The threshold setting information may be stored as part of the question DB 121.

[0169] 23 is a flowchart showing an example of a processing procedure executed by the information processing system in the fourth embodiment. The information processing system 100 starts the following processing after, for example, the flowchart shown in FIG.

[0170] The control unit 11 of the information processing device 1 determines whether a teaching plan for a specific student satisfies predetermined requirements for determining whether the teaching plan is shared (step S701). The control unit 11 determines, for example, whether the index value of the answer information corresponding to the specific student acquired in step S201 is less than a preset threshold. If different thresholds are set for multiple candidate sharing destinations, the control unit 11 executes a determination process for each sharing destination using the threshold for each sharing destination.

[0171] If it is determined that the index values ​​for all questions in the answer information are equal to or greater than a preset threshold value and therefore do not satisfy the predetermined requirement (S701: NO), the control unit 11 ends the process.

[0172] If it is determined that the index value for at least one question in the answer information is less than a preset threshold and thus satisfies the predetermined requirement (S701: YES), the control unit 11 generates shared information to be provided to the sharer corresponding to the specific student (step S702). The shared information includes, for example, answer information corresponding to the specific student, student type, teaching plan, etc. The shared information may include a screen showing the evaluation results similar to that shown in FIG. 21, a screen displaying the teaching plan, a screen displaying an explanatory text, etc. The answer information, student type, teaching plan, etc. provided to the sharer may be the same as the information provided to the instructor, or may be a part of the information provided to the instructor.

[0173] The control unit 11 transmits the generated shared information to the sharer terminal of the sharer corresponding to the specific student (step S703). The control unit 11 identifies the sharer identification information with which the information will be shared, for example, based on the correspondence between the instructor identification information and the sharer identification information shown in Fig. 22, and transmits the shared information to the sharer terminal identified by the identified sharer identification information. The sharer can check the answer information, student type, teaching plan, etc. related to the specific student through the sharer terminal.

[0174] In the above process, the control unit 11 may receive from the instructor or the like whether or not to output the shared information. If the output of the shared information is permitted, the control unit 11 executes the processes from step S702 onwards. If the output of the shared information is not permitted, the control unit 11 does not execute the processes from step S702 onwards and does not output the shared information.

[0175] When the control unit 11 receives a sharing request from the instructor through the terminal device 2, it may execute the processes from step S702 onwards.

[0176] According to this embodiment, the utility value of the generated teaching plan can be increased, and the convenience of the system can be improved. By automatically providing student information to the sharing destination, information sharing between supporters becomes smoother. By providing the student's response information, teaching plan, etc. to the sharing destination, the student's status can be shared more appropriately between supporters.

[0177] The processing entity of each process shown in the flowcharts of each of the above-mentioned embodiments is not limited. For example, some or all of the processes executed by the control unit 11 of the information processing device 1 may be executed by the terminal device 2. The instruction DB 122 may be stored in the storage unit 22 of the terminal device 2. In each of the above-mentioned embodiments, an example has been described in which the information processing device 1 functions as a generating device that generates instruction information and a providing device that generates and provides instruction information corresponding to answer information based on the generated instruction information. However, the generating device and the providing device may be provided as separate devices.

[0178] The embodiments disclosed herein are illustrative in all respects and should not be considered limiting. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and the scope equivalent to the claims. The sequences shown in each embodiment are not limited, and the order of each process may be changed within a range consistent with the present invention, and multiple processes may be executed in parallel. The entity that performs each process is not limited, and the process of each device may be executed by another device within a range consistent with the present invention.

[0179] The matters described in each embodiment can be combined with each other. In addition, the independent claims and dependent claims described in the claims can be combined with each other in any combination, regardless of the reference format. Furthermore, although the claims use a format in which a claim references two or more other claims (multiple claim format), this is not limited to this format. A multiple claim (multi-multi claim) that references at least one other multiple claim may also be used. [Explanation of symbols]

[0180] 100 Information Processing Systems 1. Information processing equipment 11 Control section 12 Storage section 13 Communications Department 121 Question DB 122 Guidance DB 123 Findings DB 1P Program 1A Recording Media 2. Terminal Device 21 Control Unit 22 Memory section 23 Communications Department 24 Display section 25 Control section 2P Program 2A Recording Media 4 Large-scale language models

Claims

1. Obtaining answer information indicating answers to multiple questions regarding characteristics of the child or student; Using a language model, guidance information is generated according to the acquired answer information. A method for generating instruction information in which processing is performed by a computer.

2. Each question is divided into several areas: Generate guidance information according to the answer information indicating answers to questions classified into each area. The method for generating instruction information according to claim 1 .

3. creating a prompt including the answer information and predetermined correspondence information between the answer information and instruction information; The instruction information is generated by providing the created prompt to the language model. The method for generating guidance information according to claim 1 or 2.

4. Based on the answer information and the generated instruction information, correspondence information between the answer information and the instruction information is updated. The method for generating instruction information according to claim 3 .

5. Acquire a student type or area of ​​interest according to the response information; creating a prompt including the acquired student type or area of ​​interest and correspondence information between the predetermined student type or area of ​​interest and instruction information; The instruction information is generated by providing the created prompt to the language model. The method for generating guidance information according to claim 1 or 2.

6. The correspondence information between the predetermined student type or attention area and the instruction information is correspondence information between the student type or attention area similar to the acquired student type or attention area and the instruction plan. The method for generating instruction information according to claim 5 .

7. A language model using correspondence information between answer information and student types is used to acquire the student type corresponding to the answer information. The method for generating instruction information according to claim 5 .

8. Derive a focus area within each area based on the answers to the questions classified into each area; creating a prompt including the derived attention area and correspondence information between the predetermined attention area and a student type; The created prompt is given to a language model to obtain the student type. The method for generating instruction information according to claim 5 .

9. Based on the student type and the generated instruction information, correspondence information between the student type and the instruction information is updated. The method for generating instruction information according to claim 5 .

10. The instruction information includes a student instruction plan; The student guidance plan includes a student guidance plan for each subject. The method for generating guidance information according to claim 1 or 2.

11. Obtaining answer information indicating answers to multiple questions regarding characteristics of the child or student; Using a language model, guidance information is generated according to the acquired answer information. A computer program that causes a computer to perform a process.

12. Obtaining answer information indicating answers to multiple questions regarding characteristics of the child or student; Using a language model, guidance information is generated according to the acquired answer information. Equipped with a control unit that executes processing A device for generating instructional information.

13. storing a plurality of pieces of guidance information corresponding to each piece of first answer information, the guidance information being generated using a language model based on a plurality of pieces of first answer information indicating answers to a plurality of questions regarding the characteristics of the child or student; acquiring second answer information indicating answers to a plurality of questions regarding characteristics of the child / student; generating guidance information corresponding to the acquired second answer information based on the plurality of pieces of guidance information stored; A computer program that causes a computer to perform a process.

14. acquiring information on the area of ​​interest or the type of student according to the second response information and the learning and / or lifestyle attitude of the student; creating a prompt including information on an area of ​​interest or a type of student according to the acquired second answer information and information on the learning and / or lifestyle attitude of the student; The created prompt is given to a language model to generate student observations.

14. A computer program according to claim 13.

15. Obtain improvements to the student's findings, Generate new student findings by inputting the obtained improvements into a language model 15. A computer program according to claim 14.

16. outputting guidance information corresponding to the acquired second answer information using a language model that uses the second answer information and correspondence information between the stored guidance information and the first answer information; 15. A computer program according to claim 13 or claim 14.

17. generating an explanatory sentence for the instruction information according to the second answer information using a language model; Output the generated commentary 15. A computer program according to claim 13 or claim 14.

18. generating, using a language model, an explanation including a summary of the instruction information or instruction information that is reduced in number to a number less than the number of pieces of instruction information corresponding to the second answer information, based on the plurality of pieces of instruction information corresponding to the second answer information; Output the generated commentary 15. A computer program according to claim 13 or claim 14.

19. providing guidance information corresponding to the second answer information to a user who provided the second answer information; If the second answer information satisfies a predetermined requirement, guidance information corresponding to the second answer information is provided to a user different from the user who provided the information.

15. A computer program according to claim 13 or claim 14.

20. The predetermined requirement is set according to the type of question related to the second answer information or the type of the different users.

20. A computer program according to claim 19.

21. acquiring a plurality of pieces of first answer information indicating answers to a plurality of questions regarding characteristics of the student, and generating instruction information according to each piece of first answer information acquired using a language model, and storing the generated plurality of pieces of instruction information; acquiring second answer information indicating answers to a plurality of questions regarding characteristics of the child / student; generating guidance information corresponding to the acquired second answer information based on the plurality of pieces of guidance information stored; An information processing method in which processing is performed by a computer.

22. acquiring a plurality of pieces of first answer information indicating answers to a plurality of questions regarding characteristics of the student, and generating instruction information according to each piece of first answer information acquired using a language model, and storing the generated plurality of pieces of instruction information; acquiring second answer information indicating answers to a plurality of questions regarding characteristics of the child / student; generating guidance information corresponding to the acquired second answer information based on the plurality of pieces of guidance information stored; Equipped with a control unit that executes processing Information processing device.

Images