Non-cognitive ability improvement support system and non-cognitive ability improvement support method

Abstract

To provide a non-cognitive ability improvement support system capable of efficiently supporting improvement in non-cognitive abilities.SOLUTION: A non-cognitive ability improvement support system includes a generation unit for generating emotion information based on biological data of a subject, a message generation unit for generating a message relating to non-cognitive abilities according to the emotion information generated by the generation unit, and an output unit for outputting the message generated by the message generation unit to the subject.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to a non-cognitive ability improvement support system and a non-cognitive ability improvement support method for supporting the improvement of non-cognitive abilities. [Background technology]

[0002] In recent years, research into non-cognitive abilities has progressed not only in Japan but all over the world, and their importance is being recognized. In Japan, we have placed particular importance on motivation and interest, but not much emphasis has been placed on nurturing tenacity and a spirit of challenge, which are important elements of non-cognitive abilities. There has been little awareness that cognitive and non-cognitive abilities develop in a way that is intertwined.

[0003] If you work hard with motivation and interest, you will naturally think deeply, innovate, and create, which will improve your cognitive abilities. By exerting your cognitive abilities in this way, you will feel a sense of accomplishment and fulfillment, which will make you want to try harder next time, and strengthen your non-cognitive abilities. By being aware of this cycle, it is possible to effectively improve both cognitive and non-cognitive abilities.

[0004] Traditionally, these attitudes and abilities were often thought of as temperament or character, but now they are considered "skills" and the potential of education is emphasized. For example, children's interest and curiosity can be intentionally increased by educators creating an appropriate environment. Also, tenacity can be developed by encouraging them. By calling them "skills," it is shown that children can become capable of doing these things through concrete support. For example, a method has been proposed that supports the improvement of non-cognitive abilities by displaying the degree of achievement of a learning plan (see Patent Document 1). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2016-218103 A Summary of the Invention [Problem to be solved by the invention]

[0006] On the other hand, when improving non-cognitive abilities, it is important to properly grasp the learner's condition.

[0007] The present disclosure is intended to solve the above-mentioned problems, and provides a non-cognitive ability improvement support system and a non-cognitive ability improvement support method that are capable of efficiently supporting the improvement of non-cognitive abilities. [Means for solving the problem]

[0008] The non-cognitive ability improvement support system disclosed herein includes a generation unit that generates emotional information based on biometric data of a subject, a message generation unit that generates a message related to non-cognitive abilities in accordance with the emotional information generated by the generation unit, and an output unit that outputs the message generated by the message generation unit to the subject.

[0009] Preferably, the device further includes an input information acquisition unit that acquires input information input by the subject to the device. The message generation unit generates a message related to the non-cognitive ability in accordance with the emotion information and the input information.

[0010] Preferably, the output unit outputs a message together with a character according to the emotion information.

[0011] Preferably, the system further comprises an evaluation unit which evaluates the subject's level of engagement with the task based on the input information, and an awarding unit which provides a reward to the subject based on a result of the evaluation by the evaluation unit.

[0012] The non-cognitive ability improvement support method disclosed herein includes the steps of generating emotional information based on biometric data of a subject, generating a message related to the non-cognitive ability in accordance with the generated emotional information, and outputting the generated message. Effect of the Invention

[0013] The non-cognitive ability improvement support system and non-cognitive ability improvement support method disclosed herein can efficiently support the improvement of non-cognitive abilities. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram illustrating an overview of a non-cognitive ability improvement support system according to a first embodiment. [Diagram 2] FIG. 1 is a diagram illustrating a configuration of a non-cognitive ability improvement support system 1 according to a first embodiment. [Diagram 3] FIG. 1 is a diagram illustrating a configuration of functional blocks of an information processing device 100 according to a first embodiment. [Figure 4] FIG. 1 is a flow diagram illustrating processing by information processing device 100 according to the first embodiment. [Diagram 5] FIG. 13 is a diagram illustrating a subroutine flow of the reaction confirmation process according to the first embodiment. [Figure 6] FIG. 2 is a diagram illustrating an example of a prompt and an answer sentence according to the first embodiment. [Figure 7] FIG. 11 is a flow diagram illustrating processing by information processing device 100 according to the second embodiment. [Figure 8] FIG. 11 is a flow diagram illustrating an input state analysis process according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. In the following, the same or corresponding parts in the drawings are denoted by the same reference characters, and their description will not be repeated in principle.

[0016] (Embodiment 1) 1 is a diagram illustrating an overview of a non-cognitive ability improvement support system according to embodiment 1. With reference to FIG. 1, a non-cognitive ability improvement support system 1 according to embodiment 1 includes a terminal 10, a network NW, an information processing device 100, and a large-scale language model 200.

[0017] The terminal 10 is a PC (Personal Computer) or the like owned by a user, and may be either portable or fixed. The terminal 10 is provided so as to be able to communicate with the information processing device 100 via a network NW. The information processing device 100 is provided so as to be able to communicate with the large-scale language model 200 via the network NW. The communication can be either wireless or wired.

[0018] A user is learning programming using a terminal 10. A learning screen 50 for learning programming is displayed on the terminal 10, and a character 52 is shown as an example on the learning screen 50.

[0019] The information processing device 100 communicates with the large-scale language model 200 to receive messages related to non-cognitive skills for improving the non-cognitive skills of a user.

[0020] The large language model (LLM) 200 is a natural language processing system that performs question and answering. The LLM 200 is a natural language processing model trained using a large amount of text data, and takes a sentence as input and outputs a sentence. When the LLM 200 is applied to a system that performs question and answering, when a question is queried to the LLM 200, an answer is output from the LLM 200. The information processing device 100 according to this embodiment queries the LLM 200 for a message related to non-cognitive abilities for improving non-cognitive abilities, and transmits the answer from the LLM 200 to the terminal 10.

[0021] The terminal 10 receives a message related to non-cognitive abilities for improving the non-cognitive abilities from the LLM 200. The terminal 10 outputs the message related to non-cognitive abilities for improving the user's non-cognitive abilities received on the learning screen 50 of the terminal 10 during program learning by voice output or displaying a speech bubble or the like. As an example, the terminal 10 may use the character 52 to execute the message output.

[0022] FIG. 2 is a diagram for explaining the configuration of the non-cognitive ability improvement support system 1 according to the first embodiment. With reference to FIG. 2, the terminal 10 includes a control unit 11, a camera 12, a communication I / F 13, an input device 14, an output device 15, a microphone 16, a storage unit 17, and an internal bus connecting each unit. The control unit 11 includes a CPU (Central Processing Unit), a RAM (Random Access Memory), and a ROM (Read Only Memory). The camera 12 acquires image data of a facial expression of a user as biometric data of the user (subject). In this example, image data of the facial expression of the user is described, but other information may be acquired as the biometric data without being limited thereto. For example, an infrared camera may be used to acquire information of the pulse wave of the user. The microphone 16 acquires voice data of the user as the biometric data of the user. The communication I / F 13 is connected to the network NW and executes data transmission and reception between the communication I / F 13 and an external device. The input device 14 includes a mouse and a keyboard. The output device 15 includes a display device such as a display, a speaker, and the like. The storage unit 17 includes various application programs, etc. For example, application programs for the user to learn programming are stored in the storage unit 17. The control unit 11 executes the application programs to generate a learning screen for learning programming, and displays the learning screen on a display device such as a display.

[0023] The terminal 10 acquires biometric data (image data and voice data, for example) and transmits it to the information processing device 100 via the communication I / F 13. The terminal 10 acquires input data and transmits it to the information processing device 100 via the communication I / F 13. The input data in this example is, for example, input data from a keyboard, a mouse, or the like that is used by a user when learning a program.

[0024] The information processing device 100 includes a control unit 101, a storage unit 107, a communication I / F 103, and an internal bus that connects each unit. The control unit 101 includes a CPU, a RAM, and a ROM. The communication I / F 103 is connected to a network NW and executes data transmission and reception with an external device. The storage unit 107 includes various application programs and the like. For example, the storage unit 107 stores application programs and the like for supporting the improvement of non-cognitive abilities. The control unit 11 realizes various processes by executing the application programs. The information processing device 100 receives biometric data and the like transmitted from the terminal 10, and generates emotion information of the user based on the biometric data and the like. The information processing device 100 queries the LLM 200 for a message related to non-cognitive abilities according to the generated emotion information.

[0025] The LLM 200 includes a question receiving unit 201, an answer generating unit 202, and a text database (DB) 204. The question receiving unit 201 receives an inquiry for a message related to non-cognitive abilities for improving non-cognitive abilities from the information processing device 100. The answer generating unit 202 analyzes the received message, and based on the analysis result, refers to the text database 204 to generate an answer to the inquiry including a message related to non-cognitive abilities, and transmits the answer to the information processing device 100. The text database (DB) has a large amount of text data for generating an answer.

[0026] The information processing device 100 receives a message related to non-cognitive abilities from the LLM 200 and transmits it to the terminal 10. The terminal 10 outputs the received message related to non-cognitive abilities on a learning screen 50 of the terminal 10 during program learning. As an example, the terminal 10 may use a character 52 provided on the learning screen 50 to execute the message output. In this example, a configuration in which the LLM 200 is provided separately from the information processing device 100 has been described, but it is also possible to provide a function equivalent to the LLM 200 within the information processing device 100.

[0027] Fig. 3 is a diagram illustrating a configuration of functional blocks of information processing device 100 according to embodiment 1. With reference to Fig. 3, control unit 101 of information processing device 100 executes an application program stored in storage unit 107 to realize various functional blocks.

[0028] Specifically, the information processing device 100 includes a biometric data acquisition unit 110, an emotion analysis unit 112, an input data acquisition unit 114, a prompt generation unit 116, an input state analysis unit 117, an output control unit 118, an evaluation unit 120, an assignment unit 122, and a response evaluation unit 124.

[0029] The biometric data acquisition unit 110 acquires biometric data (image data, voice data, etc.) transmitted from the terminal 10 via the communication I / F 103. The acquired biometric data is stored in the storage unit 107.

[0030] The emotion analysis unit 112 generates emotion information by estimating the emotion (psychology) of the user based on the acquired biometric data (image data, voice data, etc.). The emotion analysis unit 112 estimates (analyzes) emotions such as joy, anger, sadness, and fun based on the biometric data. The emotion analysis unit 112 may estimate emotions such as calm, surprise, satisfaction, boredom, disappointment, fear, relief, and anxiety, without being limited to these. Specifically, the emotion analysis unit 112 can estimate the emotion by processing the eyelid opening, gaze, eyebrow movement, presence or absence of nose wrinkles, mouth movement, mouth opening, pupil opening, and the like, contained in the facial expression of the user using the acquired image data. The emotion analysis unit 112 can also estimate (analyze) the emotion using the voice content, sighs, breathing sounds, and the like uttered by the user using voice data. The emotion analysis unit 112 may estimate the emotion using only one of the data, or may estimate the emotion by combining the data.

[0031] The input data acquisition unit 114 acquires input data (input key data, etc.) transmitted from the terminal 10 via the communication I / F 103. The acquired input data is stored in the storage unit 107.

[0032] The input state analysis unit 117 analyzes the input state of the user based on the acquired input data (input key data, etc.) Specifically, the input state analysis unit 117 estimates the input state of the user by analyzing the input speed of the user using a keyboard, etc., the number of input errors, etc.

[0033] The prompt generation unit 116 generates a sentence for inquiring of the LLM 200 about a message related to non-cognitive ability according to the emotion information generated by the emotion analysis unit 112. The prompt generation unit 116 outputs the generated sentence to the LLM 200.

[0034] The reaction evaluation unit 124 evaluates the reaction of the user to the message output related to the non-cognitive ability for improving the non-cognitive ability of the user. The reaction evaluation unit 124 adjusts various parameters based on the evaluation result of the reaction of the user.

[0035] The evaluation unit 120 evaluates the degree of engagement of the user with respect to the program learning task.

[0036] The awarding unit 122 provides a reward to the user based on the evaluation result of the evaluation unit 120.

[0037] The output control unit 118 outputs information such as a response from the LLM 200 to the terminal 10.

[0038] Fig. 4 is a flow diagram illustrating a process of information processing device 100 according to the first embodiment. With reference to Fig. 4, information processing device 100 acquires biometric data (step S0). Specifically, biometric data acquisition unit 110 acquires the biometric data transmitted from terminal 10.

[0039] Next, the information processing device 100 judges whether or not a predetermined period has elapsed (step S2). If the information processing device 100 judges that the predetermined period has not elapsed (NO in step S2), the information processing device 100 returns to step S0 and repeats the above process. The predetermined period can be set to any period, and can be set to 5 minutes as an example.

[0040] When it is determined that the predetermined period has elapsed (YES in step S2), the information processing device 100 proceeds to the next process.

[0041] The information processing device 100 executes a process of analyzing emotions (step S4). Specifically, the emotion analysis unit 112 estimates the emotion (psychology) of the user based on the biometric data (image data, voice data, etc.) stored in the storage unit 107 when a predetermined period has passed, and generates emotion information. Note that a part of the biometric data (image data, voice data, etc.) stored in the storage unit 107 within the predetermined period may be used, or all of the data may be used.

[0042] Next, the information processing device 100 generates a prompt to be sent to the LLM 200 (step S6). Specifically, the prompt generation unit 116 generates a sentence that inquires of the LLM 200 about a message related to non-cognitive abilities according to the emotion information generated by the emotion analysis unit 112.

[0043] Next, the information processing device 100 transmits a prompt to the LLM 200 (step S8). Specifically, the prompt generator 116 transmits the generated prompt, which is a sentence for making an inquiry, to the LLM 200.

[0044] Next, the information processing device 100 determines whether or not a response message has been received from the LLM 200 (step S10). Specifically, the output control unit 118 determines whether or not a response message has been received from the LLM 200.

[0045] In step S10, the information processing device 100 maintains the state until a reply message is received from the LLM 200, and when the information processing device 100 receives a reply message from the LLM 200 (YES in step S10), the information processing device 100 sets parameters for message output of the reply message based on the emotion information (step S12). Specifically, the output control unit 118 sets parameters for message output of the reply message based on the emotion information.

[0046] Next, the information processing device 100 outputs message output information to the terminal 10 (step S14). The output control unit 118 transmits message output information including the answer message to the terminal 10. When multiple answer messages are received from the LLM 200, the output control unit 118 may select one of them. When multiple answer messages are received, the output control unit 118 may combine them as appropriate, or extract only the necessary parts and appropriately process and edit them to generate the answer message.

[0047] Next, the information processing device 100 executes a process of confirming a user's reaction (a reaction confirmation process) (step S16). Specifically, the information processing device 100 executes a process of confirming a user's reaction to the message output. The details of the process will be described later.

[0048] Next, the information processing device 100 determines whether or not to end the process of supporting the improvement of non-cognitive abilities (step S18).

[0049] In step S18, when the information processing device 100 determines to end the process of supporting the improvement of non-cognitive abilities, the information processing device 100 ends the process (end). Specifically, when the information processing device 100 determines that the user has ended the application program for learning programming in the terminal 10, the information processing device 100 ends the process. For example, the information processing device 100 may end the process when it receives a command from the terminal 10 to end the application program for learning programming.

[0050] On the other hand, in step S18, when the information processing device 100 determines not to end the process of supporting the improvement of non-cognitive abilities (NO in step S18), the information processing device 100 returns to step S0 and repeats the above-mentioned process.

[0051] Fig. 5 is a diagram illustrating a subroutine flow of the reaction confirmation process according to the first embodiment. With reference to Fig. 5, information processing device 100 acquires biometric data from terminal 10 after a message is output (step S20). Specifically, biometric data acquisition unit 110 acquires the biometric data transmitted from terminal 10 after the message is output.

[0052] Next, the information processing device 100 executes a process of analyzing emotions (step S22). Specifically, the emotion analysis unit 112 estimates the emotion (psychology) of the user based on the biometric data (image data, voice data, etc.) stored in the storage unit 107 after the message is output, and generates emotion information.

[0053] Next, the information processing device 100 executes a process of adjusting parameters (step S24). The reaction evaluation unit 124 executes a process of adjusting parameters based on the emotion information generated by the emotion analysis unit 112. In this example, as an example, the reaction evaluation unit 124 adjusts the interval of the predetermined period of the process in step S2 based on the generated emotion information. For example, when a positive emotion such as joy or pleasure is estimated based on the emotion information, the reaction evaluation unit 124 shortens the interval of the predetermined period to increase the output frequency of messages related to non-cognitive abilities. On the other hand, when a negative emotion such as anger or sadness is estimated based on the emotion information, the reaction evaluation unit 124 lengthens the interval of the predetermined period to decrease the output frequency of messages related to non-cognitive abilities. It is possible to support the improvement of non-cognitive abilities by adjusting the output frequency of messages according to the user's emotional state. For example, when a positive emotion such as joy or pleasure is inferred based on the emotion information, the reaction evaluation unit 124 may only execute a process of shortening the interval of the predetermined period, and when a negative emotion such as anger or sadness is inferred, the interval of the predetermined period may not be changed, or the opposite process may be executed.

[0054] Then, the information processing device 100 ends the process (returns).

[0055] Figures 6 are diagrams illustrating an example of a prompt and a response sentence according to Embodiment 1. Figure 6(A) shows a prompt that prompt generating unit 116 transmits to LLM 200 based on emotion information.

[0056] As an example, the prompt generation unit 116 generates the following prompt based on the emotional information ("fun"). Specifically, the prompt generation unit 116 generates the following sentence as an example: "(A) Think of something to say to a child who is in the following emotional state that will enhance the child's non-cognitive abilities. When speaking to a child, use expressions that are appropriate for children. I am learning programming. (B) I find learning fun. (C) There are some things I don't understand, and I'm a little frustrated. (D) I feel it would be frustrating to give up."

[0057] 6B shows an example of a response text from the LLM 200 in response to the above prompt. As an example, the information processing device 100 receives the response text, "Great! Programming is fun, isn't it? But sometimes there are difficult parts, so it can be a little frustrating. Even if there are parts you don't understand, it's a chance to get the information into your head! Everyone had a lot of things they didn't understand at first. Let's work through those difficult parts together! Then new and interesting things might be waiting for you!"

[0058] The information processing device 100 transmits the received answer text to the terminal 10.

[0059] The terminal 10 outputs a learning screen for program learning and a message related to non-cognitive abilities for improving the user's non-cognitive abilities, which is an answer text transmitted from the information processing device 100. Through this process, the non-cognitive abilities improvement support system analyzes the user's emotional state and outputs a message of empathy that is in line with the analyzed emotional information to the user, thereby making it possible to improve and strengthen the non-cognitive abilities. In other words, the non-cognitive abilities improvement support system can appropriately grasp the state of the user who is a learner and efficiently support the improvement of non-cognitive abilities.

[0060] [Example of generating prompts] In the above, the prompt generating unit 116 has been described as generating a prompt based on emotion information ("fun"). The prompt generating unit 116 can similarly generate prompts for other emotion information.

[0061] For example, the prompt generation unit 116 generates a prompt based on emotion information ("anger"). For example, the prompt generation unit 116 may change the sentence (B) of the above sentences (A) to (D) that are the prompts to "I feel angry about learning." The prompt generation unit 116 may also fix the sentence (A) and change the sentences (B) to (D) appropriately based on the emotion information. The information processing device 100 receives a response sentence to the prompt, for example, "Anger is an emotion that arises when learning something new. If you take it one step at a time, the anger will gradually disappear. Don't overlook your growth." The information processing device 100 transmits the received response sentence to the terminal 10.

[0062] For example, the prompt generation unit 116 generates a prompt based on emotional information ("sad"). For example, the prompt generation unit 116 may change the sentence (B) of the above sentences (A) to (D) that are prompts to "I feel sad about my studies." The prompt generation unit 116 may also fix the sentence (A) and appropriately change the sentences (B) to (D) based on the emotional information. The information processing device 100 receives a response sentence to the prompt, for example, "Try to think of sadness as a step toward growth. It's natural to have things you don't understand, but it's important to have the attitude of trying your best to overcome them." The information processing device 100 transmits the received response sentence to the terminal 10.

[0063] For example, the prompt generation unit 116 generates a prompt based on emotional information ("joy"). For example, the prompt generation unit 116 may change the sentence (B) of the above sentences (A) to (D) that are the prompts to "I feel joy in learning." The prompt generation unit 116 may also fix the sentence (A) and appropriately change the sentences (B) to (D) based on the emotional information. The information processing device 100 receives a response sentence to the prompt, for example, "Your motivation for learning is excellent. By learning with joy, you can grow rapidly." The information processing device 100 transmits the received response sentence to the terminal 10.

[0064] [Message output parameter settings] The output control unit 118 sets parameters for the message output of the answer sentence based on the emotion information. For example, the output control unit 118 may set parameters for the color when outputting the message based on the emotion information. For example, the output control unit 118 may set parameters for the character color such as orange, red, blue, and yellow based on the emotion information of "joy", "anger", "sadness", and "fun" to output the message. The output control unit 118 utilizes the psychological effect of color to output a message, and outputs a message that is in line with the analyzed emotion information to the user through color, thereby improving and strengthening the non-cognitive ability. In other words, it is possible to appropriately grasp the state of the user who is a learner and efficiently support the improvement of the non-cognitive ability. Note that the setting of the parameter for the character color is one example, and is not limited to this, and other colors can be set, and the message may be output by changing the brightness or other parameters based on the emotion information.

[0065] In addition, when the output control unit 118 uses the character 52 to output a message, the output control unit 118 may adjust the movement of the character 52 based on the emotion information. For example, a plurality of movement patterns of the character 52 may be prepared in advance, and the output control unit 118 may select one of the plurality of movement patterns based on the emotion information and display it on the terminal 10. For example, movement patterns corresponding to "joy," "anger," "sadness," and "fun" may be prepared in advance, and the output control unit 118 may set a pattern among the plurality of movement patterns to match the emotion information when setting parameters for message output. For example, the non-cognitive ability improvement support system displays the movement of the character 52 in accordance with the emotion information when outputting a message, thereby outputting a message of empathy that is in line with the analyzed emotion information to the user together with the character's movement, thereby improving and strengthening non-cognitive abilities. That is, the non-cognitive ability improvement support system is capable of appropriately grasping the state of the user, who is a learner, and efficiently supporting the improvement of non-cognitive abilities.

[0066] Furthermore, when the terminal 10 uses the character 52 to output a message, the character 52 may be made to speak using a balloon display, or the message may be output as voice.

[0067] The output control unit 118 may adjust the pattern of the message output as voice based on the emotion information. For example, a plurality of voice output patterns may be prepared in advance, and the output control unit 118 may select and output one of the plurality of voice output patterns based on the emotion information. For example, voice output patterns corresponding to "joy", "anger", "sadness", and "fun" may be prepared in advance, and the output control unit 118 may set the parameter of the message output as voice to be a pattern that matches the emotion information among the plurality of voice output patterns. For example, the non-cognitive ability improvement support system outputs a message as voice in a voice output pattern that matches the emotion information, thereby outputting a voice message that sympathizes with the user based on the analyzed emotion information, thereby improving and strengthening non-cognitive abilities. In other words, the non-cognitive ability improvement support system can appropriately grasp the state of the user who is a learner and efficiently support the improvement of non-cognitive abilities.

[0068] The output control unit 118 may use the above parameter setting methods alone or in combination. In the flow of FIG. 4, the information processing device 100 has been described as executing a parameter setting process for message output based on emotion information in step S12, but this method is not a required configuration and the method may be implemented without this setting process. In the flow of FIG. 4, the information processing device 100 has been described as executing a reaction confirmation process in step S16, but this method is not a required configuration and the method may be implemented without this reaction confirmation process.

[0069] (Embodiment 2) Fig. 7 is a flow diagram illustrating the processing of information processing device 100 according to the second embodiment. With reference to Fig. 7, the processing of information processing device 100 according to the second embodiment differs from the flow diagram of Fig. 4 in that steps S1, S5A, and S5B are added, and step S6 is replaced with step S6#. The rest of the configuration is the same as that described above, and therefore detailed description thereof will not be repeated.

[0070] The information processing device 100 acquires biometric data in step S0, and then acquires input data (step S1). Specifically, the input data acquisition unit 114 acquires the input data transmitted from the terminal 10.

[0071] Next, the information processing device 100 judges whether or not a predetermined period has elapsed (step S2). If the information processing device 100 judges that the predetermined period has not elapsed (NO in step S2), the information processing device 100 returns to step S0 and repeats the above process. The predetermined period can be set to any period, and can be set to 5 minutes as an example.

[0072] When it is determined that the predetermined period has elapsed (YES in step S2), the information processing device 100 proceeds to the next process.

[0073] In step S4, the information processing device 100 executes a process of analyzing emotions when a predetermined period has elapsed, and then executes an input state analysis process (step S5A). Specifically, the input state analysis unit 117 analyzes the input state of the user based on the input data (input key data, etc.) stored in the storage unit 107. Note that the input state analysis unit 117 may use a part of the input data (input key data, etc.) stored in the storage unit 107 within the predetermined period, or may use all of the data.

[0074] Fig. 8 is a flow diagram illustrating an input state analysis process according to the second embodiment. Referring to Fig. 8, the input state analysis unit 117 evaluates the input proficiency level based on the acquired input data (step S30). Specifically, the input state analysis unit 117 analyzes the input speed of the user using a keyboard or the like, the number of input errors, and the like as information on the input data, to estimate the user's input proficiency level.

[0075] The input state analyzer 117 may estimate and classify the input proficiency as high or low, or may further classify it into a plurality of levels. The input proficiency information can be used in generating a prompt in this example.

[0076] Next, the input state analysis unit 117 evaluates the concentration level based on the acquired input data (step S32). Specifically, the input state analysis unit 117 analyzes the length of the input period of the user using a keyboard or the like as information on the input data, and evaluates the concentration level of the user.

[0077] For example, the input state analysis unit 117 can estimate that the user's concentration level is high when the length of the input period during a certain period is long. The input state analysis unit 117 may estimate that the concentration level is high when the input period during a certain period is equal to or greater than a certain threshold, and may estimate that the concentration level is not high in other cases.

[0078] Next, the input state analysis unit 117 evaluates the degree of task achievement based on the acquired input data (step S34). Specifically, the input state analysis unit 117 evaluates the degree of achievement of the programming learning given as a task based on the input data as information of the input data. Specifically, the input state analysis unit 117 may compare the correct answer information of the programming learning given as a task with the input information currently input by the user to evaluate the degree of achievement of the programming learning. As will be described later, the evaluation unit 120 evaluates the degree of effort for the task based on the information of the degree of achievement of the programming learning. In addition, the evaluation unit 120 may evaluate the degree of effort taking into account the input period and input proficiency of the programming learning. Then, the input state analysis unit 117 ends the input state analysis process (return).

[0079] 7 again, information processing device 100 determines whether the analyzed input state information indicates a high level of concentration (step S5B). Specifically, prompt generation unit 116 determines whether the analysis result of input state analysis unit 117 indicates a high level of concentration.

[0080] In step S5B, if information processing device 100 determines that the analyzed input state does not indicate a high degree of concentration (NO in step S5B), it proceeds to step S6#.

[0081] Next, the information processing device 100 generates a prompt to be sent to the LLM 200 (step S6#). Specifically, when the prompt generation unit 116 determines that the concentration level is not high as the analysis result of the input state analysis unit 117, the prompt generation unit 116 generates a sentence to inquire of the LLM 200 about a message related to non-cognitive ability according to the emotion information and the input proficiency information generated by the emotion analysis unit 112. Specifically, the prompt generation unit 116 includes a sentence related to the input proficiency when generating a prompt based on the emotion information. For example, the prompt generation unit 116 can include a sentence such as "(E) input proficiency is high" or "(E) input proficiency is low" as information related to the user's input state, in addition to the sentences (A) to (D) described in FIG. 6. Subsequent processing is the same as that described in the flow diagram of FIG. 4.

[0082] On the other hand, in step S5B, if information processing device 100 determines that the analyzed input state is a high level of concentration (YES in step S5B), it skips steps S6# to S16 and proceeds to step S18. Specifically, if prompt generation unit 116 determines that the analysis result of input state analysis unit 117 is a high level of concentration, it does not execute a prompt generation process. That is, if the level of concentration is high, information processing device 100 does not output a message to terminal 10. Information processing device 100 evaluates the level of concentration based on acquired input data, and does not output a message if it estimates that the level of concentration is high.

[0083] Message output when the user is highly concentrated may hinder the user's thinking. Therefore, the non-cognitive ability improvement support system can improve and strengthen non-cognitive abilities by stopping message output when the user is in such a state. In other words, the non-cognitive ability improvement support system can appropriately grasp the state of the user who is a learner and efficiently support the improvement of non-cognitive abilities.

[0084] In this example, the input state analysis unit 117 has been described as determining the concentration level based on the input data, but the concentration level may be determined in combination with emotion information. In this example, the non-cognitive ability improvement support system has been described as determining whether or not to output a message according to the user's concentration level, but the frequency of outputting messages related to non-cognitive abilities may be adjusted according to the user's concentration level. For example, when the user's concentration level is high, the non-cognitive ability improvement support system may lengthen the interval of the predetermined period of processing in step S2. The non-cognitive ability improvement support system can support the improvement of non-cognitive abilities by adjusting the frequency of message output according to the user's concentration level.

[0085] [Reward Granting] The evaluation unit 120 evaluates the degree of effort of the user in the program learning task. The evaluation unit 120 evaluates the degree of effort based on the task achievement level analyzed by the input state analysis unit 117. The evaluation unit 120 may evaluate the degree of effort by taking into account the input period and input proficiency of the programming learning.

[0086] For example, the evaluation unit 120 classifies the degree of the user's commitment to learning programming into a plurality of states. For example, the evaluation unit 120 classifies the degree of the user's commitment as "excellent" when the degree of the user's commitment is very excellent. The evaluation unit 120 may classify the degree of task achievement as "100%" as "excellent". The evaluation unit 120 may classify the degree of task achievement as "90% or more" as "good". The evaluation unit 120 classifies the degree of the user's commitment as "fair" when the degree of task achievement is "average". The evaluation unit 120 may classify the degree of task achievement as "less than 90%" as "fair". In this example, a case of classification into three stages will be described, but classification into multiple stages may be further performed.

[0087] The granting unit 122 grants a reward (for example, points) to a user who is classified and evaluated as having an "excellent" level of engagement in learning programming, for example.

[0088] In the case where a billing system is incorporated as a service for learning programming, the reward (points) may be used in part or in whole to enable the use of the service. Alternatively, for example, the reward (points) may be used to purchase items (clothes, hats, etc.) for the character 52 or to display another character.

[0089] In the case of a user whose learner status is classified as "excellent," which indicates a very high level of engagement in learning, the non-cognitive ability improvement support system can improve and strengthen the non-cognitive abilities by providing rewards. In other words, the non-cognitive ability improvement support system can appropriately grasp the status of the user who is a learner and efficiently support the improvement of non-cognitive abilities.

[0090] In addition, the rewards (points) awarded by the non-cognitive ability improvement support system are not limited to points but may be items, etc., and the rewards may be provided by, for example, affiliated companies (collaborating companies or supporting companies) of the non-cognitive ability improvement support system.

[0091] While the present disclosure has been specifically described above based on the embodiments, it goes without saying that the present disclosure is not limited to the embodiments and can be modified in various ways without departing from the spirit of the present disclosure. [Explanation of symbols]

[0092] 1 Non-cognitive ability improvement support system, 10 Terminal, 11,101 Control unit, 12 Camera, 14 Input device, 15 Output device, 16 Microphone, 17,107 Memory unit, 50 Learning screen, 52 Character, 100 Information processing device, 110 Biometric data acquisition unit, 112 Emotion analysis unit, 114 Input data acquisition unit, 116 Prompt generation unit, 117 Input state analysis unit, 118 Output control unit, 120 Evaluation unit, 122 Assignment unit, 124 Response evaluation unit, 200 Large-scale language model, 201 Question receiving unit, 202 Answer generation unit, 204 Text database.

Claims

1. A generation unit that generates emotion information based on biometric data of a subject; a message generating unit that generates a message related to non-cognitive ability according to the emotion information generated by the generating unit; An output unit that outputs the message generated by the message generation unit to the subject.

2. An input information acquisition unit that acquires input information input by the subject to the device, The non-cognitive ability improvement support system according to claim 1 , wherein the message generation unit generates a message related to the non-cognitive ability in accordance with the emotion information and the input information.

3. The non-cognitive ability improvement support system according to claim 1 , wherein the output unit outputs the message together with a character corresponding to the emotion information.

4. an evaluation unit that evaluates a degree of engagement of the subject with respect to the task based on the input information; The non-cognitive ability improvement support system according to claim 1 , further comprising: an awarding unit that provides a reward to the subject based on an evaluation result of the evaluation unit.

5. generating emotion information based on biometric data of a subject; generating a message related to a non-cognitive ability in response to the generated emotion information; A non-cognitive ability improvement support method comprising the step of outputting the generated message.

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