JP6756130B2 - Learning support device, robot, learning support system, learning support method and program - Google Patents

Description

The present invention relates to a learning support device, a robot, a learning support system, a learning support method and a program.

Learning support robots that support user learning are widely used. For example, Patent Document 1 discloses an information communication robot device that allows a user to learn interactively. The information communication robot device disclosed in Patent Document 1 generates output information using voice information from the user and feedback information corresponding to the educational information stored in advance, and outputs the output information to the user.

Japanese Unexamined Patent Publication No. 2001-242780

However, in the technique disclosed in Patent Document 1, since the user's voice is recognized and the predetermined learning guidance corresponding to the converted character string is executed, the learning is not diverse and uniform. There is a possibility that it will end up.

The present invention has been made in view of the above circumstances, and an object of the present invention is to provide a learning support device or the like capable of supporting a variety of learning.

In order to achieve the above object, the learning support device according to the present invention
Pseudo-emotion storage means for storing a plurality of pseudo-emotions different from each other of the learning support device,
A setting means for setting the pseudo-character of the learning support device to one of the plurality of pseudo-characters, and
Based on the set pseudo-personality, one of the plurality of pseudo-emotions includes a pseudo-emotion determining means for determining the pseudo-emotion of the learning support device.
It is provided with a learning guidance content determining means for determining the content of learning support to be provided to the learner based on the pseudo emotion determined by the pseudo emotion determining means.

According to the present invention, it is possible to provide a learning support device or the like that can support a variety of learning.

It is a block diagram of the learning support system which concerns on embodiment of this invention. (A) is a diagram showing the functional configuration of the teacher robot, and (B) is a diagram showing the functional configuration of the student robot. (A) is a diagram for explaining the classification of pseudo-personality, and (B) is a diagram for explaining the classification and transition of pseudo-emotion. It is a figure which shows an example of the learning history table for a teacher. It is a figure which shows an example of the learning-related event table for teachers. It is a figure which shows another example of the learning-related event table for teachers. It is a figure which shows an example of the biological-related event table for teachers. It is a figure which shows another example of the biological-related event table for teachers. It is a figure which shows an example of the learning instruction content change table. It is a figure which shows an example of the learning-related event table for students. (A) is a flowchart showing the flow of learning guidance processing executed by the teacher robot, and (B) is a flowchart showing the flow of learning support processing executed by the student robot.

Hereinafter, the learning support system according to the embodiment of the present invention will be described with reference to the drawings.

The learning support system according to the embodiment of the present invention is a system for supporting the learning of English conversation of the user (learner) U. As shown in FIG. 1, the learning support system 1 includes a teacher robot 100, a student robot 200, and a communication terminal 300.

The teacher robot 100 is a robot that plays a role of a teacher who teaches English conversation to user U. The teacher robot 100 has a three-dimensional shape that imitates a human body in appearance. Further, the teacher robot 100 has a pseudo-personality and a pseudo-emotion, and selects an appropriate learning curriculum according to the biometric information of the user U and the pseudo-emotion that changes depending on the learning situation, and provides learning support. Here, the pseudo-personality is one that is generated in a pseudo manner as if the teacher robot 100 generally has a personality that a person has. Further, the pseudo emotion is generated in a pseudo manner as if the teacher robot 100 had an emotion that a person who actually touched the user U would have.

As shown in FIG. 3A, the teacher robot 100 has four pseudo-characters, "cheerful", "calm", "angry", and "gloomy", depending on the degree of sociability and stability. It is classified. The user U can select a desired pseudo-personality via the communication terminal 300 when the teacher robot 100 is activated or the like.

As shown in FIG. 3B, the pseudo emotions of the teacher robot 100 are classified into four categories: “joy”, “anger”, “sorrow”, and “comfort”. "Happiness" indicates an emotional state of "joy" or "joy". "Angry" indicates an emotional state of "anger" or "grumpy". "Sorrow" indicates an emotional state such as "sad" or "anxiety". "Raku" indicates an emotional state such as "calm" or "fun". Each of these pseudo-emotions shall transition to another pseudo-emotion according to the occurrence of the event. In addition, the method of transition of pseudo emotions differs depending on the pseudo personality selected by the user U.

Functionally, as shown in FIG. 2A, the teacher robot 100 includes a control unit 110, a biological information acquisition unit 120, a storage unit 130, an expression unit 140, and a communication unit 150.

The control unit 110 controls the operation of the entire teacher robot 100. The control unit 110 is composed of a microcomputer having a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory). The control unit 110 controls each unit of the teacher robot 100 by having the CPU read the operation program stored in the ROM and execute it on the RAM.

The control unit 110 includes a biological information analysis unit 111, a learning situation determination unit 112, a pseudo emotion determination unit 113, a learning guidance content determination unit 114, and an expression determination unit 115.

The biological information analysis unit 111 analyzes the biological information acquired from the biological information acquisition unit 120, which will be described later, in chronological order, extracts changes in the biological state of the user U, and identifies the state of the user U. For example, the biometric information analysis unit 111 analyzes the voice data acquired from the biometric information acquisition unit 120, and identifies whether the user U is concentrated or sleepy from the amplitude magnitude, utterance content, utterance frequency, and the like.

The learning status determination unit 112 determines the learning status of the user U. Specifically, the learning status determination unit 112 determines whether the answer to the question is correct or incorrect, measures the response speed (time required for the answer), and learns the user U in consideration of various factors such as the correct answer rate and the progress status. Evaluate your grades.

The pseudo emotion determination unit 113 determines the pseudo emotion of the teacher robot 100. For example, at the start of learning guidance of the teacher robot 100, the pseudo emotion determination unit 113 determines the pseudo emotion by referring to the teacher learning history table stored in the storage unit 130, which will be described later. Further, for example, during execution of learning guidance, the pseudo-emotion determination unit 113 refers to a teacher's learning-related event table and a teacher's biological-related event table stored in the storage unit 130, which will be described later, to generate a new pseudo-emotion. decide.

The learning guidance content determination unit 114 determines the learning guidance content to be implemented for the user U. The control unit 110 determines the learning curriculum to be implemented with reference to the teacher learning history table stored in the storage unit 130 described later. In addition, the learning guidance content determination unit 114 refers to the learning guidance content change table stored in the storage unit 130, which will be described later, in accordance with the timing when the pseudo emotion changes by the pseudo emotion determination unit 113, and refers to the subjects included in the learning curriculum. Decide how to change the order and progress. The learning guidance content determination unit 114 transmits the determination content to the student robot 200 via the communication unit 150.

The expression determination unit 115 determines the content of the emotional expression executed by the teacher robot 100 for the user U according to the biological state of the user U, the learning situation, and the like. For example, the expression determination unit 115 refers to the teacher's learning-related event table and the teacher's biological-related event table stored in the storage unit 130, which will be described later, and executes the corresponding expression content each time a predetermined event occurs. Decide to do. The expression determination unit 115 supplies instruction data based on the determined expression content to the expression unit 140. Further, the expression determination unit 115 determines the voice to be output to the student robot 200 and the action to be executed according to the biological state and learning status of the user U, and the determined content is transmitted to the student robot 200 via the communication unit 150. introduce.

The biological information acquisition unit 120 acquires biological information that changes depending on the biological state of the user U. The biometric information acquisition unit 120 is composed of, for example, a microphone and a voice input interface, converts the voice of the user U into voice data, and outputs the voice to the biometric information analysis unit 111.

The storage unit 130 stores various data necessary for the control unit 110 to control each unit of the teacher robot 100. The storage unit 130 is composed of, for example, a non-volatile storage device such as a flash memory or an HDD (Hard Disc Drive). The storage unit 130 stores, for example, data indicating a pseudo-personality selected by the user U via the communication terminal 300 in a predetermined storage area.

Further, the data stored in the storage unit 130 includes a learning history table for teachers, a learning-related event table for teachers, a biological event table for teachers, and a learning guidance content change table.

The learning history table for teachers is a table that summarizes the history and learning results of the user U using the learning support system 1. As shown in FIG. 4, the learning history table for teachers is configured by associating each data of "start date and time", "end date and time", "learning time", and "learning result". Here, in the present embodiment, it is assumed that the user U is an infant, and the learning curriculum is set so that the learning guidance performed by the teacher robot 100 is completed in about 30 minutes at a time. In addition, the learning curriculum includes "words" that mainly teach repeated utterances of words and correction of pronunciation, "sentences" that mainly teach repeated utterances of short sentences, and pronunciation by repeatedly uttering words and sentences according to the rhythm. Includes five subjects: "Chants" to learn pronunciation and intonation, "Conversation" to exchange familiar topics in English, "Story" to read a short story, and "Review" to check the degree of understanding of the learning content. To do. "Learning grade" is evaluated on a three-point scale from "A" to "C" in consideration of the correct answer rate and the degree of proficiency for each of these subjects. In addition, "-" means that it was not carried out.

The learning-related event table for teachers is a table that defines the response when the occurrence of a learning-related event is detected, and is provided for each pseudo-personality, and different responses are defined for each. Here, the learning-related event refers to an event related to the learning acquisition status such as the correctness of the answer to the question and the learning progress. As shown in FIGS. 5 and 6, the learning-related event table for teachers is configured by associating each data of "learning-related event", "emotion point", and "expression content" for each current pseudo-emotion. Has been done. The “learning-related event” is an item indicating the degree of proficiency of the user U determined by the learning status determination unit 112. Examples of the "learning-related event" include "correct answer (wrong answer)" and "learning progress is good (bad)". The "emotion point" is a numerical value added to each pseudo emotion for each occurrence event. By setting different numerical values for the "emotion points" of each pseudo-emotion, each pseudo-emotion is weighted differently according to the importance of the event and the urgency of response. The "expression content" is an outline of the output voice and the execution operation for each event. The "expression content" shown in FIGS. 5 and 6 mainly defines the expression of the teacher robot 100 for each occurrence event, but also defines the expression to be executed by the student robot 200 in response to the occurrence event. Will be done. The pseudo-emotion determination unit 113 refers to the learning-related event table for teachers, and calculates the cumulative value of each pseudo-emotion according to the occurrence event. Further, the pseudo emotion determination unit 113 determines that when any of the cumulative values of the emotion points of each pseudo emotion exceeds a predetermined threshold value, the current pseudo emotion is changed to the pseudo emotion exceeding the threshold value. ..

The table shown in FIG. 5 is an example of a learning-related event table for teachers that is referred to when the pseudo-personality is set to “cheerful”, and the table shown in FIG. 6 has a pseudo-personality of “angry”. This is an example of a learning-related event table for teachers that is referenced when it is set.

In the learning-related event table for teachers shown in FIG. 5, for example, when the event "learning progress is poor" indicating that learning is not progressing as planned is detected in the current pseudo emotion "joy", the pseudo emotion Add emotion point "2" to "sorrow" and emotion point "1" to pseudo emotion "anger" and "comfort", and output a voice that encourages user U or perform an action that encourages it. It is stipulated in. On the other hand, in the learning-related event table for teachers shown in FIG. 6, for example, when the event "learning progress is poor" is detected in the current pseudo emotion "joy", the emotion point "2" is added to the pseudo emotion "anger". , It is stipulated that the emotion point "3" is added to the pseudo emotion "sorrow" and the voice that scolds the user U is output. In this way, the teacher robot 100 can be made to carry out various learning guidances by making the "emotion points" and "expression contents" for the learning-related events different according to the set pseudo-personality.

The biological event table for teachers is a table that defines the response when the occurrence of a biological event is detected, and is provided for each pseudo-personality, and different responses are defined for each. Here, the biological-related event is referred to as an event caused by the state of the user U determined from the biological information or its change. As shown in FIGS. 7 and 8, the teacher's biological event table is configured by associating the data of "biological event", "emotion point", and "expression content" for each current pseudo-emotion. Has been done. The "biologically related event" is an item indicating the biological state of the user U detected from the biological information acquired by the biological information acquisition unit 120. Examples of the "biologically related event" include "loud (low) voice" and "quick (slow) response". The "emotion point" is a numerical value added to each pseudo emotion for each occurrence event, as in the case of the learning-related event table for teachers. The "expression content" is an outline of the output voice and the execution operation for each event. The "expression content" shown in FIGS. 7 and 8 mainly defines the expression of the teacher robot 100 for each occurrence event, but also defines the expression to be executed by the student robot 200 in response to the occurrence event. Will be done. The pseudo-emotion determination unit 113 refers to the teacher's biological-related event table, and calculates the cumulative value of each pseudo-emotion according to the event that occurs. Further, the pseudo emotion determination unit 113 determines that when any of the cumulative values of the emotion points of each pseudo emotion exceeds a predetermined threshold value, the current pseudo emotion is changed to the pseudo emotion exceeding the threshold value. ..

The table shown in FIG. 7 is an example of a biological event table for teachers that is referred to when the pseudo-personality is set to “cheerful”, and the table shown in FIG. 8 has a pseudo-personality of “angry”. This is an example of a biological event table for teachers that is referred to when it is set.

In the biological event table for teachers shown in FIG. 7, for example, when the event "loud" indicating that the answer of the user U is loud is detected in the current pseudo emotion "anger", the pseudo emotion " It is stipulated that the emotional point "1" is added to "joy", "sorrow", and "comfort", and a voice that compliments the user U is output. On the other hand, in the biological event table for teachers shown in FIG. 8, for example, when the event "loud" is detected in the current pseudo emotion "anger", the emotion points are set to the pseudo emotions "sorrow" and "comfort". It is stipulated that "1" is added and the action of shaking the head vertically is executed. In this way, the teacher robot 100 can be made to perform various learning guidances by making the "emotion points" and "expression contents" for the biological-related events different according to the set pseudo-personality. The pseudo-emotion determination unit 113 refers to the teacher's biological-related event table, and calculates the cumulative value of each pseudo-emotion according to the event that occurs. Further, the pseudo emotion determination unit 113 determines that when any of the cumulative values of the emotion points of each pseudo emotion exceeds a predetermined threshold value, the current pseudo emotion is changed to the pseudo emotion exceeding the threshold value. ..

The learning instruction content change table is a table in which the change content of the learning instruction content is defined according to the transition of pseudo emotions. The learning instruction content change table is configured by associating each data of "pseudo-emotion after transition", "pseudo-emotion before transition", and "learning instruction content". In the learning guidance content change table shown in FIG. 9, for example, when the pseudo emotion changes from "angry" to "joy", the subject to be implemented is "changed to the subject that user U is good at" or is being implemented. It is stipulated that "questioning difficult questions" should be selectively executed in the subject of. This is intended to further motivate and challenge the user U when the pseudo-emotion of the teacher robot 100 improves as the learning result or the biological state of the user U improves. In addition, in the learning guidance content change table, for example, when the pseudo emotion changes from "easy" to "sorrow", "relax the tempo" of learning, or "talk unrelated to the subject being conducted". It is stipulated to selectively execute "to do". This is intended to give the user U a margin or change his mood when the pseudo emotion of the teacher robot 100 deteriorates a little as the learning result or the biological state of the user U deteriorates.

The expression unit 140 performs a pseudo emotional expression to the user U according to the determination content of the expression determination unit 115. The expression unit 140 has an audio output unit 141 and a movable unit 142.

The voice output unit 141 is composed of a speaker and a voice output interface, and converts the voice data generated by the expression determination unit 115 into voice and outputs the voice data to the outside.

The movable portion 142 is a partial portion of the teacher robot 100 and is displaced by driving a driving member such as a motor. The teacher robot 100 displaces the movable portion 142 according to the determination content of the expression determination unit 115, thereby performing, for example, a movement of limbs, a movement of shaking the head up / down / left / right, an operation of opening / closing the eyelids, and the like. Express your emotions.

The communication unit 150 is composed of a wireless communication module and an antenna, and performs data communication with the student robot 200 and the communication terminal 300. The communication unit 150 transmits, for example, output voice and instruction data regarding the execution operation to the student robot 200. Further, the communication unit 150 receives, for example, an operation instruction (such as a pseudo character selection operation of the teacher robot 100) and information input (such as an answer input to a question) by the user U from the communication terminal 300.

The student robot 200 is a robot that plays the role of a student who receives instruction in English conversation from the teacher robot 100 together with the user U. Like the teacher robot 100, the student robot 200 has a three-dimensional shape that imitates the human body in appearance. Further, the student robot 200 has the same pseudo-personality and pseudo-emotion as the teacher robot 100, and learns to improve the learning motivation of the user U according to the biometric information of the user U and the pseudo-emotion that changes depending on the learning situation. Implement support.

Functionally, as shown in FIG. 2B, the student robot 200 includes a control unit 210, a biological information acquisition unit 220, a storage unit 230, an expression unit 240, and a communication unit 250.

The control unit 210 controls the operation of the entire student robot 200. The control unit 210 is composed of a microcomputer having a CPU, a ROM, and a RAM. The control unit 210 controls each unit of the student robot 200 by having the CPU read the operation program stored in the ROM and execute it on the RAM.

The control unit 210 includes a biological information analysis unit 211, a learning situation determination unit 212, a pseudo emotion determination unit 213, and an expression determination unit 215. These parts are substantially the same as the biological information analysis unit 111, the learning situation determination unit 112, the pseudo emotion determination unit 113, and the expression determination unit 115 included in the teacher robot 100.

The biological information analysis unit 211 analyzes the biological information acquired from the biological information acquisition unit 220 in chronological order, extracts changes in the biological state of the user U, and identifies the state of the user U.

The learning status determination unit 212 determines the learning status of the user U. Specifically, the learning status determination unit 212 determines whether the answer to the question is correct or incorrect, measures the response speed, and evaluates the learning performance of the user U in consideration of various factors such as the correct answer rate and the progress status.

The pseudo emotion determination unit 213 determines the pseudo emotion of the student robot 200. For example, when the learning support of the student robot 200 is started, the pseudo emotion determination unit 213 determines the pseudo emotion by referring to the student learning history table stored in the storage unit 230, which will be described later. Further, for example, while the learning support is being executed, the pseudo emotion determination unit 213 refers to the student learning-related event table and the student biological-related event table stored in the storage unit 230, which will be described later, to generate a new pseudo emotion. decide.

The expression determination unit 215 determines the content of the emotional expression executed by the student robot 200 for the user U according to the biological state of the user U, the learning situation, and the like. For example, the expression determination unit 215 refers to the student learning-related event table and the student biological-related event table stored in the storage unit 230, which will be described later, and executes the corresponding expression content each time a predetermined event occurs. Decide to do. The expression determination unit 215 supplies instruction data based on the determined expression content to the expression unit 240.

The biometric information acquisition unit 220 acquires biometric information that changes depending on the state of the user U. The biometric information acquisition unit 220 is composed of, for example, a microphone and a voice input interface, converts the voice of the user U into voice data, and outputs the voice to the biometric information analysis unit 211.

The storage unit 230 stores various data necessary for the control unit 210 to control each unit of the student robot 200. The storage unit 230 is composed of, for example, a non-volatile storage device such as a flash memory or an HDD. The storage unit 230 stores, for example, data indicating a pseudo-personality selected by the user U via the communication terminal 300 in a predetermined storage area.

Further, the data stored in the storage unit 230 includes a learning history table for students, a learning-related event table for students, and a biological event table for students. Each of these tables is configured in substantially the same manner as the above-mentioned teacher learning history table, teacher learning-related event table, and teacher biological-related event table. In addition, it is desirable that the "expression content" corresponding to each occurrence event in the student learning-related event table and the student biological-related event table is suitable for students and friends.

In the learning-related event table for students referred to when the pseudo-personality shown in FIG. 10 is “cheerful”, for example, when the event “wrong answer” is detected in the current pseudo-emotion “joy”, the user U is notified. It is stipulated to output hints and cheerful voices. Further, in the current pseudo emotion "anger", when the event "learning progress is good" is detected, it is stipulated to output a voice enviing the user U.

The expression unit 240 performs a pseudo emotional expression to the user U according to the decision content of the expression determination unit 215. The expression unit 240 has an audio output unit 241 and a movable unit 242.

The voice output unit 241 is composed of a speaker and a voice output interface, converts voice data generated by the expression determination unit 215 into voice, and outputs the voice data to the outside.

The movable portion 242 is a partial portion of the student robot 200, and is displaced by driving a driving member such as a motor. The student robot 200 displaces the movable portion 242 according to the determination content of the expression determination unit 215, thereby performing, for example, a motion of moving the limbs, a motion of shaking the head up / down / left / right, and an operation of opening / closing the eyelids. Express your emotions.

The communication unit 250 is composed of a wireless communication module and an antenna, and performs data communication with the teacher robot 100 and the communication terminal 300. The communication unit 250 transmits, for example, output voice and instruction data related to the execution operation to the student robot 200. Further, the communication unit 250 receives, for example, an operation instruction (such as a pseudo character selection operation of the student robot 200) and information input (such as an answer input to a question) by the user U from the communication terminal 300.

The communication terminal 300 is composed of, for example, a smartphone or a tablet terminal, and communicates with the teacher robot 100 and the student robot 200. The communication terminal 300 displays, for example, a selected image for answering a question according to the data transmitted from the teacher robot 100, and displays an image as a hint for answering the question according to the data transmitted from the student robot 200. Further, the communication terminal 300 receives, for example, the selection input of the pseudo-characters of the teacher robot 100 and the student robot 200 by the user U, and transmits the input data to each robot.

Next, the learning guidance process executed by the teacher robot 100 will be described with reference to the flowchart shown in FIG. 11A. The learning guidance process is a process for appropriately determining the content of learning guidance to be performed for the user U based on the learning results and the biological information of the user U. The control unit 110 starts the learning guidance process in response to receiving an operation signal instructing the start of learning via the communication terminal 300 by the operation of the user U.

When the control unit 110 starts the learning guidance process, the control unit 110 sets the pseudo-character of the teacher robot 100 (step S101). The control unit 110 sets the pseudo-character of the teacher robot 100 to be "cheerful", "calm", "angry", or "gloomy" according to the selection by the operation of the user U or the setting content at the time of the previous execution. Decide on one of.

Next, the control unit 110 (pseudo-emotion determination unit 113) determines the pseudo-emotion of the teacher robot 100 (step S102). For example, the control unit 110 refers to the learning history table stored in the storage unit 130, and determines the pseudo emotion of the teacher robot 100 according to the learning frequency and the learning result of the user U. For example, when several days have passed since the last use and the learning frequency is low, the control unit 110 determines the pseudo emotion of the teacher robot 100 as "sorrow". Further, when the learning result at the time of the previous use is good, the control unit 110 determines the pseudo emotion as "joy". The control unit 110 may determine the pseudo emotion of the teacher robot 100 in consideration of the pseudo personality determined in step S101. For example, when the pseudo-personality is determined to be "angry", the pseudo-emotion may be determined to be "easy" even if the learning result at the time of the previous use is good.

Subsequently, the control unit 110 (learning instruction content determination unit 114) determines the learning curriculum to be implemented this time (step S103). The control unit 110 refers to the student learning history table stored in the storage unit 130, and determines the learning curriculum to be implemented this time according to the learning frequency of the user U and the learning results up to the previous time. For example, when the learning frequency is low, the control unit 110 may perform a relatively familiar subject "chants" for a longer time than other subjects, or may perform other subjects in order to improve the motivation of the user U for learning. Determine the learning curriculum so that it precedes the subject.

After executing the process of step S103, the control unit 110 starts learning instruction according to the determined learning curriculum (step S104). The control unit 110 controls the expression unit 140 so as to output a voice or perform an operation according to the learning content for each subject.

Subsequently, the control unit 110 determines whether or not to end the learning guidance (step S105). The control unit 110 ends the learning guidance depending on whether or not the operation signal instructing the end of learning has been received by the operation of the user U, or whether or not all the subjects of the current learning curriculum have been implemented. Judge whether or not. When it is determined that the learning guidance is finished (step S105; YES), the control unit 110 ends the learning guidance process.

When it is determined that the learning guidance is to be continued (step S105; NO), the control unit 110 determines whether or not a learning-related event or a biological-related event has occurred (step S106). The control unit 110 is defined in the teacher's learning-related event table and the teacher's biological-related event table stored in the storage unit 130 based on the analysis result of the biological information analysis unit 111 and the determination result of the learning status determination unit 112. It is determined whether or not the event has occurred. When it is determined that no learning-related event or biological-related event has occurred (step S106; NO), the control unit 110 returns the process to step S104 and continues the learning guidance.

On the other hand, when it is determined that a learning-related event or a biological-related event has occurred (step S106; YES), the control unit 110 executes the expression content corresponding to the generated event (step S107). The control unit 110 (expression determination unit 115) refers to the teacher's learning-related event table and the teacher's biological-related event table, and expresses so as to output voice and perform an operation according to the expression content corresponding to the generated event. The unit 140 is controlled.

Subsequently, the control unit 110 (pseudo emotion determination unit 113) determines whether or not the cumulative value of the emotion points of the pseudo emotion exceeds the threshold value (step S108). When it is determined that the cumulative value of the emotion points of any of the pseudo emotions does not exceed the threshold value (step S108; NO), the control unit 110 returns the process to step S104 and continues the learning guidance.

On the other hand, when it is determined that the cumulative value of the emotion points of any of the pseudo emotions exceeds the threshold value (step S108; YES), the control unit 110 (pseudo emotion determination unit 113) is a pseudo emotion that exceeds the threshold value from the current pseudo emotion. Transition to emotion (step S109).

Further, the control unit 110 (learning instruction content determination unit 114) changes the learning instruction content in accordance with the transition to the pseudo emotion (step S110). The control unit 110 (learning instruction content determination unit 114) refers to the learning instruction content change table stored in the storage unit 130, and decides to change to the learning instruction content corresponding to the pseudo emotions before and after the transition.

After executing the process of step S110, the control unit 110 returns the process to step S104, changes the order and the degree of progress of the subjects included in the learning curriculum, and continues the learning guidance.

Subsequently, the learning support process executed by the student robot 200 will be described with reference to the flowchart shown in FIG. 11B. The learning support process is a process for appropriately determining the learning support content to be performed for the user U based on the learning results and the biological information of the user U. Here, since the learning support process is substantially the same as the learning guidance process shown in FIG. 11A, detailed description thereof will be omitted. The control unit 210 starts the learning support process in response to receiving an operation signal instructing the start of learning via the communication terminal 300 by the operation of the user U.

When the control unit 210 starts the learning support process, the control unit 210 sets the pseudo-character of the student robot 200 (step S201). The control unit 210 determines the pseudo-character of the student robot 200 according to the selection by the operation of the user U or the setting content at the time of the previous execution.

Next, the control unit 210 (pseudo-emotion determination unit 213) determines the pseudo-emotion of the student robot 200 (step S202). For example, the control unit 210 refers to the learning history table stored in the storage unit 230, and determines the pseudo emotion of the student robot 200 according to the learning frequency and the learning result of the user U.

Subsequently, the control unit 210 acquires the learning curriculum to be implemented this time (step S203). The control unit 210 acquires the learning curriculum to be implemented this time, which is determined by the learning guidance content determination unit 114 of the teacher robot 100, via the communication unit 250.

After executing the process of step S203, the control unit 210 starts learning support according to the acquired learning curriculum (step S204). The control unit 210 controls the expression unit 240 so as to output a voice or perform an operation according to the learning content for each subject. When the control unit 210 receives the change contents from the teacher robot 100 as the learning guidance content determination unit 114 of the teacher robot 100 changes the order and the degree of progress of the subjects included in the learning curriculum. , The expression unit 240 is controlled so as to output a voice or perform an operation according to the changed content.

Subsequently, the control unit 210 determines whether or not to end the learning support (step S205). The control unit 210 ends the learning support depending on whether or not the operation signal instructing the end of learning has been received by the operation of the user U, or whether or not all the subjects of the current learning curriculum have been implemented. Judge whether or not. When it is determined that the learning support is terminated (step S205; YES), the control unit 210 ends the learning support process.

When it is determined that the learning support is to be continued (step S205; NO), the control unit 210 determines whether or not a learning-related event or a biological-related event has occurred (step S206). Based on the analysis result of the biological information analysis unit 211 and the determination result of the learning status determination unit 212, the control unit 210 is defined in the student learning-related event table and the student biological-related event table stored in the storage unit 230. It is determined whether or not the event has occurred. When it is determined that no learning-related event or biological-related event has occurred (step S206; NO), the control unit 210 returns the process to step S204 and continues learning support.

On the other hand, when it is determined that a learning-related event or a biological-related event has occurred (step S206; YES), the control unit 210 executes the expression content corresponding to the generated event (step S207). The control unit 210 (expression determination unit 215) refers to the student learning-related event table and the student biological-related event table, and expresses so as to output voice output or perform an operation according to the expression content corresponding to the generated event. The unit 240 is controlled.

Subsequently, the control unit 210 (pseudo emotion determination unit 213) determines whether or not the cumulative value of the emotion points of the pseudo emotion exceeds the threshold value (step S208). When it is determined that the cumulative value of the emotion points of any of the pseudo emotions does not exceed the threshold value (step S208; NO), the control unit 210 returns the process to step S204 and continues the learning guidance.

On the other hand, when it is determined that the cumulative value of the emotion points of any of the pseudo emotions exceeds the threshold value (step S208; YES), the control unit 210 (pseudo emotion determination unit 213) is a pseudo emotion that exceeds the threshold value from the current pseudo emotion. Transition to emotion (step S209), return the process to step S204, and continue learning support.

In this way, in response to the instruction to start learning by the user U, the teacher robot 100 executes the learning guidance process, the student robot 200 executes the learning support process, and the teacher robot 100 and the student perform the learning support process for the user U. Learning support is provided in collaboration with the robot 200.

As described above, according to the present embodiment, the teacher robot 100 and the student robot 200 have a pseudo-personality, and have pseudo-emotions that transition in different modes according to the set pseudo-personality. .. In addition, each robot provides learning guidance to the user U while expressing emotions by voice or motion according to the current pseudo emotions. In this way, the learning support system 1 including the teacher robot 100 and the student robot 200 can come into contact with the user U as if it were a real human being having a personality, and can support diverse learning. Further, the learning support system 1 can improve the ability of the user U to build an interpersonal relationship in addition to improving the academic ability of the user U.

The present invention is not limited to the above-described embodiment, and various modifications and applications are possible. The above embodiment may be modified as follows.

For example, in the learning support system 1, a learning support device having all or part of the functions of the teacher robot 100 may be used instead of the teacher robot 100. The learning support device may be any electronic device including at least a computer. The same applies to the student robot 200.

For example, the classification of pseudo-personality and pseudo-emotion is not limited to the above embodiment and is arbitrary. Pseudo-personality and pseudo-emotion may be further classified, and learning instruction contents corresponding to these states may be set. Further, the method of transitioning the pseudo emotion is not limited to the above-described embodiment and is arbitrary. The expression pattern of the teacher robot 100 may be simplified by omitting either the pseudo personality or the pseudo emotion.

For example, the biometric information acquisition unit 120 may be configured to acquire biometric information from a sensor installed outside. The biological information acquisition unit 120 is based on the measured values of various sensors such as a face (expression) detection sensor, a GPS (Global Positioning System) sensor, an acceleration sensor, a blood pressure sensor, a pulse sensor, a brain wave sensor, and a sweating sensor. The state of U may be recognized, and the content of expression and the content of learning guidance for the user U may be determined. Further, the expression content and the learning guidance content for the user U may be determined in consideration of the surrounding environment of the user U (for example, the lighting state and the noise level).

In the above embodiment, the teacher robot 100 and the student robot 200 have been described as having substantially the same functions, but the functions provided by the student robot 200 may be simplified. For example, the teacher robot 100 provides the student robot 200 with the result of analysis by the biological information analysis unit 111 via the communication unit 150, so that the student robot 200 is equipped with the biological information acquisition unit 220 and the biological information analysis unit 211. It may be configured not to. Further, the teacher robot 100 determines the pseudo emotions of the student robot 200 in the pseudo emotion determination unit 113, and determines the expression of the student robot 200 for the user U in the expression determination unit 115, and determines the contents of these determinations in the student robot 200. The student robot 200 may be configured not to have a pseudo emotion determination unit 213, an expression determination unit 215, or the like by instructing.

In the above embodiment, the learning instruction content change table defines the learning instruction content to be changed in association with the combination of pseudo emotions before and after the transition. However, the learning instruction content differs depending on the event that occurred immediately before the transition (immediately before the cumulative value of emotion points exceeds the predetermined threshold value) or the event that occurred most frequently before the cumulative value of emotion points exceeded the predetermined threshold value. May be specified.

The present invention allows for various embodiments and modifications without departing from the broad spirit and scope of the present invention. Moreover, the above-described embodiment is for explaining the present invention, and does not limit the scope of the present invention. That is, the scope of the present invention is indicated by the scope of claims, not by the embodiment. Then, various modifications made within the scope of the claims and the equivalent meaning of the invention are considered to be within the scope of the present invention.

Hereinafter, the inventions described in the claims of the original application of the present application will be added.

(Appendix 1)
Biometric information acquisition means for acquiring biometric information that changes depending on the learner's condition,
A learning guidance content determining means for determining the content of learning support to be provided to the learner based on the biometric information acquired by the biometric information acquiring means.
Learning support device.

(Appendix 2)
An expression determining means for determining an expression for the learner based on the biological information acquired by the biometric information acquiring means is further provided.
The learning support device according to Appendix 1.

(Appendix 3)
Further provided with a learning situation determining means for determining the learning status of the learner,
The expression determining means determines the expression for the learner based on the biological information and the determination result by the learning situation determining means.
The learning guidance content determining means determines the content of learning support to be provided to the learner based on the biological information and the determination result by the learning situation determining means.
The learning support device according to Appendix 2.

(Appendix 4)
Further provided with a pseudo emotion determining means for determining one pseudo emotion from a plurality of pseudo emotions based on the pseudo personality selected by the learner from the plurality of pseudo personalities.
The expression determining means determines the expression for the learner based on the biological information, the determination result, and the pseudo emotion determined by the pseudo emotion determining means.
The learning guidance content determining means determines the content of learning support to be provided to the learner based on the biological information, the determination result, and the pseudo emotion determined by the pseudo emotion determining means.
The learning support device according to Appendix 3.

(Appendix 5)
Each of the plurality of pseudo-emotions is given a different weight corresponding to each of the plurality of pseudo-personality.
The learning support device according to Appendix 4.

(Appendix 6)
A robot acting as a teacher equipped with the learning support device described in any one of Appendix 1 to 5.

(Appendix 7)
The robot acting as a teacher described in Appendix 6 and
A robot acting as a student who receives support from the robot acting as a teacher together with the learner.
Learning support system.

(Appendix 8)
The robot that plays the role of a student
A second biometric information acquisition means for acquiring biometric information that changes depending on the learner's state,
A second expression determining means for determining an expression for the learner based on the biological information acquired by the second biological information acquiring means is provided.
The learning support system described in Appendix 7.

(Appendix 9)
The robot acting as a student receives biological information that changes depending on the state of the learner from the robot acting as a teacher.
The learning support system described in Appendix 8.

(Appendix 10)
The learning support system according to Appendix 7, wherein the robot acting as a student receives expression determination information indicating an expression for the learner from the robot acting as a teacher.

(Appendix 11)
The biometric information acquisition step to acquire biometric information that changes depending on the learner's state,
A learning guidance content determination step for determining the content of learning support to be provided to the learner based on the biometric information acquired in the biometric information acquisition step is provided.
Learning support method.

(Appendix 12)
On the computer
Biometric information acquisition processing that acquires biometric information that changes depending on the learner's state,
Based on the biometric information acquired in the biometric information acquisition process, the learning guidance content determination process for determining the content of the learning support to be performed on the learner is executed.
,program.

1 ... Learning support system, 100 ... Teacher robot, 110 ... Control unit, 111 ... Biological information analysis unit, 112 ... Learning situation determination unit, 113 ... Pseudo-emotion determination unit, 114 ... Learning guidance content determination unit, 115 ... Expression determination unit , 120 ... biometric information acquisition unit, 130 ... storage unit, 140 ... expression unit, 141 ... audio output unit, 142 ... movable unit, 150 ... communication unit, 200 ... student robot, 210 ... control unit, 211 ... biometric information analysis unit , 212 ... Learning situation determination unit, 213 ... Pseudo emotion determination unit, 215 ... Expression determination unit, 220 ... Biological information acquisition unit, 230 ... Storage unit, 240 ... Expression unit, 241 ... Audio output unit, 242 ... Movable unit, 250 ... communication unit, 300 ... communication terminal, U ... user (learner)

Claims (13)

It is a learning support device to support learners' learning.
Pseudo-emotion storage means for storing a plurality of pseudo-emotions different from each other of the learning support device,
A setting means for setting the pseudo-character of the learning support device to one of the plurality of pseudo-characters, and
Based on the pseudo-personality set by the setting means, the pseudo-emotion determining means for determining the pseudo-emotion of the learning support device in one of the plurality of pseudo-emotions,
A learning guidance content determining means for determining the content of learning support to be provided to the learner based on the pseudo emotion determined by the pseudo emotion determining means.
Learning support device. The setting means sets the pseudo-character of the learning support device based on the user's operation.
The learning support device according to claim 1 . Further provided with a biometric information acquisition means for acquiring biometric information that changes depending on the state of the learner.
The teaching content determining means determines said acquired biometric information, based on the pseudo been pre Symbol determined emotion, the contents of the learning support be performed on the learner,
The learning support device according to claim 1 or 2 . Further provided with a learning situation determining means for determining the learning status of the learner,
The learning instruction content determining means, and determination result by said learning condition determining means, the biometric information, based on a pseudo emotion that is pre-Symbol decision, determines the content of the learning support be performed on the learner ,
The learning support device according to claim 3 . The pseudo-emotion determining means determines the pseudo-emotion of the learning support device as one of the plurality of pseudo-emotions based on the set pseudo-personality and the determined learning situation.
The learning support device according to claim 4 . Further comprising a result of judgment, and the biological information, based on the previous SL determined pseudo emotion, expression determining means for determining the expression for the learner, and
The learning support device according to claim 4 . A robot acting as a teacher equipped with the learning support device according to any one of claims 1 to 6 . The robot acting as a teacher according to claim 7 and
A robot acting as a student who receives instruction in English conversation from the robot acting as a teacher together with the learner.
Learning support system. The robot that plays the role of a student
A second biometric information acquisition means for acquiring biometric information that changes depending on the learner's state,
A second expression determining means for determining an expression for the learner based on the biological information acquired by the second biological information acquiring means is provided.
The learning support system according to claim 8 . The robot acting as a student receives biological information that changes depending on the state of the learner from the robot acting as a teacher.
The learning support system according to claim 8 . The robot acting as a student receives expression determination information indicating an expression for the learner from the robot acting as a teacher.
The learning support system according to claim 8 . It is a learning support method applied to a learning support device for supporting learners' learning.
A pseudo-emotion memory step that memorizes a plurality of pseudo-emotions different from each other of the learning support device,
A setting step for setting the pseudo-character of the learning support device to one of the plurality of pseudo-characters, and
Based on the set pseudo-personality, one of the plurality of pseudo-emotions includes a pseudo-emotion determination step of determining the pseudo-emotion of the learning support device.
A learning guidance content determination step for determining the content of learning support to be provided to the learner based on the pseudo emotion determined by the pseudo emotion determination step is provided.
Learning support method. To the computer of the learning support device to support the learner's learning,
The function of memorizing multiple pseudo emotions different from each other of the learning support device, and
A setting function that sets the pseudo-personality of the learning support device to one of the plurality of pseudo-personality,
Based on the set pseudo-personality, one of the plurality of pseudo-emotions has a pseudo-emotion determination function that determines the pseudo-emotion of the learning support device.
A function of determining the content of learning support to be provided to the learner based on the pseudo emotion determined by the pseudo emotion determination function is realized.
program.

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