KR20220128259A - Electronic apparatus for utilizing avatar matched to user's problem-solving ability, and learning management method - Google Patents

Abstract

A learning management method of an electronic device is disclosed. The learning management method may include the steps of: identifying the problem-solving ability of a user based on the user's answers to the plurality of problems; generating at least one artificial intelligence model matching the identified problem-solving ability; entering at least one problem into the artificial intelligence model and obtaining an answer of the artificial intelligence model for the input problem; and providing evaluation information on the obtained answer of the artificial intelligence model.

Description

An electronic device that uses an avatar that matches a user's problem-solving ability, and a learning management method

The present disclosure relates to an electronic device that provides a learning management method, and more particularly, to an electronic device that manages a user's learning in a way that an avatar based on an artificial intelligence model solves a test question.

The size of the domestic private education market is increasing every year.

In relation to Edutech, the conventional AI model technology mainly focuses on dialogue with students (communication), information provision, and grade analysis (analysis of academic ability), whereas specific learning such as minimizing the learning time of users (students) There was a lack of process management.

Registered Patent Publication No. 10-21916720000 (learning analysis and artificial intelligence-based customized learning method and system)

The present disclosure provides an electronic device and a learning management method for managing a user's learning through an AI model-based avatar capable of solving problems on behalf of a user (student) or competitively with the user.

Objects of the present disclosure are not limited to the above-mentioned purposes, and other objects and advantages of the present disclosure that are not mentioned may be understood by the following description, and will be more clearly understood by examples of the present disclosure. Moreover, it will be readily apparent that the objects and advantages of the present disclosure may be realized by the means and combinations thereof indicated in the claims.

A learning management method of an electronic device according to an embodiment of the present disclosure includes identifying the user's problem-solving ability based on the user's answers to a plurality of problems, and at least one matching the identified problem-solving ability. generating an artificial intelligence model of , inputting at least one problem into the artificial intelligence model, obtaining an answer of the artificial intelligence model for the problem, evaluation information on the obtained answer of the artificial intelligence model comprising the step of providing

The step of identifying the user's problem-solving ability may include, based on the difficulty of each of the plurality of problems provided by type, and whether the user's answer to each of the plurality of problems is correct, the problem-solving ability for each type of the user can be identified.

In this case, the generating of the artificial intelligence model may include generating at least one neural network model that outputs an answer matching the problem-solving ability for each type of the user.

In addition, the generating of the artificial intelligence model may include updating at least one neural network model based on the plurality of problems and the user's answers to each of the plurality of problems.

On the other hand, the learning management method of the electronic device, inputting a plurality of questions selected according to the user's input into the artificial intelligence model, obtaining a plurality of answers of the artificial intelligence model, incorrect answers among the plurality of answers The method may further include providing the user with at least one question matching at least one corresponding answer.

In addition, the learning management method of the electronic device includes providing the user with a plurality of problems included in a learning process set according to the user's problem-solving ability, based on the plurality of problems included in the set learning process. training the artificial intelligence model; when a learning period according to the set learning process expires, providing at least one evaluation problem related to the learning process to obtain the user's first answer to the evaluation problem; obtaining a second answer of the trained artificial intelligence model for the evaluation problem; providing first evaluation information for the first answer and second evaluation information for the second answer .

In this case, the training of the artificial intelligence model may include, when the answer of the artificial intelligence model to at least one problem included in the learning process is an incorrect answer, the artificial intelligence based on the problem and the correct answer to the problem. You can train the model.

Here, the learning management method of the electronic device may further include changing and setting at least one of a learning amount, a learning period, and a learning difficulty of the learning process based on the user's input. In this case, the providing of the plurality of problems includes providing the user with the plurality of problems included in the changed learning process, and the training of the artificial intelligence model is based on the changed learning process. You can train artificial intelligence models.

And, the learning management method of the electronic device compares and analyzes the first evaluation information and the second evaluation information to set a learning amount, a learning period, and a learning difficulty of at least one additional learning process after the learning process The method may further include providing the user with a plurality of problems included in the additional learning process, and training the artificial intelligence model based on the plurality of problems included in the additional learning process.

In this case, in the learning management method of the electronic device, when the first score according to the first evaluation information is lower than the second score according to the second evaluation information by a threshold or more, the first additional learning with a smaller learning amount than the learning process A plurality of problems included in the process are provided to the user, and the artificial intelligence model is trained based on a plurality of problems included in the first additional learning process, and a first score according to the first evaluation information is obtained from the first evaluation information. 2 When the second score is higher than the second score according to the evaluation information, the plurality of problems included in the second additional learning process with a larger learning amount than the learning process are provided to the user, and a plurality of problems included in the second additional learning process are provided to the user. It is also possible to train the AI model based on the problem.

The learning management method of the electronic device according to the present disclosure has an advantage of helping to efficiently configure the user's learning time by replacing at least a part of the user's problem solving process with an artificial intelligence model.

In addition, the learning management method of the electronic device according to the present disclosure has an effect of motivating a user and managing a learning schedule customized to the user based on competitive learning between the user and the artificial intelligence model.

1 is a block diagram illustrating a configuration of an electronic device according to an embodiment of the present disclosure;
2 is a flowchart illustrating a learning management method of an electronic device according to an embodiment of the present disclosure;
3 is a view for explaining operations of an electronic device and a user terminal for guiding the creation of an avatar according to an embodiment of the present disclosure;
4 is a view for explaining the operation of an electronic device and a user terminal that provide a user interface (UI) related to problem solving of an avatar (artificial intelligence model) according to an embodiment of the present disclosure;
5A to 5B are diagrams for explaining the operation of an electronic device and a user terminal that provide answers and evaluation information of an artificial intelligence model according to an embodiment of the present disclosure;
6 is a block diagram illustrating an operation in which an electronic device manages a user's learning through competitive problem-solving training of an artificial intelligence model according to an embodiment of the present disclosure;
7 is a block diagram illustrating a configuration of an electronic device according to various embodiments of the present disclosure.

Prior to describing the present disclosure in detail, a description will be given of the description of the present specification and drawings.

First, terms used in the present specification and claims have been selected in consideration of functions in various embodiments of the present disclosure. However, these terms may vary depending on the intention or legal or technical interpretation of a person skilled in the art, and the emergence of new technology. Also, some terms are arbitrarily selected by the applicant. These terms may be interpreted in the meaning defined in the present specification, and if there is no specific term definition, it may be interpreted based on the general content of the present specification and common technical knowledge in the art.

Also, the same reference numerals or reference numerals in each drawing attached to this specification indicate parts or components that perform substantially the same functions. For convenience of description and understanding, the same reference numerals or reference numerals are used in different embodiments. That is, even though all components having the same reference number are illustrated in a plurality of drawings, the plurality of drawings do not mean one embodiment.

In addition, in this specification and claims, terms including an ordinal number, such as "first" and "second", may be used to distinguish between elements. This ordinal number is used to distinguish the same or similar elements from each other, and the meaning of the term should not be construed as limited due to the use of the ordinal number. For example, the components combined with such an ordinal number should not be limited in the order of use or arrangement by the number. If necessary, each ordinal number may be used interchangeably.

In this specification, the singular expression includes the plural expression unless the context clearly dictates otherwise. In the present application, terms such as "comprises" or "consisting of" are intended to designate that the features, numbers, steps, operations, components, parts, or combinations thereof described in the specification exist, and are intended to indicate that one or more other It is to be understood that this does not preclude the possibility of addition or presence of features or numbers, steps, operations, components, parts, or combinations thereof.

In an embodiment of the present disclosure, terms such as “module”, “unit”, “part”, etc. are terms for designating a component that performs at least one function or operation, and such component is hardware or software. It may be implemented or implemented as a combination of hardware and software. In addition, a plurality of "modules", "units", "parts", etc. are integrated into at least one module or chip, except when each needs to be implemented as individual specific hardware, and thus at least one processor. can be implemented as

In addition, in an embodiment of the present disclosure, when a part is connected to another part, this includes not only direct connection but also indirect connection through another medium. In addition, the meaning that a certain part includes a certain component means that other components may be further included without excluding other components unless otherwise stated.

1 is a block diagram illustrating a configuration of an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 1 , the electronic device 100 may include a memory 110 and a processor 120 .

The electronic device 100 may correspond to a server device capable of communicating with various user terminals. In this case, the electronic device 100 may interwork with various user terminals through at least one web page or application, and may provide a learning management method according to various embodiments to be described later through the user terminal.

Alternatively, the electronic device 100 may correspond to various terminal devices such as a smart phone, a notebook PC, a desktop PC, a tablet PC, a wearable device, a TV, a set-top box, and a console device.

The memory 110 is a configuration for storing an operating system (OS) for controlling overall operations of components in the electronic device 100 , at least one instruction, and data.

The memory 110 may include non-volatile memory such as ROM and flash memory, and may include volatile memory such as DRAM. In addition, the memory 110 may include a hard disk, a solid state drive (SSD), or the like.

Memory 110 may contain one or more problems per category and/or per type.

The categories are divided according to subjects and may include math, English, Korean (language), science, social studies, and the like.

*Category may be divided according to the type of test even in the same subject. For example, even within English, it can be divided into elementary English, secondary English, English for the SAT, and English related to English tests accredited by various organizers/organizations.

The type may correspond to the nature of each question or the intention of the question. Specifically, in the case of language or English problems, understanding the purpose of the text, understanding the mood/mind/toning, understanding the implication, understanding the gist/argument, understanding the topic, understanding the content match/inconsistency, identifying the appropriateness of the vocabulary, understanding the grammar accuracy, inferring the reference , flow identification, sequence identification, summary, reading comprehension, etc.

Memory 110 may contain one or more problems per various sources. Source means a subject that creates/provides a problem, and may correspond to various education-related companies, education-related institutions, test administration/management institutions, etc., but is not limited thereto.

The memory 110 may include information on the difficulty level for each problem. Here, the difficulty level may be set to a value preset by the problem creator, or may be set based on the percentage of correct answers of a certain number of students who have solved the problem.

The difficulty level can be matched with the problem-solving ability for each step. Here, the problem-solving ability means the ability or level to solve a problem, and may be understood as an index for evaluating the problem-solving level of at least one user (student) or an artificial intelligence model to be described later.

That is, in embodiments to be described later, it may be assumed that the higher the difficulty of the problem that the user (student) or the AI model can solve, the higher the problem solving ability of the user or the AI model is. In addition, it can be assumed that the higher the percentage of correct answers (of the user or the artificial intelligence model) for a plurality of problems of the same difficulty, the higher the problem-solving ability of the user or the artificial intelligence model.

Meanwhile, the memory 110 may include at least one artificial intelligence model for problem solving.

This artificial intelligence model may correspond to a network model (neural network model) based on a neural network. The network model may include a plurality of network nodes having weights. A plurality of network nodes may form a connection relationship based on weights between nodes of different layers.

Neural network models include Convolutional Neural Network (CNN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Bidirectional Recurrent Deep Neural Network (BRDNN), and GAN (GAN). Generative Adversarial Network) and deep Q-networks (Deep Q-Networks), etc. may be configured, but is not limited thereto.

As an example, when at least one (solving) problem is input, at least one artificial intelligence model that outputs an answer to the corresponding problem may be stored in the memory 110 .

Here, the question may correspond to a multiple-choice (including examples) or multiple-choice type, and the answer may be an answer that selects at least one answer or a short answer/declaration type answer.

As a specific example, it is assumed that a multiple-choice problem including a query, a fingerprint, and a plurality of views (ex. Nos. 1 to 5) is input to the AI model.

In this case, the AI model may select at least one answer from among the plurality of views based on the text corresponding to each of the query, the fingerprint, and the plurality of views.

To this end, the AI model may include at least one layer (module) for understanding natural language, at least one layer (module) for selecting a view, and the like, but is not limited thereto.

Also, as a specific example, it is assumed that a subjective type problem including a query or a question and a fingerprint is input.

In this case, the artificial intelligence model may output an answer (text) to the query based on the text corresponding to the query and/or the fingerprint. To this end, the AI model may include at least one layer for providing an answer to a query.

In this case, the AI model may extract at least one text (related to a query/fingerprint) from at least one answer database constructed to be linked with the AI model, and construct an answer using the extracted text.

On the other hand, when the input problem is a listening problem in the form of a voice, the electronic device 100 acquires a text matching the voice of the listening problem using a voice recognition module (ex. Automatic Speech Recognition), and artificially uses the acquired text. It can also be entered into an intelligence model.

The processor 120 is a configuration for overall controlling each configuration included in the electronic device 100 , and various units such as a central processing unit (CPU), an application processor (AP), a graphic processing unit (GPU), a VPU, and an NPU. can be composed of

The processor 120 may control the electronic device 100 by executing an instruction stored in the memory 110 .

Hereinafter, an operation of the processor 120 will be described with reference to the drawings.

2 is a flowchart illustrating a learning management method of an electronic device according to an embodiment of the present disclosure.

Referring to FIG. 2 , the processor 120 may identify the user's problem-solving ability based on the user's answers to a plurality of problems ( S210 ).

For example, the processor 120 of the electronic device 100 implemented as a server may communicate with the user terminal through the communication unit, and may control the user terminal to sequentially display a plurality of problems.

As a specific example, when login is performed based on user information (eg, ID, password, etc.) received through the user terminal, the electronic device 100 may identify the user.

In addition, a plurality of problems may be provided through the user terminal according to the request of the user who has logged in.

Here, the user's request may be a request to be provided with a plurality of problems, or may be a request to generate an avatar (artificial intelligence model) matching the user's problem-solving ability.

When a plurality of questions are provided, the processor 120 may obtain the user's answer for each question according to a user input received through the user terminal.

Here, the processor 120 may identify whether the user's answer is a correct answer or an incorrect answer, and may identify the user's problem-solving ability based on a correct ratio of the user's answer to each of a plurality of questions.

Specifically, the processor 120 may identify the user's problem-solving ability for each type based on the difficulty of each of the plurality of problems provided for each type and whether the user's answer to each of the plurality of problems is correct.

The problem-solving ability for each type, for example, may be divided into a plurality of steps (eg, steps 1 to 9), but is not limited thereto.

In this case, the processor 120 may set a time limit for each problem (or for a plurality of problems) or measure the time taken until the user's answer is obtained for each problem.

When the answer is the correct answer, the processor 120 may identify the problem-solving ability of the user higher as the time taken from the time the problem is provided to the time the user's answer is obtained is shorter.

When the user's problem-solving ability is identified according to the above-described embodiment, the processor 120 may generate at least one artificial intelligence model matching the identified problem-solving ability (S220).

Specifically, the processor 120 may generate at least one artificial intelligence model that outputs an answer matching the user's problem-solving ability for each type.

Here, the processor 120 may train the neural network model based on problems of difficulty matching the problem-solving ability for each type of the user.

As an example, it is assumed that the user's problem-solving ability for the reading comprehension problem is three levels, and the user's problem-solving ability for the referential reasoning problem is four levels.

In this case, the processor 120 may update the weights between nodes of the neural network model by inputting and outputting one or more reading comprehension problems matching the level of difficulty of three or less and correct answers to the corresponding reading comprehension problems, respectively. In addition, the processor 120 may update the weights between nodes of the neural network model by inputting and outputting one or more referential inference problems matching the difficulty level of 4 or less, and correct answers to the corresponding referential inference problems, respectively.

In addition, the processor 120 may update at least one neural network model based on the plurality of problems previously provided to the user and the user's answers to the plurality of problems.

In this case, since the updated neural network model (artificial intelligence model) is trained based on the user's answer whether the user's answer is correct or incorrect, it may have problem-solving ability matching the user's problem-solving ability.

As described above, when the AI model matching the user's problem-solving ability is generated, the processor 120 may obtain an answer by inputting at least one problem into the AI model (S230).

In this case, the processor 120 may select at least one problem according to the user input and input the selected problem into the AI model.

In this case, the processor 120 may provide evaluation information on the answer of the artificial intelligence model (S240).

The evaluation information corresponds to an index indicating the problem-solving ability of the AI model.

The evaluation information may include information on whether the answer is correct or not. When the answer is an incorrect answer, the evaluation information may include information about a correct answer (a model answer).

The evaluation information may include the number of correct answers and a percentage of correct answers for a plurality of questions. The evaluation information may include information on a total evaluation score of answers to a plurality of questions.

Also, the evaluation information may include information on the number of correct answers or the percentage of correct answers for each type of question. That is, the evaluation information may include an index for problem-solving ability for each type.

In the case of an answer to a short-form question, the evaluation information may include information about a score of an answer to the question.

Specifically, in the case of a short-form question, the processor 120 may calculate a match rate by comparing the text of the model answer with the text of the answer, and may calculate a score according to the calculated match rate. Alternatively, the processor 120 may identify at least one keyword included in the model answer, and calculate a score according to whether the identified keyword is included in the answer.

In addition, the evaluation information may include information on the correct rate/score of at least one other user for the same problem, the correct rate/score of at least one other artificial intelligence model, and the like.

Hereinafter, a more detailed operation of the learning management method of the electronic device 100 and related embodiments will be described with reference to the drawings. Meanwhile, although the electronic device 100 implemented as a server provides a learning management method through the user terminal 200 in FIGS. 3, 4, and 5A to 5B below, the electronic device 100 is illustrated. Of course, it can also be implemented as a user terminal (eg, a smart phone, a notebook PC, a desktop PC, etc.).

3 is a diagram for explaining operations of an electronic device and a user terminal for guiding generation of an avatar according to an embodiment of the present disclosure;

Referring to FIG. 3 , the electronic device 100 may provide a user interface (UI) for generating at least one avatar matching the user's problem-solving ability through the user terminal 200 .

An avatar is a medium for providing an AI model service, and may be defined as a virtual character or entity, and may have at least one name.

The electronic device 100 may generate an avatar (artificial intelligence model) for each category of the problem. Referring to FIG. 3 , a user input for selecting a category in which an avatar (artificial intelligence model) is to be generated may be received through the UI 310 of the user terminal 200 .

In FIG. 3 , the name of the avatar corresponds to “A.I Hong Gil-dong”, but it goes without saying that it may be changed according to a user input.

And, when at least one category is selected, the electronic device 100 may provide a plurality of problems matching the selected category through the user terminal 200 .

Specifically, referring to FIG. 3 , a plurality of problems may be displayed through the UI 320 of the user terminal 200 , and each problem may be sequentially provided through the scroll 321 .

The plurality of problems provided may include various types of problems. For example, the plurality of problems may include two problems for each of the 18 types, but the number of types or the number of problems is not limited thereto.

Here, the electronic device 100 may obtain the user's answer to each problem through a user input (eg, a touch, a keyboard input, a mouse click, etc.) received through the user terminal 200 .

The UI 325 indicating the progress rate may indicate in real time a ratio of the number of questions for which the user's answer is obtained among a plurality of questions.

In this case, when a user input for selecting the UI 322 corresponding to completion is received, the process of obtaining the user's answers to a plurality of problems may be completed.

As such, the process of obtaining the user's answer to a plurality of problems corresponds to a prerequisite for generating an artificial intelligence model (avatar) that matches the user's problem-solving ability.

When the user's answers to a plurality of questions are obtained, the electronic device 100 may analyze the user's answers to identify the user's problem-solving ability.

Specifically, the electronic device 100 may determine the problem-solving ability of the user for each type according to the correct answer/ratio of the answers to the questions for each type.

In addition, the electronic device 100 may generate at least one artificial intelligence model that matches the user's problem-solving ability for each type.

FIG. 4 is a diagram for explaining operations of an electronic device and a user terminal that provide a user interface (UI) related to problem solving of an avatar (artificial intelligence model) according to an embodiment of the present disclosure.

Referring to FIG. 4 , the electronic device 100 may control the user terminal 200 to provide UI items corresponding to 'AI problem solving 410' and 'my solving 420', respectively.

The 'AI problem solving 410' is an item for controlling the process of allowing the avatar (artificial intelligence model) to solve the problem.

When the 'AI problem solving 410' item is selected, the electronic device 100 may input at least one problem selected according to a user's input into the AI model to obtain an answer of the AI model.

Referring to FIG. 4 , a problem to be solved by the artificial intelligence model may be selected according to a user input for each UI item 411 , 412 , and 413 .

Specifically, the UI item 411 is an item for selecting one or more categories of a problem, the UI item 412 is an item for selecting one or more sources of a problem, and the UI item 413 is an item for selecting other problems It may correspond to an item for additionally setting filtering for various conditions (eg, period, difficulty, etc.).

As such, when a category and a source of a problem are selected, the electronic device 100 may output a UI 415 for providing a plurality of selected problems through the user terminal 200 .

Referring to FIG. 4 , the UI 415 is designed to classify problems by source and solve the problems of each source (select “solve”), but the configuration of the UI 415 does not need to be limited thereto.

Referring to FIG. 4 , a UI item 415 ′ for receiving all selected problems may also be provided.

On the other hand, 'I solve 420' is an item for providing a plurality of problems so that the user can directly solve the problem.

When the item 'I solve 420' is selected, similarly to FIG. 4 , the electronic device 100 may provide problems of various categories/sources, and in this case, a user's input for selecting/writing an answer for each problem can be received.

Meanwhile, as the AI model and/or the user's problem solving 410 and 420 is performed as shown in FIG. 4 , the electronic device 100 may provide evaluation information on the AI model and/or the user's answer. .

5A to 5B are diagrams for explaining operations of an electronic device and a user terminal that provide answers and evaluation information of an artificial intelligence model according to an embodiment of the present disclosure.

Referring to FIG. 5A , the electronic device 100 performs 'question view 510', 'AI score report 520', 'I solve (530)', and 'my score report card 540' through the user terminal 200 . UI items such as

'Problem view 510' is an item input to the AI model, that is, an item for providing a problem solved by the AI model, and 'AI report card 520' is an item for providing evaluation information about the answer of the AI model , 'I solve (530)' is an item for providing a corresponding problem so that the user can solve the same problem as the problem solved by the artificial intelligence model, 'My report card 540' is the evaluation of the user's answer to the problem This item is for providing information.

FIG. 5A corresponds to a case in which the 'problem view 510' item is selected according to a user's input.

Referring to FIG. 5A , the electronic device 100 may provide a 'problem solved by AI 511' item and a 'view only incorrect answer 512' item through the user terminal 200, respectively.

As shown in FIG. 5A , when the item 'a problem 511 solved by the AI' is selected, the electronic device 100 may provide a UI 511' including at least one problem solved by the artificial intelligence model.

The UI 511 ′ may include a visual indication (indicating that it is correct or incorrect) for checking whether the answer of the artificial intelligence model for each problem is correct or not.

Also, the UI 511' may include at least one UI item 511'-1 for providing a correct answer to a problem in which the AI model is incorrect.

If the 'view only incorrect answer (512)' item is selected, the electronic device 100 controls the user terminal 200 to provide only the problem in which the answer of the artificial intelligence model was an incorrect answer among the problems solved by the artificial intelligence model. can

As such, by providing what kind of problem the artificial intelligence model matching the user's problem-solving ability is wrong, the electronic device 100 provides an incorrect answer problem according to the result similar to that solved by the user directly, even if the user does not directly solve all of the plurality of problems. be able to provide

In other words, since the user can only solve the problem for which the artificial intelligence model is wrong and receive the correct answer to the problem, there is no need to solve a problem that the user knows or is too easy for the user, as a result, the user can solve the problem directly The time required for it can be reduced.

The electronic device 100 may obtain answers and evaluation information of at least one other artificial intelligence model to a problem solved by the artificial intelligence model.

Here, the other artificial intelligence model may correspond to an artificial intelligence model (avatar) generated according to the problem-solving ability of another user.

As a specific example, the electronic device 100 may input a problem input to the user's artificial intelligence model (avatar) into another user's artificial intelligence model (avatar) to obtain an answer of the other artificial intelligence model.

In addition, the electronic device 100 may provide evaluation information on the answer of the artificial intelligence model together with the evaluation information on the answer of the other artificial intelligence model.

In relation to this, FIG. 5B corresponds to a case in which the item 'AI score report 520' is selected according to a user's input.

Referring to FIG. 5B , the electronic device 100 displays a user's avatar (AI model) and other users' avatars (AI models) by type (word, structure) through the UI 521 of the user terminal 200 . , sentences, etc.) can provide problem-solving ability (evaluation information), respectively.

In this case, the user's problem-solving ability and the problem-solving ability of other users may be indirectly compared, and evaluation information may be provided in an interesting manner according to the comparison method through the avatar.

Meanwhile, the electronic device 100 may provide the artificial intelligence model and the user's competitive training to motivate the user to learn and manage the user's learning schedule.

In relation to this, FIG. 6 is a block diagram for explaining an operation in which an electronic device manages a user's learning through competitive problem-solving training of an artificial intelligence model according to an embodiment of the present disclosure.

Referring to FIG. 6 , the electronic device 100 may provide the user with a plurality of problems included in a learning process set according to the user's problem-solving ability ( S610 ).

The learning process may refer to a structured plan or schedule for learning a certain amount of educational materials within a certain period of time.

The educational material may correspond to at least one problem, a lecture video, a textbook, and the like.

Specifically, the electronic device 100 may set a learning process corresponding to a learning amount, a learning period, and a learning difficulty matching the user's problem-solving ability.

The amount of learning corresponds to the number of educational materials (ex. the number of problems to be solved), the learning period corresponds to the period given to the learning of the educational materials (the target period), and the learning difficulty corresponds to the difficulty of the educational materials (ex. average difficulty of the problem).

In this case, the electronic device 100 may set at least one of the learning amount and the learning period to be higher as the user's problem-solving ability is higher. Also, the electronic device 100 may increase the difficulty of the provided educational material as the user's problem-solving ability increases.

However, it goes without saying that the electronic device 100 may change and set at least one of the learning amount of the learning process, the learning period, and the learning difficulty based on the user's input.

As such, when the learning process is set, the electronic device 100 may provide a plurality of problems included in the learning process to the user. In this case, the correct answer to each problem may be provided together according to the user's request.

In addition, the electronic device 100 may also perform training on the artificial intelligence model according to the above-described learning process set according to the user's problem-solving ability ( S620 ).

If the learning process is changed according to a user's input, training of the artificial intelligence model may be performed with respect to the changed learning process.

Specifically, the electronic device 100 may train the artificial intelligence model based on a plurality of problems included in the learning process.

In this case, the electronic device 100 may input a plurality of problems into the AI model, respectively, and obtain an answer of the AI model for each problem.

Here, when the answer of the AI model to at least one problem is an incorrect answer, the electronic device 100 may train the AI model based on the problem and the correct answer to the problem. In this case, the problem-solving ability of the AI model can be improved.

As such, after training of the user and the AI model is performed, an evaluation problem may be given to each of the user and the AI model.

For example, when the learning period (eg, one week) according to the above-described learning process expires, the electronic device 100 provides the user with at least one evaluation problem related to the learning process, An answer can be obtained (S630).

The evaluation problem is a problem for evaluating the learning achievement of the corresponding learning process, and may mean a problem that is expected to be solved when the learning process is smoothly completed.

Also, the electronic device 100 may obtain a second answer of the AI model by inputting the same evaluation problem to the trained AI model (S640).

In addition, the electronic device 100 may provide first evaluation information for the user's first answer and second evaluation information for the second answer of the artificial intelligence model, respectively ( S650 ).

In this case, the user's achievement in the learning process may be presented by comparison with the artificial intelligence model that has undergone the same learning process. In particular, since the user and the AI model are evaluated through the same learning process with substantially the same problem-solving ability, the effect of competition-based motivation for learning by period can be expected.

In addition, according to an embodiment, the electronic device 100 compares and analyzes the above-described first evaluation information and the second evaluation information to obtain a learning amount, a learning period, and a learning value of at least one additional learning process after the above-described learning process. You can also set the difficulty level.

In this case, the additional learning process may be set differently according to a comparison between the first score of the first evaluation information for the user and the second score of the second evaluation information for the artificial intelligence model.

As a specific example, it is assumed that the first score according to the first evaluation information is lower than the second score according to the second evaluation information by more than a threshold. This means that the user's achievement in the above-described learning process is relatively poor.

In this case, the electronic device 100 may set an additional learning process with a smaller learning amount than the above-described learning process. Also, the electronic device 100 may set an additional learning process having a shorter learning period than the aforementioned learning process. Also, the electronic device 100 may set an additional learning process having the same difficulty as the above-described learning process or having a higher difficulty only by a first level difference (<threshold level) than the above-described learning process.

However, if the first score is lower than the second score by more than a special threshold (the learning achievement is very low), the electronic device 100 may set an additional learning process having a lower difficulty than the above-described learning process.

As another example, it is assumed that the first score according to the first evaluation information is higher than the second score according to the second evaluation information by more than a threshold. This means that the user's achievement in the above-described learning process is relatively good.

In this case, the electronic device 100 may set a second additional learning process in which a learning amount is greater than that of the above-described learning process. Also, the electronic device 100 may set an additional learning process having a longer learning period than the above-described learning process. Also, the electronic device 100 may set an additional learning process having the same difficulty as the above-described learning process or having a higher difficulty than the above-described learning process by a second level difference (>threshold level) or more.

As another example, when the first score according to the first evaluation information and the second score according to the second evaluation information are the same, the electronic device 100 may set an additional learning process with the same learning period and learning amount as the above-described learning process. . Also, the electronic device 100 may set an additional learning process having a difficulty level higher than that of the above-described learning process by a threshold level.

When the additional learning process is set according to the above-described embodiments, the electronic device 100 may provide a plurality of problems included in the additional learning process to the user, and artificial intelligence based on the plurality of problems included in the additional learning process model can be trained.

And, when the learning period of the additional learning process expires, the electronic device 100 may also provide at least one evaluation problem (related to the additional learning process) to the user and the artificial intelligence model, similar to the above-described embodiment. The action may be repeated.

Meanwhile, FIG. 7 is a block diagram illustrating a configuration of an electronic device according to various embodiments of the present disclosure.

Referring to FIG. 7 , the electronic device 100 may further include at least one of a communication unit 130 , a user input unit 140 , and an output unit 150 in addition to the memory 110 and the processor 120 .

The communication unit 130 is a configuration for the electronic device 100 to transmit/receive data to and from various external devices, and may include at least one circuit for communication.

The communication unit 130 is TCP/IP (Transmission Control Protocol/Internet Protocol), UDP (User Datagram Protocol), HTTP (Hyper Text Transfer Protocol), HTTPS (Secure Hyper Text Transfer Protocol), FTP (File Transfer Protocol), SFTP ( A communication protocol (protocol) such as Secure File Transfer Protocol) and MQTT (Message Queuing Telemetry Transport) may be used to transmit/receive various information to and from one or more external electronic devices.

To this end, the communication unit 130 may be connected to an external device based on a network implemented through wired communication and/or wireless communication. In this case, the communication unit 130 may be directly connected to an external device, or may be connected to an external electronic device through one or more external servers (eg, Internet Service Providers (ISPs)) that provide a network.

The network may be a personal area network (PAN), a local area network (LAN), a wide area network (WAN), etc. depending on the area or size, and depending on the openness of the network, an intranet, It may be an extranet or the Internet.

Wireless communication includes long-term evolution (LTE), LTE Advance (LTE-A), 5th generation (5G) mobile communication, code division multiple access (CDMA), wideband CDMA (WCDMA), universal mobile telecommunications system (UMTS), WiBro (Wireless Broadband), GSM (Global System for Mobile Communications), DMA (Time Division Multiple Access), WiFi (Wi-Fi), WiFi Direct, Bluetooth, NFC (near field communication), at least one of the communication methods such as Zigbee may include

Wired communication may include at least one of communication methods such as Ethernet, optical network, USB (Universal Serial Bus), and ThunderBolt.

Here, the communication unit 130 may include a network interface or a network chip according to the above-described wired/wireless communication method. Meanwhile, the communication method is not limited to the above-described example, and may include a communication method newly appearing according to the development of technology.

When the electronic device 100 is a server, the electronic device 100 may communicate with various user terminals through the communication unit 130 and interwork with each user terminal through at least one web page or application to enable each user. It is possible to provide a learning management method according to the above-described various embodiments for

In this case, the electronic device 100 may receive information about a user input received through the user terminal from the user terminal through the communication unit 130 .

Also, the electronic device 100 may receive information about the problem and the correct answer by performing communication with external servers in which various problems are stored through the communication unit 130 .

When the electronic device 100 is a user terminal such as a smart phone, the electronic device 100 manages learning according to various embodiments described above through an application/web page provided by at least one external server connected through the communication unit 130 . You can also provide a method.

The user input unit 140 is configured to receive a user command or user information.

When the electronic device 100 is implemented as a user terminal such as a smartphone or a notebook PC, the user input unit 140 may be implemented as a touch sensor, a button, a camera, a microphone, a keyboard, and the like.

When the electronic device 100 is a desktop PC, the user input unit 140 may be implemented as a terminal connected to various input interfaces such as a keyboard or a mouse.

The output unit 150 is configured to output various information and provide it to the user.

When the electronic device 100 is implemented as a user terminal such as a smart phone, the output unit 150 may include a display, a speaker, an earphone/headset terminal, and the like.

As an embodiment, the processor 120 may provide various problems, evaluation information, and other UIs according to various embodiments described above through the output unit 150 .

Meanwhile, the various embodiments described above may be implemented by combining a plurality of embodiments as long as they do not conflict with each other.

Meanwhile, the various embodiments described above may be implemented in a recording medium readable by a computer or a similar device using software, hardware, or a combination thereof.

According to the hardware implementation, the embodiments described in the present disclosure are ASICs (Application Specific Integrated Circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays) ), processors, controllers, micro-controllers, microprocessors, and other electrical units for performing other functions may be implemented using at least one.

In some cases, the embodiments described herein may be implemented by the processor itself. According to the software implementation, embodiments such as the procedures and functions described in this specification may be implemented as separate software modules. Each of the above-described software modules may perform one or more functions and operations described herein.

Meanwhile, computer instructions or a computer program for performing a processing operation in each device in the system according to various embodiments of the present disclosure described above are stored in a non-transitory computer-readable medium. can be saved. When the computer instructions or computer program stored in the non-transitory computer-readable medium are executed by the processor of the specific device, the specific device performs the processing operation in the electronic device according to the various embodiments described above.

The non-transitory computer-readable medium refers to a medium that stores data semi-permanently, rather than a medium that stores data for a short moment, such as a register, cache, memory, etc., and can be read by a device. Specific examples of the non-transitory computer-readable medium may include a CD, DVD, hard disk, Blu-ray disk, USB, memory card, ROM, and the like.

In the above, preferred embodiments of the present disclosure have been illustrated and described, but the present disclosure is not limited to the specific embodiments described above, and is commonly used in the technical field pertaining to the present disclosure without departing from the gist of the present disclosure as claimed in the claims. Various modifications may be made by those having the knowledge of

100: electronic device 110: memory
120: processor

Claims (1)

In the avatar-based learning management method of the electronic device performed by a processor of the electronic device, the method comprising:
identifying the user's problem-solving ability based on the user's answers to a plurality of problems;
generating at least one artificial intelligence model matching the identified problem-solving ability;
inputting at least one problem into the AI model to obtain an answer of the AI model for the problem; and
Including; providing evaluation information for the obtained answer of the artificial intelligence model;
The learning management method of the electronic device,
providing the user with a plurality of problems included in a learning process set according to the user's problem-solving ability;
training the artificial intelligence model based on the plurality of problems included in the set learning process;
when the learning period according to the set learning process expires, providing at least one evaluation question related to the learning process to obtain the user's first answer to the evaluation problem;
obtaining a second answer of the trained artificial intelligence model to the evaluation problem; and
Including; providing first evaluation information for the first answer and second evaluation information for the second answer;
The learning management method of the electronic device,
Based on the user's input, changing and setting at least one of a learning amount, a learning period, and a learning difficulty of the learning process; further comprising,
Providing the plurality of problems comprises:
Provide the user with a plurality of problems included in the changed learning process,
The step of training the artificial intelligence model is,
An avatar-based learning management method of an electronic device, wherein the artificial intelligence model is trained based on the changed learning process.

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