CN112990677B - Teaching system, computer equipment and storage medium based on artificial intelligence - Google Patents
Teaching system, computer equipment and storage medium based on artificial intelligence Download PDFInfo
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Abstract
The invention belongs to the technical field of artificial intelligence education, and discloses a teaching system based on artificial intelligence, computer equipment and a storage medium, wherein the teaching system based on artificial intelligence comprises: the system comprises a user identity registration login module, a user level selection module, a central control module, a preliminary evaluation module, a learning weak point determination module, a teaching module, an image acquisition module, an image analysis module, a learning monitoring module, a learning process grading module, an evaluation module, a teaching evaluation module and a teaching adjustment module. According to the invention, the learning state of the student is automatically determined according to the facial expression of the student based on a preset artificial intelligence algorithm model by acquiring the information of the facial image influenced by the learning state of the student; the learning content of the student is determined according to the learning state and other information of the student, so that the student can achieve a convenient and efficient learning effect; the evaluation and the teaching quality evaluation are carried out aiming at the learning of students, and the self-adaptive evaluation of the teaching can be carried out.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence education, and particularly relates to a teaching system, computer equipment and a storage medium based on artificial intelligence.
Background
At present: in a traditional artificial intelligence teaching system, complex software environment configuration and hardware upgrading are required to be carried out on a computer for teaching, and meanwhile students are required to have certain mathematics and computer knowledge reserves, so that the application range is small.
The learning idea of adaptive education has existed for a long time, and in recent years, with the application of artificial intelligence in the education industry, adaptive education based on artificial intelligence has come into play. The self-adaptive education learning mode can collect the learning data of students in real time, evaluate the learning content of the students, realize the personalized learning of thousands of people and thousands of faces and improve the learning efficiency. The self-adaptive education based on artificial intelligence is in an initial development stage in China at present, an industrial chain is immature, labor division is not clear, and the cognition degree of a user is low. With the mature technology and the rich effective data in the future, the adaptive education is expected to be applied in more disciplines and subdivision fields.
However, the existing self-adaptive teaching is low in efficiency, the learning condition cannot be monitored, and teaching evaluation and adaptability adjustment cannot be carried out.
Through the above analysis, the problems and defects of the prior art are as follows: the existing self-adaptive teaching is low in teaching efficiency, the learning condition cannot be monitored, and teaching evaluation and adaptability adjustment cannot be carried out.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a teaching system, computer equipment and a storage medium based on artificial intelligence.
The invention is realized in this way, a teaching system based on artificial intelligence, the teaching system based on artificial intelligence includes:
the learning vulnerability determining module is connected with the central control module and is used for determining the learning vulnerability of the user based on the user score;
the teaching module is connected with the central control module and is used for performing targeted teaching based on the determined learning weak points of the user;
the image acquisition module is connected with the central control module and is used for acquiring a face image and body action image data in the user teaching process;
the image analysis module is connected with the central control module and used for analyzing based on the acquired corresponding images and determining the learning state of the user;
the learning monitoring module is connected with the central control module and is used for acquiring learning data of the user and monitoring the learning condition of the user;
the learning process scoring module is connected with the central control module and is used for scoring the learning process of the user based on the learning state and the learning condition of the user;
the evaluation module is connected with the central control module and is used for evaluating the user at the current stage based on the teaching content;
the teaching evaluation module is connected with the central control module and is used for evaluating teaching quality based on the progress values of the evaluation result and the preliminary evaluation result of the user at the current stage and the learning process score;
the teaching quality assessment method of the teaching assessment module comprises the following steps:
firstly, taking the evaluation result of the user at the current stage, the progress value of the preliminary evaluation result and the learning process score as evaluation factors, and combining the factor molecules with the fuzzy mathematical membership to obtain the factor membership, wherein the formula is as follows:
wherein x0 represents the result of preliminary evaluation; x1 represents the evaluation result at this stage; x represents a learning process score;
secondly, evaluating the single index of the teaching quality; calculating comprehensive weight;
the calculating the summation weight comprises:
calculating the weight of a single index:
in the formula: ai represents the current index value; lk represents an index energy level;
using normalized weight calculation in the fuzzy model:
in the formula: wk represents a single index weight;
and (3) obtaining a teaching quality comprehensive weight matrix B by n teaching quality indexes:
B＝[W1，W2，......，Wn]
finally, performing composite calculation on the matrix R and the matrix, and calculating to obtain the teaching quality condition;
and the teaching adjusting module is connected with the central control module and is used for adjusting the teaching task and the teaching mode based on the teaching quality assessment result.
Further, the artificial intelligence based teaching system further comprises:
the user identity registration and login module is connected with the central control module and is used for registering and logging in the identity of the user of the teaching system;
the user level selection module is connected with the central control module and is used for selecting the current learning degree based on the self learning level of the user;
the central control module is connected with the user identity registration login module, the user level selection module, the preliminary evaluation module, the learning weak point determination module, the teaching module, the image acquisition module, the image analysis module, the learning monitoring module, the learning process grading module, the evaluation module, the teaching evaluation module and the teaching adjustment module and is used for controlling each module to normally work by utilizing a single chip microcomputer or a controller;
and the preliminary evaluation module is connected with the central control module and is used for automatically determining the current level of the corresponding test question evaluation user based on the learning strength selected by the user and outputting the user score.
Further, the method for determining the user learning vulnerability by the learning vulnerability determination module comprises:
1) determining the number of wrong questions of the user based on the preliminary evaluation scores of the user, and determining problem solving thought information of the wrong test questions and standard problem solving information of the wrong test questions for each wrong question of the user;
2) and judging whether the weak points exist in the learning of the user or not based on the problem solving thought information of the wrong test problems and the standard problem solving information of the wrong test problems.
Further, the step of judging whether the weak points exist in the user learning based on the problem solving thought information of the wrong test problems and the standard problem solving information of the wrong test problems comprises the steps of:
and if the wrong test question does not have a question solving thought, or the question solving thought is not clear, judging that the user has weak points.
Further, the method for determining the learning state of the user by the image analysis module comprises the following steps:
(1) collecting a first image learned by a user; analyzing whether the first image contains designated content information or not; if the first image contains designated content information, acquiring a second image;
(2) analyzing whether the second image contains the person information or not; if the second image contains the personal information, analyzing whether the personal information meets a preset personal state standard or not; if not, forming a first monitoring result corresponding to the learning state of the reader; if so, forming a second monitoring result corresponding to the learning state of the reader;
(3) acquiring a third image after a first preset time; analyzing whether the third image is the same as the first image; if the third image is the same as the first image, acquiring a fourth image;
(4) analyzing whether the fourth image contains the person information or not; if the fourth image contains the personal information, analyzing whether the personal information meets a preset personal state standard or not; and if the character information does not accord with the preset character state standard, forming the first monitoring result corresponding to the learning state of the reader.
Further, the character information includes an angle between shoulders and a horizontal direction, a height to width ratio of a mouth, and a time during which eyes are continuously closed.
Further, the analyzing whether the personal information meets a preset personal status standard includes:
1) analyzing whether the angle between the shoulders and the horizontal direction is larger than a first preset angle or not; if the angle between the shoulders and the horizontal direction is larger than a first preset angle, judging that the character information does not accord with a preset character state standard;
2) analyzing whether the ratio of the height to the width of the mouth is greater than a preset value; if the ratio of the height to the width of the mouth is larger than a preset value, judging that the character information does not accord with a preset character state standard;
3) analyzing whether the time for which the eyes are continuously closed exceeds a second preset time; if the time for continuously closing the eyes exceeds second preset time, judging that the character information does not accord with preset character state standards;
4) outputting a comprehensive judgment result based on the judgment results of the steps 1) to 3).
Further, the teaching quality single index evaluation formula is as follows:
the following equation is established:
wherein, W is a sample set of each index of teaching quality, L is a pollution level set of each index of teaching quality, and A is a sample numerical value; n is the index number; m is data of teaching quality energy level;
calculating the factor membership degree of the single index through the following formula, and obtaining a matrix R of m x n corresponding to n teaching quality indexes;
A1L1 ... A1Lm
... ... ...
R＝AnL1 ... AnLm。
it is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the functions of the artificial intelligence based teaching system.
It is another object of the present invention to provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the functions of the artificial intelligence based teaching system.
By combining all the technical schemes, the invention has the advantages and positive effects that: according to the invention, the learning state of the student is automatically determined according to the facial expression of the student based on a preset artificial intelligence algorithm model by acquiring the information of the facial image influenced by the learning state of the student; the learning content of the student is determined according to the learning state and other information of the student, so that the student can achieve a convenient and efficient learning effect; the evaluation and the teaching quality evaluation are carried out aiming at the learning of students, and the self-adaptive evaluation of the teaching can be carried out.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of an artificial intelligence-based teaching system provided by an embodiment of the present invention;
in the figure: 1. a user identity registration login module; 2. a user level selection module; 3. a central control module; 4. a preliminary evaluation module; 5. a learning vulnerability determination module; 6. a teaching module; 7. an image acquisition module; 8. an image analysis module; 9. a learning monitoring module; 10. a learning process scoring module; 11. an evaluation module; 12. a teaching evaluation module; 13. and a teaching adjusting module.
FIG. 2 is a flowchart of an artificial intelligence-based teaching method provided by an embodiment of the present invention.
Fig. 3 is a flowchart of a method for determining a user learning vulnerability by a learning vulnerability determination module according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for determining a learning state of a user by an image analysis module according to an embodiment of the present invention.
Fig. 5 is a flowchart of a method for analyzing whether personal information meets a predetermined personal status standard according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In view of the problems in the prior art, the present invention provides a teaching system, a computer device, and a storage medium based on artificial intelligence, and the following describes the present invention in detail with reference to the accompanying drawings.
As shown in fig. 1, the artificial intelligence-based teaching system provided in the embodiment of the present invention includes:
the user identity registration login module 1 is connected with the central control module 3 and is used for registering and logging in the identity of the user of the teaching system;
the user level selection module 2 is connected with the central control module 3 and is used for selecting the current learning degree based on the self learning level of the user;
the central control module 3 is connected with the user identity registration login module 1, the user level selection module 2, the preliminary evaluation module 4, the learning weak point determination module 5, the teaching module 6, the image acquisition module 7, the image analysis module 8, the learning monitoring module 9, the learning process scoring module 10, the evaluation module 11, the teaching evaluation module 12 and the teaching adjustment module 13, and is used for controlling each module to normally work by utilizing a single chip microcomputer or a controller;
the preliminary evaluation module 4 is connected with the central control module 3 and used for automatically determining the current level of the corresponding test question evaluation user based on the learning strength selected by the user and outputting the user score;
the learning vulnerability determining module 5 is connected with the central control module 3 and is used for determining the learning vulnerability of the user based on the user score;
the teaching module 6 is connected with the central control module 3 and is used for performing targeted teaching based on the determined learning weak points of the user;
the image acquisition module 7 is connected with the central control module 3 and is used for acquiring a face image and body action image data in the user teaching process;
the image analysis module 8 is connected with the central control module 3 and used for analyzing based on the collected corresponding images and determining the learning state of the user;
the learning monitoring module 9 is connected with the central control module 3 and used for acquiring learning data of the user and monitoring the learning condition of the user;
the learning process scoring module 10 is connected with the central control module 3 and is used for scoring the learning process of the user based on the learning state and the learning condition of the user;
the evaluation module 11 is connected with the central control module 3 and is used for carrying out user evaluation at the current stage based on the teaching content;
the teaching evaluation module 12 is connected with the central control module 3 and used for evaluating teaching quality based on the progress values of the evaluation result and the preliminary evaluation result of the user at the current stage and the learning process score;
and the teaching adjusting module 13 is connected with the central control module 3 and used for adjusting teaching tasks and teaching modes based on the teaching quality assessment result.
As shown in fig. 2, the teaching method based on artificial intelligence provided by the embodiment of the present invention includes:
s101, registering and logging in the identity of a teaching system user through a user identity registration and logging-in module; selecting the current learning degree based on the self learning level by a user through a user level selection module; the central control module controls each module to work normally by utilizing a single chip microcomputer or a controller;
s102, automatically determining the current level of a corresponding test question evaluation user through a primary evaluation module based on the learning strength selected by the user, and outputting the user score; determining, by a learning vulnerability determination module, a user learning vulnerability based on the user score;
s103, performing targeted teaching based on the determined learning weak points of the user through a teaching module; acquiring a face image and body action image data in the teaching process of a user through an image acquisition module; analyzing based on the acquired corresponding images through an image analysis module to determine the learning state of the user;
s104, acquiring learning data of the user through a learning monitoring module, and monitoring the learning condition of the user; scoring the learning process of the user based on the learning state and the learning condition of the user through a learning process scoring module; the evaluation module is used for evaluating the user at the current stage based on the teaching content;
s105, performing teaching quality evaluation through a teaching evaluation module based on the progress values of the evaluation result and the preliminary evaluation result of the user at the current stage and the learning process score; and adjusting the teaching task and the teaching mode based on the teaching quality assessment result through a teaching adjustment module.
And the teaching adjusting module is connected with the central control module and is used for adjusting the teaching task and the teaching mode based on the teaching quality assessment result.
As shown in fig. 3, in step S103, the method for determining a learning vulnerability of a user by a learning vulnerability determining module according to an embodiment of the present invention includes:
s201, determining the number of wrong questions of the user based on the preliminary evaluation scores of the user, and determining problem solving thought information of the wrong test questions and standard problem solving information of the wrong test questions for each wrong question of the user;
s202, judging whether weak points exist in the user learning based on the problem solving thought information of the wrong test problems and the standard problem solving information of the wrong test problems.
The method for judging whether the weak points exist in the user learning based on the problem solving thought information of the wrong test questions and the standard problem solving information of the wrong test questions provided by the embodiment of the invention comprises the following steps:
and if the wrong test question does not have a question solving thought, or the question solving thought is not clear, judging that the user has weak points.
As shown in fig. 4, in step S103, the method for determining the learning state of the user by the image analysis module according to the embodiment of the present invention includes:
s301, collecting a first image learned by a user; analyzing whether the first image contains designated content information or not; if the first image contains designated content information, acquiring a second image;
s302, analyzing whether the second image contains the person information or not; if the second image contains the personal information, analyzing whether the personal information meets a preset personal state standard or not; if not, forming a first monitoring result corresponding to the learning state of the reader; if so, forming a second monitoring result corresponding to the learning state of the reader;
s303, acquiring a third image after the first preset time; analyzing whether the third image is the same as the first image; if the third image is the same as the first image, acquiring a fourth image;
s304, analyzing whether the fourth image contains the person information or not; if the fourth image contains the personal information, analyzing whether the personal information meets a preset personal state standard or not; and if the character information does not accord with the preset character state standard, forming the first monitoring result corresponding to the learning state of the reader.
The character information provided by the embodiment of the invention comprises the angle between shoulders and the horizontal direction, the height-width ratio of the mouth and the continuous eye closing time.
As shown in fig. 5, the analyzing whether the personal information meets the preset personal status criteria according to the embodiment of the present invention includes:
s401, analyzing whether an angle between each shoulder and the horizontal direction is larger than a first preset angle or not; if the angle between the shoulders and the horizontal direction is larger than a first preset angle, judging that the character information does not accord with a preset character state standard;
s402, analyzing whether the ratio of the height to the width of the mouth is larger than a preset value or not; if the ratio of the height to the width of the mouth is larger than a preset value, judging that the character information does not accord with a preset character state standard;
s403, analyzing whether the continuous eye closing time exceeds a second preset time or not; if the time for continuously closing the eyes exceeds second preset time, judging that the character information does not accord with preset character state standards;
s404, outputs the comprehensive determination result based on the determination results of steps S401 to S403.
In step S105, the evaluation of teaching quality by the teaching evaluation module based on the progress values of the evaluation result and the preliminary evaluation result at the current stage of the user and the learning process score according to the embodiment of the present invention includes:
firstly, taking the evaluation result of the user at the current stage, the progress value of the preliminary evaluation result and the learning process score as evaluation factors, and combining the factor molecules with the fuzzy mathematical membership to obtain the factor membership, wherein the formula is as follows:
wherein x0 represents the result of preliminary evaluation; x1 represents the evaluation result at this stage; x represents a learning process score;
secondly, evaluating the single index of the teaching quality:
the following equation is established:
wherein, W is a sample set of each index of teaching quality, L is a pollution level set of each index of teaching quality, and A is a sample numerical value; n is the index number; m is data of teaching quality energy level;
calculating the factor membership degree of the single index through the following formula, and obtaining a matrix R of m x n corresponding to n teaching quality indexes;
A1L1 ... A1Lm
... ... ...
R＝AnL1 ... AnLm；
then, calculating comprehensive weight;
the calculating the summation weight comprises:
calculating the weight of a single index:
in the formula: ai represents the current index value; lk represents an index energy level;
using normalized weight calculation in the fuzzy model:
in the formula: wk represents a single index weight;
and (3) obtaining a teaching quality comprehensive weight matrix B by n teaching quality indexes:
B＝[W1，W2，......，Wn]
and finally, performing composite calculation on the matrix R and the matrix, and calculating to obtain the teaching quality condition.
It should be noted that the embodiments of the present invention can be realized by hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided on a carrier medium such as a disk, CD-or DVD-ROM, programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier, for example. The apparatus and its modules of the present invention may be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., or by software executed by various types of processors, or by a combination of hardware circuits and software, e.g., firmware.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention disclosed herein, which is within the spirit and principle of the present invention, should be covered by the present invention.
Claims (10)
1. An artificial intelligence based teaching system, comprising:
the learning vulnerability determining module is connected with the central control module and is used for determining the learning vulnerability of the user based on the user score;
the teaching module is connected with the central control module and is used for performing targeted teaching based on the determined learning weak points of the user;
the image acquisition module is connected with the central control module and is used for acquiring a face image and body action image data in the user teaching process;
the image analysis module is connected with the central control module and used for analyzing based on the acquired corresponding images and determining the learning state of the user;
the learning monitoring module is connected with the central control module and is used for acquiring learning data of the user and monitoring the learning condition of the user;
the learning process scoring module is connected with the central control module and is used for scoring the learning process of the user based on the learning state and the learning condition of the user;
the evaluation module is connected with the central control module and is used for evaluating the user at the current stage based on the teaching content;
the teaching evaluation module is connected with the central control module and is used for evaluating teaching quality based on the progress values of the evaluation result and the preliminary evaluation result of the user at the current stage and the learning process score; the method specifically comprises the following steps: firstly, taking the evaluation result of the user at the current stage, the progress value of the preliminary evaluation result and the learning process score as evaluation factors, and combining the factor molecules with the fuzzy mathematical membership to obtain the factor membership, wherein the formula is as follows:
wherein x0 represents the result of preliminary evaluation; x1 represents the evaluation result at this stage; x represents a learning process score;
secondly, evaluating the single index of the teaching quality; calculating comprehensive weight; the calculating the comprehensive weight comprises:
calculating the weight of a single index:
In the formula: ai represents the current index value; lk represents an index energy level;
using normalized weight calculation in the fuzzy model:
in the formula: wk represents a single index weight;
and (3) obtaining a teaching quality comprehensive weight matrix B by n teaching quality indexes:
B＝[W1，W2，......，Wn]
finally, performing composite calculation on the matrix R and the matrix, and calculating to obtain the teaching quality condition;
the single index evaluation formula is as follows:
the following equation is established:
wherein, W is a sample set of each index of teaching quality, L is a pollution level set of each index of teaching quality, and A is a sample numerical value; n is the index number; m is data of teaching quality energy level;
calculating the factor membership degree of the single index through the following formula, and obtaining a matrix R of m x n corresponding to n teaching quality indexes;
and the teaching adjusting module is connected with the central control module and is used for adjusting the teaching task and the teaching mode based on the teaching quality assessment result.
2. The artificial intelligence based teaching system of claim 1 wherein said artificial intelligence based teaching system further comprises:
the user identity registration and login module is connected with the central control module and is used for registering and logging in the identity of the user of the teaching system;
the user level selection module is connected with the central control module and is used for selecting the current learning degree based on the self learning level of the user;
the central control module is connected with the user identity registration login module, the user level selection module, the preliminary evaluation module, the learning weak point determination module, the teaching module, the image acquisition module, the image analysis module, the learning monitoring module, the learning process grading module, the evaluation module, the teaching evaluation module and the teaching adjustment module and is used for controlling each module to normally work by utilizing a single chip microcomputer or a controller.
3. The artificial intelligence based teaching system of claim 1 wherein said artificial intelligence based teaching system further comprises:
and the preliminary evaluation module is connected with the central control module and is used for automatically determining the current level of the corresponding test question evaluation user based on the learning strength selected by the user and outputting the user score.
4. The artificial intelligence based teaching system of claim 1 wherein the method of the learning vulnerability determination module determining user learning vulnerabilities comprises:
1) determining the number of wrong questions of the user based on the preliminary evaluation scores of the user, and determining problem solving thought information of the wrong test questions and standard problem solving information of the wrong test questions for each wrong question of the user;
2) and judging whether the weak points exist in the learning of the user or not based on the problem solving thought information of the wrong test problems and the standard problem solving information of the wrong test problems.
5. The artificial intelligence based teaching system of claim 4, wherein said determining whether said user learns to have a weak point based on said problem solving idea information of wrong test questions and said normative problem solving information of wrong test questions comprises:
and if the wrong test question does not have a question solving thought, or the question solving thought is not clear, judging that the user has weak points.
6. The artificial intelligence based teaching system of claim 1 wherein the method of the image analysis module determining the learning state of the user comprises:
(1) collecting a first image learned by a user; analyzing whether the first image contains designated content information or not; if the first image contains designated content information, acquiring a second image;
(2) analyzing whether the second image contains the person information or not; if the second image contains the personal information, analyzing whether the personal information meets a preset personal state standard or not; if not, forming a first monitoring result corresponding to the learning state of the reader; if so, forming a second monitoring result corresponding to the learning state of the reader;
(3) acquiring a third image after a first preset time; analyzing whether the third image is the same as the first image; if the third image is the same as the first image, acquiring a fourth image;
(4) analyzing whether the fourth image contains the person information or not; if the fourth image contains the personal information, analyzing whether the personal information meets a preset personal state standard or not; and if the character information does not accord with the preset character state standard, forming the first monitoring result corresponding to the learning state of the reader.
7. An artificial intelligence based tutorial system as claimed in claim 6 wherein the character information includes the angle between shoulders and horizontal, the height to width ratio of the mouth, and the duration of eye closure.
8. The artificial intelligence based tutoring system of claim 6, wherein analyzing whether the persona information meets preset persona status criteria comprises:
1) analyzing whether the angle between the shoulders and the horizontal direction is larger than a first preset angle or not; if the angle between the shoulders and the horizontal direction is larger than a first preset angle, judging that the character information does not accord with a preset character state standard;
2) analyzing whether the ratio of the height to the width of the mouth is greater than a preset value; if the ratio of the height to the width of the mouth is larger than a preset value, judging that the character information does not accord with a preset character state standard;
3) analyzing whether the time for which the eyes are continuously closed exceeds a second preset time; if the time for continuously closing the eyes exceeds second preset time, judging that the character information does not accord with preset character state standards;
4) outputting a comprehensive judgment result based on the judgment results of the steps 1) to 3).
9. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the functions of the artificial intelligence based teaching system of any of claims 1-7.
10. A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the functions of the artificial intelligence based teaching system of any of claims 1-7.
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