Personalized education system and method based on artificial intelligence

Abstract

The invention relates to the technical field of interactive education, in particular to a personalized education system and method based on artificial intelligence, and the method comprises the steps: obtaining two progress groups which have been given in a target teaching stage in live teaching; obtaining interaction data of each interaction in the target teaching stage corresponding to each progress group; obtaining an interaction positive index of each interaction in each progress group according to the interaction data; obtaining an interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positive index of each interaction in each progress group; and according to the interaction rhythm adjustment coefficient, adjusting interaction moments of other progress groups which do not start the target teaching stage. According to the invention, the live teaching interaction quality can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of interactive education, and particularly relates to a personalized education system and method based on artificial intelligence. Background Art

[0002] As a new education mode that relies on Internet technology and platforms, online interactive education realizes real-time or non-real-time interaction between teachers and students. It has successfully broken through the dual limitations of traditional education in terms of geography and time, making the distribution of educational resources more balanced and reasonable, and at the same time bringing more flexible and diverse learning paths for students.

[0003] Currently, mainstream education platforms mainly focus on the analysis of data such as students' learning progress and homework completion status, and provide personalized teaching recommendations based on this. However, these platforms largely ignore the interactive experience during live teaching. Due to the lack of an intuitive interactive feeling, it is difficult for teachers to quickly and effectively grasp the teaching effect during the teaching process, nor can they timely understand the learning status of students.

[0004] Existing technologies usually determine the interactive period by statistically counting the number of interactive participants and the threshold of participation duration. However, in the actual situation where the participants in online education have a large mobility, this pre-set period screening method is difficult to meet the actual needs of participants at different levels, resulting in poor interactive quality of live teaching. Summary of the Invention

[0005] In order to solve the technical problem of poor interactive quality in live teaching, the purpose of the present invention is to provide a personalized education system and method based on artificial intelligence, and the specific technical solutions adopted are as follows: In the first aspect of the present invention, a personalized education method based on artificial intelligence is provided, and the method includes: Obtain two progress groups that have completed teaching in the target teaching stage of live teaching; Obtain the interactive data of each interaction in the target teaching stage corresponding to each progress group; Obtain the interactive positive index of each interaction in each progress group according to the interactive data; Obtain the interactive rhythm adjustment coefficient of the target teaching stage according to the interactive positive index of each interaction in each progress group; Adjust the interaction time of other progress groups that have not started the target teaching stage according to the interactive rhythm adjustment coefficient.

[0006] In one embodiment, the interaction data includes: the total number of students, the interaction duration, and the actual response durations of each student who actually participates in the interaction; obtaining the interaction activity index for each interaction in each progress group according to the interaction data includes: Performing the following steps on the interaction data of each interaction in each progress group: Obtaining a response status parameter according to the actual response duration of the current interaction and the total number of students; Obtaining an interference coefficient according to the interaction duration of the current interaction; Obtaining the interaction activity index of the current interaction according to the response status parameter and the interference coefficient.

[0007] In one embodiment, the interaction data further includes: the actual number of students who actually participate in the interaction; obtaining the response status parameter according to the actual response duration of the current interaction and the total number of students includes: Obtaining the first number of people whose actual response duration is less than or equal to the first preset duration; Obtaining the second number of people whose actual response duration is greater than the first preset duration; Obtaining a first ratio according to the first number of people and the total number of students; Obtaining a second ratio according to the second number of people and the total number of students; Obtaining a participation ratio coefficient according to the actual number of people and the total number of students; Obtaining the total response duration according to the actual response duration; Obtaining the response status parameter according to the participation ratio coefficient, the total response duration, the actual number of people, the first ratio, and the second ratio.

[0008] In one embodiment, obtaining the interference coefficient according to the interaction duration of the current interaction includes: Obtaining the reference time required to complete each question in the current interaction; Obtaining the interference coefficient according to the interaction duration and all the reference times.

[0009] In one embodiment, obtaining the interaction rhythm adjustment coefficient for the target teaching stage according to the interaction activity index of each interaction in each progress group includes: Obtaining an interaction transition coefficient corresponding to each adjacent two interactions in each progress group according to the interaction activity indices of each adjacent two interactions in each progress group; Obtaining the same-order participation index of each progress group according to the interaction transition coefficient; Obtaining the interaction rhythm adjustment coefficient according to the same-order participation index.

[0010] In one embodiment, obtaining the interaction transformation coefficient corresponding to each adjacent two interactions of each progress group according to the interaction positive index of each adjacent two interactions of each progress group includes: Performing the following steps on the interaction positive index corresponding to each adjacent two interactions: Obtaining the minimum value in the interaction positive indices of the current adjacent two interactions; Obtaining the absolute value of the difference between the interaction positive indices of the current adjacent two interactions; Obtaining the interaction transformation coefficient of the current adjacent two interactions according to the absolute value of the difference and the minimum value.

[0011] In one embodiment, obtaining the same - order participation index of each progress group according to the interaction transformation coefficient includes: Performing the following steps on the interaction transformation coefficient of each progress group: Obtaining the mean value of all the interaction transformation coefficients of the current progress group; Obtaining the maximum interaction transformation coefficient value among the interaction transformation coefficients; [[ID=D20]] Obtaining the target interaction positive index corresponding to the maximum interaction transformation coefficient value; Obtaining the same - order participation index of the current progress group according to the mean value, the maximum interaction transformation coefficient value, and the target interaction positive index.

[0012] In one embodiment, obtaining the interaction rhythm adjustment coefficient according to the same - order participation index includes: Obtaining the maximum value among the same - order participation indices; Obtaining the interaction rhythm adjustment coefficient according to all the interaction durations corresponding to each progress group and the maximum value among the same - order participation indices.

[0013] In one embodiment, adjusting the interaction moment of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient includes: Performing the following steps on each other progress group: Obtaining the time of the teaching stage that the current other progress group has completed; Obtaining the interaction moment when the current other progress group starts the target teaching stage according to the time and the interaction rhythm adjustment coefficient.

[0014] In a second aspect of the present invention, there is provided a personalized education system based on artificial intelligence, and the system includes: A progress group acquisition module, configured to acquire two progress groups that have completed teaching in the target teaching stage of live teaching; An interaction data acquisition module, configured to acquire interaction data for each interaction in the target teaching stage corresponding to each of the progress groups; An interaction positive index acquisition module, configured to acquire an interaction positive index for each interaction in each of the progress groups according to the interaction data; An interaction rhythm adjustment coefficient acquisition module, configured to acquire an interaction rhythm adjustment coefficient for the target teaching stage according to the interaction positive index for each interaction in each of the progress groups; An adjustment module, configured to adjust the interaction time of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient.

[0015] In one embodiment, the interaction data includes: the total number of students, the interaction duration, and the actual answering durations of each student actually participating in the interaction; specifically, the interaction positive index acquisition module is configured to: Perform the following steps on the interaction data for each interaction in each of the progress groups: Acquire an answering status parameter according to the actual answering duration of the current interaction and the total number of students; Acquire an interference coefficient according to the interaction duration of the current interaction; Acquire the interaction positive index of the current interaction according to the answering status parameter and the interference coefficient.

[0016] In one embodiment, the interaction data further includes: the actual number of students actually participating in the interaction; specifically, the interaction positive index acquisition module is configured to: Acquire a first number of persons whose actual answering duration is less than or equal to a first preset duration; Acquire a second number of persons whose actual answering duration is greater than the first preset duration; Acquire a first ratio according to the first number of persons and the total number of students; Acquire a second ratio according to the second number of persons and the total number of students; Acquire a participation number ratio coefficient according to the actual number of persons and the total number of students; Acquire a total answering duration according to the actual answering duration; Acquire the answering status parameter according to the participation number ratio coefficient, the total answering duration, the actual number of persons, the first ratio, and the second ratio.

[0017] In one embodiment, the interaction positive index acquisition module is specifically configured to: Acquire the reference time required to complete each question in the current interaction; Acquire the interference coefficient according to the interaction duration and all the reference times.

[0018] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically configured to: Obtain the interactive transition coefficient corresponding to each adjacent two interactions of each progress group according to the interactive positive index of each adjacent two interactions; Obtain the same-order participation index of each progress group according to the interactive transition coefficient; Obtain the interactive rhythm adjustment coefficient according to the same-order participation index.

[0019] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically configured to: Perform the following steps on the interactive positive index corresponding to each adjacent two interactions: Obtain the minimum value in the interactive positive indexes of the current adjacent two interactions; Obtain the absolute value of the difference between the interactive positive indexes of the current adjacent two interactions; Obtain the interactive transition coefficient of the current adjacent two interactions according to the absolute value of the difference and the minimum value.

[0020] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically configured to: Perform the following steps on the interactive transition coefficient of each progress group: Obtain the mean value of all the interactive transition coefficients of the current progress group; Obtain the maximum interactive transition coefficient value in the interactive transition coefficients; Obtain the target interactive positive index corresponding to the maximum interactive transition coefficient value; Obtain the same-order participation index of the current progress group according to the mean value, the maximum interactive transition coefficient value and the target interactive positive index.

[0021] In one embodiment, the interactive rhythm adjustment coefficient acquisition module is specifically configured to: Obtain the maximum value in the same-order participation indexes; Obtain the interactive rhythm adjustment coefficient according to all the interactive durations corresponding to each progress group and the maximum value in the same-order participation indexes.

[0022] In one embodiment, the adjustment module is specifically configured to: Perform the following steps on each other progress group: Obtain the time of the teaching stage that the current other progress group has completed; Obtain the interactive moment when the current other progress group starts the target teaching stage according to the time and the interactive rhythm adjustment coefficient.

[0023] The present invention has the following beneficial effects: By analyzing the interaction data of the participating students in the live interactive teaching during the target teaching stage in the live teaching process, the present invention obtains the interaction positive index for each interaction. Based on this index, it can accurately determine the participation willingness of the participating students, and based on the interaction positive index of each interaction, obtains the interaction rhythm adjustment coefficient for the target teaching stage. With this interaction rhythm adjustment coefficient, it adaptively adjusts the interaction time of the target teaching stage of other progress groups that have not carried out the target teaching stage. Since the interaction data of the previous participating students is considered when adjusting the interaction time, the obtained interaction rhythm adjustment coefficient is more accurate, which can improve the student adaptability in the teaching process and improve the teaching quality. That is, since this coefficient is calculated based on the rich and real interaction data of the previous participating students, fully covering the behavior characteristics and feedback of the students in different interaction scenarios, it can comprehensively and accurately reflect the ideal rhythm and mode of interaction in this teaching stage. With this accurate interaction rhythm adjustment coefficient, the present invention can adaptively adjust the interaction time of the target teaching stage of other progress groups that have not carried out the target teaching stage. During the adjustment process, since the interaction data of the previous participating students is always referred to, the setting of the interaction time closely conforms to the actual participation rules and learning needs of the students, thereby further optimizing the accuracy of the interaction rhythm adjustment coefficient. When the interaction rhythm adjustment coefficient is more accurate, the teaching process can more precisely adapt to the learning rhythm and participation willingness of the students. Teachers can flexibly adjust the teaching methods and interaction links according to the actual state of the students, making the teaching activities highly compatible with the needs of the students, greatly improving the student adaptability in the teaching process, and finally achieving a significant improvement in teaching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0025] Figure 1 is the flow of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure One ; Figure 2 is the flow of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure Two ; Figure 3 is the flow of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure Three ; Figure 4 The flowchart of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure Four ; Figure 5 The schematic diagram of the interactive interval duration provided by an embodiment of the present invention; Figure 6 The schematic diagram of the interactive time of different classes provided by an embodiment of the present invention; Figure 7 The flowchart of a personalized education method based on artificial intelligence provided by an embodiment of the present invention Figure Five ; Figure 8 The schematic diagram of the functional modules of a personalized education system based on artificial intelligence provided by an embodiment of the present invention. Detailed implementation manners

[0026] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following combines the accompanying drawings and preferred embodiments to elaborate in detail on a personalized education system and method based on artificial intelligence proposed according to the present invention, its specific implementation manners, structures, features and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0028] The following specifically describes the specific solutions of a personalized education system and method based on artificial intelligence provided by the present invention with reference to the accompanying drawings.

[0029] Existing education platforms mainly focus on the analysis of data such as the learning progress and homework completion status of students to provide personalized teaching recommendations, but they ignore the interactive experience in the live teaching process, resulting in a lack of intuitive interactive links. This kind of neglect makes it difficult for teachers to evaluate the teaching effect in a timely and effective manner during the teaching process and also unable to fully understand the learning status of students. The personalized education system and method based on artificial intelligence provided by the present invention can be applied to existing education platforms. By analyzing the interactive data in each stage of live teaching, the interactive positive index of students can be evaluated, and based on the interactive positive index, an interactive rhythm adjustment coefficient can be obtained. The teaching rhythm can be dynamically adjusted based on the interactive rhythm adjustment coefficient, so as to effectively improve the learning enthusiasm and assist teachers in accurately grasping the learning dynamics of students.

[0030] Please refer to Figure 1, which shows the flowchart of the personalized education method based on artificial intelligence provided by an embodiment of the present invention, as Figure 1 shown, the method includes the following steps S101 - S105: Step S101, obtain two progress groups that have completed the teaching of the target teaching stage in the live teaching.

[0031] The progress group in the present invention can be a group of students with the same or similar learning progress. For example: a class, and one class is a progress group.

[0032] Then in step S101, it is necessary to obtain the classes that have simultaneously completed the teaching of a certain teaching stage.

[0033] For example: Class 1 has completed the teaching of teaching stage 1, Class 2 has also completed the teaching of teaching stage 1, and Class 3 has also completed the teaching of teaching stage 1. Then step 101 can randomly select any two classes from Class 1, Class 2, and Class 3 to execute S102 - S105.

[0034] Step S102, obtain the interaction data of each interaction in the target teaching stage corresponding to each of the progress groups.

[0035] The interaction data includes: the total number of students, the preset interaction duration, the actual number of students actually participating in the interaction, and the actual answering duration of each of the students actually participating in the interaction.

[0036] The present invention is based on artificial intelligence for online interactive education. During the process of online education for users (teachers, students), various interaction data generated will be recorded, including various status data such as teacher live content, questions, homework, and student answers, as well as student emotion, attention, and expression data obtained by AI. Therefore, for the research on the method of online interactive education, the overall interaction positive index can be analyzed by obtaining the interaction data in the teacher's teaching process. The technologies that can be used to obtain data by AI are as follows: Facial expression recognition: Analyze the facial expressions of students to judge whether they are focused, confused, or interested.

[0037] Attention analysis: By analyzing the eye gaze direction, head posture, etc. of students, judge whether they are paying attention to the screen content.

[0038] Emotion classification: Analyze the emotional tendency (positive, negative, neutral) in the students' answers or discussions in the discussion area to understand the students' learning status and enthusiasm for participation.

[0039] Attention analysis, facial expression recognition, and emotion classification belong to common analysis tasks in artificial intelligence, and their technical principles will not be elaborated here.

[0040] During the live teaching process, the teacher distributes questionnaires or pop-up windows to the computer terminals of each student (total number of students ), and the interactive interface is displayed on the student's terminal for the preset interactive duration each time it is sent , that is, the duration of the interactive interface (pop-up window) on the student's terminal is . The system will record each interaction and mark it as , that is, represents the th interaction, and the total number of interactions can also be counted .

[0041] It should be noted that one interaction refers to the pop-up of one pop-up window, and multiple questions can be asked in one interaction.

[0042] The students operate through the computer terminal for the teacher's questions, and the system will record the operation results and actual answering duration of each student according to the operations of different students (if the actual answering duration of the student exceeds the specified answering time , then and always ).

[0043] For the answering parameters of the students' answers to the questions, it is necessary to count the initiative of different students' answers to the teacher. If a student participates in answering, 1 will be added to the number of interaction records of the student. If the student does not participate in answering within the specified answering time , no record will be made.

[0044] It is also necessary to count the actual number of students who actually participate in the interaction, that is, it is necessary to count the number of students who actually participate in answering the questions, denoted as .

[0045] The present invention is a personalized education system and method based on artificial intelligence. Since the teacher is conducting live teaching in the system, to ensure the seriousness of the students and improve the participation of the students, during the teaching process, the teacher may ask questions to the students at any time. Subsequently, the students answer the questions according to the knowledge they have learned. Furthermore, the system analyzes the learning status of the students based on the classroom question data parameters and the completion status of the homework.

[0046] Step S103: Obtain the interaction activity index of each interaction in each progress group according to the interaction data.

[0047] In one embodiment, as Figure 2 shown, the step of obtaining the interaction activity index of each interaction in each progress group according to the interaction data in step S103 includes the following sub-steps S1031-S1033 for the interaction data of each interaction in each progress group: S1031. Obtain the response status parameter based on the actual response duration of the current interaction and the total number of students.

[0048] During the online interactive teaching process, the teacher asks questions to the students. Due to the differences in the mastery of the question content among different students, there are situations such as participating in answering (including fully answering and not fully answering) and not participating in answering during the answering process of the teacher's questions. Therefore, when analyzing the teaching response status, it is necessary to obtain the response status parameter for each answer.

[0049] In one embodiment, obtaining the response status parameter based on the actual response duration of the current interaction and the total number of students in step S1031 includes the following sub-steps A1 - A7: A1. Obtain the number of the first group of people whose actual response duration is less than or equal to the first preset duration.

[0050] A2. Obtain the number of the second group of people whose actual response duration is greater than the first preset duration.

[0051] Among the data of each student who has participated in answering, the better the interactive effect is if the student can complete the question answering timely and accurately. Therefore, when analyzing the participation degree of the students, it is necessary to analyze the efficiency status of different students in answering the questions.

[0052] During the process of completing an answer, based on the time required for each student to answer respectively, set the first preset duration as , when the actual response duration of the students actually participating in the interaction , it is called the reasonable time , when , it is called the general time (when , it exceeds the answering time and is not included in the participation in answering).

[0053] Obtain the number of the first group of people corresponding to the reasonable time and the number of the second group of people corresponding to the general time respectively.

[0054] A3. Obtain the first ratio according to the number of the first group of people and the total number of students, and the formula is: ; Wherein, is the first ratio, is the number of the first group of people, represents the reasonable time, which refers to the time when the actual response duration of the students actually participating in the interaction is less than or equal to the first preset duration, is the total number of students.

[0055] A4. Obtain a second ratio based on the second number of personnel and the total number of trainees.

[0056] ; Wherein, is the second ratio, is the second number of personnel, represents the general time, which refers to the time when the actual answering duration of the trainees actually participating in the interaction is greater than the first preset duration, is the total number of trainees.

[0057] A5. Obtain a participation ratio coefficient based on the actual number of people and the total number of trainees.

[0058] For the participation degree of different trainees in the teacher's questions, after each question is asked, obtain the participation ratio coefficient ( is the actual number of trainees actually participating in the interaction, is the total number of trainees), and then record the obtained ratio coefficients of the number of people for each question number i.

[0059] A6. Obtain the total answering duration based on the actual answering duration.

[0060] A7. Obtain the answering status parameter based on the participation ratio coefficient, the total answering duration, the actual number of people, the first ratio, and the second ratio.

[0061] Analyze the answering status parameter of the current interaction by combining the participation ratio coefficient, the total answering duration, the actual number of people, the first ratio, and the second ratio .

[0062] In the formula, is the number of the interaction, is the th participation ratio coefficient of the interaction, is the sum of the actual answering durations, is the actual number of trainees actually participating in the interaction, is the ratio of the sum of the actual answering durations to the actual number of people, representing the average answering duration, is the comparison coefficient between the first ratio and the second ratio. The larger this value is, the better the answering effect. Use function for normalization processing.

[0063] The above formula is based on the difference relationship between the number of people participating in the answering and the quantity occupied within the answering time. The larger the number of people answering and the shorter the time required for answering, the better the status of this question-and-answer interaction.

[0064] In another embodiment, the answer status parameter of the current interaction can be reflected by the product of the proportion of the student's concentration state and the proportion of the positive emotional tendency of the answer.

[0065] S1032. Obtain the interference coefficient according to the interaction duration of the current interaction.

[0066] In one embodiment, obtaining the interference coefficient according to the interaction duration of the current interaction in step S1032 includes the following sub-steps B1 - B2: B1. Obtain the reference time required to complete each question in the current interaction.

[0067] B2. Obtain the interference coefficient according to the interaction duration and all the reference times.

[0068] For the random questions corresponding to the currently taught knowledge points in the system question bank retrieved by the teacher, there are deviations between the preset expected time of different questions and the interaction operation time of the student. The greater the deviation, the worse the overall interaction effect of the student. Therefore, when directly detecting the live teaching interaction process, it is necessary to analyze the interference coefficient of question thinking on the overall answering efficiency.

[0069] Based on each question in the current interaction (question number), obtain each question in the question bank The reference time required to complete .

[0070] Regarding the difference in the reference time required for different questions in the current interaction and the preset interaction duration of the current interaction , obtain the interference coefficient in each interaction process , reflecting the adaptability between the question asked and the student user, specifically as follows: ; In the formula, is the number of the interaction, n represents the number of questions in an interaction, is the ratio of the reference duration required for each question in the th interaction to the preset interaction duration of the th interaction. The closer this value is to 1, the higher the adaptability of the knowledge point corresponding to the keyword in the currently selected question to the current batch of participating users. represents the preset interaction duration displayed on the student's terminal for each sent interaction interface, that is, the duration of the interaction interface (pop-up window) on the student's terminal.

[0071] Due to the existence of phenomenon, use function for Perform normalization processing so that The value range of is

[0072] S1033. Obtain the interaction positive index of the current interaction according to the response status parameter and the interference coefficient.

[0073] Further, in combination with the interference coefficient Take it as an interference factor to obtain the interaction positive index of the students for the current interaction in the live teaching interaction process : ; In the formula, represents the interaction positive index of the th interaction, represents the response status parameter of the th interaction, represents the interference coefficient of the th interaction, and the result is normalized using the function. The interference coefficient characterizes the suitability between the question asked and the student user. For each interaction process, by analyzing the response status of the interaction process and the student's response time to reflect the suitability between the question and the student in these two aspects, comprehensively reflect the enthusiasm of each interaction process. The larger the value of the interaction positive index, the better the interaction state of the students in the corresponding interaction process, the better the interaction effect, and the more active the students are in the interaction process. The smaller the value of the interaction positive index, the worse the interaction state of the students in the corresponding interaction process, the worse the interaction effect, and the less active the students are in the interaction process.

[0074] Step S104. Obtain the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positive index of each interaction in each progress group.

[0075] In one embodiment, as Figure 3 shown, in step S104, obtaining the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positive index of each interaction in each progress group includes the following sub-steps S1041 - S1043: S1041. Obtain the interaction transition coefficient corresponding to each adjacent two interactions in each progress group according to the interaction positive index of each adjacent two interactions in each progress group.

[0076] In one embodiment, as Figure 4As shown, in step S1041, obtaining the interaction transition coefficient corresponding to each adjacent two interactions of each progress group according to the interaction positive index of each adjacent two interactions of each progress group includes performing the following steps S10411-S10413 on the interaction positive index corresponding to each adjacent two interactions: S10411. Obtain the minimum value in the interaction positive indexes of the current adjacent two interactions.

[0077] S10412. Obtain the absolute value of the difference between the interaction positive indexes of the current adjacent two interactions.

[0078] S10413. Obtain the interaction transition coefficient of the current adjacent two interactions according to the absolute value of the difference and the minimum value.

[0079] In one embodiment, the present invention also provides a method for dividing teaching stages: During the live teaching process of teachers, the frequency and specific stages of interaction in different time periods will affect the interaction effect. Therefore, it is necessary to update and adjust through the overall division of data, so as to obtain better live teaching interaction effects in different teaching stages, thereby improving the interaction state parameters.

[0080] During the live teaching of teachers, the serious state of students' live learning changes in different time periods. Teachers cannot monitor the teaching participants all the time, which leads to changes in the adaptability of the preset interaction plan to the participants during the interactive teaching process due to the change of teaching personnel in multiple interactions.

[0081] Therefore, aiming at the deviation of the adaptability between the preset interaction plan and students in the teaching process of each teacher at different times, based on the change fluctuation of the overall interaction positive index of students during the interactive questioning process in different time stages that have been obtained, the relevance of the keyword content of knowledge points in the teaching process of the same course is screened out.

[0082] The teaching stage in the present invention refers to the difference in knowledge points. For example, Figure 5 As shown, when the same knowledge point is being taught, the interval between interactions is shorter. That is, the interaction interval in the same teaching stage is shorter. When different knowledge points are being taught, due to the time occupied by the knowledge points during the brief description, there will be a longer time interval between the interaction of the new knowledge point and the previous knowledge point. That is, the interaction interval of non-same teaching stages is longer.

[0083] First, regarding the teacher's explanation process, when a knowledge point is finished, questions will be asked about the knowledge point being taught to analyze the relevance of the content of the preset interaction plan participated by the students. Regarding the knowledge point explanation, the keyword of the knowledge point content and the scale of the preset interaction plan reflect the importance of the knowledge point. Therefore, the teaching stage of knowledge explanation is divided according to the deviation of adjacent explanation durations.

[0084] ; Among them, represents the duration deviation between adjacent interactions, is the interval duration between adjacent interactions, represents the average interaction time of adjacent interactions.

[0085] Regarding the marked number of questions, those that are adjacent and are divided into the same teaching stage.

[0086] During the teaching process, the teacher will teach different groups with the same progress and, regarding the same major (occupation), will teach based on the same knowledge body. During the explanation process, different groups with the same progress may have different reactions to the same knowledge body. That is, within different classes, during the interactive explanation by the teacher, the interactive activity index of the students may be different. As Figure 6 shown, the interaction times in different classes are different. Therefore, in order to have a better interactive explanation effect, it is advisable to choose to conduct question-and-answer interactions during the period (stage) when the students have a relatively high degree of enthusiasm.

[0087] First, analyze the differences in the interactive activity indices of the students under each adjacent interaction count within the target teaching stage for each progress group to obtain the interaction conversion coefficient : ; ; In the formula, represents the interaction conversion coefficient at the -th interaction of the first progress group, is the -th interaction, is the interactive activity index when the first progress group conducts the -th interaction, is the interactive activity index when the first progress group conducts the -(th) interaction, is to use the function to obtain the smaller value of the interactive activity indices of adjacent interactions of the first progress group, is the ratio of the difference in the interactive activity indices of adjacent interactions of the first progress group to the smaller value of the interactive activity indices of adjacent interactions; Indicates the interaction transition coefficient corresponding to the th interaction in the second progress group, where is the th interaction, is the interaction positive index when the second progress group conducts the -1th interaction, is the interaction positive index when the second progress group conducts the th interaction, is to obtain the minimum value of the interaction positive index of adjacent interactions of the second progress group by using the function, is the ratio of the difference in the interaction positive index of adjacent interactions of the second progress group to the minimum value of the interaction positive index of adjacent interactions. It should be noted that when obtaining the interaction transition coefficient,

[0088] wherein, it should be noted that the first progress group and the second progress group represent the progress groups corresponding to the completion of the same teaching stage, that is, both represent the progress groups that have completed the target teaching stage and are the closest to the time of the next progress group that will complete the target teaching stage.

[0089] S1042. Obtain the same-order participation index of each of the progress groups according to the interaction transition coefficient.

[0090] In one embodiment, as Figure 7 shown, in step S1042, obtaining the same-order participation index of each of the progress groups according to the interaction transition coefficient includes performing the following steps S10421-S10424 on the interaction transition coefficient of each of the progress groups: S10421. Obtain the mean value of all the interaction transition coefficients of the current progress group.

[0091] S10422. Obtain the maximum interaction transition coefficient value among the interaction transition coefficients.

[0092] S10423. Obtain the target interaction positive index corresponding to the maximum interaction transition coefficient value.

[0093] S10424. Obtain the same-order participation index of the current progress group according to the mean value, the maximum interaction transition coefficient value, and the target interaction positive index.

[0094] Regarding the interaction positive index of the students towards the teacher within the target teaching stage, by analyzing the change in interaction enthusiasm presented during the questions asked within the target teaching stage, obtain the same-order participation index of each progress group within the target teaching stage : ; ; In the formula, represents the same-order participation index of the first progress group within the target teaching stage, is the mean value of all interaction transition coefficients of the first progress group within the target teaching stage, , representing the mean value of the sum of all interaction transition coefficients of the first progress group within the target teaching stage, represents the th interaction, represents the number of interactions, represents the first progress group at the th interaction corresponding interaction transition coefficient, , representing the maximum value of the interaction transition coefficients of the first progress group within the target teaching stage, represents the interaction transition coefficient at the second interaction of the first progress group, represents the interaction transition coefficient at the third interaction of the first progress group, represents the first progress group at the th interaction interaction transition coefficient, is the proportion of the difference between the maximum value of the interaction transition coefficients and the mean value of the interaction transition coefficients of the first progress group within the target education stage in the mean value of the interaction transition coefficients, representing the overall interaction effect of the target teaching stage. The smaller this value is, the better the overall effect.

[0095] In the first progress group within the target teaching stage, one interaction corresponds to one interaction transition coefficient, and at the same time one interaction also corresponds to one interaction positive index. Obtain the number of interactions corresponding to the maximum value of the interaction transition coefficients of the first progress group within the target teaching stage, and record the interaction positive index corresponding to this interaction as the target interaction positive index corresponding to the maximum interaction transition coefficient value . Based on this, is corresponding target interaction positive index, representing the actual participation index, is its ( corresponding target interaction positive index) reciprocal, so that the result is positively correlated with changes. Use function to normalize the result, so that the larger the value, the better the participation effect of the target teaching stage. represents the same-order participation index of the second progress group within the target teaching stage, is the mean value of all interaction transition coefficients of the second progress group within the target teaching stage, , representing the mean value of the sum of all interaction transition coefficients of the second progress group within the target teaching stage, represents the The next interaction, indicating the number of interactions, indicating that the second progress group is at the interaction conversion coefficient corresponding to the next interaction, indicating the maximum value of the interaction conversion coefficient of the second progress group within the target teaching stage, indicating the interaction conversion coefficient at the second interaction of the second progress group, indicating the interaction conversion coefficient at the third interaction of the second progress group, indicating that the second progress group is at the interaction conversion coefficient at the next interaction, is the proportion of the difference between the maximum value of the interaction conversion coefficient and the average value of the interaction conversion coefficient of the second progress group within the target education stage in the average value of the interaction conversion coefficient, indicating the overall interaction effect of the target teaching stage. The smaller this value is, the better the overall effect is. The corresponding target interaction positive index, indicating the actual participation index, is the reciprocal of it ( the corresponding target interaction positive index), making the result positively correlated with the change. Use function to normalize the result, making the better the participation effect of the target teaching stage when the value is larger.

[0096] S1043. Obtain the interaction rhythm adjustment coefficient according to the same-order participation index.

[0097] In one embodiment, obtaining the interaction rhythm adjustment coefficient according to the same-order participation index in step S1043 includes the following sub-steps S10431 - S10432: S10431. Obtain the maximum value in the same-order participation index.

[0098] Obtain the maximum value in the same-order participation index of the second progress group and the same-order participation index of the first progress group.

[0099] S10432. Obtain the interaction rhythm adjustment coefficient according to all the interaction durations corresponding to each progress group and the maximum value in the same-order participation index.

[0100] Obtain the overall duration of different interactions of each progress group according to the progress of teaching time in the classroom teaching process.

[0101] Based on the same-order participation index of the target teaching stage during the teaching process of the first progress group , the same-order participation index and the overall duration of the second progress group within the target teaching stage , obtain the interactive rhythm adjustment coefficient : ; In the formula, is the interactive rhythm adjustment coefficient during the teaching process of the target teaching stage in the future, is the same-level participation index of the first progress group in the target teaching stage, is the same-level participation index of the second progress group in the target teaching stage, is the overall duration of the first progress group in the target teaching stage, that is, the sum of all interactive durations corresponding to the first progress group during the development of the target teaching stage, is the overall duration of the second progress group in the target teaching stage, that is, the sum of all interactive durations corresponding to the second progress group during the development of the target teaching stage, and use The function screens the classroom effects with better participation status, and then, based on the time difference coefficient of the classroom, as the adjustment parameter.

[0102] Step S105. Adjust the interaction time of other progress groups that have not started the target teaching stage according to the interactive rhythm adjustment coefficient.

[0103] In one embodiment, in step S105, adjusting the interaction time of other progress groups that have not started the target teaching stage according to the interactive rhythm adjustment coefficient includes performing the following steps S1051 - S1052 for each other progress group: S1051. Obtain the time of the teaching stage that the current other progress group has completed.

[0104] S1052. Obtain the interaction time when the current other progress group starts the target teaching stage according to the time and the interactive rhythm adjustment coefficient.

[0105] When adjusting the interaction time of other progress groups that have not carried out the target teaching stage during the development of the target teaching stage, the teaching time of the teaching stage that the progress group to be adjusted has completed can be obtained first , and then through Obtain the interaction time when the other progress group to be adjusted executes the target teaching stage, and the teacher teaches the progress group to be adjusted according to the obtained interaction time . In the formula, is the interaction time of the target teaching stage of the other progress group to be adjusted, is the interactive rhythm adjustment coefficient during the teaching process of the target teaching stage, It is the teaching time of the teaching stage completed by the progress group to be adjusted.

[0106] For example, assume that the target teaching stage is the third teaching stage, and the progress groups that have carried out the third teaching stage are the first progress group and the second progress group. If there are multiple progress groups that have carried out the third teaching stage, then obtain the two progress groups that are about to start the third teaching stage time away from the third progress group for analysis. The progress group that has not carried out the third teaching stage is the third progress group, and the third progress group has completed the teaching of the first teaching stage and the second teaching stage. Then, first obtain the interaction rhythm adjustment coefficient of the third teaching stage according to steps S101 - S104 , when carrying out the third teaching stage for the third progress group, first obtain the teaching time of the first teaching stage and the second teaching stage completed by the third progress group , and then through the formula obtain the interaction moment when the third progress group starts the third teaching stage , finally, when the interaction moment arrives, the teacher will carry out the teaching of the third teaching stage for the third progress group. For example, the teaching of the third teaching stage can be that the teacher mobilizes random questions from the database question bank corresponding to the target teaching stage to interact with the students for teaching and explanation.

[0107] The obtaining of the interaction moment when the current other progress groups start the target teaching stage according to the time and the interaction rhythm adjustment coefficient can also be realized by a neural network. Train a regression neural network, such as an RNN, to predict the interaction moment when the current other progress groups start the target teaching stage. The specific neural network training is a well-known technology, and the specific process is not elaborated here.

[0108] By analyzing the interaction data of the live interactive teaching of the participating students during the target teaching stage in the live teaching process, the present invention obtains the interaction positive index for each interaction. Based on this index, the participation willingness of the participating students can be accurately determined. And according to the interaction positive index of each interaction, the interaction rhythm adjustment coefficient of the target teaching stage is obtained. By virtue of this interaction rhythm adjustment coefficient, the interaction moments of the target teaching stage of other progress groups that have not carried out the target teaching stage are adaptively adjusted. Since the interaction data of the previous participating students is considered when adjusting the interaction moments, the obtained interaction rhythm adjustment coefficient is more accurate, which can improve the student adaptability in the teaching process and enhance the teaching quality. That is to say, since this coefficient is calculated based on the rich and real interaction data of the previous participating students, fully covering the behavioral characteristics and feedback of the students in different interaction scenarios, it can comprehensively and accurately reflect the ideal rhythm and mode of interaction in this teaching stage. By virtue of this accurate interaction rhythm adjustment coefficient, the present invention can adaptively adjust the interaction moments of the target teaching stage of other progress groups that have not carried out the target teaching stage. During the adjustment process, because the interaction data of the previous participating students is always referred to, the setting of the interaction moments closely conforms to the actual participation rules and learning needs of the students, thereby further optimizing the accuracy of the interaction rhythm adjustment coefficient. When the interaction rhythm adjustment coefficient is more accurate, the teaching process can more precisely adapt to the learning rhythm and participation willingness of the students. The teacher can flexibly adjust the teaching method and interaction links according to the actual state of the students, making the teaching activities highly compatible with the needs of the students, greatly improving the student adaptability in the teaching process, and finally achieving a significant improvement in the teaching quality.

[0109] In addition, the present invention can also well cope with the interaction quality in the case of large personnel mobility in online education. This is because when analyzing the interaction data of the participating students during the target teaching stage in the live teaching, the obtained interaction positive index and interaction rhythm adjustment coefficient completely originate from the real performance of the students actually participating in the interaction. The interaction behaviors of these students comprehensively reflect their states and enthusiasm during the learning process. Therefore, based on these data indicators obtained from the real performance, the participation patterns and needs of the students can be accurately understood. In the case of large personnel mobility in new student education, the interaction performance data of the previous actual participating students provides strong support for the optimization of the interaction link. On the one hand, according to the interaction positive index and interaction rhythm adjustment coefficient, the interaction time can be flexibly and reasonably arranged to ensure that the interaction link conforms to the participation habits and learning states of the students, avoiding the disconnection between the interaction and the students due to personnel flow. On the other hand, these data help the teacher to more accurately grasp the teaching rhythm and timely adjust the teaching strategy according to the actual feedback of the students, thereby effectively improving the interaction quality and enabling the online teaching to remain efficient in the complex and changeable personnel flow environment.

[0110] The present invention also includes the following implementation manners. First, obtain the interaction data of two progress groups during the live teaching process, and based on the interaction data, obtain the interaction positive index of each interaction of each progress group (the calculation method of the interaction positive index is similar to that in step S103 of the above embodiment and will not be elaborated here). Then, through the teaching stage division method in step S1041, divide the activities of each progress group into different teaching stages. Then, for the two progress groups in each teaching stage, obtain the interaction rhythm adjustment coefficient (the calculation method of the interaction rhythm adjustment coefficient is similar to that in step S104 of the above embodiment and will not be elaborated here). Then, based on the obtained interaction rhythm adjustment coefficient of the corresponding teaching stage, adjust the interaction moments of other progress groups that have not carried out this teaching stage.

[0111] In the present invention, assume that the live broadcast sequence is that progress group 1 finishes the first teaching stage, then progress group 2 broadcasts the first teaching stage. After progress group 2 finishes the first teaching stage, then progress group 3 broadcasts the first teaching stage. After progress group 3 finishes the first teaching stage, then progress group 4 broadcasts the first teaching stage, and so on. Then, when determining the interaction rhythm adjustment coefficient, after both progress group 1 and progress group 2 have completed the first teaching stage, obtain the first interaction rhythm adjustment coefficient through the interaction data of progress group 1 and progress group 2. When progress group 3 broadcasts the first teaching stage, adjust the interaction moment of progress group 3 broadcasting the first teaching stage according to the first interaction rhythm adjustment coefficient. After progress group 3 has completed the first teaching stage, obtain the second interaction rhythm adjustment coefficient through the interaction data of progress group 2 and progress group 3. When progress group 4 broadcasts the first teaching stage, adjust the interaction moment of progress group 4 broadcasting the first teaching stage according to the second interaction rhythm adjustment coefficient, and so on.

[0112] Based on the same inventive concept, an embodiment of the present application also provides an artificial intelligence-based personalized education system for implementing the above-mentioned artificial intelligence-based personalized education method. The implementation solutions provided by this system for solving problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the following artificial intelligence-based personalized education system can refer to the limitations on the artificial intelligence-based personalized education method in the above text and will not be elaborated here.

[0113] In an exemplary embodiment, as Figure 8 shown, there is provided an artificial intelligence-based personalized education system including: A progress group acquisition module 11, configured to acquire two progress groups that have completed teaching in the target teaching stage during live teaching; An interaction data acquisition module 12, configured to acquire the interaction data of each interaction in the target teaching stage corresponding to each of the progress groups; The interaction positive index acquisition module 13 is used to acquire the interaction positive index of each interaction in each progress group according to the interaction data; The interaction rhythm adjustment coefficient acquisition module 14 is used to acquire the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positive index of each interaction in each progress group; The adjustment module 15 is used to adjust the interaction time of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient.

[0114] In one embodiment, the interaction data includes: the total number of students, the interaction duration, and the actual answering duration of each student who actually participates in the interaction; the interaction positive index acquisition module is specifically used for: Perform the following steps on the interaction data of each interaction in each progress group: Obtain the answering state parameter according to the actual answering duration of the current interaction and the total number of students; Obtain the interference coefficient according to the interaction duration of the current interaction; Obtain the interaction positive index of the current interaction according to the answering state parameter and the interference coefficient.

[0115] In one embodiment, the interaction data further includes: the actual number of students who actually participate in the interaction; the interaction positive index acquisition module is specifically used for: Obtain the first number of people whose actual answering duration is less than or equal to the first preset duration; Obtain the second number of people whose actual answering duration is greater than the first preset duration; Obtain the first ratio according to the first number of people and the total number of students; Obtain the second ratio according to the second number of people and the total number of students; Obtain the participation ratio coefficient according to the actual number of people and the total number of students; Obtain the total answering duration according to the actual answering duration; Obtain the answering state parameter according to the participation ratio coefficient, the total answering duration, the actual number of people, the first ratio, and the second ratio.

[0116] In one embodiment, the interaction positive index acquisition module is specifically used for: Obtain the reference time required to complete each question in the current interaction; Obtain the interference coefficient according to the interaction duration and all the reference times.

[0117] In one embodiment, the interaction rhythm adjustment coefficient acquisition module is specifically used for: Obtain the interaction transformation coefficient corresponding to each adjacent pair of interactions of each of the progress groups according to the interaction positive index of each adjacent pair of interactions; Obtain the same - order participation index of each of the progress groups according to the interaction transformation coefficient; Obtain the interaction rhythm adjustment coefficient according to the same - order participation index.

[0118] In one embodiment, the interaction rhythm adjustment coefficient obtaining module is specifically configured to: Perform the following steps on the interaction positive index corresponding to each adjacent pair of interactions: Obtain the minimum value among the interaction positive indexes of the current adjacent pair of interactions; Obtain the absolute value of the difference between the interaction positive indexes of the current adjacent pair of interactions; Obtain the interaction transformation coefficient of the current adjacent pair of interactions according to the absolute value of the difference and the minimum value.

[0119] In one embodiment, the interaction rhythm adjustment coefficient obtaining module is specifically configured to: Perform the following steps on the interaction transformation coefficient of each of the progress groups: Obtain the mean value of all the interaction transformation coefficients of the current progress group; Obtain the maximum interaction transformation coefficient value among the interaction transformation coefficients; Obtain the target interaction positive index corresponding to the maximum interaction transformation coefficient value; Obtain the same - order participation index of the current progress group according to the mean value, the maximum interaction transformation coefficient value, and the target interaction positive index.

[0120] In one embodiment, the interaction rhythm adjustment coefficient obtaining module is specifically configured to: Obtain the maximum value among the same - order participation indexes; Obtain the interaction rhythm adjustment coefficient according to all the interaction durations corresponding to each of the progress groups and the maximum value among the same - order participation indexes.

[0121] In one embodiment, the adjustment module is specifically configured to: Perform the following steps on each of the other progress groups: Obtain the time of the teaching stage that the current other progress group has completed; Obtain the interaction moment when the current other progress group starts the target teaching stage according to the time and the interaction rhythm adjustment coefficient.

[0122] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. An artificial intelligence-based personalized education method, characterized in that, The method includes: Obtaining two progress groups that have completed the teaching in the target teaching stage of live teaching; Obtaining the interaction data of each interaction in the target teaching stage corresponding to each of the progress groups; Obtaining the interaction positive index of each interaction in each of the progress groups according to the interaction data; Obtaining the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positive index of each interaction in each of the progress groups; Adjusting the interaction moments of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient; The interaction data includes: the total number of students, the interaction duration, and the actual answering durations of each student who actually participates in the interaction; the obtaining the interaction positive index of each interaction in each of the progress groups according to the interaction data includes: Performing the following steps on the interaction data of each interaction in each of the progress groups: Obtaining a response status parameter according to the actual answering duration and the total number of students of the current interaction; Obtaining an interference coefficient according to the interaction duration of the current interaction; Obtaining the interaction positive index of the current interaction according to the response status parameter and the interference coefficient.

2. The personalized education method based on artificial intelligence according to claim 1, wherein The interaction data further includes: the actual number of students who actually participate in the interaction; the obtaining the response status parameter according to the actual answering duration and the total number of students of the current interaction includes: Obtaining the number of the first group of people whose actual answering duration is less than or equal to the first preset duration; Obtaining the number of the second group of people whose actual answering duration is greater than the first preset duration; Obtaining a first ratio according to the number of the first group of people and the total number of students; Obtaining a second ratio according to the number of the second group of people and the total number of students; Obtaining a participation number ratio coefficient according to the actual number of people and the total number of students; Obtaining the total answering duration according to the actual answering duration; Obtaining the response status parameter according to the participation number ratio coefficient, the total answering duration, the actual number of people, the first ratio, and the second ratio.

3. The personalized education method based on artificial intelligence according to claim 2, characterized in that The obtaining the interference coefficient according to the interaction duration of the current interaction includes: Obtaining the reference time required to complete each question in the current interaction; Obtaining the interference coefficient according to the interaction duration and all the reference times.

4. The personalized education method based on artificial intelligence according to claim 3, characterized in that, The obtaining the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positive index of each interaction in each of the progress groups includes: Obtaining the interaction transition coefficient corresponding to each adjacent two interactions of each of the progress groups according to the interaction positive index of each adjacent two interactions of each of the progress groups; Obtaining the same-order participation index of each of the progress groups according to the interaction transition coefficient; Obtaining the interaction rhythm adjustment coefficient according to the same-order participation index.

5. The personalized education method based on artificial intelligence according to claim 4, characterized in that The obtaining the interaction transition coefficient corresponding to each adjacent two interactions of each of the progress groups according to the interaction positive index of each adjacent two interactions of each of the progress groups includes: Performing the following steps on the interaction positive index corresponding to each adjacent two interactions: Obtaining the minimum value of the interaction positive index of the current adjacent two interactions; Obtaining the absolute value of the difference between the interaction positive index of the current adjacent two interactions; Obtain the interaction transition coefficient of the current two adjacent interactions according to the absolute value of the difference and the minimum value.

6. The personalized education method based on artificial intelligence according to claim 5, characterized in that The obtaining of the same-order participation indexes of each progress group according to the interaction transition coefficient includes: Perform the following steps on the interaction transition coefficient of each progress group: Obtain the mean value of all the interaction transition coefficients of the current progress group; Obtain the maximum value of the interaction transition coefficients; Obtain the target interaction positive index corresponding to the maximum value of the interaction transition coefficients; Obtain the same-order participation index of the current progress group according to the mean value, the maximum value of the interaction transition coefficients, and the target interaction positive index.

7. The personalized education method based on artificial intelligence according to claim 5, characterized in that, The obtaining of the interaction rhythm adjustment coefficient according to the same-order participation index includes: Obtain the maximum value in the same-order participation indexes; Obtain the interaction rhythm adjustment coefficient according to all the interaction durations corresponding to each progress group and the maximum value in the same-order participation indexes.

8. The personalized education method based on artificial intelligence according to claim 1, wherein, The adjustment of the interaction moments of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient includes: Perform the following steps on each other progress group: Obtain the time of the teaching stage that the current other progress group has completed; Obtain the interaction moment when the current other progress group starts the target teaching stage according to the time and the interaction rhythm adjustment coefficient.

9. The personalized education system based on artificial intelligence is characterized in that, The system includes: A progress group obtaining module, configured to obtain two progress groups that have completed teaching in the target teaching stage of live teaching; An interaction data obtaining module, configured to obtain the interaction data of each interaction in the target teaching stage corresponding to each progress group; An interaction positive index obtaining module, configured to obtain the interaction positive index of each interaction in each progress group according to the interaction data; An interaction rhythm adjustment coefficient obtaining module, configured to obtain the interaction rhythm adjustment coefficient of the target teaching stage according to the interaction positive index of each interaction in each progress group; An adjustment module, configured to adjust the interaction moments of other progress groups that have not started the target teaching stage according to the interaction rhythm adjustment coefficient; The interaction data includes: the total number of students, the interaction duration, and the actual answering durations of each student who actually participates in the interaction; specifically, the interaction positive index obtaining module is configured to: Perform the following steps on the interaction data of each interaction in each progress group: Obtain a response status parameter according to the actual answering duration of the current interaction and the total number of students; Obtain an interference coefficient according to the interaction duration of the current interaction; Obtain the interaction positive index of the current interaction according to the response status parameter and the interference coefficient.

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