CN116453387B - An AI intelligent teaching robot control system and method - Google Patents

Abstract

The invention belongs to the technical field of teaching robots, and particularly relates to an AI intelligent teaching robot control system and method, wherein the system comprises the following steps of. According to the invention, the attention of students can be concentrated by setting the questioning information, meanwhile, the quality score of the current period of test teaching can be calculated according to the accuracy of the response information of the students, a corresponding review plan can be generated according to the quality score of the teaching, and the teaching content of the subsequent teaching video can be regulated and controlled in real time according to the execution time of the review plan, so that the playing of the teaching video can be completed within the teaching plan, and the intelligent teaching robot can be ensured to improve the learning effect of the students and not to influence the teaching plan after being put into use.

Description

An AI intelligent teaching robot control system and method

Technical field

The invention belongs to the technical field of teaching robots, and specifically relates to an AI intelligent teaching robot control system and method.

Background technique

Education is an activity that cultivates people with purpose and plan. It is the cornerstone of our country's development. However, with the rapid development of artificial intelligence and computer technology, intelligent teaching robots have gradually appeared in the field of education. In schools, intelligent teaching robots It can assist teachers in teaching students knowledge. At home, intelligent teaching robots can help parents tutor students with homework, review learned knowledge, etc. Intelligent teaching robots are often equipped with a display screen for playing teaching pictures. Compared with teachers or Parents use narration to teach. Video teaching can fully reflect the teaching scene according to the teaching content, increase the interest of the teaching content, and attract students' attention. This will undoubtedly increase the students' receptive ability and improve the students' ability to accept the teaching. learning outcomes.

In the existing technology, most intelligent teaching robots play preset teaching videos according to inherent programs. However, students are prone to distraction during class, and they cannot understand the students by just playing teaching videos. If they master the teaching content, and continue to play the teaching video according to the established procedures, some students will be disconnected from the previous teaching content, which will undoubtedly greatly reduce the expected teaching quality. Based on this, this program proposes a method that can An intelligent teaching robot control method that tests teaching quality and regulates subsequent teaching videos in real time based on teaching quality.

Contents of the invention

The purpose of the present invention is to provide an AI intelligent teaching robot control system and method that can test the teaching quality and regulate subsequent teaching videos in real time according to the teaching quality, so that after the intelligent teaching robot is put into use, it can not only ensure that students' learning effects are improved, but also There will be no impact on the teaching plan.

The technical solutions adopted by the present invention are as follows:

An AI intelligent teaching robot control method, including:

Obtain the student group of the teaching point and match the teaching video data set according to the student group;

Obtain student needs and retrieve corresponding teaching videos from the teaching video data set based on student needs;

Obtain the basic information of each student in the student group and summarize it into a basic information set;

Obtain the question information in the teaching video and extract responding students based on the basic information set;

Obtain the response information corresponding to the question information answered by all the responding students, and calculate the accuracy of the response information in real time;

Input the accuracy rate of the response information into the data conversion model, and calibrate the conversion result as a teaching quality score;

Obtain evaluation thresholds and compare with said teaching quality score;

If the teaching quality score is less than the evaluation threshold, the teaching quality effect is determined to be poor, and the teaching video is played again;

If the teaching quality score is greater than or equal to the evaluation threshold, then the difference between the teaching quality score and the evaluation threshold is measured and calibrated as the evaluation difference, and the teaching video continues to be played;

The evaluation interval is obtained, compared with the evaluation difference, a review plan is output according to the comparison result, and the teaching content of the next teaching video is adjusted according to the review plan.

In a preferred solution, the step of obtaining the student group of the teaching point and matching the teaching video data set according to the student group includes:

Obtain the geographical location of the teaching point and match all teaching videos that match the local teaching characteristics;

Obtain the specific school stage of the student group, filter the teaching videos according to the school stage, and summarize the screening results into a teaching video data set;

Wherein, the teaching robot includes an interactive screen, and all the teaching videos in the teaching video data set can be released through the interactive screen.

In a preferred solution, the step of obtaining question information in the teaching video and extracting student responses based on the basic information set includes:

Obtain the basic information of each student from the basic information set, where the basic information includes the student's name and academic performance;

Obtain the classification threshold and compare it with the learning performance, classify the student group into multiple sampling intervals, and then arrange the multiple sampling intervals in order from high to low learning performance;

According to the arrangement order of the sampling intervals, a sampling plan is generated, and responding students are selected from the sampling intervals according to the sampling plan.

In a preferred solution, the step of generating a sampling plan based on the arrangement sequence of the sampling intervals includes:

Obtain the duration of the teaching video and the time nodes at which the question information is distributed in the teaching video, and calibrate them as question nodes;

Obtain the number of question contents in the question information, wherein the question information includes multiple question contents, and the number of question contents is not less than 3;

Obtain the center node of the teaching video and compare it with the question node;

If the question node is distributed after the central node, the question content is distributed in the order of the sampling interval;

If the question node is distributed before the central node, the question content is distributed in the reverse order of the sampling interval;

Wherein, if the number of the question content is higher than the number of sampling intervals, repeated allocation will be performed according to the order of allocation of the question content.

In a preferred solution, the step of obtaining the response information corresponding to the question information answered by all the responding students and calculating the accuracy of the response information in real time includes:

Get response information;

Obtain the standard answer corresponding to the question information, compare it with the response information, obtain the repetition rate between the response information and the standard answer, and calibrate it as a parameter to be evaluated;

Obtain the standard parameters corresponding to the standard answer and compare them with the parameters to be evaluated, where each of the standard parameters corresponds to one standard parameter;

If the value of the parameter to be evaluated is less than the standard parameter, it is determined that the response information is incorrect;

If the value of the parameter to be evaluated is greater than or equal to the standard parameter, it is determined that the response information is correct;

Obtain the measurement function, input the number of correct response information and the number of question information into the measurement function, and calibrate the measurement result as the accuracy of the response information.

In a preferred solution, after the response information is determined to be correct, the basic information of the student corresponding to the response information is obtained and filtered out from the sampling interval;

After the response information is determined to be wrong, the basic information of the student corresponding to the response information is obtained and retained within the sampling interval.

In a preferred solution, the step of inputting the accuracy of the response information into the data conversion model and calibrating the conversion result as a teaching quality score includes:

Obtain the evaluation interval from the data conversion model, where the evaluation interval is (0, a], (a, b], (b, c]...;

Obtain the teaching quality score corresponding to each evaluation interval;

The accuracy of the response information is compared with the evaluation interval, and the corresponding teaching quality score is output.

In a preferred solution, the step of obtaining the evaluation interval and comparing it with the evaluation difference, then outputting a review plan based on the comparison results, and then adjusting the teaching content of the next teaching video based on the review plan includes:

Obtain the evaluation interval corresponding to the evaluation difference and output the review plan. The review plan includes repeatedly playing the teaching video, repeatedly setting the question information and briefly discussing the video;

Obtain the review duration of the review plan and the teaching duration of the next teaching period, and use the difference between the teaching duration and the review duration as the effective duration;

Obtain the teaching content of the next teaching video, the teaching content includes necessary teaching content and non-essential teaching content, and the non-essential teaching content can be screened out from the next teaching video;

Mark the time occupied by the necessary teaching content to the necessary time and compare it with the effective time;

If the effective duration is less than the necessary duration, it means that the necessary teaching content cannot be fully played, and the necessary teaching content will be postponed to the next teaching video, and the non-essential teaching content in the next teaching video will be screened. remove;

If the effective duration is greater than or equal to the necessary duration, it means that the necessary teaching content can be fully played, and there is no need to postpone the necessary teaching content to the next teaching video.

The invention also provides an AI intelligent teaching robot control system, applied to the above-mentioned AI intelligent teaching robot control method, including:

A first acquisition module, the first acquisition module is used to acquire the student group of the teaching point and match the teaching video data set according to the student group;

The video calling module obtains student needs and retrieves corresponding teaching videos from the teaching video data set based on student needs;

a second acquisition module, the second acquisition module is used to acquire the basic information of each student in the student group and summarize it into a basic information set;

An extraction module, the extraction module is used to obtain the question information in the teaching video, and extract responding students according to the basic information set;

A calculation module, the calculation module is used to obtain the response information corresponding to the question information answered by all the responding students, and calculate the accuracy of the response information in real time;

A data conversion module, the data conversion module is used to input the accuracy of the response information into the data conversion model, and calibrate the conversion results as a teaching quality score;

A comparison module, which is used to obtain the evaluation threshold and compare it with the teaching quality score;

If the teaching quality score is less than the evaluation threshold, the teaching quality effect is determined to be poor, and the teaching video is played again;

If the teaching quality score is greater than or equal to the evaluation threshold, then the difference between the teaching quality score and the evaluation threshold is measured and calibrated as the evaluation difference, and the teaching video continues to be played;

A control module, the control module is used to obtain the evaluation interval, compare it with the evaluation difference, output a review plan according to the comparison result, and then control the teaching content of the next teaching video according to the review plan.

And, an AI intelligent teaching robot control terminal, including:

at least one processor;

and a memory communicatively connected to the at least one processor;

Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor, so that the at least one processor can execute the above-mentioned AI intelligent teaching robot control. method.

The technical effects achieved by the present invention are:

This invention improves students' tension by setting question information so that students can focus their attention. At the same time, the current experimental teaching quality score can be calculated based on the accuracy of the students' response information, and corresponding review can be generated based on the teaching quality score. plan, and then adjust the teaching content of subsequent teaching videos in real time according to the execution time of the review plan, so that the teaching video can be played within the teaching plan, ensuring that after the intelligent teaching robot is put into use, it can not only improve students' learning effects, but also There will be no impact on the teaching plan.

Description of drawings

Figure 1 is a flow chart of the method provided by the present invention;

Figure 2 is a system module diagram provided by the present invention.

Detailed ways

In order to make the above objects, features and advantages of the present invention more obvious and understandable, the specific implementation modes of the present invention will be described in detail below with reference to the accompanying drawings.

Many specific details are set forth in the following description to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described here. Those skilled in the art can do so without departing from the connotation of the present invention. Similar generalizations are made, and therefore the present invention is not limited to the specific embodiments disclosed below.

Second, reference herein to "one embodiment" or "an embodiment" refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. “In a preferred embodiment” appearing in different places in this specification does not all refer to the same embodiment, nor is it a separate or selective embodiment that is mutually exclusive with other embodiments.

Referring to Figures 1 and 2, the present invention provides an AI intelligent teaching robot control method, including:

S1. Obtain the student group of the teaching point and match the teaching video data set according to the student group;

S2. Obtain student needs and retrieve corresponding teaching videos from the teaching video data set according to student needs;

S3. Obtain the basic information of each student in the student group and summarize it into a basic information set;

S4. Obtain the question information in the teaching video and extract the answering students based on the basic information set;

S5. Obtain the response information corresponding to the question information answered by all responding students, and calculate the accuracy of the response information in real time;

S6. Input the accuracy of the response information into the data conversion model, and calibrate the conversion results as teaching quality scores;

S7. Obtain the evaluation threshold and compare it with the teaching quality score;

If the teaching quality score is less than the evaluation threshold, the teaching quality is judged to be poor, and the teaching video is replayed;

If the teaching quality score is greater than or equal to the evaluation threshold, the difference between the teaching quality score and the evaluation threshold is calculated and calibrated as the evaluation difference, and the teaching video continues to be played;

S8. Obtain the evaluation interval, compare it with the evaluation difference, output a review plan based on the comparison results, and then adjust the teaching content of the next teaching video according to the review plan.

As mentioned in steps S1-S8 above, with the rapid development of artificial intelligence and computer technology, intelligent teaching robots have gradually appeared in the field of education. In schools, they can assist teachers in teaching students knowledge. At home, It can help students with homework, review learned knowledge, etc. In order to ensure that the teaching content is interesting and attract students' attention, intelligent teaching robots are often equipped with a display screen for playing teaching pictures. Compared with teachers or parents who use narration Teaching method, video teaching can fully reflect the teaching picture according to the teaching content, which will undoubtedly increase the students' receptive ability and improve the students' learning effect. Based on this, this plan determines the student group of the teaching point. , will match the teaching video data set that is consistent with the teaching plan of the teaching point, and then determine the teaching video that needs to be played according to the students' learning needs. In order to ensure the student's learning effect, question information is interspersed in the teaching video. These question information need to be set based on the played teaching content, and when the question information is sent out, responding students will be randomly selected from the student group to answer the questions, and then the accuracy of these responding students' answering questions will be counted and summarized, and through data conversion The model converts the accuracy rate into a teaching quality score, and an evaluation threshold is preset in this implementation. The evaluation threshold is used to compare with the teaching quality score to determine the teaching quality evaluation effect, and based on the evaluation effect Determine whether to replay the teaching video and whether to generate a corresponding review plan. Under the premise that the review plan is generated, the teaching content of the next teaching video can also be adjusted so that the teaching video can be played within the teaching plan. After the intelligent teaching robot is put into use, it can not only achieve the teaching purpose, but also will not affect the teaching plan.

In a preferred implementation, the steps of obtaining the student group of the teaching point and matching the teaching video data set according to the student group include:

S101. Obtain the geographical location of the teaching point and match all teaching videos that meet the local teaching characteristics;

S102. Obtain the specific academic period of the student group, filter the teaching videos according to the academic period, and summarize the screening results into a teaching video data set;

Among them, the teaching robot includes an interactive screen, and all teaching videos in the teaching video data set can be released through the interactive screen.

As mentioned in the above steps S101-S102, the teaching materials used may be different for different regions. For example, the teaching materials implemented in our country are the Human Education Edition, the Jiangsu Education Edition, the Beijing Normal University Edition, and the Hunan Education Edition. , Hubei Education Edition, Western Normal University Edition, Chinese Edition, and Ministry Edition, etc., then the teaching content in the textbooks is also different from each other. Before determining the teaching videos to be released, it is necessary to determine the geographical location of the teaching points first, based on The geographical location is used to determine the teaching materials used, and then the teaching video data set is determined based on the teaching materials, and then the matching teaching videos are screened out according to the specific academic stages and learning progress of the student group, and finally the teaching videos are processed through the interactive screen. Just place it.

In a preferred implementation, the question information in the teaching video is obtained, and the steps to answer the student are extracted based on the basic information set, including:

S401. Obtain the basic information of each student from the basic information set, where the basic information includes the student's name and academic performance;

S402. Obtain the classification threshold and compare it with the learning performance, classify the student group into multiple sampling intervals, and then arrange the multiple sampling intervals in order from high to low learning performance;

S403. Generate a sampling plan according to the arrangement order of the sampling intervals, and select responding students within the sampling interval according to the sampling plan.

As described in the above steps S401-S403, before the question information is sent out, it is necessary to obtain basic information of the student group. This implementation method also groups the students by their academic performance. This purpose is to distinguish the students' ability to accept learning, but not based on this. To evaluate the students' merits and demerits, and this extraction process does not involve subjective factors and can be carried out without violating the concept of students promoted in our country. Moreover, for different subjects, each student's academic performance is not the same. are the same, then it may exist within any interval. Of course, the placement of teaching videos is set according to preset requirements, so the sampling interval it matches should also be consistent with the subject. In order to ensure that the question information is given To determine the accuracy of the feedback results, after the sampling interval is determined, random sampling within the interval is used to select responding students to avoid overgeneralization.

In a preferred implementation, the steps of generating a sampling plan based on the sequence of sampling intervals include:

Stp1. Obtain the duration of the teaching video and the time nodes at which the question information is distributed in the teaching video, and calibrate them as question nodes;

Stp2. Obtain the number of question contents in the question information. The question information includes multiple question contents, and the number of question contents is not less than 3;

Stp3. Obtain the center node of the teaching video and compare it with the question node;

If the question node is distributed after the central node, the question content will be distributed in the order of the sampling interval;

If the question node is distributed before the central node, the question content will be distributed in the reverse order of the sampling interval;

Among them, if the number of question contents is higher than the number of sampling intervals, repeated allocation will be carried out according to the order of allocation of question contents.

As mentioned in the above steps Stp1-Sto3, the teaching video is pre-recorded, so its duration can also be directly obtained. As for the setting of the question information, it can be specifically formulated by the teacher teaching in the video. Under normal circumstances, the teacher is teaching the students During the learning process, the order from easy to difficult will be followed, so that students can understand the teaching content step by step. In this implementation, the question content is allocated according to the distribution of the question nodes relative to the central node. In the early stage of the teaching content playback , even students with poor receptive ability can quickly understand the teaching content. At this time, the question content is allocated according to the reverse order of the sampling interval, mainly to test the accuracy of students with poor receptive ability in answering questions. As the teaching deepens Students with poor receptive ability may not be able to understand the teaching content in a short period of time. Taking them as the main sampling objects will obviously cause deviations in the assessment of teaching quality. Therefore, the forward order of sampling intervals is used to allocate question content. As for students with poor receptive ability, Poor students can find the teacher to solve their doubts after class without taking up normal classroom teaching time. Here, what needs further explanation is that after the response information is judged to be correct, the basic information of the student corresponding to the response information is obtained. information and filter it out from the sampling interval so that each student has a probability of being asked a question. After the response information is judged to be wrong, the basic information of the student corresponding to the response information is obtained and retained within the sampling interval, so that This retains the probability of the student being drawn again, ensuring the student's sense of tension and prompting the student to listen carefully to the lecture and understand the teaching content. Of course, after the student answers an error, the teaching robot does not directly issue an instruction to answer the error, but can respond accordingly. Set up some humanized sentences to appease their emotions, such as: "Great, but unfortunately there is still some discrepancy with the correct answer", "That's a good answer, we are almost close to the correct answer" and "Keep up the good work, I will be able to answer correctly next time" ", etc. This method can also stimulate the curiosity of students who answer incorrectly.

In a preferred implementation, the steps of obtaining the response information corresponding to the question information answered by all responding students and calculating the accuracy of the response information in real time include:

S501. Obtain response information;

S502. Obtain the standard answer corresponding to the question information, compare it with the response information, obtain the repetition rate between the response information and the standard answer, and calibrate it as a parameter to be evaluated;

S503. Obtain the standard parameters corresponding to the standard answers and compare them with the parameters to be evaluated, where each standard parameter corresponds to a standard parameter;

If the value of the parameter to be evaluated is less than the standard parameter, the response information is determined to be incorrect;

If the value of the parameter to be evaluated is greater than or equal to the standard parameter, the response information is determined to be correct;

S504. Obtain the measurement function, input the number of correct response information and the number of question information into the measurement function, and calibrate the measurement result as the accuracy of the response information.

As described in the above steps S501-S504, when calculating the accuracy of the response information, the first thing that needs to be determined is the standard answer corresponding to the question content, and each standard answer corresponds to a standard parameter. For example, in Chinese teaching, the value of a poem When asking a question, if there is any typo, it means that the answer is wrong. At this time, the corresponding standard parameter should be 100%. For the question about the interpretation of the poem, even if there is a little inconsistent expression, as long as it does not deviate from the central idea , then it can also be determined that the answer is correct, the repetition rate between the response information answered by the responding student and the standard answer is calibrated as the parameter to be evaluated, and then the size between the parameter to be evaluated and the standard parameter is compared to determine the response The accuracy of the information, where the measurement function of the accuracy is , in the formula,/> Indicates the accuracy of the response information,/> Indicates the number of response messages judged to be correct,/> Represents the total number of question contents. Based on the above formula, the accuracy of the response information can be clearly measured.

In a preferred implementation, the steps of inputting the accuracy of the response information into the data conversion model and calibrating the conversion results as teaching quality scores include:

S601. Obtain the evaluation interval from the data conversion model, where the evaluation interval is (0, a], (a, b], (b, c]...;

S602. Obtain the teaching quality score corresponding to each evaluation interval;

S603. Compare the accuracy rate of the response information with the evaluation interval, and output the corresponding teaching quality score.

As described in the above steps S601-S603, when converting the accuracy of the response information into a teaching quality score, the first thing that needs to be determined is the evaluation interval. The number of evaluation intervals can be set according to specific needs. For example, the evaluation interval is set to ( 0, 0.6], (0.6, 0.8], (0.8, 1], the teaching quality scores corresponding to these three evaluation intervals are 1, 2, 3 respectively. Of course, other scoring forms can also be used. This implementation only makes For example, the specific scoring form can be set according to actual needs, and there are no clear restrictions in this plan.

In a better implementation, the steps of obtaining the evaluation interval, comparing it with the evaluation difference, outputting a review plan based on the comparison results, and then adjusting the teaching content of the next teaching video based on the review plan include:

S801. Obtain the evaluation interval corresponding to the evaluation difference, and output a review plan, where the review plan includes repeatedly playing the teaching video, repeatedly setting the question information and briefly discussing the video;

S802. Obtain the review duration of the review plan and the teaching duration of the next teaching class, and use the difference between the teaching duration and the review duration as the effective duration;

S803. Obtain the teaching content of the next teaching video. The teaching content includes necessary teaching content and non-essential teaching content, and the non-essential teaching content can be filtered out from the next teaching video;

S804. Mark the duration of necessary teaching content to the necessary duration and compare it with the effective duration;

If the effective duration is less than the required duration, it means that the necessary teaching content cannot be fully played, and the necessary teaching content will be postponed to the next teaching video, and the non-essential teaching content in the next teaching video will be screened out;

If the effective duration is greater than or equal to the required duration, it means that the necessary teaching content can be fully played, and there is no need to postpone the necessary teaching content to the next teaching video.

As described in the above steps S801-S804, in order to ensure the quality of learning, before each implementation of new teaching content, review of previously learned content is essential, which can be review content related to the teaching content. It can also be irrelevant, but the review content whose learning node is relatively recent to this node will not be listed one by one here. Before outputting the review plan, what needs to be determined first is the learning effect of the student group. The main basis is the evaluation threshold. Comparing the results with the teaching quality score, if the teaching quality score is less than the evaluation threshold, the teaching video needs to be played again, and the review plan will not be executed at this time. However, if the teaching quality score is greater than or equal to the evaluation threshold, the evaluation will be output first. Difference, for the evaluation difference, can intuitively judge the student learning effect. For example, if the teaching quality rating adopts a 10-point system, and the evaluation threshold is set to 7, the actual teaching quality rating of the student group is 10. Obviously, the student group If you have a good grasp of the teaching content, you don’t need to spend a lot of time when implementing the review plan. You only need to play a brief discussion video to help students review what they have learned. If the actual teaching quality score of the student group is 7 , although most students can master the teaching content, there are still many students who cannot fully master the teaching content. Therefore, in order to ensure the smooth teaching of the new teaching content, the implementation of the review plan obviously needs to add a certain amount of time. Specifically, this can be done through repetition It is carried out by combining playing teaching videos with repeated setting of question information. The execution time of the review plan should be planned by the teacher. There is no restriction here. After the execution time of the review plan is determined, within the teaching plan, the following The playback duration of the teaching video needs to be adjusted accordingly. Each teaching video must contain necessary teaching content and non-essential teaching content. For example, taking poetry in Chinese teaching as an example, the non-essential teaching content includes information about the author. Background introduction and other supplementary knowledge that does not affect students' learning of teaching content can be supplemented by arranging students to read after class. Based on this, after the execution duration of the review plan is determined, the duration of the teaching content in the next teaching video can be adjusted. , when the effective time is less than the necessary time, the non-essential teaching content will be screened out. When the effective time is greater than or equal to the necessary time, the teacher can selectively retain the non-essential teaching content based on the importance.

The invention also provides an AI intelligent teaching robot control system, applied to the above-mentioned AI intelligent teaching robot control method, including:

The first acquisition module is used to acquire the student group of the teaching point and match the teaching video data set according to the student group;

The video calling module obtains student needs and retrieves corresponding teaching videos from the teaching video data set based on student needs;

The second acquisition module is used to acquire the basic information of each student in the student group and summarize it into a basic information set;

The extraction module is used to obtain the question information in the teaching video and extract the responding students based on the basic information set;

The measurement module is used to obtain the response information corresponding to the question information answered by all responding students, and calculate the accuracy of the response information in real time;

The data conversion module is used to input the accuracy of the response information into the data conversion model, and calibrate the conversion results as teaching quality scores;

The comparison module is used to obtain the evaluation threshold and compare it with the teaching quality score;

If the teaching quality score is less than the evaluation threshold, the teaching quality is judged to be poor, and the teaching video is replayed;

If the teaching quality score is greater than or equal to the evaluation threshold, the difference between the teaching quality score and the evaluation threshold is calculated and calibrated as the evaluation difference, and the teaching video continues to be played;

The control module is used to obtain the evaluation interval, compare it with the evaluation difference, output a review plan based on the comparison result, and then control the teaching content of the next teaching video according to the review plan.

As mentioned above, when the intelligent teaching robot is working, it first obtains the student group of the teaching point through the first acquisition module, and matches the teaching video data set according to the student group, and then uses the video calling module to call the teaching video that needs to be played according to the needs of the students, and Multiple question information is set in the teaching video. Based on the basic information of the student group collected by the second acquisition module, the extraction module is used to extract the responding students, and the response information of the responding students is summarized. The calculation module is then used to calculate the accuracy of the response information. rate, and convert the accuracy of the response information into a teaching quality score through the data conversion module, and then use the comparison module to evaluate the effect of teaching quality, and generate a corresponding review plan. Finally, according to the review plan, use the control module to adjust the next step The teaching content of the teaching video ensures that the required teaching content can be completed within the teaching plan. As for the judgment process involved in the above, the if...else function can be used for nesting. Of course, other algorithms that can achieve this purpose are also applicable. In this scheme, there is no restriction in this article.

And, an AI intelligent teaching robot control terminal, including:

at least one processor;

and memory communicatively connected to at least one processor;

The memory stores a computer program that can be executed by at least one processor, and the computer program is executed by at least one processor, so that at least one processor can execute the above-mentioned AI intelligent teaching robot control method.

It should be noted that, in this document, the terms "include", "comprises" or any other variation thereof are intended to cover a non-exclusive inclusion, such that a process, device, article or method that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, apparatus, article or method. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, apparatus, article or method that includes that element.

The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be made. regarded as the protection scope of the present invention. The structures, devices and operating methods that are not specifically described and explained in the present invention are all implemented according to conventional means in the art unless otherwise specified or limited.

Claims (8)

1. An AI intelligent teaching robot control method is characterized in that: comprising the following steps:

acquiring a student group of a teaching point, and matching a teaching video data set according to the student group;

acquiring student demands, and calling corresponding teaching videos from the teaching video data set according to the student demands;

basic information of each student in the student group is acquired and summarized into a basic information set;

acquiring questioning information in the teaching video, and extracting response students according to the basic information set;

acquiring response information corresponding to the questioning information, which is answered by all the answering students, and calculating the accuracy of the response information in real time;

inputting the accuracy of the response information into a data conversion model, and calibrating a conversion result as a teaching quality score;

the step of inputting the accuracy of the response information into a data conversion model and calibrating the conversion result as a teaching quality score comprises the following steps:

obtaining an evaluation interval from the data conversion model, wherein the evaluation interval is (0, a), (a, b), (b, 1);

acquiring a teaching quality score corresponding to each evaluation interval;

comparing the accuracy of the response information with an evaluation interval, and outputting a corresponding teaching quality score;

acquiring an evaluation threshold value and comparing the evaluation threshold value with the teaching quality score;

if the teaching quality score is smaller than the evaluation threshold, judging that the teaching quality effect is poor, and replaying the teaching video;

if the teaching quality score is greater than or equal to the evaluation threshold, measuring and calculating the difference between the teaching quality score and the evaluation threshold, calibrating the difference as the evaluation difference, and continuing playing the teaching video;

acquiring an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and adjusting the teaching content of the next teaching video according to the review plan;

the step of obtaining an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and adjusting the teaching content of the next teaching video according to the review plan comprises the following steps:

acquiring an evaluation interval corresponding to the evaluation difference and outputting a review plan, wherein the review plan comprises repeated playing of teaching videos, repeated setting of questioning information and brief discussion of videos;

acquiring the review time length of the review plan and the teaching time length of the next teaching lesson, and taking the difference value between the teaching time length and the review time length as the effective time length;

acquiring teaching contents of a next teaching video, wherein the teaching contents comprise necessary teaching contents and unnecessary teaching contents, and the unnecessary teaching contents can be screened out from the next teaching video;

locating the time mark occupied by the necessary teaching content to the necessary time, and comparing the necessary time with the effective time;

if the effective duration is smaller than the necessary duration, indicating that the necessary teaching content cannot be completely played, and smoothly extending the necessary teaching content to the next teaching video, and screening unnecessary teaching content in the next teaching video;

if the effective time length is longer than or equal to the necessary time length, the necessary teaching content can be completely played, and the necessary teaching content does not need to be forward to the next teaching video.

2. The AI intelligent teaching robot control method according to claim 1, characterized by: the step of obtaining the student group of the teaching point and matching the teaching video data set according to the student group comprises the following steps:

obtaining the geographic position of the teaching point, and matching all teaching videos conforming to the local teaching characteristics;

the specific school segments of the student group are obtained, the teaching video is screened according to the school segments, and the screening result is summarized into a teaching video data set;

the teaching robot comprises an interaction screen, and teaching videos in the teaching video data set can be put in through the interaction screen.

3. The AI intelligent teaching robot control method according to claim 1, characterized by: the step of obtaining the questioning information in the teaching video and extracting the answering students according to the basic information set comprises the following steps:

acquiring basic information of each student from the basic information set, wherein the basic information comprises student names and learning achievements;

the classification threshold value is obtained, the classification threshold value is compared with the learning score, the student group is classified into a plurality of sampling intervals, and the sampling intervals are arranged according to the order of the learning score from high to low;

and generating a sampling plan according to the arrangement sequence of the sampling intervals, and extracting response students in the sampling intervals according to the sampling plan.

4. The AI intelligent teaching robot control method according to claim 3, characterized in that: the step of generating a sampling plan according to the arrangement sequence of the sampling intervals comprises the following steps:

acquiring the duration of the teaching video and the time nodes of the questioning information distributed in the teaching video, and calibrating the time nodes as questioning nodes;

acquiring the number of the questioning contents in the questioning information, wherein the questioning information comprises a plurality of questioning contents, and the number of the questioning contents is not less than 3;

acquiring a central node of the teaching video and comparing the central node with the question node;

if the questioning nodes are distributed behind the central node, distributing the questioning contents according to the arrangement sequence of the sampling intervals;

if the questioning nodes are distributed in front of the central node, distributing the questioning contents according to the reverse arrangement sequence of the sampling intervals;

and if the number of the questioning contents is higher than the number of the sampling intervals, repeatedly distributing the questioning contents according to the distribution sequence of the questioning contents.

5. The AI intelligent teaching robot control method according to claim 1, characterized by: the step of obtaining the response information corresponding to the question information and responding to the student responses and calculating the accuracy of the response information in real time comprises the following steps:

obtaining response information;

obtaining a standard answer corresponding to the questioning information, comparing the standard answer with the response information to obtain the repetition rate between the response information and the standard answer, and calibrating the repetition rate as a parameter to be evaluated;

obtaining standard parameters corresponding to standard answers, and comparing the standard parameters with the parameters to be evaluated, wherein each standard parameter corresponds to one standard parameter;

if the value of the parameter to be evaluated is smaller than the standard parameter, judging that the response information is wrong;

if the value of the parameter to be evaluated is larger than or equal to the standard parameter, judging that the response information is correct;

obtaining a measuring and calculating function, inputting the number of the response information and the number of the questioning information to the measuring and calculating function, and calibrating the measuring and calculating result as the accuracy of the response information.

6. The AI intelligent teaching robot control method according to claim 5, characterized in that: after the response information is judged to be correct, basic information of students corresponding to the response information is acquired, and screened out from the sampling interval;

and after the answer information is judged to be wrong, acquiring basic information of the student corresponding to the answer information, and retaining the basic information in a sampling interval.

7. An AI intelligent teaching robot control system, applied to the AI intelligent teaching robot control method of any one of claims 1 to 6, characterized in that: comprising the following steps:

the first acquisition module is used for acquiring a student group of the teaching point and matching the teaching video data set according to the student group;

the video calling module is used for obtaining the demands of students and calling corresponding teaching videos from the teaching video data set according to the demands of the students;

the second acquisition module is used for acquiring basic information of each student in the student group and summarizing the basic information into a basic information set;

the extraction module is used for acquiring the questioning information in the teaching video and extracting the answering students according to the basic information set;

the measuring and calculating module is used for acquiring response information corresponding to the questioning information, which is answered by all the answering students, and calculating the accuracy of the response information in real time;

the data conversion module is used for inputting the accuracy rate of the response information into the data conversion model and calibrating the conversion result as a teaching quality score;

the comparison module is used for acquiring an evaluation threshold value and comparing the evaluation threshold value with the teaching quality score;

if the teaching quality score is smaller than the evaluation threshold, judging that the teaching quality effect is poor, and replaying the teaching video;

if the teaching quality score is greater than or equal to the evaluation threshold, measuring and calculating the difference between the teaching quality score and the evaluation threshold, calibrating the difference as the evaluation difference, and continuing playing the teaching video;

the regulation and control module is used for acquiring an evaluation interval, comparing the evaluation interval with the evaluation difference, outputting a review plan according to a comparison result, and regulating and controlling the teaching content of the next teaching video according to the review plan.

8. AI intelligent teaching robot control terminal, its characterized in that: comprising the following steps:

at least one processor;

and a memory communicatively coupled to the at least one processor;

wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the AI intelligent teaching robot control method of any of claims 1-6.