A method, system, device and medium for generating teaching content based on artificial intelligence

Abstract

The present invention discloses a method, system, device and medium for generating teaching content based on artificial intelligence, which relates to the technical field of teaching content generation. By receiving multiple content tags in the teaching syllabus sent by the client, a preliminary screening is performed in the database to obtain a first target set, and the complexity level of each module is obtained according to the number of text words, the number of pictures and the number of videos in each module of the first target set; and a second target set is screened out according to the complexity level of each module in the first target set; for each module in the second target set, a priority selection index is calculated, and the modules with the largest priority selection index among the multiple content tags in the second target set are combined to generate the final teaching content. In this way, it is more convenient for teachers to make selections, and the finally combined teaching content meets the requirements of the teaching courseware; at the same time, it reduces the time and energy of teachers in selection and arrangement, lightens the burden of preparing teaching courseware, and reduces the impact on the lesson preparation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of teaching content generation, and particularly relates to a method, a system, a device and a medium for generating teaching content based on artificial intelligence. Background Art

[0002] The generation of teaching content based on artificial intelligence refers to using AI technology to automatically analyze the teaching materials required by users, screening and generating personalized corresponding teaching materials from a vast amount of teaching resources, so as to customize teaching content suitable for the needs of each user, thereby improving teaching efficiency and teaching effect; especially when teachers need to generate teaching courseware content, teachers input the labels of the teaching materials they need on the client (the device storing the teaching content), and perform screening to generate the corresponding teaching content, which greatly saves the time for teachers to make courseware, enabling teachers to devote more energy to teaching methods and classroom interactions;

[0003] When teachers conduct teaching courseware screening, in addition to inputting the labels of the teaching materials they need on the client, they also screen separately according to the approximate time of the entire teaching courseware, each teaching content type module (such as the introduction module, the explanation module, the interaction module, etc.) and the teaching time corresponding to each teaching content type module. The client combines each screened teaching content type module. In this way, it helps teachers accurately match teaching content, make reasonable time allocation, make the classroom flow smoothly, and the phased screening can flexibly arrange the teaching rhythm, thereby improving classroom efficiency and teaching effect;

[0004] However, when actually screening and combining teaching content type modules, screening teaching materials only through labels, teaching content type modules and the corresponding teaching time may lead to a relatively chaotic complexity of the content of the screened teaching content type modules, making it difficult for teachers to select, and the finally combined teaching content does not meet the requirements of the teaching courseware; in addition, for the large number of teaching content type modules screened by screening teaching materials through labels, teaching content type modules and the corresponding teaching time, it may cause teachers to spend more time and energy in selection and arrangement, increasing the burden of preparing teaching courseware and possibly affecting the lesson preparation efficiency. Summary of the Invention

[0005] The purpose of the present invention is to solve the above-mentioned problems, and provide a method, a system, a device and a medium for generating teaching content based on artificial intelligence.

[0006] In the first aspect of the implementation of the present invention, a method for generating teaching content based on artificial intelligence is first proposed. The method is applied to a cloud server and includes the following steps:

[0007] Receive the syllabus sent by the client; the syllabus includes course tags and multiple content tags; the target content tag is any one of the multiple content tags, and the target content tag includes a type tag and a teaching time tag;

[0008] Screen in the database for modules that match the course tag and the target content tag as the first target set, and determine the number of text words, the number of pictures, and the number of videos in each module of the first target set to obtain the complexity level of each module;

[0009] Send the complexity levels of the modules in the first target set to the client; so that the client receives a selection instruction to screen out a second target set that meets the selection instruction from the first target set;

[0010] For each module in the second target set, obtain its usage frequency, corresponding time, and form type to get a priority selection index; so that the client combines the modules with the largest priority selection index in the second target set of multiple content tags to generate the final teaching content.

[0011] Optionally, determining the number of text words, the number of pictures, and the number of videos in each module of the first target set to obtain the complexity level of each teaching material includes:

[0012] Obtain the number of different words and the total number of words in the text contained in each module, and divide the number of different words by the total number of words to obtain the lexical richness;

[0013] Obtain the total number of sentences contained in each module, and divide the total number of words by the total number of sentences to obtain the average sentence length;

[0014] Normalize the lexical richness and average sentence length of each module, and obtain the text complexity of each module according to the normalized lexical richness and average sentence length;

[0015] The calculation formula is: In the formula, dc and df are the normalized lexical richness and average sentence length respectively, Bn is the text complexity, a1 and a2 are the preset proportionality coefficients of dc and df respectively, and both a1 and a2 are greater than 0;

[0016] Obtain the complexity level of each module according to the text complexity, the number of pictures, and the number of videos of each module.

[0017] Optionally, obtaining the complexity level of each module according to the text complexity, the number of pictures, and the number of videos of each module includes:

[0018] Obtain the total number of images contained in each module, and decompose each image into different frequency sub-bands through wavelet transform using the Daubechies wavelet function;

[0019] For each frequency sub-band, calculate the grayscale histogram H s (i), where H represents the grayscale histogram, s represents a certain frequency sub-band obtained after wavelet decomposition, and i represents the grayscale level;

[0020] Calculate the information entropy of each sub-band, and the calculation formula is:

[0021]

[0022] In the formula, represents the information entropy of the nth sub-band, and P s (i)·log2(P s (i)) represents the contribution of each grayscale level i to the sub-band entropy value;

[0023] Calculate the information entropy index of the entire image, and the calculation formula is: In the formula, EFG is the information entropy index of the entire image, and u is the total number of sub-bands;

[0024] Calculate the mean value of the information entropy indices of all images contained in each module as the image complexity of each module;

[0025] Obtain the number of videos contained in each module, extract frames from each video to obtain the image set of the corresponding video, and calculate the mean value of the information entropy indices of the images in the video image set as the complexity index of the corresponding video. Take the mean value of the complexity indices of all videos contained in each module as the video complexity of each module;

[0026] Obtain the complexity coefficient of each module according to the text complexity, image complexity, and video complexity of each module, and obtain the complexity level of each module according to the preset stage complexity coefficient threshold.

[0027] Optionally, obtaining the complexity coefficient of each module according to the text complexity, image complexity, and video complexity of each module, and obtaining the complexity level of each module according to the preset stage complexity coefficient threshold includes:

[0028] Normalize the text complexity, image complexity, and video complexity of each module, and obtain the complexity coefficient of each module according to the normalized text complexity, image complexity, and video complexity. The calculation formula is:

[0029] Ghc = b1×tgh + b2×yhl + b3×kmu

[0030] Wherein, Ghc is the complexity coefficient, tgh, yhl, and kmu are respectively the text complexity, image complexity, and video complexity after normalization processing, b1, b2, and b3 are respectively the preset proportional coefficients of tgh, yhl, and kmu, and b1, b2, and b3 are all greater than 0;

[0031] Compare the complexity coefficient of each module with the first threshold of the preset complexity coefficient and the second threshold of the preset complexity coefficient, and the first threshold of the preset complexity coefficient is less than the second threshold of the preset complexity coefficient;

[0032] When the complexity coefficient is not greater than the first threshold of the preset complexity coefficient, mark the complexity level of the corresponding module as simple;

[0033] When the complexity coefficient is greater than the first threshold of the preset complexity coefficient but less than the second threshold of the preset complexity coefficient, mark the complexity level of the corresponding module as medium;

[0034] When the complexity coefficient is not less than the second threshold of the preset complexity coefficient, mark the complexity level of the corresponding module as difficult.

[0035] Optionally, for each module in the second target set, obtaining its usage frequency, corresponding time, and form type to obtain the priority selection index includes:

[0036] Obtain the total number of times each module in the second target set is used, usage frequency, and average value of usage effect scores, and perform normalization processing to obtain the usage effect index of each module. The calculation formula is: hd = ln(c1×rt + c2×ed + c3×ws + 1), where rt, ed, and ws are respectively the total number of times used, usage frequency, and average value of usage effect scores after normalization processing; c1, c2, and c3 are respectively the preset proportional coefficients of rt, ed, and ws, and c1, c2, and c3 are all greater than 0;

[0037] Obtain the actual duration of the teaching content corresponding to each module in the second target set, calculate the absolute difference between the actual duration and the preset standard time, and use the absolute difference as the time deviation index;

[0038] Obtain the priority selection index of the corresponding module according to the usage effect index, time deviation index, and form type of each module in the second target set.

[0039] Optionally, obtaining the priority selection index of the corresponding module according to the usage effect index, time deviation index, and form type of each module in the second target set includes:

[0040] Obtain all form types included in each module in the second target set, and obtain the usage times and preset interesting weight values corresponding to each form type, so as to obtain the form diversity coefficient of each module. The calculation formula is: In the formula, TE is the form diversity index, e i is the usage times corresponding to the i-th form type, ω i is the preset interesting weight value corresponding to the i-th form type, and q is the total number of form types;

[0041] Obtain the priority selection index of the corresponding module according to the usage effect index, time deviation index and form diversity index of each module in the second target set.

[0042] Optionally, obtaining the priority selection index of the corresponding module according to the usage effect index, time deviation index and form diversity index of each module in the second target set includes:

[0043] Perform normalization processing on the usage effect index, time deviation index and form diversity index, and obtain the priority selection index of the corresponding module according to the normalized usage effect index, time deviation index and form diversity index. The calculation formula is:

[0044]

[0045] In the formula, sx, zp, and mp are the normalized usage effect index, time deviation index and form diversity index respectively; f1, f2, and f3 are the preset proportionality coefficients of sx, zp, and mp respectively, and f1, f2, and f3 are all greater than 0.

[0046] In the second aspect of the implementation of the present invention, a system for generating teaching content based on artificial intelligence is proposed. The system includes a client and a cloud server, and the cloud server is connected to the client, where:

[0047] The client sends a teaching syllabus to the cloud server; the teaching syllabus includes course tags and multiple content tags; the target content tag is any one of the multiple content tags, and the target content tag includes a type tag and a teaching time tag;

[0048] The cloud server is used to screen modules that match the course tags and the target content tags in the database as the first target set, determine the number of text words, the number of pictures and the number of videos of each module in the first target set to obtain the complexity level of each module; and send the complexity levels of the modules in the first target set to the client;

[0049] The client is further used to receive a selection instruction to screen out a second target set that meets the selection instruction from the first target set;

[0050] The cloud server is further configured to obtain a priority selection index for each module in the second target set by acquiring its usage frequency, corresponding time, and form type;

[0051] The client is further configured to combine the modules with the maximum priority selection index in the second target set of multiple content tags to generate the final teaching content.

[0052] In the third aspect of the implementation of the present invention, a device for generating teaching content based on artificial intelligence is proposed. The device is applied to a cloud server and includes:

[0053] A first screening module: receiving a teaching syllabus sent by the client; the teaching syllabus includes course tags and multiple content tags; a target content tag is any one of the multiple content tags, and the target content tag includes a type tag and a teaching time tag;

[0054] A complexity level module: screening modules that match the course tags and the target content tags in a database as a first target set, and determining the number of text words, the number of pictures, and the number of videos of each module in the first target set to obtain the complexity level of each module;

[0055] A second screening module: sending the complexity levels of the modules in the first target set to the client; so that the client receives a selection instruction and screens out a second target set that meets the selection instruction from the first target set;

[0056] A content generation module: obtaining a priority selection index for each module in the second target set by acquiring its usage frequency, corresponding time, and form type; so that the client combines the modules with the maximum priority selection index in the second target set of multiple content tags to generate the final teaching content.

[0057] Advantages of the present invention:

[0058] The present invention provides a method, a system, a device and a medium for generating teaching content based on artificial intelligence. By receiving a teaching syllabus sent by a client, modules that match the course tags and target content tags are screened in a database as a first target set, and the number of text words, the number of pictures, and the number of videos of each module in the first target set are determined to obtain the complexity level of each module; and the complexity levels of the modules in the first target set are sent to the client, so that the client receives a selection instruction and screens out a second target set that meets the selection instruction from the first target set; for each module in the second target set, its usage frequency, corresponding time, and form type are obtained to obtain a priority selection index; so that the client combines the modules with the largest priority selection index in the second target set of multiple content tags to generate the final teaching content. In this way, it is more convenient for teachers to make selections, and the finally combined teaching content meets the requirements of teaching courseware; at the same time, it reduces the time and energy of teachers in selection and sorting, lightens the burden of preparing teaching courseware, and reduces the impact on lesson preparation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The present invention will be further described below with reference to the accompanying drawings.

[0060] Figure 1 It is a flowchart of a method for generating teaching content based on artificial intelligence;

[0061] Figure 2 It is a framework diagram of a system for generating teaching content based on artificial intelligence;

[0062] Figure 3 It is a framework diagram of a device for generating teaching content based on artificial intelligence. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0065] An embodiment of the present invention provides a method for generating teaching content based on artificial intelligence. Refer to Figure 1 , Figure 1 It is a flowchart of a method for generating teaching content based on artificial intelligence provided by an embodiment of the present invention. This method is applied to a cloud server and includes the following steps:

[0066] Receive the syllabus sent by the client; the syllabus includes course tags and multiple content tags; the target content tag is any one of the multiple content tags, and the target content tag includes a type tag and a teaching time tag;

[0067] Screen in the database for modules that match the course tag and the target content tag as the first target set, and determine the number of text words, the number of pictures, and the number of videos of each module in the first target set to obtain the complexity level of each module;

[0068] Send the complexity levels of the modules in the first target set to the client; so that the client receives a selection instruction to screen out a second target set that meets the selection instruction from the first target set;

[0069] For each module in the second target set, obtain its usage frequency, corresponding time, and form type to obtain a priority selection index; so that the client combines the modules with the largest priority selection index in the second target set of multiple content tags to generate the final teaching content.

[0070] Based on the method for generating teaching content based on artificial intelligence provided by the embodiments of the present invention, through the above method, it is more convenient for teachers to make selections, and the finally combined teaching content meets the requirements of teaching courseware; at the same time, it reduces the time and energy of teachers in selection and arrangement, lightens the burden of preparing teaching courseware, and reduces the impact on the lesson preparation efficiency.

[0071] It should be noted that the teacher inputs the required syllabus on the client, and the syllabus includes course tags and multiple content tags; the target content tag is any one of the multiple content tags, and the target content tag includes a type tag and a teaching time tag;

[0072] For example: The teacher inputs the required course tags (such as "mathematics, Fourier function", etc.) on the client, and multiple content tags: Suppose there are a total of three content tags. One content tag is "introduction", the type tag corresponding to "introduction" is "introduction module", and the corresponding teaching time tag is "introduction module for 15 minutes"; one content tag is "explanation", the type tag corresponding to "explanation" is "explanation module", and the corresponding teaching time tag is "explanation module for 30 minutes"; one content tag is "interaction", the type tag corresponding to "interaction" is "interaction module", and the corresponding teaching time tag is "interaction module for 15 minutes";

[0073] And according to the above teaching syllabus, modules that match the course tags and target content tags are screened in the database as the first target set; for example: in the first target set of the "introduction" content tag, there are several introduction modules, and each introduction module lasts about 15 minutes; in the first target set of the "explanation" content tag, there are several explanation modules, and each explanation module lasts about 30 minutes; in the first target set of the "interaction" content tag, there are several interaction modules, and each interaction module lasts about 15 minutes;

[0074] In addition, the content corresponding to each module in the first target set of all content tags is "mathematics, Fourier function"; this preliminary screening is mainly completed through artificial intelligence algorithms and data analysis techniques. The client automatically matches relevant teaching content modules from the huge teaching resource database according to the teaching syllabus input by the teacher; it can quickly and accurately screen out teaching materials that meet the teacher's needs, reduce the cumbersome operations in lesson preparation, and at the same time ensure the pertinence and systematicness of the teaching content, improving the efficiency and quality of teaching preparation.

[0075] In one embodiment, determining the number of text words, the number of pictures, and the number of videos in each module of the first target set to obtain the complexity level of each teaching material includes:

[0076] Obtain the number of different words and the total number of words in the text contained in each module, and divide the number of different words by the total number of words to obtain the word richness of each module;

[0077] Obtain the total number of sentences contained in each module, and divide the total number of words by the total number of sentences to obtain the average sentence length of each module;

[0078] Normalize the word richness and average sentence length of each module, and obtain the text complexity of each module according to the normalized word richness and average sentence length;

[0079] The calculation formula is: In the formula, dc and df are the normalized word richness and average sentence length respectively, Bn is the text complexity, a1 and a2 are the preset proportionality coefficients of dc and df respectively, and both a1 and a2 are greater than 0;

[0080] Obtain the complexity level of each module according to the text complexity, the number of pictures, and the number of videos of each module.

[0081] It should be noted that a1 and a2 are set by professionals according to the actual situation. Generally, the sum of a1 and a2 is 1. For example, a1 and a2 can be 0.4 and 0.6 respectively, or other numbers, and there is no specific limitation; in addition, common normalization methods include Min-Max normalization, Z-Score standardization, etc. The specific method is selected by professionals according to the actual situation, and there is no specific limitation and elaboration.

[0082] It should be noted that natural language processing (NLP) technology can be used to analyze the text content of each module, extract the number of different words, the total number of words, and the total number of sentences; it can be achieved by using tools such as word segmentation algorithms and word frequency statistics, or other methods for extraction, and there is no specific limitation and elaboration;

[0083] It should be noted that the greater the text complexity of each module, the greater the complexity of the teaching content in the corresponding module. The reason is that the increase in text complexity usually means the use of more unique words and longer sentences, indicating that the expressed ideas and concepts are more rich and in-depth. Therefore, when selecting teaching modules, teachers can judge their suitability based on the text complexity of the module to ensure that the selected content meets the requirements of the teachers.

[0084] In one embodiment, obtaining the complexity level of each module according to the text complexity, the number of pictures, and the number of videos of each module includes:

[0085] Obtain the total number of images included in each module, and decompose each image into different frequency subbands through wavelet transform using the Daubechies wavelet function;

[0086] For each frequency subband, calculate the gray level histogram H s (i), where H represents the gray level histogram, s represents a certain frequency subband obtained after wavelet decomposition, and i represents the gray level;

[0087] Calculate the information entropy of each subband, and the calculation formula is:

[0088]

[0089] In the formula, represents the information entropy of the nth subband, and P s (i)·log2(P s (i)) represents the contribution of each gray level i to the subband entropy value;

[0090] Calculate the information entropy index of the entire image, and the calculation formula is: In the formula, EFG is the information entropy index of the entire image, and u is the total number of subbands;

[0091] Calculate the mean value of the information entropy index of all the images included in each module as the image complexity of each module;

[0092] Obtain the number of videos included in each module, extract frames from each video to obtain the image set of the corresponding video, and calculate the mean value of the information entropy index of the images in the image set of the video as the complexity index of the corresponding video. Take the mean value of the complexity indices of all the videos included in each module as the video complexity of each module;

[0093] Obtain the complexity coefficient of each module according to the text complexity, image complexity, and video complexity of each module, and obtain the complexity level of each module according to the preset stage complexity coefficient threshold.

[0094] It should be noted that frame extraction of each video can be implemented through a video processing library (such as OpenCV, FFmpeg, etc.). These tools can conveniently read video files and extract specific frames, or it can be other methods, which are not specifically limited and elaborated here.

[0095] It should be noted that the information entropy index of an image refers to the content complexity of the image. When the information entropy index of the image is larger, it indicates that the content conveyed by the image is more complex. The reason is that information entropy measures the diversity and uniformity of the gray-level distribution in the image. Specifically, a higher information entropy value means that the image contains rich details and features, and the gray-level distribution is more extensive, reflecting the complexity of the image content, such as the combination of different objects, textures, and shapes. While a lower information entropy value indicates that the image content is relatively simple, and the gray levels are concentrated, perhaps only containing a single color or shape. Therefore, by analyzing the information entropy, we can effectively evaluate the expressiveness of the images in each module and the richness of the information conveyed, so as to more accurately judge its complexity level, and ultimately help teachers make more appropriate decisions in teaching content selection.

[0096] In one implementation, apply the Daubechies wavelet function for wavelet transform to decompose the image into different frequency subbands, which can effectively capture the multi-scale features and detailed information of the image. The advantage of wavelet transform lies in its good time-frequency localization ability, which can provide both the spatial and frequency information of the image, and this is especially important for analyzing complex images. By calculating the information entropy for each frequency subband, the complexity and information content of different frequency components can be quantified, thus revealing the structural and textural features of the image; this method not only improves the accuracy of evaluating the content complexity of the image, but also helps to select more expressive images in teaching content generation, thereby enhancing the teaching effect.

[0097] In one embodiment, the complexity coefficient of each module is obtained based on the text complexity, image complexity, and video complexity of each module, and the complexity level of each module is obtained according to a preset stage complexity coefficient threshold, including:

[0098] Normalize the text complexity, image complexity, and video complexity of each module, and obtain the complexity coefficient of each module based on the normalized text complexity, image complexity, and video complexity. The calculation formula is:

[0099] Ghc = b1 × tgh + b2 × yhl + b3 × kmu

[0100] In the formula, Ghc is the complexity coefficient, tgh, yhl, and kmu are the text complexity, image complexity, and video complexity after normalization respectively, b1, b2, and b3 are the preset proportional coefficients of tgh, yhl, and kmu respectively, and b1, b2, and b3 are all greater than 0;

[0101] Compare the complexity coefficient of each module with a preset first complexity coefficient threshold and a preset second complexity coefficient threshold, and the preset first complexity coefficient threshold is less than the preset second complexity coefficient threshold;

[0102] When the complexity coefficient is not greater than the preset first complexity coefficient threshold, mark the complexity level of the teaching content of the corresponding module as simple;

[0103] When the complexity coefficient is greater than the preset first complexity coefficient threshold but less than the preset second complexity coefficient threshold, mark the complexity level of the teaching content of the corresponding module as medium;

[0104] When the complexity coefficient is not less than the preset second complexity coefficient threshold, mark the complexity level of the teaching content of the corresponding module as difficult.

[0105] It should be noted that b1, b2, and b3 are set by professionals according to the actual situation. Generally, the sum of b1, b2, and b3 is 1. For example, b1, b2, and b3 can be 0.3, 0.3, and 0.4 respectively, or other numbers, which are not specifically limited; in addition, common normalization methods include Min - Max normalization, Z - Score standardization, etc. The specific method is selected by professionals according to the actual situation and will not be specifically limited and elaborated.

[0106] It should be noted that the preset first complexity coefficient threshold and the preset second complexity coefficient threshold are set by professionals according to the actual situation, which will not be specifically limited and elaborated;

[0107] It should be noted that the complexity levels of each module in the first target set are sent to the client; so that the client can receive a selection instruction to filter out a second target set that meets the selection instruction from the first target set; for example: for the topic of "Fourier function", if a teacher wants to select some basic Fourier transform concepts and simple examples in the "introduction" to ensure that all students can understand the basic background knowledge, the teacher can select the module with a complexity level of "simple" in the first target set of the "introduction" content label as the selection instruction, and the client will use all the modules with a complexity level of "simple" in the teaching content of the first target set of the "introduction" content label as the second target set;

[0108] In the explanation module, assuming that the teacher wants to introduce slightly more complex formula derivations and application examples and teach the students in a slightly deeper way, the teacher can select the module with a complexity level of "medium" in the first target set of the "explanation" content label as the selection instruction, and the client will use all the modules with a complexity level of "medium" in the teaching content of the first target set of the "explanation" content label as the second target set;

[0109] Finally, in the interaction module, assuming that the teacher wants to design some challenging exercises or discussion questions to test the teaching effect, the teacher can select the module with a complexity level of "difficult" in the first target set of the "interaction" content label as the selection instruction, and the client will use all the modules with a complexity level of "difficult" in the teaching content of the first target set of the "interaction" content label as the second target set;

[0110] In this way, allowing the teacher to automatically select the complexity levels of different content labels can effectively improve the pertinence and flexibility of teaching. The systematic module selection also saves time for the teacher, simplifies the lesson preparation process, enables them to focus more on teaching itself and interaction with students, and thus improves the overall quality of the classroom.

[0111] In one embodiment, for each module in the second target set, obtaining its usage frequency, corresponding time, and form type to obtain a priority selection index includes:

[0112] Obtain the total number of times each module in the second target set is used, the usage frequency, and the average value of the usage effect score, and perform normalization processing to obtain the usage effect index of each module. The calculation formula is: hd = ln(c1×rt + c2×ed + c3×ws + 1), where rt, ed, and ws are the total number of times used, the usage frequency, and the average value of the usage effect score after normalization processing respectively; c1, c2, and c3 are the preset proportionality coefficients of rt, ed, and ws respectively, and c1, c2, and c3 are all greater than 0;

[0113] Obtain the actual duration of the teaching content corresponding to each module in the second target set, calculate the absolute difference between the actual duration and the preset standard time, and use the absolute difference as the time deviation index;

[0114] Obtain the priority selection index of the corresponding module according to the usage effect index, time deviation index and form type of each module in the second target set.

[0115] It should be noted that c1, c2, and c3 are set by professionals according to the actual situation. Generally, the sum of c1, c2, and c3 is 1. For example, c1, c2, and c3 can be 0.4, 0.3, 0.3 respectively, or other numbers, which are not specifically limited; in addition, common normalization methods include Min-Max normalization, Z-Score standardization, etc. The specific method is selected by professionals according to the actual situation, which is not specifically limited and elaborated; in addition, the preset standard time is set by the teacher according to the actual situation. For example, the preset standard time for the introduction module is 15 minutes, the preset standard time for the explanation module is 30 minutes, the preset standard time for the interaction module is 15 minutes, etc., which is specifically determined according to the actual situation and is not specifically limited and elaborated.

[0116] It should be noted that through the background data collection function of the client management system (LMS), the total number of times each module is used, the usage frequency, and the usage effect score can be recorded. The actual teaching duration of each module can be obtained through the recording system of the client, or other acquisition methods, which are not specifically limited and elaborated;

[0117] It should be noted that the better the usage effect index of each module in the second target set, the larger the corresponding module priority selection index, and the greater the priority of the final teaching content. The reason is that the better the usage effect index, it means that the actual application performance of the module in the teaching process is more excellent, specifically manifested in the frequent use times, high usage frequency, and good student feedback scores. These factors together reflect the effectiveness and popularity of the module, thus increasing its priority selection index in the teaching content; therefore, the improvement of the usage effect index not only indicates that the module can better meet the teaching objectives and student needs, but also helps teachers to be more targeted when selecting teaching content, ensuring a higher priority of the final teaching content, and thus improving the overall teaching effect.

[0118] It should be noted that the time deviation index refers to the degree of deviation between the actual duration of the teaching content corresponding to each module in the second target set and the preset standard time; the greater the degree of deviation, the smaller the priority selection index corresponding to the module, and the lower the priority as the final teaching content. The reason is that a large degree of deviation may indicate problems in the design or implementation of the module, resulting in reduced teaching efficiency; therefore, an increase in the time deviation index will lead to a decrease in the priority selection index of the corresponding module, thereby reducing its priority in the final teaching content. This mechanism encourages teachers to more accurately grasp the teaching time, ensure that each module can effectively convey knowledge within the scheduled time, and thus improve the overall teaching effect.

[0119] In one embodiment, obtaining the priority selection index of each module according to the usage effect index, time deviation index, and form type of each module in the second target set includes:

[0120] Obtain all form types included in each module in the second target set, and obtain the usage times and preset interest weight values corresponding to each form type to obtain the form diversity coefficient of each module. The calculation formula is: In the formula, TE is the form diversity index, e i is the usage times corresponding to the i-th form type, ω i is the preset interest weight value corresponding to the i-th form type, and q is the total number of form types;

[0121] Obtain the priority selection index of each module according to the usage effect index, time deviation index, and form diversity index of each module in the second target set.

[0122] It should be noted that the preset interest weight value is set by professionals according to the actual situation, and specific details are not limited and will not be elaborated; in addition, form types can be, for example, video lectures, mini-program game interactions, etc. The specific form types are determined according to the actual situation and are not limited and will not be elaborated.

[0123] It should be noted that through the background data collection function of the client management system (LMS), the form types included in each module, as well as the usage times and preset interest weight values corresponding to each form type, can be recorded, or it can be other methods. Specific details are not limited and will not be elaborated;

[0124] It should be noted that the form diversity index refers to the degree of form diversity of the teaching content corresponding to each module in the second goal set; if the degree of form diversity is greater, the corresponding module priority index is greater, and the priority as the final teaching content is greater. The reason is that the form diversity index reflects the richness of the teaching content in the design and implementation process. The diversity of forms can meet the learning styles and interests of different students, promote more active participation and higher learning motivation. When a module contains multiple form types, such as video lectures, mini-program game interactions, etc., students can access and understand knowledge in different ways, which not only improves the fun of learning, but also enhances the absorption and memory of knowledge; therefore, the greater the degree of form diversity, the higher the module priority index, and the teaching content obtains greater priority in the teaching process, thereby improving the overall teaching quality.

[0125] In one embodiment, obtaining the priority index of the corresponding module according to the usage effect index, time deviation index and form diversity index of each module in the second target set includes:

[0126] The use effect index, time deviation index and form diversity index are normalized, and the priority index of the corresponding module is obtained according to the normalized use effect index, time deviation index and form diversity index. The calculation formula is:

[0127]

[0128] Wherein, sx, zp, and mp are respectively the normalized usage effect index, time deviation index, and form diversity index; f1, f2, and f3 are respectively the preset proportional coefficients of sx, zp, and mp, and f1, f2, and f3 are all greater than 0.

[0129] It should be noted that f1, f2, and f3 are set by professionals according to actual conditions. Generally, the sum of f1, f2, and f3 is 1. For example, f1, f2, and f3 can be 0.27, 0.41, and 0.32, respectively, or other numbers without specific limitation. In addition, commonly used normalization processing methods include Min-Max normalization, Z-Score standardization, etc. The specific method is selected by professionals according to actual conditions, and no specific limitation or elaboration is made.

[0130] It should be noted that the teacher, on the client side, prioritizes the modules with the largest indexes in the second target focus of multiple content tags to combine and generate the final teaching content.

[0131] For example, assume that the module with the largest preference index in the second target set of the "import" content label is A, the module with the largest preference index in the second target set of the "explanation" content label is H, and the module with the largest preference index in the second target set of the "interaction" content label is U. Then the teacher first selects the teaching content corresponding to module A as the introductory content, then selects the teaching content corresponding to module H for in-depth knowledge explanation, and finally selects the teaching content corresponding to module U to stimulate students' sense of participation and interactivity. Such a combination not only ensures the continuity and logic of the teaching content, but also takes into account the connection between different modules, thus forming a complete and systematic teaching plan; in this way, the teacher can more effectively achieve the teaching goals, improve students' learning effects and classroom participation; at the same time, it reduces the time and energy of the teacher in selection and arrangement, lightens the burden of preparing teaching courseware, and reduces the impact on the lesson preparation efficiency.

[0132] Based on the same inventive concept, an embodiment of the present invention further provides a system for generating teaching content based on artificial intelligence. Refer to Figure 2 , Figure 2 which is a framework diagram of a device for generating teaching content based on artificial intelligence provided by an embodiment of the present invention. The system includes a client and a cloud server, and the cloud server is connected to the client, wherein:

[0133] The client sends a teaching syllabus to the cloud server; the teaching syllabus includes course labels and multiple content labels; the target content label is any one of the multiple content labels, and the target content label includes a type label and a teaching time label;

[0134] The cloud server is used to screen modules that match the course label and the target content label in the database as the first target set, determine the number of text words, the number of pictures, and the number of videos of each module in the first target set to obtain the complexity level of each module; and send the complexity levels of the modules in the first target set to the client;

[0135] The client is further used to receive a selection instruction to screen out a second target set that meets the selection instruction from the first target set;

[0136] The cloud server is further used to obtain the usage frequency, the corresponding time, and the form type of each module in the second target set to obtain a preference index;

[0137] The client is further used to combine the modules with the largest preference index in the second target sets of the multiple content labels to generate the final teaching content.

[0138] Based on the system for generating teaching content based on artificial intelligence provided by the embodiments of the present invention, through the above-mentioned manner, it is more convenient for teachers to make selections, and the finally combined teaching content meets the requirements of the teaching courseware; at the same time, it reduces the time and energy of teachers in selection and arrangement, lightens the burden of preparing teaching courseware, and reduces the impact on the lesson preparation efficiency.

[0139] Based on the same inventive concept, the embodiments of the present invention also provide an apparatus for generating teaching content based on artificial intelligence. Refer to Figure 3 , Figure 3 which is a framework diagram of an apparatus for generating teaching content based on artificial intelligence provided by the embodiments of the present invention. This apparatus is applied to a cloud server and includes:

[0140] The first screening module: receiving the teaching syllabus sent by the client; the teaching syllabus includes course tags and multiple content tags; the target content tag is any one of the multiple content tags, and the target content tag includes a type tag and a teaching time tag;

[0141] The complexity level module: screening modules that match the course tags and target content tags in the database as the first target set, and determining the number of text words, the number of pictures, and the number of videos of each module in the first target set to obtain the complexity level of each module;

[0142] The second screening module: sending the complexity levels of the modules in the first target set to the client; so that the client receives a selection instruction and screens out a second target set that meets the selection instruction from the first target set;

[0143] The content generation module: for each module in the second target set, obtaining its usage frequency, corresponding time, and form type to obtain a priority selection index; so that the client combines the modules with the largest priority selection index in the second target set of multiple content tags to generate the final teaching content.

[0144] Based on the apparatus for generating teaching content based on artificial intelligence provided by the embodiments of the present invention, through the above-mentioned manner, it is more convenient for teachers to make selections, and the finally combined teaching content meets the requirements of the teaching courseware; at the same time, it reduces the time and energy of teachers in selection and arrangement, lightens the burden of preparing teaching courseware, and reduces the impact on the lesson preparation efficiency.

[0145] In another embodiment provided by the present invention, there is also provided a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the steps of any one of the above-mentioned methods for generating teaching content based on artificial intelligence are implemented.

[0146] In another embodiment provided by the present invention, there is also provided a computer program product containing instructions, which, when running on a computer, causes the computer to execute any one of the methods for generating teaching content based on artificial intelligence in the above embodiments.

[0147] The above has described in detail one embodiment of the present invention, but the content is only the preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for generating teaching content based on artificial intelligence, characterized in that, The method is applied to a cloud server and includes the following steps: Receive a syllabus sent by a client; the syllabus includes course tags and multiple content tags; a target content tag is any one of the multiple content tags, and the target content tag includes a type tag and a teaching time tag; Screen modules that match the course tag and the target content tag in a database as a first target set, and determine the number of text words, the number of pictures, and the number of videos in each module of the first target set to obtain the complexity level of each module; Send the complexity levels of the modules in the first target set to the client; so that the client receives a selection instruction to screen out a second target set that meets the selection instruction from the first target set; For each module in the second target set, obtain its usage frequency, corresponding time, and form type to obtain a priority selection index; so that the client combines the modules with the largest priority selection index in the second target set of multiple content tags to generate the final teaching content.

2. The method for generating teaching content based on artificial intelligence according to claim 1, characterized in that, Determining the number of text words, the number of pictures, and the number of videos in each module of the first target set to obtain the complexity level of each teaching material includes: Obtain the number of different words and the total number of words in the text contained in each module, and divide the number of different words by the total number of words to obtain the word richness; Obtain the total number of sentences contained in each module, and divide the total number of words by the total number of sentences to obtain the average sentence length; Perform normalization processing on the word richness and average sentence length of each module, and obtain the text complexity of each module according to the normalized word richness and average sentence length; Obtain the complexity level of each module according to the text complexity, the number of pictures, and the number of videos of each module.

3. The method for generating teaching content based on artificial intelligence according to claim 2, wherein Obtaining the complexity level of each module according to the text complexity, the number of pictures, and the number of videos of each module includes: Obtain the total number of images contained in each module, and decompose each image into different frequency subbands through wavelet transform using the Daubechies wavelet function; For each frequency subband, calculate the information entropy of each subband, and use the sum of the information entropies of each subband as the information entropy index of the corresponding image; Calculate the mean value of the information entropy indexes of all the images contained in each module as the image complexity of each module; Obtain the number of videos contained in each module, extract frames from each video to obtain an image set corresponding to the video, and calculate the mean value of the information entropy indexes of the images in the image set of the video as the complexity index of the corresponding video, and use the mean value of the complexity indexes of all the videos contained in each module as the video complexity of each module; Obtain the complexity coefficient of each module according to the text complexity, image complexity, and video complexity of each module, and obtain the complexity level of each module according to the preset stage complexity coefficient threshold.

4. A method for generating teaching content based on artificial intelligence according to claim 3, characterized in that Obtaining the complexity coefficient of each module according to the text complexity, image complexity, and video complexity of each module, and obtaining the complexity level of each module according to the preset stage complexity coefficient threshold includes: Normalize the text complexity, image complexity, and video complexity of each module, and obtain the complexity coefficient of each module based on the normalized text complexity, image complexity, and video complexity. The calculation formula is as follows: Ghc = b1×tgh + b2×yhl + b3×kmu In the formula, Ghc is the complexity coefficient, tgh, yhl, and kmu are the normalized text complexity, image complexity, and video complexity respectively, b1, b2, and b3 are the preset proportionality coefficients of tgh, yhl, and kmu respectively, and b1, b2, and b3 are all greater than 0; Compare the complexity coefficient of each module with the first preset complexity coefficient threshold and the second preset complexity coefficient threshold, and the first preset complexity coefficient threshold is less than the second preset complexity coefficient threshold; When the complexity coefficient is not greater than the first preset complexity coefficient threshold, mark the complexity level of the corresponding module as simple; When the complexity coefficient is greater than the first preset complexity coefficient threshold but less than the second preset complexity coefficient threshold, mark the complexity level of the corresponding module as medium; When the complexity coefficient is not less than the second preset complexity coefficient threshold, mark the complexity level of the corresponding module as difficult.

5. A method for generating teaching content based on artificial intelligence according to claim 1, characterized in that, For each module in the second target set, obtaining its usage frequency, corresponding time, and form type to get the priority selection index includes: Obtain the total number of times each module in the second target set is used, usage frequency, and average usage effect score, and perform normalization to obtain the usage effect index of each module. The calculation formula is: hd = ln(c1×rt + c2×ed + c3×ws + 1), where rt, ed, and ws are the total number of times used, usage frequency, and average usage effect score after normalization respectively; c1, c2, and c3 are the preset proportionality coefficients of rt, ed, and ws respectively, and c1, c2, and c3 are all greater than 0; Obtain the actual duration of the teaching content corresponding to each module in the second target set, calculate the absolute difference between the actual duration and the preset standard time, and use the absolute difference as the time deviation index; Obtain the priority selection index of the corresponding module according to the usage effect index, time deviation index, and form type of each module in the second target set.

6. The method for generating teaching content based on artificial intelligence according to claim 5, characterized in that, Obtaining the priority selection index of the corresponding module according to the usage effect index, time deviation index, and form type of each module in the second target set includes: Obtain all form types included in each module in the second target set, and obtain the usage times and preset interesting weight values corresponding to each form type, to obtain the form diversity coefficient of each module. The calculation formula is as follows: In the formula, TE is the form diversity index, e i is the usage times corresponding to the i-th form type, ω i is the preset interesting weight value corresponding to the i-th form type, and q is the total number of form types; Obtain the priority selection index of the corresponding module according to the usage effect index, time deviation index, and form diversity index of each module in the second target set.

7. A method for generating teaching content based on artificial intelligence according to claim 6, characterized in that, Obtaining the priority selection index of the corresponding module according to the usage effect index, time deviation index, and form diversity index of each module in the second target set includes: Normalize the usage effect index, time deviation index, and form diversity index, and obtain the priority selection index of the corresponding module according to the normalized usage effect index, time deviation index, and form diversity index. The calculation formula is: Where sx, zp, and mp are the usage effect index, time deviation index, and diverse form index after normalization respectively; f1, f2, and f3 are the preset proportionality coefficients of sx, zp, and mp respectively, and f1, f2, and f3 are all greater than 0.

8. A system for generating teaching content based on artificial intelligence, characterized in that, The system includes a client and a cloud server, which are connected to each other. Among them: The client sends a teaching syllabus to the cloud server; The teaching syllabus includes a course label and multiple content labels; The target content label is any one of the multiple content labels, and the target content label includes a type label and a teaching time label; The cloud server is used to screen modules that match the course label and the target content label in the database as the first target set, determine the number of text words, the number of pictures, and the number of videos of each module in the first target set to obtain the complexity level of each module; and send the complexity levels of the modules in the first target set to the client; The client is further used to receive a selection instruction to screen out a second target set that meets the selection instruction from the first target set; The cloud server is further used to obtain the usage frequency, corresponding time, and form type of each module in the second target set to obtain a priority selection index; The client is further used to combine the modules with the largest priority selection index in the second target set of multiple content labels to generate the final teaching content.

9. An apparatus for generating teaching content based on artificial intelligence, which is used to implement the method for generating teaching content based on artificial intelligence according to any one of the above claims 1-7, characterized in that, The device is applied to a cloud server, and the device includes: A first screening module: receiving a teaching syllabus sent by the client; The teaching syllabus includes a course label and multiple content labels; The target content label is any one of the multiple content labels, and the target content label includes a type label and a teaching time label; A complexity level module: screening modules that match the course label and the target content label in the database as the first target set, determining the number of text words, the number of pictures, and the number of videos of each module in the first target set to obtain the complexity level of each module; A second screening module: sending the complexity levels of the modules in the first target set to the client; so that the client receives a selection instruction to screen out a second target set that meets the selection instruction from the first target set; A content generation module: obtaining the usage frequency, corresponding time, and form type of each module in the second target set to obtain a priority selection index; so that the client combines the modules with the largest priority selection index in the second target set of multiple content labels to generate the final teaching content.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed, it implements the method according to any one of claims 1-7.

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