AI fused digital intelligence teaching case library construction and management method

Abstract

The invention discloses an AI fused digital intelligence teaching case library construction and management method, and belongs to the technical field of teaching. The method comprises the following steps: S1, screening a first culture gene; s2, screening a second culture gene; s3, constructing a cultural gene hierarchy map; s4, perfecting a cultural gene hierarchy map; s5, teaching cases are constructed based on the constructed cultural gene hierarchy atlas, a teaching case library is formed, and management is carried out based on dynamic updating of the cultural gene hierarchy atlas; according to the method, historical inheritance logic in theory is deconstructed, value fit logic after student teaching and self-confidence state logic generated dynamically are combined, a time sequence atlas analysis model of'culture gene-cognitive evolution-self-confidence generation 'is constructed, updating real-time performance can be met, a precise path of culture leading under digital intelligence technology enabling is achieved, and the method has the advantages of being high in practicability and high in practicability. And a theoretical support and a technical solution are provided for new-age young growth.

Description

Technical Field

[0001] This invention belongs to the field of teaching technology, specifically relating to a method for constructing and managing an AI-integrated digital teaching case library. Background Technology

[0002] Teaching cases refer to specific materials or contextual carriers designed in course teaching to achieve the fundamental goal of cultivating morality and fostering talent. They carry clear elements and value orientations and guide students to actively analyze, think and reflect by presenting real events, typical figures, social phenomena or virtual scenarios, ultimately helping them to establish a correct worldview, outlook on life and values.

[0003] Currently, teaching cases are generally compiled by experts based on their solid theoretical foundation and rich practical experience, and a teaching case library is built by them. This method has the following problems: it cannot meet the requirement of real-time updates, resulting in teaching delays.

[0004] In view of this, we design an AI-integrated digital teaching case library construction and management method to solve the above problems. Summary of the Invention

[0005] To address the problems mentioned in the background, this invention provides a method for constructing and managing an AI-integrated digital teaching case library. This method features real-time updates, a precise path for cultural guidance empowered by digital technology, and provides theoretical support and technical solutions for cultivating youth in the new era.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing and managing an AI-integrated digital teaching case library, comprising: S1: Invite experts to list cultural genes, construct an initial cultural gene pool, and select core cultural genes from the initial cultural gene pool as the first cultural genes; S2: Search for teaching cases, select teaching cases related to the first cultural gene, and periodically send questionnaires to students of such teaching cases. Based on the results of the questionnaires, determine the degree of cognitive fit between the first cultural gene and the students' teaching, and select the second cultural gene. S3: Construct a hierarchical map of cultural genes in Graphiti. The root node is the general outline of excellent Chinese culture, the first child node is each first cultural gene, the weight of the edge is the historical inheritance strength of the first cultural gene, the second child node is the cognitive state of each cycle, and the weight of the edge is the fit between the second cultural gene and the cognitive state. It is updated in real time. S4: Graphiti dynamically analyzes students' confidence status after teaching based on the cognitive states of each cycle in the constructed cultural gene hierarchy graph. The confidence status is used as the third node of the cultural gene hierarchy graph, and the weight of the edge is the conversion coefficient between the cognitive state and the confidence state. S5: Based on the constructed cultural gene hierarchy map, teaching cases are built to form a teaching case library, which is managed based on the dynamic updates of the cultural gene hierarchy map.

[0007] Furthermore, the screening of core cultural genes in step S1 is performed using a preset threshold for the intensity of inheritance, and the expression for calculating the intensity of inheritance is as follows: ; In the formula: and Indicates the preset weight; Experts representing cultural genes listed the frequencies; Indicates the number of experts; The frequency of documents representing cultural genes; This represents the maximum frequency of all cultural gene documents.

[0008] Furthermore, the step of selecting teaching cases related to the first cultural gene in step S2 includes: Obtain the specific content of the teaching case, segment the specific content into words, and obtain a collection of words; The similarity between the word vectors within a word set and the first cultural gene vector is determined by the following expression: ; In the formula: and Words and the first cultural gene vector Vector representation of; The L2 norm of a vector; A preset similarity threshold is used to determine the frequency of words in the word set that are similar to the first cultural gene. A preset frequency threshold is set. If the frequency exceeds the preset threshold, it will be selected as a teaching case related to the first cultural gene.

[0009] Furthermore, the questionnaire in step S2 is conducted using options, including cultural identity, value identity, and behavioral tendencies.

[0010] Furthermore, the step S2 in determining the cognitive fit between the first cultural gene and the student's teaching includes: The quantitative standards for each option in the questionnaire are preset, as are the cognitive status standards for each quantitative result in the questionnaire. The results of the questionnaire survey were quantified using standardized quantitative criteria. The quantitative results of the questionnaire are used to determine the cognitive state results based on the cognitive state criteria. The cognitive state results are cognitive state adjectives. The similarity between cognitive states and first cultural genes is calculated, summarized, and sorted according to timestamps. The evolution of similarity over time is judged, and if it increases, the cognitive fit is higher.

[0011] Furthermore, the step S4, which involves Graphiti dynamically analyzing students' post-teaching confidence, includes: The confidence conversion coefficient is calculated by comparing students' post-teaching cognitive state with the degree of alignment with their second cultural genes. The expression for the conversion coefficient is as follows: ; In the formula: It indicates the degree of fit between the later cycles in adjacent cycles; It indicates the degree of fit between the preceding and following cycles in adjacent cycles; This represents the sum of the fit differences across all cycles.

[0012] Compared with the prior art, the beneficial effects of the present invention are: This invention deconstructs the theoretical logic of historical inheritance, combines it with the logic of value alignment after students' teaching and the logic of dynamically generated self-confidence, and constructs a time-series graph analysis model of "cultural genes - cognitive evolution - self-confidence generation". This model can meet the requirements of real-time updates and realize a precise path for cultural guidance empowered by digital technology, providing theoretical support and technical solutions for cultivating young people in the new era. Attached Figure Description

[0013] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] A method for constructing and managing an AI-integrated digital teaching case library, including: S1: Invite experts to list cultural genes, construct an initial cultural gene pool, and select core cultural genes from the initial cultural gene pool as the first cultural genes; Experts refer to professionals with solid theoretical foundations and rich practical experience. The cultural genes they list, including "patriotism" and "craftsmanship", are of professional reference value. Based on the cultural genes listed by experts, duplicate cultural genes are removed, and the initial cultural gene pool constructed is also of professional reference value. Cultural genes are dynamically evolving in practice. Some cultural genes fade out due to their low adaptability to the needs of the times, while others are passed down through generations due to intergenerational identity. Therefore, cultural genes that are passed down through generations better reflect the main development of culture. Core cultural genes are selected as the primary cultural genes by setting a threshold for the intensity of the continuity of inheritance. The formula for calculating the strength of succession is: ; In the formula: and Indicates the preset weight; Experts representing cultural genes listed the frequencies; Indicates the number of experts; The frequency of documents representing cultural genes; This represents the maximum frequency of all cultural gene documents; S2: Search for teaching cases, select teaching cases related to the first cultural gene, and periodically send questionnaires to students of such teaching cases. Based on the results of the questionnaires, determine the degree of cognitive fit between the first cultural gene and the students' teaching, and select the second cultural gene. Different cultural genes are suited to different age groups and their cognition varies. Since this is aimed at the teaching stage, it is necessary to find cultural genes that fit the students. Teaching cases refer to specific materials or situational carriers designed in course teaching to achieve the fundamental goal of cultivating morality and fostering talent. They carry clear elements and value orientations. By presenting real events, typical figures, social phenomena or virtual scenarios, they guide students to actively analyze, think and reflect, and ultimately help them establish a correct worldview, outlook on life and values. By searching and screening teaching cases related to the first cultural gene and investigating the teaching results of such teaching cases, we can find the cultural genes that fit the students more quickly. Determining whether a teaching case is related to the First Cultural Gene by using cosine similarity between words involves: obtaining the specific content of the teaching case; segmenting the specific content into words to obtain a word set; determining the similarity between the word vectors in the word set and the First Cultural Gene vector; setting a similarity threshold; determining the frequency of words in the word set that are similar to the First Cultural Gene based on the similarity threshold; and setting a frequency threshold. If the frequency exceeds the preset frequency threshold, it is considered a selected teaching case related to the First Cultural Gene. The expression for calculating similarity is: ; In the formula: and Words and the first cultural gene vector Vector representation of; The L2 norm of a vector; Since teaching cases are aimed at a large number of students, it is convenient to use a questionnaire to investigate the teaching outcomes of such teaching cases. The questionnaire content is based on the options, including cultural identity, value identity and behavioral tendencies. Each option in the questionnaire has a quantitative standard. The survey results of the questionnaire are uniformly quantified based on the quantitative standard. Each quantitative result of the questionnaire has a corresponding cognitive state standard. The cognitive state results of the questionnaire are determined based on the corresponding cognitive state standards. The cognitive state results are cognitive state adjectives. The similarity between the cognitive state and the first cultural gene is calculated, summarized, and sorted according to the timestamp. The evolution process of similarity over time is judged. If it is higher and higher, the cognitive fit is higher. The expression for calculating similarity is: ; In the formula: and Adjectives describing cognitive states and the first cultural gene vector Vector representation of; The L2 norm of a vector; Cognitive fit refers to the degree of matching between cultural genes and cognitive state. It is used to quantify the correlation and consistency between cultural elements and individual cognitive state. It is the core indicator for measuring the fit between cultural input and cognitive response. Therefore, the higher the cognitive fit, the more suitable the first cultural gene is for student teaching, so as to screen out the second cultural gene. S3: Construct a hierarchical map of cultural genes in Graphiti. The root node is the general outline of excellent Chinese culture, the first child node is each first cultural gene, the weight of the edge is the historical inheritance strength of the first cultural gene, the second child node is the cognitive state of each cycle, and the weight of the edge is the fit between the second cultural gene and the cognitive state. It is updated in real time. The data on the intensity of cultural gene inheritance, the first cultural gene selected based on it, the cognitive state of students after teaching, the compatibility of cultural genes, and the second cultural gene selected based on it are numerous and messy. Constructing a teaching case library based on this requires a lot of time for data sorting and comparison each time. Therefore, by using the flexible structure of graph database and the efficient calculation of time-series engine, Graphiti can realize the dynamic evolution of nodes, edges and edge attributes to construct a cultural gene hierarchy graph, which can reduce the time for data sorting and improve the construction speed and management effect of the teaching case library. S4: Graphiti dynamically analyzes students' confidence status after teaching based on the cognitive states of each cycle in the constructed cultural gene hierarchy graph. The confidence status is used as the third node of the cultural gene hierarchy graph, and the weight of the edge is the conversion coefficient between the cognitive state and the confidence state. Graphiti not only enables the dynamic evolution of nodes, edges, and edge attributes, but also enables in-depth analysis of nodes, edges, and edge attributes. The higher the alignment between students' post-teaching cognitive state and their second cultural genes, the higher their self-confidence. The conversion coefficient between the two is calculated using an expression. The expression for calculating the conversion factor is: ; In the formula: It indicates the degree of fit between the later cycles in adjacent cycles; It indicates the degree of fit between the preceding and following cycles in adjacent cycles; This represents the sum of the fit differences across all cycles; S5: Based on the constructed cultural gene hierarchy map, teaching cases are built to form a teaching case library, which is managed based on the dynamic updates of the cultural gene hierarchy map.

[0016] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for constructing and managing an AI-integrated digital teaching case library, characterized in that, include: S1: Invite experts to list cultural genes, construct an initial cultural gene pool, and select core cultural genes from the initial cultural gene pool as the first cultural genes; S2: Search for teaching cases, select teaching cases related to the first cultural gene, and periodically send questionnaires to students of such teaching cases. Based on the results of the questionnaires, determine the degree of cognitive fit between the first cultural gene and the students' teaching, and select the second cultural gene. S3: Construct a hierarchical map of cultural genes in Graphiti. The root node is the general outline of excellent Chinese culture, the first child node is each first cultural gene, the weight of the edge is the historical inheritance strength of the first cultural gene, the second child node is the cognitive state of each cycle, and the weight of the edge is the fit between the second cultural gene and the cognitive state. It is updated in real time. S4: Graphiti dynamically analyzes students' confidence status after teaching based on the cognitive states of each cycle in the constructed cultural gene hierarchy graph. The confidence status is used as the third node of the cultural gene hierarchy graph, and the weight of the edge is the conversion coefficient between the cognitive state and the confidence state. S5: Based on the constructed cultural gene hierarchy map, teaching cases are built to form a teaching case library, which is managed based on the dynamic updates of the cultural gene hierarchy map.

2. The method for constructing and managing an AI-integrated digital teaching case library according to claim 1, characterized in that: The selection of core cultural genes in step S1 is based on a preset threshold for the intensity of inheritance. The expression for calculating the intensity of inheritance is as follows: ; In the formula: and Indicates the preset weight; Experts representing cultural genes listed the frequencies; Indicates the number of experts; The frequency of documents representing cultural genes; This represents the maximum frequency of all cultural gene documents.

3. The method for constructing and managing an AI-integrated digital teaching case library according to claim 2, characterized in that: The screening steps for teaching cases related to the first cultural gene in step S2 include: Obtain the specific content of the teaching case, segment the specific content into words, and obtain a collection of words; The similarity between the word vectors within a word set and the first cultural gene vector is determined by the following expression: ; In the formula: and Words and the first cultural gene vector Vector representation of; The L2 norm of a vector; A preset similarity threshold is used to determine the frequency of words in the word set that are similar to the first cultural gene. A preset frequency threshold is set. If the frequency exceeds the preset threshold, it will be selected as a teaching case related to the first cultural gene.

4. The method for constructing and managing an AI-integrated digital teaching case library according to claim 3, characterized in that: The questionnaire in step S2 is conducted by surveying the options, including cultural identity, value identity, and behavioral tendencies.

5. The method for constructing and managing an AI-integrated digital teaching case library according to claim 4, characterized in that: The step S2, which involves determining the cognitive fit between the first cultural gene and the student's teaching, includes: The quantitative standards for each option in the questionnaire are preset, as are the cognitive status standards for each quantitative result in the questionnaire. The results of the questionnaire survey were quantified using standardized quantitative criteria. The quantitative results of the questionnaire are used to determine the cognitive state results based on the cognitive state criteria. The cognitive state results are cognitive state adjectives. The similarity between cognitive states and first cultural genes is calculated, summarized, and sorted according to timestamps. The evolution of similarity over time is judged, and if it increases, the cognitive fit is higher.

6. The method for constructing and managing an AI-integrated digital teaching case library according to claim 5, characterized in that: The steps in step S4 of the Graphiti dynamic analysis of students' post-teaching confidence include: The confidence conversion coefficient is calculated by comparing students' post-teaching cognitive state with the degree of alignment with their second cultural genes. The expression for the conversion coefficient is as follows: ; In the formula: It indicates the degree of fit between the later cycles in adjacent cycles; It indicates the degree of fit between the preceding and following cycles in adjacent cycles; This represents the sum of the fit differences across all cycles.

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