An AI agent-based learning plan updating method and device

Abstract

This application discloses a learning plan updating method and apparatus based on an AI intelligent agent, relating to the field of educational technology. The method includes: responding to acquiring learning behavior data of a student learning questions based on a learning plan in a peer learning system; determining the question information and note information of the student's target questions based on the learning behavior data; determining the set of implicit knowledge points in the knowledge point set corresponding to the question information and note information based on a first set of knowledge points marked for the target questions; determining the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set based on the prerequisite and relevance information between the implicit knowledge point set and the first knowledge point set; and updating the target implicit knowledge point set in the knowledge point learning grid corresponding to the learning plan to update the learning plan to a target learning plan including the target implicit knowledge point set. This application can improve learning efficiency and the systematic nature of knowledge construction.

Description

Technical Field

[0001] This application relates to the field of educational technology, and in particular to a method and apparatus for updating learning plans based on AI intelligent agents. Background Technology

[0002] In knowledge graph-based adaptive learning systems, inaccurate learning plans have become a prominent technical bottleneck. The root cause of this problem lies primarily in the inherent flaws in the construction of knowledge graphs themselves. Current knowledge graphs largely rely on subjective expert annotation or automated data mining. The former struggles to fully cover the implicit relationships between knowledge points, while the latter is susceptible to data noise, leading to problems such as insufficient confidence and weak logical support in the extracted relationships.

[0003] Existing systems generally lack a dynamic calibration mechanism for the strength of knowledge associations. The weights of relationships in the graph are often based on static rules or historical data presets, and cannot be adjusted in real time according to actual learning outcomes. When students learn according to the plan, the system cannot accurately determine whether the mastery of prior knowledge truly meets the learning requirements of subsequent content, nor can it identify knowledge jumps or path redundancy caused by inaccurate associations.

[0004] The inaccuracy of this underlying model directly leads to a mismatch between the generated learning plan and the student's actual cognitive state. Students may be required to learn content for which they have not yet acquired the necessary prerequisites, or spend excessive time on knowledge points they have already mastered. Because the plan generation process lacks interpretable connections, students struggle to actively identify problems and are forced to passively progress through inefficient learning paths, severely impacting learning efficiency and the effectiveness of knowledge construction. This "inaccuracy" is not only reflected in the improper arrangement of knowledge point sequences but also profoundly reflects the gap between the knowledge representation model and actual learning patterns. Summary of the Invention

[0005] In view of this, this application provides a learning plan updating method and apparatus based on AI intelligent agents. The main purpose is to improve the technical problem in the current technology that, due to the lack of interpretable correlation display in the learning plan generation process, students find it difficult to actively identify the problems and can only passively advance in an inefficient learning path, which seriously affects the learning efficiency and the effectiveness of knowledge construction.

[0006] Firstly, this application provides a learning plan update method based on an AI agent, including:

[0007] In response to obtaining learning behavior data of students learning questions based on learning plans in the learning companion system, the question information and note information of the student's learning target questions are determined based on the learning behavior data;

[0008] Based on the first set of knowledge points marked on the target question, determine the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information;

[0009] Based on the prerequisite and relevance information between the implicit knowledge point set and the first knowledge point set, the target implicit knowledge point set corresponding to the learning plan is determined from the implicit knowledge point set.

[0010] The set of target implicit knowledge points is updated in the knowledge point learning grid corresponding to the learning plan, so as to update the learning plan to include the set of target implicit knowledge points.

[0011] Optionally, determining the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information based on the first set of knowledge points marked for the target question includes:

[0012] Knowledge points are extracted from the question information and the note information respectively to obtain a second set of knowledge points corresponding to the question information and a third set of knowledge points corresponding to the note information.

[0013] The knowledge points in the second set of knowledge points that are not included in the first set of knowledge points are determined as the first subset of implicit knowledge points corresponding to the target question;

[0014] The knowledge points in the third set of knowledge points that are not included in the first set of knowledge points are determined as the second subset of implicit knowledge points corresponding to the target question;

[0015] The set of implicit knowledge points is determined based on the first subset of implicit knowledge points and the second subset of implicit knowledge points.

[0016] Optionally, determining the target set of implicit knowledge points corresponding to the learning plan from the set of implicit knowledge points based on the prior knowledge relationship and relevance information between the implicit knowledge point set and the first knowledge point set includes:

[0017] Based on the first subset of implicit knowledge points and the first set of knowledge points, multiple prerequisite relationships for knowledge points are established to determine the knowledge points in the first subset of implicit knowledge points as prerequisite knowledge points for the knowledge points in the first set of knowledge points.

[0018] Multiple knowledge point correlation relationships are established based on the second subset of implicit knowledge points and the first set of knowledge points, so as to associate the knowledge points in the second subset of implicit knowledge points with the knowledge points in the first set of knowledge points;

[0019] A first confidence level is determined by using the prerequisite relationships of the multiple knowledge points as the prerequisite relationships of the target knowledge points corresponding to the learning plan, and a second confidence level is determined by using the correlation relationships of the multiple knowledge points as the correlation relationships of the target knowledge points corresponding to the learning plan.

[0020] The target set of implicit knowledge points corresponding to the learning plan is determined from the set of implicit knowledge points based on the first confidence level and the second confidence level.

[0021] Optionally, determining the first confidence level of the prerequisite relationships of the multiple knowledge points as the prerequisite relationships of the target knowledge points corresponding to the learning plan includes:

[0022] Determine the student's first level of mastery of the first subset of implicit knowledge points, and determine the student's answer to the question based on the student's answer data to the question in the question information;

[0023] Based on the first mastery level data and the answer results, determine the first support data for each knowledge point prerequisite relationship in the multiple knowledge point prerequisite relationships;

[0024] The confidence level of each knowledge point prior relationship is updated based on the historical confidence level of the first support data to obtain the first confidence level of using the multiple knowledge point prior relationships as the prior relationship of the target knowledge point.

[0025] Optionally, determining the second confidence level of the correlation between multiple knowledge points as the correlation between the target knowledge points corresponding to the learning plan includes:

[0026] Determine the student's second level of mastery of the second subset of implicit knowledge points, and the contribution of the note information to the correlation relationship of the multiple knowledge points;

[0027] Based on the mastery data and the contribution data, a second support data is determined for the relevance relationship of each knowledge point in the multiple knowledge point relevance relationships;

[0028] The data is updated based on the historical confidence of the relevance relationship of each knowledge point in the second support data to obtain the second confidence of using the relevance relationship of the multiple knowledge points as the relevance relationship of the target knowledge point.

[0029] Optionally, determining the target set of implicit knowledge points corresponding to the learning plan from the set of implicit knowledge points based on the first confidence level and the second confidence level includes:

[0030] The prerequisite relationships and correlation relationships of the multiple knowledge points are stored in the candidate relationship library corresponding to the learning plan, so as to monitor the first confidence level and the second confidence level through the candidate relationship library;

[0031] In response to the fact that the first confidence of the first knowledge point prerequisite relationship in multiple knowledge point prerequisite relationships is greater than the prerequisite confidence threshold and the number of times the first implicit knowledge point corresponding to the first knowledge point prerequisite relationship is identified is greater than the number of times threshold, the first implicit knowledge point is determined as the first target implicit knowledge point corresponding to the learning plan.

[0032] In response to the fact that the first confidence level of the first knowledge point correlation relationship among multiple knowledge point correlation relationships is greater than the correlation confidence level threshold and the number of times the first implicit knowledge point corresponding to the first knowledge point correlation relationship is identified is greater than the number of times threshold, the first implicit knowledge point is determined as the second target implicit knowledge point corresponding to the learning plan;

[0033] The set of target implicit knowledge points corresponding to the learning plan is determined based on the first target implicit knowledge point and the second target implicit knowledge point.

[0034] Optionally, after storing the prerequisite relationships and correlation relationships of the multiple knowledge points in the candidate relationship library corresponding to the learning plan, so as to monitor the first confidence level and the second confidence level through the candidate relationship library, the method further includes:

[0035] Determine the monitoring period for the first confidence level and the second confidence level of the candidate relation database;

[0036] If, during the monitoring period, the first confidence level of the second knowledge point prerequisite relationship among the multiple knowledge point prerequisite relationships and / or the second confidence level of the second knowledge point prerequisite relationship among the multiple knowledge point correlation relationships are lower than the monitoring threshold, the second knowledge point prerequisite relationship and / or the second knowledge point prerequisite relationship will be removed from the candidate relationship database.

[0037] In response to the end of the monitoring period, the multiple knowledge point prerequisite relationships and the multiple knowledge point correlation relationships are removed from the candidate relationship database.

[0038] Secondly, this application provides a learning plan updating device based on an AI agent, comprising:

[0039] The determination module is configured to, in response to acquiring learning behavior data of a student learning questions based on a learning plan in the learning companion system, determine the question information and note information of the student's learning target questions based on the learning behavior data;

[0040] The determination module is further configured to determine the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information based on the first set of knowledge points marked for the target question;

[0041] The determining module is further configured to determine the target set of implicit knowledge points corresponding to the learning plan from the set of implicit knowledge points based on the prior relationship and relevance information between the set of implicit knowledge points and the first set of knowledge points.

[0042] The update module is configured to update the target implicit knowledge point set in the knowledge point learning grid corresponding to the learning plan, so as to update the learning plan to include the target implicit knowledge point set.

[0043] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the AI ​​agent-based learning plan update method described in the first aspect.

[0044] Fourthly, this application provides an electronic device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein the processor executes the computer program to implement the AI ​​agent-based learning plan update method described in the first aspect.

[0045] By employing the above technical solutions, this application provides a learning plan updating method and apparatus based on AI intelligent agents. Compared with existing technologies, this application provides accurate data for updating learning plans by collecting student learning behavior data and extracting key information from questions and notes; it constructs implicit relationships in a knowledge graph by identifying a set of implicit knowledge points; it improves the accuracy of selecting target implicit knowledge points by analyzing the prerequisite and relevance relationships of knowledge points; and it achieves precise matching between the learning plan and the student's cognitive state by updating the knowledge point learning grid and dynamically optimizing the learning plan, thereby improving learning efficiency and the systematic nature of knowledge construction. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0047] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1The illustration shows a flowchart of a learning plan update method based on an AI agent provided in an embodiment of this application;

[0049] Figure 2 This application illustrates a knowledge point learning grid provided in an embodiment.

[0050] Figure 3 The illustration shows a flowchart of a learning plan update method based on an AI agent provided in an embodiment of this application;

[0051] Figure 4 This illustration shows a schematic diagram of a learning plan update device based on an AI agent, according to an embodiment of this application.

[0052] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown. Detailed Implementation

[0053] The embodiments of this application will now be described in more detail with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0054] To address the technical problem in existing technologies where the learning plan generation process lacks interpretable relational displays, making it difficult for students to actively identify problems and forcing them to passively progress through inefficient learning paths, thus severely impacting learning efficiency and the effectiveness of knowledge construction, this embodiment provides a learning plan updating method based on an AI agent. Figure 1 As shown, the method includes:

[0055] Step 101: In response to obtaining the student's learning behavior data based on the learning plan in the learning companion system, determine the question information and note information of the student's learning target questions based on the learning behavior data.

[0056] In this embodiment of the application, the learning companion system can be the backend system corresponding to the AI ​​learning assistant installed on the learning tablet. The learning companion system can store data such as students' basic information, learning records, and course materials.

[0057] In this application embodiment, learning behavior data can be various types of operational data generated by students when learning questions in the tutoring system. For example, the learning behavior data in this application implementation may specifically include question answering data (such as answer results, answering time, and incorrect question markings), note data (such as note text and the corresponding learning position of the notes), and learning interaction data (such as online class dwell time and number of repeated views).

[0058] In this embodiment of the application, the target question can be a specific question that the student is currently studying based on the current learning plan.

[0059] In this embodiment of the application, the question information can be detailed information related to the target question. Specifically, the question information may include the question ID, question stem text, answer explanation, system-pre-annotated knowledge points, and student's answer results. (1 indicates correct, 0 indicates incorrect), answer timestamp, and other information.

[0060] In this embodiment of the application, the note information can be the notes recorded by the student during the learning of the target questions. Specifically, in this embodiment, the note information can be... This can be represented as note information, which may include note text (Text), note location (Loc), and the contextual knowledge points (kcontext) corresponding to the note.

[0061] In this embodiment, the system can monitor the operational behavior of the learning companion system on the learning tablet in real time. When it detects that a student has entered the question learning module based on the current learning plan and begun answering the target question, it can automatically trigger the collection of learning behavior data. During the data collection process, the system first records the student's answering actions, generating answer data including the question ID, question stem, student's answer, and answering time. Simultaneously, it retrieves the preset answer analysis and explicit knowledge point annotations for the question. If the student triggers the note-taking function (such as handwriting or inputting notes) during the answering process, the system can simultaneously record the note text, the corresponding learning location, and the contextual knowledge points. The system can perform structured processing on the collected learning behavior data to determine the question information directly related to the target question and the student's self-recorded notes.

[0062] Step 102: Determine the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information based on the first set of knowledge points marked for the target question.

[0063] In this embodiment, the first set of knowledge points can be the set of explicit knowledge points that mark the target question in the learning companion system. For example, the first set of knowledge points in this embodiment can be specifically represented by Kexplicit.

[0064] In this embodiment, the set of knowledge points corresponding to the question information can be the set of all knowledge points extracted from the question information (especially the answer analysis) of the target question. The set of knowledge points corresponding to the question information can include explicit knowledge points and knowledge points indirectly involved in the question. For example, the set of knowledge points corresponding to the question information in this embodiment can be specifically represented by Kanalysis.

[0065] In this embodiment, the set of knowledge points corresponding to the note information can be the set of all knowledge points extracted from the student's recorded notes. This set of knowledge points can reflect the knowledge points the student focuses on when learning the target questions. The set of knowledge points can also include additional knowledge points not marked by the system. For example, the set of knowledge points corresponding to the note information in this embodiment can be specifically represented by Knote.

[0066] In this embodiment, the implicit knowledge point set can be a set of knowledge points not included in the first knowledge point set, which are from the knowledge point set corresponding to the question information and the knowledge point set corresponding to the note information. For example, the implicit knowledge point set in this embodiment can be specifically represented by Khidden.

[0067] In this embodiment of the application, the system can extract knowledge points from the question information and the note information to obtain the corresponding knowledge point set. Specifically, for the question information, the system can use Natural Language Processing (NLP) technology to extract knowledge points, including but not limited to using a BERT-based subject knowledge point recognition model to perform entity linking and semantic analysis on the question answer parsing. For the note information, the system can combine a subject-specific dictionary and the TF-IDF text feature extraction algorithm to perform word segmentation and semantic matching on the note text, extracting the knowledge point set corresponding to the note information.

[0068] In this embodiment of the application, the system compares the set of knowledge points corresponding to the question information and the set of knowledge points corresponding to the note information with the first set of knowledge points respectively, and filters out implicit knowledge points. Specifically, this may include: first comparing the set of knowledge points corresponding to the question information... First knowledge point collection The implicit knowledge points from the question source are obtained by filtering out the knowledge points not included in the first knowledge point set of the notes information; finally, these two sets of implicit knowledge points are merged and duplicates are removed to obtain the implicit knowledge point set. .

[0069] Step 103: Based on the prerequisite and correlation information between the implicit knowledge point set and the first knowledge point set, determine the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set.

[0070] In the embodiments of this application, the prerequisite relationship can be a dependency relationship between knowledge points. The prerequisite relationship can be expressed as mastering one knowledge point (prerequisite knowledge point) is a prerequisite for understanding or mastering another knowledge point (subsequent knowledge point).

[0071] In the embodiments of this application, the relevance information can be the association information between knowledge points. The relevance information can indicate that two knowledge points are related in semantic description, application scenario or learning logic, but there is no strict order of mastery.

[0072] In this embodiment of the application, the target implicit knowledge point set can be a set of implicit knowledge points selected from the implicit knowledge point set that have a high confidence prior relationship or correlation relationship with the first knowledge point set.

[0073] In this embodiment, the system can establish prerequisite relationships and relevance relationships based on the implicit knowledge point set and the first knowledge point set, respectively; the system can determine prerequisite relationships based on the implicit knowledge points extracted from the question information and the first knowledge point set, referring to the teaching syllabus, subject expert experience, and historical student learning data; the system can determine relevance relationships based on the implicit knowledge points extracted from the note information and the first knowledge point set, through text similarity calculation (TF-IDF vectorization technology can be used to convert knowledge point descriptions into vectors and calculate cosine similarity) and knowledge point co-occurrence frequency statistics.

[0074] In the embodiments of this application, the system can calculate the confidence level of the prior relationship and the confidence level of the relevance relationship, and filter target implicit knowledge points based on the confidence level of the prior relationship and the confidence level of the relevance relationship; if the confidence level of the prior relationship between the implicit knowledge point and the first knowledge point set is greater than the prior confidence level threshold, or the confidence level of the relevance relationship between the implicit knowledge point and the first knowledge point set is greater than the relevance confidence level threshold, then the implicit knowledge point is included in the target implicit knowledge point set.

[0075] Step 104: Update the target implicit knowledge point set in the knowledge point learning grid corresponding to the learning plan, so as to update the learning plan to include the target implicit knowledge point set.

[0076] In this embodiment of the application, the knowledge point learning grid can be a two-dimensional grid formed by mapping the learning content of the student's current semester's courses according to logical relationships (such as chapter order, knowledge dependencies), such as... Figure 2 As shown, in Figure 2 Each grid cell represents a knowledge point. The proficiency level of a knowledge point can be marked by filling the cell with different circles, different symbols on different circles, or different shapes. The specific marking method for the proficiency level of a knowledge point is not limited in this embodiment.

[0077] As an optional method, when marking the proficiency of knowledge points by filling different circles, red can be used to indicate not mastered, yellow can be used to indicate mastered but not proficient, and green can be used to indicate proficient; when marking the proficiency of knowledge points by filling different shapes, 1 can be used to indicate not mastered, 2 can be used to indicate mastered but not proficient, and 3 can be used to indicate proficient; when marking the proficiency of knowledge points by filling different shapes, circles can be used to indicate not mastered, triangles can be used to indicate mastered but not proficient, squares can be used to indicate proficient, and so on, without further examples.

[0078] In this embodiment of the application, the target learning plan can be an updated learning plan. For example, the target learning plan in this embodiment of the application can specifically be based on the original learning plan, incorporating a set of target implicit knowledge points, and specifying the learning order and priority of the target implicit knowledge points.

[0079] For the embodiments of this application, the system can first determine the position of the target implicit knowledge point in the knowledge point learning grid, and ensure that the connected knowledge points (with prior or related relationships) in the grid are as adjacent as possible. The specific method for determining the position of the target implicit knowledge point in the knowledge point learning grid can be, but is not limited to, the force-directed algorithm.

[0080] In this embodiment of the application, the system can mark the target implicit knowledge points with colors based on the students' mastery data of the target implicit knowledge points; the system can incorporate the set of target implicit knowledge points into the original learning plan, and generate a target learning plan by combining the priority of knowledge points (priority knowledge points have higher priority than related knowledge points) and mastery (red knowledge points have higher priority than yellow and green knowledge points).

[0081] Compared with existing technologies, this embodiment provides accurate data for updating learning plans by collecting students' learning behavior data and extracting key information from questions and notes; it constructs implicit relationships in a knowledge graph by identifying a set of implicit knowledge points; it improves the accuracy of selecting target implicit knowledge points by analyzing the prerequisite and relevance relationships of knowledge points; and it achieves precise matching between the learning plan and students' cognitive state by updating the knowledge point learning grid and dynamically optimizing the learning plan, thereby improving learning efficiency and the systematic nature of knowledge construction.

[0082] As an optional approach, when performing the step of "determining the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information based on the first set of knowledge points marked for the target question," the following methods can be used, but are not limited to: Figure 3 As shown, the method includes:

[0083] Step 201: Extract knowledge points from the question information and the note information respectively to obtain a second set of knowledge points corresponding to the question information and a third set of knowledge points corresponding to the note information.

[0084] In this embodiment of the application, the second knowledge point set can be a set composed of all knowledge points extracted from the question information of the target question. For example, the second knowledge point set in this embodiment of the application can specifically be represented as Kanalysis.

[0085] In this embodiment, the third knowledge point set is a set composed of all knowledge points extracted from the student's recorded notes. For example, the second knowledge point set in this embodiment can be specifically represented as... .

[0086] In this embodiment of the application, knowledge points are extracted from the question information and the note information respectively to obtain the second set of knowledge points corresponding to the question information. The system can use various NLP techniques to extract knowledge points from the question information. The system can use a rule-based knowledge point extraction method (such as matching keywords in the answer analysis through a preset subject knowledge point keyword library) or a sequence labeling model based on conditional random field (CRF) (performing part-of-speech tagging and entity recognition on the answer analysis text to determine knowledge point entities) to obtain the second set of knowledge points.

[0087] In this embodiment of the application, the third set of knowledge points corresponding to the question information and the note information can be obtained by extracting knowledge points from the question information and the note information respectively. This can be achieved by the system using Semantic Role Labeling (SRL) technology to analyze the grammatical structure and semantic relationships of the note text.

[0088] Step 202: Determine the knowledge points in the second knowledge point set that are not included in the first knowledge point set as the first implicit knowledge point subset corresponding to the target question.

[0089] In this embodiment of the application, the first subset of implicit knowledge points may be a subset composed of implicit knowledge points that are not included in the first set of knowledge points and are selected from the second set of knowledge points.

[0090] In this embodiment of the application, the system can compare the elements of the second set of knowledge points with the first set of knowledge points, subtract the first set of knowledge points from the second set of knowledge points, and then filter out the knowledge points not included in the first set of knowledge points. The filtered knowledge points not included in the first set of knowledge points are determined as the first subset of implicit knowledge points corresponding to the target question.

[0091] Step 203: Determine the knowledge points in the third knowledge point set that are not included in the first knowledge point set as the second implicit knowledge point subset corresponding to the target question.

[0092] In this embodiment of the application, the second subset of implicit knowledge points may be a subset composed of implicit knowledge points that are not included in the first set of knowledge points and are selected from the third set of knowledge points.

[0093] In this embodiment of the application, the system can compare the elements of the third set of knowledge points with the first set of knowledge points, subtract the first set of knowledge points from the third set of knowledge points, and then filter out the knowledge points not included in the first set of knowledge points. The filtered knowledge points not included in the first set of knowledge points are determined as the second subset of implicit knowledge points corresponding to the target question.

[0094] Step 204: Determine the set of implicit knowledge points based on the first subset of implicit knowledge points and the second subset of implicit knowledge points.

[0095] In this embodiment of the application, determining the implicit knowledge point set based on the first implicit knowledge point subset and the second implicit knowledge point subset can be achieved by the system merging the first implicit knowledge point subset and the second implicit knowledge point subset, and removing duplicate knowledge point elements while performing a set union operation to obtain the implicit knowledge point set.

[0096] As an optional approach, when performing the step of "determining the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set based on the prerequisite relationships and relevance information between the implicit knowledge point set and the first knowledge point set," the following method can be used, but is not limited to: establishing multiple prerequisite relationships between knowledge points based on the first subset of implicit knowledge points and the first knowledge point set, so as to determine the knowledge points in the first subset of implicit knowledge points as prerequisite knowledge points for the knowledge points in the first knowledge point set; establishing multiple relevance relationships between knowledge points based on the second subset of implicit knowledge points and the first knowledge point set, so as to associate the knowledge points in the second subset of implicit knowledge points with the knowledge points in the first knowledge point set; determining a first confidence level for using the multiple prerequisite relationships between knowledge points as prerequisite relationships for the target knowledge points corresponding to the learning plan and a second confidence level for using the multiple relevance relationships between knowledge points as relevance relationships for the target knowledge points corresponding to the learning plan; and determining the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set based on the first confidence level and the second confidence level.

[0097] In the embodiments of this application, the system can establish a prerequisite relationship based on the implicit knowledge point set and the first knowledge point set. Specifically, it can determine the prerequisite relationship based on the implicit knowledge points extracted from the question information and the first knowledge point set, with reference to the teaching syllabus, subject expert experience, and historical student learning data, and determine the knowledge points in the first implicit knowledge point subset as the prerequisite knowledge points of the knowledge points in the first knowledge point set.

[0098] In this embodiment of the application, the system can establish a correlation relationship based on the implicit knowledge point set and the first knowledge point set. Specifically, it can determine the correlation relationship based on the implicit knowledge points extracted from the note information and the first knowledge point set through text similarity calculation (TF-IDF vectorization technology can be used to convert the knowledge point description into a vector and calculate the cosine similarity) and knowledge point co-occurrence frequency statistics, and associate the knowledge points in the second implicit knowledge point subset with the knowledge points in the first knowledge point set.

[0099] In this embodiment of the application, the first confidence level can be used to represent the confidence level of the prior relationship of the multiple knowledge points as the prior relationship of the target knowledge points corresponding to the learning plan.

[0100] In this embodiment of the application, the second confidence level can be used to represent and use the correlation relationship of multiple knowledge points as the confidence level of the correlation relationship of the target knowledge points corresponding to the learning plan.

[0101] In the embodiments of this application, the calculation of the first confidence level and the second confidence level can be based on the student's actual learning data (mastery, answer results, note contribution) and adopt a dynamic update mechanism. As new learning data accumulates, the first confidence level and the second confidence level will be continuously updated to ensure the timeliness of the relationship credibility.

[0102] In this embodiment, determining the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set based on the first confidence level and the second confidence level can be based on a confidence threshold. The confidence threshold can be set based on subject characteristics and teaching experience. Then, the system can check the confidence level of the relationship between the implicit knowledge point and the first knowledge point set. If the confidence level of the prerequisite relationship between implicit knowledge point A and explicit knowledge point A is greater than or equal to the first confidence threshold, or the confidence level of the correlation relationship between implicit knowledge point A and explicit knowledge point A is greater than or equal to the first confidence threshold, then implicit knowledge point A is included in the target implicit knowledge point set. If the confidence level of the prerequisite relationship between implicit knowledge point A and explicit knowledge point A is less than the first confidence threshold, or the confidence level of the correlation relationship between implicit knowledge point A and explicit knowledge point A is less than the first confidence threshold, then implicit knowledge point A is not included in the target implicit knowledge point set.

[0103] As an optional approach, when performing the step of "determining the first confidence level of using the multiple knowledge point prerequisite relationships as the target knowledge point prerequisite relationships corresponding to the learning plan", the following method can be used, but is not limited to: determining the student's first mastery level data on the first subset of implicit knowledge points, and determining the student's answer to the question based on the student's answer data on the question information; determining the first support level data for each knowledge point prerequisite relationship among the multiple knowledge point prerequisite relationships based on the first mastery level data and the answer results; updating the confidence level of each knowledge point prerequisite relationship based on the historical confidence level of the first support level data to obtain the current first confidence level of using the multiple knowledge point prerequisite relationships as the target knowledge point prerequisite relationships.

[0104] In the embodiments of this application, the first level of mastery data can be the probability of a student mastering a first implicit knowledge point, calculated by the system through a model. For example, the first level of mastery data in the embodiments of this application can be represented as P(k).

[0105] In this embodiment of the application, the answer data can be data related to the student's answers to the target question. For example, the answer data in this embodiment may specifically include the answer, the answering time, and whether the answer was modified.

[0106] In this embodiment of the application, the answer result can be a judgment on the correctness of the answer (correct=0 or 1). For example, if a student answers question A incorrectly, the answer result can be correct=0; if a student answers question A correctly, the answer result can be correct=1.

[0107] In this embodiment, the first support data can be evidence strength data based on the student's first level of mastery data and answer results to determine the establishment of a priori relationship. For example, the first support data in this embodiment can specifically be represented as... , The value can be in the range of [0,1]. The larger the value, the stronger the evidence for the prior relationship.

[0108] For example, if a student answers the target question incorrectly (correct=0) and has not mastered the first implicit knowledge point (P(k) is less than 0.7, and the mastery threshold is 0.7), then the student's incorrect answer to the target question and failure to master the first implicit knowledge point can be considered strong evidence, and the first support data Snew can be 0.5.

[0109] For example, if a student answers the target question correctly (correct=1) but has not mastered the first implicit knowledge point (P(k) less than 0.7), then the student's correct answer to the target question but failure to master the first implicit knowledge point can be considered weak evidence, with the first support data being weak. It can be 0.2.

[0110] For example, if a student answers the target question correctly (correct=1) and possesses the first implicit knowledge point (P(k) greater than or equal to 0.7), then the student's correct answer to the target question and possession of the first implicit knowledge point can be considered moderate evidence, representing first-level support data. It can be 0.3.

[0111] For example, if a student answers the target question incorrectly (correct=0) but has not mastered the first implicit knowledge point (P(k) less than 0.7), then the student's incorrect answer and failure to master the first implicit knowledge point can be considered strong evidence, representing the first support data. It can be 0.5.

[0112] In this embodiment, historical confidence can be the confidence of the prior relationship of knowledge points before the current update. The initial value of historical confidence can be 0 (when the relationship is first established), and historical confidence can be continuously updated as support data accumulates. For example, in this embodiment, historical confidence can be specifically represented as Confold.

[0113] In this embodiment, the first confidence level can be an updated confidence level, which can be used to reflect the credibility of the current prior relationship. For example, the historical confidence level in this embodiment can be specifically represented as Confnew.

[0114] In this embodiment of the application, the formula for updating the first confidence level can be as shown in Formula 1, wherein, It can represent historical confidence level. This can represent the first level of confidence. The weighting coefficient can represent the historical confidence level, and r can represent the prior knowledge relationship. This can represent the first level of support data.

[0115] (Formula 1)

[0116] As an optional approach, when performing the "determining the second confidence level of using the correlation relationships of multiple knowledge points as the correlation relationships of the target knowledge points corresponding to the learning plan", the following method can be used, but is not limited to: determining the student's second mastery level data on the second subset of implicit knowledge points, and the contribution data of the note information to the correlation relationships of the multiple knowledge points; determining the second support data of each correlation relationship of the multiple knowledge points based on the mastery level data and the contribution data; updating the second confidence level of each correlation relationship of the multiple knowledge points according to the historical confidence level of the second support data to obtain the current second confidence level of using the correlation relationships of the multiple knowledge points as the correlation relationships of the target knowledge points.

[0117] In the embodiments of this application, the second level of mastery data can be the probability of a student's mastery of the second implicit knowledge point, calculated by the system through a model.

[0118] In the embodiments of this application, the contribution data can be data on the degree of support of the note information for the establishment of the correlation relationship. For example, the contribution data in the embodiments of this application can be specifically represented as Contribution(i,j), and the value range of the contribution data can be [0,1].

[0119] In this embodiment of the application, the formula for calculating the contribution data can be as shown in Formula 2, where base can represent the basic contribution coefficient. If the knowledge point in the context of the notes belongs to any knowledge point in the relevance relationship, then base can be 1; otherwise, base can be 0.5. P(ki) can represent the mastery probability of knowledge point ki, and P(kj) can represent the mastery probability of knowledge point kj.

[0120] (Formula 2)

[0121] In this embodiment, the second support data can be evidence strength data based on the student's second mastery level data and contribution data to determine the correlation. For example, the first support data in this embodiment can specifically range from [0,1], where a larger Snew indicates stronger evidence of the correlation.

[0122] In this embodiment of the application, the formula for updating the second confidence level can also be as shown in Formula 1, wherein, It can represent historical confidence level. The confidence level can be represented by the figure. The weighting coefficient can represent the historical confidence level, and r can represent the prior knowledge relationship. This can represent the second support data.

[0123] As an optional approach, when performing the step of "determining the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set based on the first confidence level and the second confidence level", the following method can be used, but is not limited to: storing the multiple knowledge point prerequisite relationships and the multiple knowledge point correlation relationships in the candidate relationship library corresponding to the learning plan, so as to monitor the first confidence level and the second confidence level through the candidate relationship library; in response to the first confidence level of the first knowledge point prerequisite relationship being greater than the prerequisite confidence level threshold and the number of times the first implicit knowledge point corresponding to the first knowledge point prerequisite relationship is identified being greater than the number of times threshold, determining the first implicit knowledge point as the first target implicit knowledge point corresponding to the learning plan; in response to the first confidence level of the first knowledge point correlation relationship being greater than the correlation confidence level threshold and the number of times the first implicit knowledge point corresponding to the first knowledge point correlation relationship is identified being greater than the number of times threshold, determining the second implicit knowledge point as the first target implicit knowledge point corresponding to the learning plan; and determining the target implicit knowledge point set corresponding to the learning plan based on the first target implicit knowledge point and the second target implicit knowledge point.

[0124] In this embodiment, the candidate relation database can be a database used in the learning companion system to store the prerequisite and relevance relationships of the knowledge points to be verified. For example, the candidate relation database in this embodiment may specifically include a Neo4j graph database, a MySQL relational database, etc. Each record in the candidate relation database may contain a relation ID, relation type (prerequisite / relevance), implicit knowledge point ID, explicit knowledge point ID, current confidence level, number of times it has been identified, first identification time, and last update time, etc.

[0125] In this embodiment of the application, the prerequisite relation of the first knowledge point can be a prerequisite relation in the candidate relation library.

[0126] In this embodiment, the prior repair confidence threshold can be the minimum confidence level set by the system to determine whether the prior repair relationship meets the standard.

[0127] For the embodiments of this application, the number of times threshold can be the minimum number of times that the system sets to determine whether the prior relationship has been sufficiently verified.

[0128] In the embodiments of this application, the first target implicit knowledge point can be an implicit knowledge point selected from the first implicit knowledge point subset that meets the corresponding prerequisite relationship.

[0129] In this embodiment, the system can check the first confidence level and the number of times each prerequisite relation is identified during the periodic monitoring of the candidate relation database. If the first confidence level is greater than a threshold and the number of times it is identified is greater than a threshold, the first implicit knowledge point corresponding to the candidate relation can be determined as the first target implicit knowledge point. If the first confidence level is greater than a threshold and the number of times it is identified is less than a threshold, the first implicit knowledge point corresponding to the candidate relation is not determined as the first target implicit knowledge point. If the first confidence level is less than a threshold and the number of times it is identified is greater than a threshold, the first implicit knowledge point corresponding to the candidate relation is not determined as the first target implicit knowledge point. If the first confidence level is less than a threshold and the number of times it is identified is less than a threshold, the first implicit knowledge point corresponding to the candidate relation is not determined as the first target implicit knowledge point.

[0130] In this embodiment of the application, the first knowledge point relevance relationship can be a relevance relationship in the candidate relationship library.

[0131] In this embodiment of the application, the correlation confidence threshold can be the minimum confidence level set by the system to determine whether the correlation relationship meets the standard.

[0132] In the embodiments of this application, the second target implicit knowledge point can be an implicit knowledge point selected from the subset of second implicit knowledge points that meets the corresponding relevance criteria.

[0133] In the embodiments of this application, the system can check the second confidence level and the number of times each relevance relationship is identified during the periodic monitoring of the candidate relationship database. If the second confidence level is greater than a threshold and the number of times it is identified is greater than a threshold, the first implicit knowledge point corresponding to the candidate relationship can be determined as the first target implicit knowledge point. If the second confidence level is greater than a threshold and the number of times it is identified is less than a threshold, the first implicit knowledge point corresponding to the candidate relationship is not determined as the first target implicit knowledge point. If the second confidence level is less than a threshold and the number of times it is identified is greater than a threshold, the first implicit knowledge point corresponding to the candidate relationship is not determined as the first target implicit knowledge point. If the confidence level of the e-th person is less than a threshold and the number of times it is identified is less than a threshold, the first implicit knowledge point corresponding to the candidate relationship is not determined as the first target implicit knowledge point.

[0134] In this embodiment of the application, the system can merge the first target implicit knowledge point and the second target implicit knowledge point, remove duplicate elements (if any), and obtain a set of target implicit knowledge points; if the first target implicit knowledge point and the second target implicit knowledge point are duplicated, one of the first target implicit knowledge point or the second target implicit knowledge point can be retained during the merging.

[0135] As an optional approach, after performing the step of "storing the multiple knowledge point prerequisite relationships and the multiple knowledge point correlation relationships in the candidate relation library corresponding to the learning plan, so as to monitor the first confidence level and the second confidence level through the candidate relation library", the following method can also be used, but is not limited to: determining the monitoring period of the candidate relation library for the first confidence level and the second confidence level; in response to the first confidence level of the second knowledge point prerequisite relationship among the multiple knowledge point prerequisite relationships and / or the second confidence level of the second knowledge point prerequisite relationship among the multiple knowledge point correlation relationships being lower than the monitoring threshold during the monitoring period, removing the second knowledge point prerequisite relationship and / or the second knowledge point prerequisite relationship from the candidate relation library; in response to the end of the monitoring period, removing the multiple knowledge point prerequisite relationships and the multiple knowledge point correlation relationships from the candidate relation library.

[0136] In the embodiments of this application, the monitoring period can be the monitoring duration of the candidate relation library for relations that do not meet the standards. The setting of the monitoring period can be adjusted according to the learning cycle of the subject chapters, and candidate relations that still do not meet the standards after the monitoring period can be removed.

[0137] In the embodiments of this application, the second knowledge point prerequisite relation can be a prerequisite relation in the candidate relation library that does not meet the standard.

[0138] In this embodiment of the application, the second knowledge point relevance relationship can be a relevance relationship in the candidate relationship library that does not meet the standard.

[0139] In this embodiment, the monitoring threshold can be a minimum confidence level set by the system to determine whether a relationship is worthless; candidate relationships below the monitoring threshold can be removed.

[0140] In the embodiments of this application, if the first confidence level of the prerequisite relationship of the second knowledge point and the second confidence level of the relevance relationship of the second knowledge point are both lower than the monitoring threshold, then the prerequisite relationship of the second knowledge point and the relevance relationship of the second knowledge point can both be removed from the candidate relationship library and incorporated into the formal knowledge graph; if the first confidence level of the prerequisite relationship of the second knowledge point is lower than the monitoring threshold or the second confidence level of the relevance relationship of the second knowledge point is lower than the monitoring threshold, then the prerequisite relationship of the second knowledge point or the relevance relationship of the second knowledge point can be removed from the candidate relationship library and incorporated into the formal knowledge graph.

[0141] Compared to existing technologies, this embodiment accurately determines the set of implicit knowledge points by splitting and merging subsets of implicit knowledge points from the sources of questions and notes, removing duplicates; it provides a reliable basis for selecting target implicit knowledge points by establishing two types of relationships: prerequisite and relevance, and calculating confidence levels; it improves the accuracy and reliability of prerequisite relationship judgment by combining students' mastery of implicit knowledge points with their answer results; it calculates the confidence level of relevance relationships based on students' mastery and note contribution, making the judgment of knowledge point associations more aligned with students' self-directed learning focus; it accurately selects high-value target implicit knowledge points by monitoring confidence levels and recognition frequency through a candidate relationship database, ensuring the relevance of learning plan updates. By setting monitoring cycles and removal rules, it purifies the candidate relationship database data to avoid interference from invalid associations, improving the efficiency and accuracy of learning plan optimization.

[0142] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0143] Example 1: Based on the knowledge point set corresponding to the course material information, determine the basic learning information of the knowledge points in the knowledge point set, as well as the prerequisite knowledge information and correlation information between the knowledge points; generate attribute information of the knowledge points based on the basic learning information, the prerequisite knowledge information, and the correlation information, and construct a knowledge graph corresponding to the knowledge point set based on the attribute information; map the knowledge graph into the knowledge point learning grid corresponding to the course material information to obtain the target knowledge point learning grid; generate the student's learning plan for the target time period based on the knowledge point learning grid in the knowledge point learning grid module, and generate the student's learning path based on the learning plan; the knowledge point learning grid module is used to display the learning path to the student through the knowledge point learning grid; wherein, the target knowledge point learning grid is a learning grid that includes the attribute information of the knowledge points and the correlation information between the knowledge points.

[0144] Example 11: Based on attribute information, determine the frequency data of knowledge points appearing in the same historical learning sessions, and generate a first association strength corresponding to the knowledge points in the knowledge point set based on the frequency data; based on attribute information, determine the mastery level data of knowledge points, and generate a second association strength corresponding to the knowledge points in the knowledge point set based on the mastery level data; based on attribute information, determine the proximity of the learning order of knowledge points, and generate a third association strength corresponding to the knowledge points in the knowledge point set based on the proximity of the learning order; based on attribute information, determine the probability data of incorrect answers to knowledge points, and generate a fourth association strength corresponding to the knowledge points in the knowledge point set based on the probability data of incorrect answers; generate a target association strength corresponding to the knowledge points in the knowledge point set based on the first association strength, second association strength, third association strength, and fourth association strength.

[0145] Example 12: Determine the first association type between knowledge points based on basic learning information, the second association type between knowledge points based on prerequisite knowledge information, and the third association type between knowledge points based on relevance information; generate directed edges corresponding to the knowledge point set based on the first association type between knowledge points corresponding to basic learning information and the second association type between knowledge points corresponding to prerequisite knowledge information, and generate undirected edges corresponding to the knowledge point set based on the first association type between knowledge points corresponding to the first association type and the third association type between knowledge points corresponding to relevance information; determine the target association strength corresponding to the knowledge points in the knowledge point set based on attribute information, and generate an association matrix based on the knowledge point set, directed edges, undirected edges, and association strength; construct graph edges based on the association matrix, and construct graph nodes based on the knowledge point set and attribute information, and combine graph nodes and graph edges to generate a knowledge graph.

[0146] Example 13: Quality assessment of the knowledge graph. In the case of isolated nodes in the knowledge graph, the knowledge point set is refined, and the knowledge graph is adjusted based on the refined knowledge point set. In the case of node density in the knowledge graph that is greater than the density threshold, the knowledge point set is merged, and the knowledge graph is adjusted based on the merged knowledge point set.

[0147] Example 14: In response to acquiring learning behavior data of students learning based on the learning plan, the system determines the students' question information and note information during the learning process based on the learning behavior data; identifies implicit knowledge points in the question information and note information, and determines the implicit attribute information of the implicit knowledge points, including implicit prerequisite knowledge information and implicit relevance information; when the implicit attribute information meets the knowledge graph update conditions, the system updates the knowledge graph based on the implicit knowledge points and implicit attribute information to obtain the target knowledge graph, which is used to generate a learning grid that includes implicit knowledge points and their attribute information.

[0148] Example 15: Determine the prerequisite knowledge points corresponding to the implicit knowledge points based on implicit prerequisite knowledge information; determine the students' mastery of the implicit knowledge points and prerequisite knowledge points based on the question information, as well as the answer results data of the questions containing the implicit knowledge points and prerequisite knowledge points; determine the confidence level of the implicit prerequisite knowledge information based on the mastery level and answer results data; in response to the confidence level of the implicit prerequisite knowledge information being greater than the confidence level threshold and the number of times the implicit knowledge points are identified being greater than the number of times threshold, update the graph nodes in the knowledge graph based on the implicit knowledge points, and update the directed edges in the knowledge graph based on the implicit prerequisite knowledge information.

[0149] Example 16: Determine the relevant knowledge points corresponding to the implicit knowledge points based on implicit correlation information; determine the student's mastery of the implicit knowledge points and relevant knowledge points based on note information, as well as the information contribution data of note information to implicit correlation information; determine the confidence level of implicit correlation information based on the mastery level and information contribution data; in response to the confidence level of implicit correlation information being greater than the confidence level threshold and the number of times implicit knowledge points are identified being greater than the number of times threshold, update the graph nodes in the knowledge graph based on the implicit knowledge points, and update the undirected edges in the knowledge graph based on implicit prerequisite knowledge information.

[0150] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0151] Example 2: Based on the student's learning location information in the target knowledge point learning grid, determine the target learning course corresponding to the learning location information; divide the student's first learning behavior data in the target learning course into at least one learning behavior data set, and generate at least one behavioral feature set corresponding to the at least one learning behavior data set; analyze the student's mastery of knowledge points in the target learning course based on the at least one behavioral feature set, and determine the student's unmastered knowledge point set in the target learning course; match the unmastered knowledge point set with the target knowledge graph corresponding to the target knowledge point learning grid to obtain the target knowledge point network corresponding to the unmastered knowledge point set in the target knowledge graph; generate the student's target learning path in the target knowledge point learning grid based on the target knowledge point network, and the target learning path is used to assist the student in mastering the unmastered knowledge point set.

[0152] Example 21: Determine the time information corresponding to each behavioral feature in at least one set of behavioral features, and evaluate the effectiveness of each behavioral feature in at least one set of behavioral features based on the time information to obtain effective value data for each behavioral feature in at least one set of behavioral features; filter the behavioral features in at least one set of behavioral features based on the effective value data to obtain target behavioral features that meet the effective conditions in at least one set of behavioral features, and form at least one set of target behavioral features; analyze the students' mastery of knowledge points in the target learning course based on at least one set of target behavioral features to determine the set of knowledge points that students have not mastered in the target learning course.

[0153] Example 22: Match at least one set of target behavioral features with each knowledge point in the target learning course to determine at least one target behavioral feature corresponding to each knowledge point in the target learning course; identify the importance data of at least one target behavioral feature corresponding to each knowledge point in the target learning course to each knowledge point in the target learning course through a target model, which is trained based on students' historical behavioral features and historical knowledge point mastery levels; determine students' mastery data of each knowledge point in the target learning course based on at least one target behavioral feature and importance data corresponding to each knowledge point; determine the set of unmastered knowledge points in the target learning course based on the mastery data.

[0154] Example 23: Based on importance data, determine the influence coefficient of at least one behavioral feature corresponding to each knowledge point on the degree of mastery of each knowledge point; analyze at least one behavioral feature based on the influence coefficient to generate data on the student's mastery of each knowledge point in the target learning course.

[0155] Example 24: Map the mastery data of each knowledge point to the objective function to obtain the mastery probability data of each knowledge point; select knowledge points that meet the unmastered conditions from the knowledge corresponding to the target learning course based on the mastery probability data, and form a set of unmastered knowledge points from the knowledge points selected from the knowledge corresponding to the target learning course.

[0156] Example 25: Generate a sequence of unmastered knowledge points based on the set of unmastered knowledge points; mark the sequence of unmastered knowledge points with unmastered identifiers in the target learning grid, and extract the paths of unmastered knowledge points corresponding to the sequence of unmastered knowledge points from the target learning grid; match the paths of unmastered knowledge points with the target knowledge graph according to the unmastered identifiers to obtain the target knowledge point network corresponding to the set of unmastered knowledge points in the target knowledge graph.

[0157] Example 26: This method is used to match the paths of unmastered knowledge points with the target knowledge graph based on the unmastered identifier, thereby obtaining the network of knowledge points to be screened corresponding to the set of unmastered knowledge points in the target knowledge graph; and to determine the target knowledge point network from the network of knowledge points to be screened based on the prerequisite relationships and relevance information of the paths of unmastered knowledge points in the network of knowledge points to be screened.

[0158] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0159] Example 3: Obtain second learning behavior data of the student learning knowledge points in the set of unmastered knowledge points within the knowledge point learning grid; determine first note information and first note information of the student learning knowledge points in the set of unmastered knowledge points based on the second learning behavior data; evaluate the student's learning needs for knowledge points in the set of unmastered knowledge points based on the first note information and first note information to obtain learning intention intensity data corresponding to knowledge points in the knowledge point learning grid; identify the student's target learning intention for knowledge points in the set of unmastered knowledge points based on the learning intention intensity data, the first note information and first note information; update the set of unmastered knowledge points based on the target learning intention and the knowledge point location information of the set of unmastered knowledge points in the knowledge point learning grid to obtain a target set of unmastered knowledge points, and recommend the target set of unmastered knowledge points to the student.

[0160] Example 31: Based on note information, determine the update frequency of students' notes within the target time range, and evaluate students' learning activity data within the target time range based on the update frequency; based on note information and question information, determine the question frequency of students' doubts about knowledge points in the set of unmastered knowledge points, and evaluate students' difficulty in understanding knowledge points in the set of unmastered knowledge points based on the question frequency; evaluate students' learning needs for knowledge points in the set of unmastered knowledge points based on learning activity data and difficulty in understanding data, and obtain the learning intention intensity data corresponding to knowledge points in the knowledge point learning grid.

[0161] Example 32: Based on the second learning behavior data, determine the learning end time for students to learn the knowledge points in the set of unmastered knowledge points; determine the decay factor of learning intention intensity over time based on the learning end time and time decay coefficient; evaluate the urgency data of students learning the knowledge points in the set of unmastered knowledge points based on the decay factor; perform weighted analysis on learning activity data, comprehension difficulty data and urgency data to evaluate students' learning needs for the knowledge points in the set of unmastered knowledge points, and obtain the learning intention intensity data corresponding to the knowledge points in the knowledge point learning grid.

[0162] Example 33: Based on the first note information, generate student learning content data and corresponding behavior sequence data for the second learning behavior data; evaluate the student's learning intention for the knowledge points in the set of unmastered knowledge points as a first probability data of deep understanding intention, a second probability data of basic consolidation intention, and a third probability data of conceptual inquiry intention based on the learning intention intensity data, learning content data, and behavior sequence data; determine the target learning intention from deep understanding intention, basic consolidation intention, and conceptual inquiry intention based on the first probability data, second probability data, and third probability data.

[0163] Example 34: Evaluate the student's required level of understanding of the knowledge points in the set of unmastered knowledge points based on the first probability data, the second probability data, and the third probability data; if the required level of understanding data is greater than the first threshold for required understanding, determine the student's intention to deepen understanding as the target learning intention for the knowledge points in the set of unmastered knowledge points; if the required level of understanding data is less than or equal to the first threshold for required understanding and greater than the second threshold for required understanding, determine the student's intention to consolidate basic knowledge as the target learning intention for the knowledge points in the set of unmastered knowledge points, wherein the second threshold for required understanding is less than the first threshold for required understanding; if the required level of understanding data is less than the second threshold for required understanding, determine the student's intention to raise conceptual questions as the target learning intention for the knowledge points in the set of unmastered knowledge points.

[0164] Example 35: Generate student learning content data based on the first note information; map the second learning behavior data into the knowledge graph corresponding to the knowledge point learning grid to obtain the student's learning knowledge point data in the knowledge graph; perform multi-dimensional information matching between the learning content data and the learning knowledge point data to generate knowledge point location information for the set of unmastered knowledge points; update the set of unmastered knowledge points according to the target learning intention and the knowledge point location information to obtain the target set of unmastered knowledge points, generate the target set of unmastered knowledge points and the target learning path corresponding to the target set of unmastered knowledge points; generate recommendation information for the target set of unmastered knowledge points and the target learning path so that students can master the knowledge points in the target set of unmastered knowledge points based on the target set of unmastered knowledge points and the target learning path.

[0165] Example 36: Obtain second learning behavior data of students learning knowledge points in the set of unmastered knowledge points within the knowledge point learning grid from the learning companion system; divide the second learning behavior data of students in the target knowledge point learning grid into at least one learning behavior data set, and generate a set of behavioral features corresponding to at least one learning behavior data set; determine the time information corresponding to each behavioral feature in the behavioral feature set, and evaluate the effectiveness of each behavioral feature in the behavioral feature set based on the time information to obtain the effective value data of each behavioral feature in the behavioral feature set; filter the behavioral features in the behavioral feature set based on the effective value data to obtain the target behavioral features that meet the effective conditions in the behavioral feature set, and form a target behavioral feature set; analyze the students' mastery of knowledge points in the target knowledge point learning grid based on the target behavioral feature set to determine the set of unmastered knowledge points in the target knowledge point learning grid; identify the box selection behavior from the target behavioral feature set, and classify the learning content selected by the box selection behavior to obtain the first note information and first note information of students learning knowledge points in the set of unmastered knowledge points.

[0166] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0167] Example 4: The second learning behavior data is mapped into the knowledge graph corresponding to the knowledge point learning grid to obtain the student's learning knowledge point data in the knowledge graph; the student's learning content data is generated based on the question information and the note information, and the learning content data is matched with the learning knowledge point data in multiple dimensions to generate the knowledge point location information of the set of unmastered knowledge points.

[0168] Example 41: Generate student learning content data based on question information and note information; perform similarity matching based on the first semantic embedding vector corresponding to the learning content data and the second semantic embedding vector corresponding to the learning knowledge point data to match the content similarity between the learning content data and the learning knowledge point data, and obtain a first similarity matching result; perform similarity matching based on the context data corresponding to the learning content data and the adjacent knowledge point data corresponding to the learning knowledge point data to match the overall similarity between the learning content data and the learning knowledge point data in the knowledge graph, and obtain a second similarity matching result; perform similarity matching based on the first position data of the learning content data in the textbook and the second position data of the learning knowledge point data in the textbook to match the position similarity between the learning content data and the learning knowledge point data, and obtain a third similarity matching result; based on the first similarity matching result, the second similarity matching result, and the third similarity matching result, generate knowledge point location information for the set of unmastered knowledge points.

[0169] Example 42: A fusion analysis is performed based on the first similarity matching result, the second similarity matching result, and the third similarity matching result to evaluate the accuracy of the location of knowledge points in the set of unmastered knowledge points, and to obtain the location reliability data of the learning content data and the learning knowledge point data; based on the location reliability data, unmastered knowledge points that meet the confidence conditions are selected from the set of unmastered knowledge points to form a location knowledge point set; based on the prerequisite relationships and relevance relationships of the knowledge points in the location knowledge point set in the knowledge graph, the knowledge point location information of the set of unmastered knowledge points is generated.

[0170] Example 43: Based on the prerequisite relationships of knowledge points in the knowledge point set located in the knowledge graph, determine the first knowledge point set consisting of the prerequisite knowledge points corresponding to the knowledge point set located in the knowledge graph, and the second knowledge point set with the knowledge points in the knowledge point set as prerequisite knowledge points; extract the knowledge point dependency relationship chain corresponding to the unmastered knowledge point set from the knowledge graph based on the knowledge point set located, the first knowledge point set, and the second knowledge point set; determine the third knowledge point set related to the knowledge point set located in the knowledge graph based on the correlation relationships of knowledge points in the knowledge point set located in the knowledge graph, and extract the knowledge point related relationship chain corresponding to the unmastered knowledge point set based on the knowledge point dependency relationship chain and the knowledge point related relationship chain; generate the knowledge point location information of the unmastered knowledge point set based on the knowledge point dependency relationship chain and the knowledge point related relationship chain.

[0171] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0172] Example 5: Based on the student's review path for already learned knowledge points within the target knowledge point learning grid, determine the knowledge points to be reviewed indicated by the review path; extract the network of knowledge points to be reviewed corresponding to the knowledge points to be reviewed from the knowledge graph corresponding to the target knowledge point learning grid, wherein the network of knowledge points to be reviewed includes at least one prerequisite knowledge point and at least one related knowledge point corresponding to the knowledge point to be reviewed; update the review path based on the student's mastery of the knowledge point to be reviewed, the at least one prerequisite knowledge point, and the at least one related knowledge point to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed; determine the student's target knowledge points to be reviewed based on the target knowledge point review path, generate recommendation information for the target knowledge points to be reviewed, and recommend the review of the target knowledge points to the student.

[0173] Example 51: Determine the knowledge point mastery level indicator of the knowledge point to be reviewed from the knowledge point network; when the knowledge point mastery level indicator of the knowledge point to be reviewed is not mastered, determine the first knowledge point mastery level indicator of at least one prerequisite knowledge point and the second knowledge point mastery level indicator of at least one related knowledge point in the knowledge point network to be reviewed; based on the first knowledge point mastery level indicator and the second knowledge point mastery level indicator, update the knowledge point review path to obtain the target knowledge point review path corresponding to the knowledge point to be reviewed.

[0174] Example 52: Based on the mastery level identifier of the first knowledge point and the mastery level identifier of the second knowledge point, determine the set of first knowledge points that the student has mastered from at least one prerequisite knowledge point and at least one related knowledge point; remove the set of first knowledge points from the network of knowledge points to be reviewed, and use the knowledge points to be reviewed as the starting point of the review path to update the network of knowledge points to be reviewed, so as to obtain the target knowledge point review path corresponding to the knowledge points to be reviewed.

[0175] Example 53: Based on the mastery level indicators of the first and second knowledge points, determine the set of second knowledge points that the student has not mastered from at least one prerequisite knowledge point and at least one related knowledge point; determine the learning priority information of the knowledge points in the second knowledge point set; based on the learning priority information, select the first target knowledge point that meets the priority condition from the second knowledge point set; use the first target knowledge point as the starting point of the review path, update the network of knowledge points to be reviewed, and obtain the review path of the target knowledge point.

[0176] Example 54: Determine the mastery probability data and knowledge point difficulty data for each knowledge point in the second knowledge point set; evaluate the learning priority of each knowledge point in the second knowledge point set based on the mastery probability data and knowledge point difficulty data to obtain the learning priority information of each knowledge point in the second knowledge point set; determine the knowledge point with the highest learning priority information in the second knowledge point set as the first target knowledge point.

[0177] Example 55: When the knowledge point mastery level indicator of the knowledge point to be reviewed is marked as "mastered", the knowledge point traversal range is determined based on the knowledge point to be reviewed; the knowledge points are traversed within the knowledge point traversal range to obtain the third set of knowledge points that the student has not mastered; the set of prerequisite knowledge points and the set of related knowledge points corresponding to the third set of knowledge points are determined in the knowledge graph; the second target knowledge point that meets the priority condition is determined from the third set of knowledge points, the set of prerequisite knowledge points, and the set of related knowledge points; the knowledge point to be reviewed is removed from the knowledge point network to be reviewed, and the second target knowledge point is used as the starting point of the review path to update the knowledge point network to obtain the target knowledge point review path.

[0178] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0179] Example 6: In response to obtaining the third learning behavior data of the student reviewing the target knowledge point, the first mastery level identifier of the target knowledge point is updated; the update influence range of the mastery level of the target knowledge point in the knowledge graph corresponding to the knowledge point learning grid is determined, and a set of knowledge points to be updated is formed based on the knowledge points covered by the update influence range; the mastery level data of the knowledge points in the set of knowledge points to be updated is updated, and the second mastery level identifier of the knowledge points in the set of knowledge points to be updated is updated according to the updated mastery level data; the knowledge point learning grid is updated to the target knowledge point learning grid based on the second mastery level identifier, and the target review content of the student is determined according to the target knowledge point learning grid.

[0180] Example 61: Based on the target prerequisite relationships and target relevance relationships of target knowledge points in the knowledge graph, determine the direct dependencies of target knowledge points, and determine the direct impact range based on the direct dependencies; based on the indirect connection relationships between target knowledge points and other knowledge points in the knowledge graph, determine the indirect dependencies of target knowledge points, and determine the indirect impact range based on the indirect dependencies; determine the set of directly impacted knowledge points covered by the direct impact range, and the set of indirect impacted knowledge points covered by the indirect impact range; generate a set of knowledge points to be updated based on the set of directly impacted knowledge points and the set of indirect impacted knowledge points.

[0181] Example 62: Determine the first indirect dependency relationship established by a target knowledge point through an indirect knowledge point in the knowledge graph, and the second indirect dependency relationship connected by multiple indirect knowledge points; based on the first influence decay degree corresponding to the first indirect dependency relationship, determine the first influence range of the target knowledge point based on the first indirect dependency relationship in the knowledge graph; based on the second influence decay degree corresponding to the second indirect dependency relationship, determine the second influence range of the target knowledge point based on the second indirect dependency relationship in the knowledge graph; determine the indirect influence range based on the first influence range and the second influence range.

[0182] Example 63: Based on direct and indirect dependencies, determine the dependency strength data between knowledge points in the set of knowledge points to be updated and the target knowledge points; identify the importance data of knowledge points in the set of knowledge points to be updated through the target model, which is trained based on students' historical behavioral characteristics and their mastery of historical knowledge points; determine the update priority information for updating knowledge points in the set of knowledge points to be updated based on dependency strength data, importance data, and mastery level data; update the mastery level data according to the update priority information, and update the second mastery level label of the knowledge points in the set of knowledge points to be updated based on the updated mastery level data.

[0183] Example 64: Determine the knowledge point update order list corresponding to the knowledge point set to be updated according to the update priority information, so as to determine the update order of knowledge points in the knowledge point set to be updated; for the first target knowledge point in the knowledge point set to be updated, determine the centrality adjustment coefficient corresponding to the first target knowledge point based on the connection strength data of the first target knowledge point in the knowledge graph, the distance data between the first target knowledge point and other knowledge points, and the degree centrality coefficient. The first target knowledge point is any knowledge point in the knowledge point set to be updated; update the mastery data of the first target knowledge point based on the centrality adjustment coefficient and the change in the mastery data of the target knowledge point; update the second mastery label of the knowledge points in the knowledge point set to be updated based on the updated mastery data of the knowledge points in the knowledge point set to be updated.

[0184] Example 65: When the connection strength data in the centrality adjustment coefficient is greater than the connection strength threshold and the change in the mastery level data is less than the first change threshold, the first target knowledge point is identified as a key knowledge point, and the second mastery level identifier of the first target knowledge point is marked as an unmastered identifier; when the degree centrality coefficient in the centrality adjustment coefficient is greater than the degree centrality threshold and the mastery level data is greater than the second mastery level threshold, the first target knowledge point is identified as a basic knowledge point, and the second mastery level identifier of the first target knowledge point is marked as a mastered identifier.

[0185] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0186] Example 7: Obtain fourth learning behavior data of students learning knowledge points in the knowledge point learning grid, and obtain third mastery level identifiers of knowledge points in the knowledge point learning grid; determine second mastery level data of students learning knowledge points in the knowledge point learning grid based on the fourth learning behavior data, and evaluate second knowledge point status data of knowledge points in the knowledge point learning grid based on the second mastery level data; determine abnormal knowledge points from the knowledge point learning grid based on the first knowledge point status data corresponding to the second mastery level data and the second knowledge point status data; update the knowledge graph corresponding to the knowledge point learning grid based on the abnormal knowledge points to obtain an updated target knowledge graph, which is used to generate the target knowledge point learning grid.

[0187] Example 71: Based on the first knowledge point status data and the second knowledge point status data, perform a status data difference analysis to evaluate the degree of difference between the knowledge point mastery status displayed in the knowledge point learning grid and the student's mastery status of the knowledge points in the knowledge point learning grid; based on the fourth learning behavior data, determine the question information and note information of the student's learning of the knowledge points in the knowledge point learning grid, and determine the student's learning status information of the knowledge points in the knowledge point learning grid based on the question information, note information, and the second mastery level data; based on the learning status information and the degree of difference, identify the knowledge points with abnormal status from the knowledge point learning grid, and update the knowledge graph corresponding to the knowledge point learning grid based on the knowledge points with abnormal status to obtain the updated target knowledge graph.

[0188] Example 72: Based on note information, determine the frequency of student note updates within a target time range, and evaluate the student's learning activity data for knowledge points in the knowledge point learning grid within the target time range based on the update frequency; based on note information and question information, determine the frequency of student questions about knowledge points in the knowledge point learning grid, and evaluate the student's difficulty in understanding knowledge points in the knowledge point learning grid based on the question frequency; if the learning activity data is greater than the activity threshold and / or the difficulty in understanding data is greater than the difficulty threshold and / or the second mastery level data is less than the mastery level threshold, determine the student's learning status information for knowledge points in the knowledge point learning grid as abnormal learning status information.

[0189] Example 73: When the student's learning status information for knowledge points in the knowledge point learning grid is abnormal, the stability data of knowledge points in the knowledge point learning grid in the knowledge graph is evaluated based on the betweenness centrality data and proximity centrality data of knowledge points in the knowledge point learning grid; the credibility data of abnormal learning status information is determined based on the stability data and the degree of difference; abnormal knowledge points are identified from the knowledge point learning grid based on the credibility data, learning status information, and degree of difference; the knowledge graph corresponding to the knowledge point learning grid is updated based on the abnormal knowledge points to obtain the updated target knowledge graph.

[0190] Example 74: Divide the fourth learning behavior data of students in the target knowledge point learning grid into at least one learning behavior data set, and generate a behavior feature set corresponding to at least one learning behavior data set; determine the time information corresponding to each behavior feature in the behavior feature set, and evaluate the effectiveness of each behavior feature in the behavior feature set based on the time information to obtain the effective value data of each behavior feature in the behavior feature set; filter the behavior features in the behavior feature set based on the effective value data to obtain the target behavior features in the behavior feature set that meet the effective conditions, and form a target behavior feature set; identify the box selection behavior from the target behavior feature set, and classify the learning content selected by the box selection behavior to obtain the question information and note information of students learning knowledge points in the abnormal knowledge point set.

[0191] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0192] Example 8: Determine the state correction content corresponding to the abnormal knowledge point; based on the correction content, perform state correction on the abnormal knowledge point in the knowledge point learning grid, and determine the mastery level impact data after the abnormal knowledge point has been state corrected; determine the influence range of the mastery level impact data in the knowledge graph corresponding to the knowledge point learning grid based on the mastery level impact data, and update the knowledge graph within the influence range.

[0193] Example 81: When the first mastery level identifier is a mastery identifier, determine the question information and note information of the student's learning of knowledge points in the knowledge point learning grid based on learning behavior data; evaluate the student's learning needs for knowledge points with abnormal status based on question information and note information, and obtain the learning intention intensity data corresponding to the knowledge points with abnormal status; determine the first mastery level data corresponding to the knowledge points with abnormal status based on the first mastery level identifier; when the learning intention intensity data is greater than the learning intention intensity threshold and the first mastery level data is less than the mastery level threshold, determine the first non-mastery identifier as the second mastery level identifier; determine the first state correction content corresponding to the knowledge points with abnormal status based on the mastery identifier and the first non-mastery identifier.

[0194] Example 82: When the first mastery level identifier is the first non-mastery identifier, the student's question information and note information for learning knowledge points in the knowledge point learning grid are determined based on learning behavior data; the frequency of questions the student has about knowledge points with abnormal status is determined based on the note information and question information, and the student's difficulty in understanding knowledge points with abnormal status is evaluated based on the question frequency; the student's score data for the questions corresponding to the knowledge points with abnormal status is determined; when the difficulty in understanding data is greater than the difficulty in understanding threshold and the score data is less than the score threshold, the second non-mastery identifier is determined as the second mastery level identifier; based on the first non-mastery identifier and the second non-mastery identifier, the second status correction content corresponding to the knowledge points with abnormal status is determined.

[0195] As an alternative approach, this application also provides the following embodiments, but is not limited thereto, including:

[0196] Example 9: Obtain the student's historical learning data; based on the student's historical learning data, determine the student's learning ability data and the student's learning status information for the knowledge points in the knowledge point learning grid; based on the learning ability data and the learning status information, determine the student's learning plan type, and determine the set of knowledge points to be learned in the knowledge point learning grid according to the learning plan type; generate the student's learning path in the knowledge point learning grid based on the set of knowledge points to be learned; determine the set of learning materials that match the learning path from the learning materials, so as to recommend that the student learn the set of knowledge points to be learned based on the set of learning materials.

[0197] Example 91: Extract students' learning characteristics from their historical learning data, and determine their historical learning status for the corresponding historical knowledge points based on these characteristics. The learning characteristics include at least one of learning speed, learning memory, and answering characteristics. Determine students' learning ability data based on their historical learning status. Determine the mastery probability data and centrality data of knowledge points in the knowledge point learning grid, and determine the intensity of students' learning intention for the knowledge points in the knowledge point learning grid based on their historical learning data. Construct a learning status matrix for students' knowledge points in the knowledge point learning grid based on the mastery probability data, centrality data, and intensity of learning intention data. This learning status matrix represents the students' learning status information for the knowledge points in the knowledge point learning grid.

[0198] Example 92: Based on learning ability data and learning status information, determine the time limit of the student's learning plan, and generate at least one learning task information corresponding to the student according to the learning plan time limit; match the at least one learning task information with the knowledge graph corresponding to the knowledge point learning grid to map the at least one learning task information to at least one candidate knowledge point set in the knowledge point learning grid; determine the priority data of the knowledge points in the at least one candidate knowledge point set based on mastery probability data, centrality data, and learning intention intensity data; and form a learning knowledge point set based on the priority data and the knowledge points in the at least one candidate knowledge point set that meet the priority conditions.

[0199] Example 93: Based on the dependencies of knowledge points in the knowledge graph corresponding to the knowledge point learning grid, generate a first knowledge point sequence corresponding to the knowledge points in the set of knowledge points to be learned. The dependencies include prerequisite relationships and relevance relationships. Determine the learning speed information corresponding to the first knowledge point sequence. If the learning speed is greater than the speed threshold, determine the parallel knowledge points corresponding to the first knowledge point sequence, and generate a learning path based on the parallel knowledge points and the first knowledge point sequence. If the learning speed is less than or equal to the speed threshold, generate a learning path based on the first knowledge point sequence.

[0200] Example 94: Based on the prerequisite relationships of knowledge points in the set of knowledge points to be learned in the knowledge graph, determine the set of prerequisite knowledge points corresponding to the set of knowledge points to be learned; generate an initial knowledge point sequence based on the set of knowledge points to be learned, and insert the knowledge points in the set of prerequisite knowledge points into the preceding positions of the knowledge points in the set of knowledge points to be learned in the initial knowledge point sequence to obtain the first knowledge point sequence.

[0201] Example 95: Based on at least one first material feature in the content dimension and at least one second material feature in the question dimension, a feature vector corresponding to the learning material is constructed; the feature vector and student ability data are subjected to correlation matching and similarity matching to obtain a comprehensive matching result of the feature vector and student ability data, and candidate learning materials suitable for the student are selected from the learning materials according to the comprehensive matching result; the student's learning duration is determined based on the student's ability data, and at least one target learning plan is generated for the student according to the learning duration and the learning path to be learned; a set of learning materials matching each of the at least one target learning plan is determined from the learning materials.

[0202] Example 96: Determine the students' already-learned plans in at least one target learning plan, and the set of questions that the students have already learned in the already-learned plans; based on the students' learning of the set of questions they have already learned, analyze the students' mastery data of the knowledge points corresponding to the set of questions they have already learned; update the students' unlearned plans and the set of learning materials corresponding to the unlearned plans in at least one target learning plan based on the mastery data.

[0203] Furthermore, as Figure 1 and Figure 3 The specific implementation of the method shown in this embodiment provides a learning plan update device based on an AI agent, such as... Figure 4 As shown, the device includes: a determination module 31 and an update module 32.

[0204] The determination module 31 is configured to, in response to acquiring learning behavior data of a student learning questions based on a learning plan in the learning companion system, determine the question information and note information of the student's learning target questions based on the learning behavior data;

[0205] The determining module 31 is further configured to determine the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information based on the first set of knowledge points marked on the target question;

[0206] The determining module 31 is further configured to determine the target set of implicit knowledge points corresponding to the learning plan from the set of implicit knowledge points based on the prior relationship and relevance information between the set of implicit knowledge points and the first set of knowledge points.

[0207] The update module 31 is configured to update the target implicit knowledge point set in the knowledge point learning grid corresponding to the learning plan, so as to update the learning plan to include the target implicit knowledge point set.

[0208] In some examples of this embodiment, the determining module 31 is specifically configured to extract knowledge points from the question information and the note information respectively, to obtain a second set of knowledge points corresponding to the question information and a third set of knowledge points corresponding to the note information; to determine the knowledge points in the second set that are not included in the first set of knowledge points as a first subset of implicit knowledge points corresponding to the target question; to determine the knowledge points in the third set that are not included in the first set of knowledge points as a second subset of implicit knowledge points corresponding to the target question; and to determine the implicit knowledge point set based on the first subset of implicit knowledge points and the second subset of implicit knowledge points.

[0209] In some examples of this embodiment, the determining module 31 is further configured to: establish multiple knowledge point prerequisite relationships based on the first subset of implicit knowledge points and the first set of knowledge points, so as to determine the knowledge points in the first subset of implicit knowledge points as prerequisite knowledge points for the knowledge points in the first set of knowledge points; establish multiple knowledge point correlation relationships based on the second subset of implicit knowledge points and the first set of knowledge points, so as to associate the knowledge points in the second subset of implicit knowledge points with the knowledge points in the first set of knowledge points; determine a first confidence level for using the multiple knowledge point prerequisite relationships as the prerequisite relationships for the target knowledge points corresponding to the learning plan and a second confidence level for using the multiple knowledge point correlation relationships as the correlation relationships for the target knowledge points corresponding to the learning plan; and determine the target implicit knowledge point set corresponding to the learning plan from the implicit knowledge point set based on the first confidence level and the second confidence level.

[0210] In some examples of this embodiment, the determining module 31 is further configured to determine the student's first mastery level data on the first subset of implicit knowledge points, and to determine the student's answer to the question based on the student's answer data on the question information; to determine the first support level data for each knowledge point prerequisite relationship in the plurality of knowledge point prerequisite relationships based on the first mastery level data and the answer result; and to update the first confidence level of each knowledge point prerequisite relationship based on the historical confidence level of the first support level data to obtain the first confidence level of using the plurality of knowledge point prerequisite relationships as the target knowledge point prerequisite relationship.

[0211] In some examples of this embodiment, the determining module 31 is further configured to determine the student's second mastery level data on the second subset of implicit knowledge points, and the contribution data of the note information to the relevance relationship of the multiple knowledge points; determine the second support data of the relevance relationship of each knowledge point in the multiple knowledge point relevance relationships based on the mastery level data and the contribution data; update the second support data based on the historical confidence of the relevance relationship of each knowledge point according to the second support data, and obtain the second confidence of using the relevance relationship of the multiple knowledge points as the relevance relationship of the target knowledge point.

[0212] In some examples of this embodiment, the determining module 31 is further configured to store the multiple knowledge point prerequisite relationships and the multiple knowledge point correlation relationships in the candidate relationship library corresponding to the learning plan, so as to monitor the first confidence and the second confidence through the candidate relationship library; in response to the first confidence of the first knowledge point prerequisite relationship being greater than the prerequisite confidence threshold and the number of times the first implicit knowledge point corresponding to the first knowledge point prerequisite relationship is identified being greater than the number of times threshold, the first implicit knowledge point is determined as the first target implicit knowledge point corresponding to the learning plan; in response to the first confidence of the first knowledge point correlation relationship being greater than the correlation confidence threshold and the number of times the first implicit knowledge point corresponding to the first knowledge point correlation relationship is identified being greater than the number of times threshold, the first implicit knowledge point is determined as the second target implicit knowledge point corresponding to the learning plan; and the target implicit knowledge point set corresponding to the learning plan is determined based on the first target implicit knowledge point and the second target implicit knowledge point.

[0213] In some examples of this embodiment, the determining module 31 is further configured to determine the monitoring period of the candidate relation library for the first confidence level and the second confidence level; during the monitoring period, in response to the first confidence level of the second knowledge point prerequisite relationship among the plurality of knowledge point prerequisite relationships and / or the second confidence level of the second knowledge point prerequisite relationship among the plurality of knowledge point correlation relationships being lower than the monitoring threshold, the second knowledge point prerequisite relationship and / or the second knowledge point prerequisite relationship are removed from the candidate relation library; in response to the end of the monitoring period, the plurality of knowledge point prerequisite relationships and the plurality of knowledge point correlation relationships are removed from the candidate relation library.

[0214] It should be noted that other corresponding descriptions of the functional units involved in the AI ​​agent-based learning plan update device provided in this embodiment can be found in [reference]. Figure 1 and Figure 3 The corresponding descriptions in [the document] will not be repeated here.

[0215] Based on the above, Figure 1 and Figure 3 Accordingly, this embodiment also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. Figure 1 and Figure 3 The method shown.

[0216] Based on this understanding, the technical solution of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of this application.

[0217] like Figure 5 The diagram shown is a hardware structure schematic of an electronic device according to the present invention, comprising:

[0218] At least one processor 401; and,

[0219] A memory 402 is communicatively connected to at least one of the processors 401; wherein,

[0220] The memory 402 stores instructions that can be executed by at least one of the processors to enable the at least one of the processors to perform the AI ​​agent-based learning plan update method as described above.

[0221] Figure 5 Take a processor 401 as an example.

[0222] The electronic device may also include an input device 403 and a display device 404.

[0223] The processor 401, memory 402, input device 403, and display device 404 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0224] Memory 402, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the AI ​​agent-based learning plan update method in the embodiments of this application, for example, Figure 1 and Figure 3 The method flow is shown. The processor 401 executes various functional applications and data processing by running non-volatile software programs, instructions, and modules stored in the memory 402, thereby implementing the AI ​​agent-based learning plan update method in the above embodiments.

[0225] Memory 402 may include a program storage area and a data storage area. The program storage area may store an operating system and applications required for at least one function; the data storage area may store data created based on the use of the AI ​​agent-based learning plan update method. Furthermore, memory 402 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory 402 may optionally include memory remotely located relative to processor 401, and these remote memories may be connected via a network to the apparatus performing the AI ​​agent-based learning plan update method. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0226] Input device 403 can receive user clicks and generate signal inputs related to user settings and function control for the AI-based intelligent agent learning plan update method. Display device 404 may include display devices such as a display screen.

[0227] When one or more modules are stored in the memory 402, and are run by one or more processors 401, the learning plan update method based on AI agent in any of the above method embodiments is executed.

[0228] Optionally, the aforementioned physical devices may also include a user interface, a network interface, a camera, radio frequency (RF) circuitry, sensors, audio circuitry, a Wi-Fi module, etc. The user interface may include a display screen, input units such as a keyboard, etc., and optional user interfaces may also include USB interfaces, card reader interfaces, etc. The network interface may optionally include standard wired interfaces, wireless interfaces (such as Wi-Fi interfaces), etc.

[0229] Those skilled in the art will understand that the physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or combine certain components, or have different component arrangements.

[0230] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the aforementioned physical device, supporting the operation of information processing programs and other software and / or programs. The network communication module is used to enable communication between the various components within the storage medium, as well as communication with other hardware and software in the information processing physical device.

[0231] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms, or it can be implemented by hardware. By applying the solution of this embodiment, compared with the prior art, this embodiment provides accurate data for updating learning plans by collecting student question learning behavior data and extracting key information from questions and notes; it constructs implicit relationships in a knowledge graph by determining the set of implicit knowledge points; it improves the accuracy of selecting target implicit knowledge points by analyzing the prerequisite and relevance relationships of knowledge points; it achieves precise matching between the learning plan and the student's cognitive state by updating the knowledge point learning grid and dynamically optimizing the learning plan, thereby improving learning efficiency and the systematic nature of knowledge construction; and it further improves the accuracy of selecting target implicit knowledge points by splitting questions and notes. The implicit knowledge points of the source are subsetted and deduplicated to accurately determine the set of implicit knowledge points. By establishing two types of relationships—prerequisite and relevance—and calculating confidence levels, a reliable basis is provided for selecting target implicit knowledge points. The confidence level of prerequisite relationships is calculated by combining students' mastery of implicit knowledge points with their answer results, improving the accuracy and reliability of prerequisite relationship judgments. The confidence level of relevance relationships is calculated based on students' mastery and note-taking contributions, making knowledge point association judgments more aligned with students' self-directed learning focuses. By monitoring confidence levels and recognition frequency through a candidate relationship database, high-value target implicit knowledge points are accurately selected, ensuring the relevance of learning plan updates. By setting monitoring cycles and removal rules, the candidate relationship database data is purified to avoid interference from invalid associations, improving the efficiency and accuracy of learning plan optimization.

[0232] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0233] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments described herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. An AI agent-based learning plan updating method, characterized by, include: In response to obtaining learning behavior data of students learning questions based on learning plans in the learning companion system, the question information and note information of the student's learning target questions are determined based on the learning behavior data; Based on the first set of knowledge points marked on the target question, determine the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information; Based on the prerequisite and relevance information between the implicit knowledge point set and the first knowledge point set, the target implicit knowledge point set corresponding to the learning plan is determined from the implicit knowledge point set. The set of target implicit knowledge points is updated in the knowledge point learning grid corresponding to the learning plan, so as to update the learning plan to include the set of target implicit knowledge points. The step of determining the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information based on the first set of knowledge points marked on the target question includes: Knowledge points are extracted from the question information and the note information respectively to obtain a second set of knowledge points corresponding to the question information and a third set of knowledge points corresponding to the note information. The knowledge points in the second set of knowledge points that are not included in the first set of knowledge points are determined as the first subset of implicit knowledge points corresponding to the target question; The knowledge points in the third set of knowledge points that are not included in the first set of knowledge points are determined as the second subset of implicit knowledge points corresponding to the target question; The set of implicit knowledge points is determined based on the first subset of implicit knowledge points and the second subset of implicit knowledge points; The step of determining the target set of implicit knowledge points corresponding to the learning plan from the set of implicit knowledge points based on the prior knowledge relationship and relevance information between the set of implicit knowledge points and the first set of knowledge points includes: Based on the first subset of implicit knowledge points and the first set of knowledge points, multiple prerequisite relationships for knowledge points are established to determine the knowledge points in the first subset of implicit knowledge points as prerequisite knowledge points for the knowledge points in the first set of knowledge points. Multiple knowledge point correlation relationships are established based on the second subset of implicit knowledge points and the first set of knowledge points, so as to associate the knowledge points in the second subset of implicit knowledge points with the knowledge points in the first set of knowledge points; A first confidence level is determined by using the prerequisite relationships of the multiple knowledge points as the prerequisite relationships of the target knowledge points corresponding to the learning plan, and a second confidence level is determined by using the correlation relationships of the multiple knowledge points as the correlation relationships of the target knowledge points corresponding to the learning plan. The target set of implicit knowledge points corresponding to the learning plan is determined from the set of implicit knowledge points based on the first confidence level and the second confidence level.

2. The method of claim 1, wherein, Determining the first confidence level of the prerequisite relationships of the multiple knowledge points as the prerequisite relationships of the target knowledge points corresponding to the learning plan includes: Determine the student's first level of mastery of the first subset of implicit knowledge points, and determine the student's answer to the question based on the student's answer data to the question in the question information; Based on the first mastery level data and the answer results, determine the first support data for each knowledge point prerequisite relationship in the multiple knowledge point prerequisite relationships; The confidence level of each knowledge point prior relationship is updated based on the historical confidence level of the first support data to obtain the first confidence level of using the multiple knowledge point prior relationships as the prior relationship of the target knowledge point.

3. The method of claim 1, wherein, Determining the second confidence level for the correlation between multiple knowledge points as the correlation between the target knowledge points corresponding to the learning plan includes: Determine the student's second level of mastery of the second subset of implicit knowledge points, and the contribution of the note information to the correlation relationship of the multiple knowledge points; Based on the mastery data and the contribution data, a second support data is determined for the relevance relationship of each knowledge point in the multiple knowledge point relevance relationships; The data is updated based on the historical confidence of the relevance relationship of each knowledge point in the second support data to obtain the second confidence of using the relevance relationship of the multiple knowledge points as the relevance relationship of the target knowledge point.

4. The method according to any one of claims 1 to 3, characterized in that, The step of determining the target set of implicit knowledge points corresponding to the learning plan from the set of implicit knowledge points based on the first confidence level and the second confidence level includes: The prerequisite relationships and correlation relationships of the multiple knowledge points are stored in the candidate relationship library corresponding to the learning plan, so as to monitor the first confidence level and the second confidence level through the candidate relationship library; In response to the fact that the first confidence of the first knowledge point prerequisite relationship in multiple knowledge point prerequisite relationships is greater than the prerequisite confidence threshold and the number of times the first implicit knowledge point corresponding to the first knowledge point prerequisite relationship is identified is greater than the number of times threshold, the first implicit knowledge point is determined as the first target implicit knowledge point corresponding to the learning plan. In response to the fact that the first confidence level of the first knowledge point correlation relationship among multiple knowledge point correlation relationships is greater than the correlation confidence level threshold and the number of times the first implicit knowledge point corresponding to the first knowledge point correlation relationship is identified is greater than the number of times threshold, the first implicit knowledge point is determined as the second target implicit knowledge point corresponding to the learning plan; The set of target implicit knowledge points corresponding to the learning plan is determined based on the first target implicit knowledge point and the second target implicit knowledge point.

5. The method according to claim 4, characterized in that, After storing the prerequisite relationships and correlation relationships of the multiple knowledge points in the candidate relationship library corresponding to the learning plan, so as to monitor the first confidence level and the second confidence level through the candidate relationship library, the method further includes: Determine the monitoring period for the first confidence level and the second confidence level of the candidate relation database; If, during the monitoring period, the first confidence level of the second knowledge point prerequisite relationship among the multiple knowledge point prerequisite relationships and / or the second confidence level of the second knowledge point prerequisite relationship among the multiple knowledge point correlation relationships are lower than the monitoring threshold, the second knowledge point prerequisite relationship and / or the second knowledge point prerequisite relationship will be removed from the candidate relationship database. In response to the end of the monitoring period, the multiple knowledge point prerequisite relationships and the multiple knowledge point correlation relationships are removed from the candidate relationship database.

6. A learning plan update device based on an AI intelligent agent, characterized in that, include: The determination module is configured to, in response to acquiring learning behavior data of a student learning questions based on a learning plan in the learning companion system, determine the question information and note information of the student's learning target questions based on the learning behavior data; The determination module is further configured to determine the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information based on the first set of knowledge points marked for the target question; The determining module is further configured to determine the target set of implicit knowledge points corresponding to the learning plan from the set of implicit knowledge points based on the prior relationship and relevance information between the set of implicit knowledge points and the first set of knowledge points. The update module is configured to update the target implicit knowledge point set in the knowledge point learning grid corresponding to the learning plan, so as to update the learning plan to include the target implicit knowledge point set. The step of determining the set of implicit knowledge points in the set of knowledge points corresponding to the question information and the note information based on the first set of knowledge points marked on the target question includes: Knowledge points are extracted from the question information and the note information respectively to obtain a second set of knowledge points corresponding to the question information and a third set of knowledge points corresponding to the note information. The knowledge points in the second set of knowledge points that are not included in the first set of knowledge points are determined as the first subset of implicit knowledge points corresponding to the target question; The knowledge points in the third set of knowledge points that are not included in the first set of knowledge points are determined as the second subset of implicit knowledge points corresponding to the target question; The set of implicit knowledge points is determined based on the first subset of implicit knowledge points and the second subset of implicit knowledge points; The step of determining the target set of implicit knowledge points corresponding to the learning plan from the set of implicit knowledge points based on the prior knowledge relationship and relevance information between the set of implicit knowledge points and the first set of knowledge points includes: Based on the first subset of implicit knowledge points and the first set of knowledge points, multiple prerequisite relationships for knowledge points are established to determine the knowledge points in the first subset of implicit knowledge points as prerequisite knowledge points for the knowledge points in the first set of knowledge points. Multiple knowledge point correlation relationships are established based on the second subset of implicit knowledge points and the first set of knowledge points, so as to associate the knowledge points in the second subset of implicit knowledge points with the knowledge points in the first set of knowledge points; A first confidence level is determined by using the prerequisite relationships of the multiple knowledge points as the prerequisite relationships of the target knowledge points corresponding to the learning plan, and a second confidence level is determined by using the correlation relationships of the multiple knowledge points as the correlation relationships of the target knowledge points corresponding to the learning plan. The target set of implicit knowledge points corresponding to the learning plan is determined from the set of implicit knowledge points based on the first confidence level and the second confidence level.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

8. An electronic device comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

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