AI enabling college gynaecology and obstetrics nursing professional course evaluation method and system

Abstract

The invention is suitable for the technical field of medical education, and provides an AI enabling college gynaecology and obstetrics nursing science major course evaluation method and system, and the method comprises the following steps: collecting the multi-source learning data of students; based on a natural language processing model, a learning behavior analysis engine and a virtual operation evaluation model, performing AI analysis on the text data, the learning behavior data and the virtual operation data to obtain an AI analysis report; based on a preset PDCA cycle engine, according to the AI analysis report, generating a teaching optimization suggestion to the teacher end, triggering an adaptive learning path to the student end, and performing PDCA cycle; and generating a multi-dimensional evaluation report according to the data of each link in the PDCA cycle. According to the invention, artificial intelligence and college education are deeply fused, clinical guidelines of gynaecology and obstetrics are converted into a technical path capable of calculating evaluation indexes, medical rule digitization is realized, the method can be applied to college gynaecology and obstetrics teaching, and a multi-dimensional and quantifiable remarkable effect is brought.

Description

Technical Field

[0001] This invention belongs to the field of medical education technology, and in particular relates to an AI-enabled method and system for evaluating courses in obstetrics and gynecology nursing majors in colleges and universities. Background Technology

[0002] The rapid development of artificial intelligence (AI) technology brings new opportunities and challenges to the education field. Obstetrics and gynecology nursing is an important branch of medical education. Nursing education must focus on job competency to improve the quality of nursing talent training and provide high-quality clinical care. The curriculum system of obstetrics and gynecology nursing programs in universities needs to keep pace with the times and cultivate nursing talents that meet the needs of the era. The mechanism of childbirth is a crucial core content of obstetrics and gynecology nursing. Currently, the traditional teaching method, based on surveys and analyses of the current situation and evaluation systems of obstetrics and gynecology nursing courses in universities, has achieved teaching results but also revealed problems such as a single evaluation method, lack of personalization, and untimely feedback. Therefore, it is urgent to take effective measures to improve the quality of talent training to meet the needs of economic and social development for high-quality, innovative medical professionals.

[0003] AI technology is convenient and efficient, enabling richer and more diverse teaching scenarios: publishing intelligent question banks, learning analytics, virtual simulations, and more. A feasible solution for applying AI technology to the evaluation of courses on the mechanisms of childbirth involves using natural language processing to develop an intelligent question-and-answer system to assess students' understanding of the mechanisms of childbirth; using virtual reality technology to construct virtual childbirth scenarios to assess students' clinical operational skills and adaptability; and using learning analytics to track students' learning process and provide personalized learning suggestions and feedback.

[0004] Formative assessment is a student-centered, real-time, dynamic, and repeated evaluation method that possesses multiple teaching functions such as diagnosis, motivation, feedback, and guidance. It effectively guides teaching strategies, promotes student learning, and improves teaching outcomes. Based on AI technology, an evaluation model for the course on the mechanism of childbirth is constructed, including evaluation indicators, evaluation methods, and an implementation evaluation model. In course assessment, the assessment ratio is reset: formative assessment accounts for 70%, and summative assessment accounts for 30%. Formative assessment includes (attendance - AI-powered assignments / tests - practical training - course mind map design - AI-based case reproduction - group case analysis); summative assessment is a closed-book examination, graded on a 100-point scale. The degree of achievement of course objectives is calculated through performance analysis.

[0005] However, the nursing profession is unique in that it deals with human life and health, allowing no room for error. This requires students not only to master basic theoretical knowledge and operational skills, but also to possess excellent professional ethics. Traditional obstetrics and gynecology nursing teaching models suffer from rote learning, i.e., one-way instruction, where students passively memorize theories without training in active thinking and critical thinking; there is an overemphasis on theory and a neglect of practice: classroom theoretical teaching accounts for too high a proportion, while clinical practice opportunities are insufficient, leading to students' unfamiliarity with procedures and poor adaptability when facing real cases. Furthermore, there is a significant gap between simulations and real-world scenarios: traditional model exercises (such as mannequins) cannot fully simulate complex clinical situations (such as maternal emotional changes and emergency resuscitation).

[0006] Clinical work and study should be conducted with strong professional ethics, awareness, conduct, and attitude. Students rarely consider what professional qualities they should possess to meet future workplace demands. Therefore, professional instructors need to integrate nursing background into their teaching of knowledge and skills, interpreting the professional qualities of nurses appropriately. Currently, there is insufficient cultivation of humanistic care and communication skills, a tendency towards technical overemphasis, and an overemphasis on operational skills while neglecting the cultivation of humanistic qualities such as maternal psychological support, family communication, and cultural sensitivity. Training in doctor-patient communication is lacking, and students lack simulated training in dealing with scenarios such as maternal anxiety and family conflicts.

[0007] In summary, current traditional professional course evaluation systems are simplistic, relying solely on grades and written examinations while neglecting the evaluation of comprehensive qualities such as clinical practical skills, teamwork, and emergency response. Furthermore, feedback mechanisms are weak, lacking dynamic assessment and personalized guidance of students' learning processes. The application of digital tools is also insufficient, with modern technologies such as virtual reality (VR) and online learning platforms not fully integrated into teaching. Therefore, how to construct an intelligent evaluation system that can simultaneously assess theoretical knowledge, practical skills, and professional qualities, and provide real-time feedback and personalized teaching intervention remains a pressing technical challenge in higher education. Summary of the Invention

[0008] The purpose of this invention is to provide an AI-enabled evaluation method for obstetrics and gynecology nursing courses in colleges and universities, aiming to solve the above-mentioned technical problems.

[0009] This invention is implemented as follows: an AI-enabled method for evaluating courses in obstetrics and gynecology nursing at universities, comprising the following steps: Collect students' multi-source learning data; the multi-source learning data includes text data, learning behavior data, and virtual operation data; Based on natural language processing models, learning behavior analysis engines, and virtual operation evaluation models, AI analysis is performed on text data, learning behavior data, and virtual operation data to obtain AI analysis reports. Based on the preset PDCA cycle engine, and according to the AI ​​analysis report, teaching optimization suggestions are generated for teachers and adaptive learning paths are triggered for students to carry out the PDCA cycle. A multi-dimensional evaluation report is generated based on the data from each stage of the PDCA cycle.

[0010] Furthermore, the text data includes student attendance records, online assignments, stage test scores, theoretical test results, case analyses, and short answer questions; the learning behavior data includes assignment submission time, classroom interaction data, online learning duration, and learning video viewing progress; and the virtual operation data includes virtual operation recordings and virtual simulation operation logs.

[0011] Furthermore, the natural language processing model includes the BERT model and / or the GPT model; the learning behavior analysis engine includes a time series model; and the virtual operation evaluation model includes a computer vision algorithm.

[0012] Furthermore, the steps for AI analysis of text data, learning behavior data, and virtual operation data based on natural language processing models, learning behavior analysis engines, and virtual operation evaluation models specifically include: Based on the BERT model and / or GPT model, AI analysis is performed on text data to automatically assess students' knowledge mastery. Based on time series models, students' learning trajectories are analyzed using learning behavior data and virtual operation data to identify learning patterns and predict students' weaknesses. Based on computer vision algorithms, AI analysis is performed on virtual operation data to compare students' operation steps, techniques, and paths with standard operation procedures frame by frame and calculate deviation scores.

[0013] Furthermore, the preset PDCA cycle engine includes: Plan: Generate class and / or individual learning plans based on AI analysis reports, and dynamically adjust the priority of the teaching resource library; Implementation: Pushing personalized learning plans to students and collecting learning data through real-time classroom interaction tools; Inspection: Compare with preset goals, output gap reports based on students' learning data; and check the standardization of virtual simulation operations through a virtual operation evaluation model; Processing: Based on the inspection results, generate teaching optimization suggestions for teachers and trigger adaptive learning paths for students.

[0014] Another objective of this invention is to provide an AI-enabled evaluation system for obstetrics and gynecology nursing courses in higher education institutions, for implementing the aforementioned AI-enabled evaluation method for obstetrics and gynecology nursing courses in higher education institutions, comprising: The data acquisition module is used to collect students' multi-source learning data, which includes text data, learning behavior data, and virtual operation data. The AI ​​analysis module is used to perform AI analysis on text data, learning behavior data, and virtual operation data based on natural language processing models, learning behavior analysis engines, and virtual operation evaluation models, and to generate AI analysis reports. The PDCA cycle module is used to generate teaching optimization suggestions for teachers and trigger adaptive learning paths for students based on the preset PDCA cycle engine and AI analysis reports, and to carry out the PDCA cycle. The evaluation report generation module is used to generate multi-dimensional evaluation reports based on data from each stage of the PDCA cycle.

[0015] Furthermore, the PDCA cycle module includes: The planning unit is used to generate class and / or individual learning plans based on AI analysis reports and dynamically adjust the priority of the teaching resource library. The execution unit is used to push personalized learning plans to students and collect learning data through real-time classroom interaction tools; The inspection unit is used to compare with preset targets, output gap reports based on students' learning data, and check the standardization of virtual simulation operations through a virtual operation evaluation model. The processing unit is used to generate teaching optimization suggestions for teachers and trigger adaptive learning paths for students based on the inspection results.

[0016] Furthermore, the AI-enabled course evaluation system for obstetrics and gynecology nursing in higher education institutions also includes: The virtual simulation module is used to construct high-fidelity obstetric and gynecological nursing scenarios and to conduct standardized evaluations of students' operations based on motion capture technology.

[0017] This invention provides an AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities. It deeply integrates artificial intelligence with higher education, transforming obstetrics and gynecology clinical guidelines into calculable evaluation indicators. This digitizes medical rules and can be applied to obstetrics and gynecology teaching in universities, bringing significant multi-dimensional and quantifiable effects. It not only improves the skill qualification rate at the "point" level, but also reconstructs the teaching process at the "line" level and optimizes the educational ecosystem at the "surface" level, ultimately achieving the fundamental goal of cultivating high-quality and highly adaptable nursing talents. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities, as provided in this embodiment of the invention.

[0019] Figure 2This is a schematic diagram illustrating the process of performing AI analysis on text data, as provided in an embodiment of the present invention.

[0020] Figure 3 This is a flowchart illustrating the AI ​​analysis of virtual operation data provided in an embodiment of the present invention.

[0021] Figure 4 A flowchart illustrating the evaluation framework provided in this embodiment of the invention.

[0022] Figure 5 The structural block diagram of the AI-enabled evaluation system for obstetrics and gynecology nursing courses in colleges and universities provided in this embodiment of the invention.

[0023] Figure 6 This is a structural block diagram of the PDCA cycle module provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0025] like Figure 1 As shown, in another embodiment of the present invention, an AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities is provided, comprising the following steps: S100. Collect students' multi-source learning data; the multi-source learning data includes text data, learning behavior data, and virtual operation data; S200, based on a natural language processing model, a learning behavior analysis engine, and a virtual operation evaluation model, performs AI analysis on text data, learning behavior data, and virtual operation data to obtain an AI analysis report. S300, based on a preset PDCA cycle engine, generates teaching optimization suggestions for teachers based on AI analysis reports and triggers adaptive learning paths for students to carry out PDCA cycles. S400. Generate a multi-dimensional evaluation report based on the data from each stage of the PDCA cycle.

[0026] It should be noted that the method provided in the embodiments of the present invention can be applied to the evaluation of the curriculum system of obstetrics and gynecology nursing. The following embodiments are illustrated using the childbirth professional course as an example, but are not limited thereto.

[0027] Specifically, textual data includes, but is not limited to, student attendance records, online assignments, stage test scores, theoretical test results, case analyses, and short-answer questions; learning behavior data includes, but is not limited to, assignment submission time, classroom interaction data, online learning duration, and learning video viewing progress; virtual operation data includes, but is not limited to, virtual operation recordings and virtual simulation operation logs. In practical applications, the above step S100 can be implemented through a data acquisition layer; multi-source data input is adopted: integrating student attendance records, online assignments, stage test scores, virtual simulation operation logs, and classroom interaction data (such as Q&A and discussions); virtual operation data is collected through virtual simulation devices (such as VR gloves and motion capture cameras) based on sensors and interfaces; and learning data such as theoretical test results can be obtained through the application programming interface (API) of the learning management system (LMS).

[0028] In a preferred embodiment of the present invention, the natural language processing (NLP) model includes the BERT model and / or the GPT model; the learning behavior analysis engine includes a time series model; and the virtual operation evaluation model includes a computer vision algorithm.

[0029] In a preferred embodiment of the present invention, the step of performing AI analysis on text data, learning behavior data, and virtual operation data based on a natural language processing model, a learning behavior analysis engine, and a virtual operation evaluation model specifically includes: Based on the BERT model and / or GPT model, AI analysis is performed on text data to automatically assess students' knowledge mastery. Based on time series models, students' learning trajectories are analyzed using learning behavior data and virtual operation data to identify learning patterns and predict students' weaknesses. Based on computer vision algorithms, AI analysis is performed on virtual operation data to compare students' operation steps, techniques, and paths with standard operation procedures frame by frame and calculate deviation scores.

[0030] Specifically, such as Figure 2 As shown in the AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities provided in this embodiment of the invention, the core task of the natural language processing (NLP) model is to analyze students' textual answers (such as case analysis and short answer questions) and automatically assess their knowledge mastery. BERT and GPT are currently the most advanced NLP models. By using BERT (precise understanding and scoring of student answers) or GPT (generating parsing and interactive feedback) models to analyze students' textual answers (such as case analysis questions), assess the depth of knowledge mastery, and automatically generate labels for incorrect answers (such as "incorrect differential diagnosis of placental abruption").

[0031] In addition, the learning behavior analysis engine analyzes learning trajectories based on time series models (LSTM, which records student learning behaviors in chronological order), including theoretical learning trajectories, weekly test scores, homework submission times, and online learning durations; and practical operation records: the time sequence of completing the delivery steps in virtual simulation (such as the time consumed in each stage of "cervical dilation → fetal head descent → delivery"), thereby predicting weak points.

[0032] like Figure 3 As shown, virtual operation data such as practical actions collected by VR devices, including time-series data, can be preprocessed using the methods described above. Then, it can be compared with standard operation sequences to generate deviation scores, which can be used to assess students' mastery of virtual simulation operations.

[0033] In a preferred embodiment of the present invention, the preset PDCA cycle engine includes: Plan: Generate class and / or individual learning plans based on AI analysis reports (such as "focusing on strengthening teaching of labor stages next week"), and dynamically adjust the priority of teaching resource library (such as pinning micro-lessons related to frequently missed knowledge points). Do: Push personalized learning plans to students (e.g., assign extra virtual training to students who are weak in "breech delivery"), and collect learning data through real-time classroom interaction tools (e.g., timed quiz machines); Check: Compare with preset goals (e.g., "80% of students master uterine contraction monitoring"), output gap reports based on students' learning data; and check the standardization of virtual simulation operations through virtual operation evaluation models (e.g., "whether the fetal head flexion angle meets the standard"). Processing (Act): Based on the inspection results, generate teaching optimization suggestions to the teacher (such as "increase the case discussion class time for abnormal labor process") and trigger adaptive learning paths to the student (such as unlocking the basic theory review module when the error rate is >50%).

[0034] In this embodiment of the invention, structured (test scores, etc.) and unstructured (virtual operation recordings, etc.) data are processed uniformly through multi-source learning data fusion. This involves embedding text (homework), video (operation recordings), and numerical values ​​(test scores) into a unified vector space for joint AI analysis. This embodiment employs a dynamic evaluation algorithm, building an intelligent question bank based on the BERT model to automatically generate test questions with appropriate difficulty. Specifically, the dynamic test question generation algorithm is as follows: Input: student's historical incorrect knowledge points and the current chapter (e.g., "Chapter 8, Complications of Pregnancy"). Processing: Item Response Theory (IRT) is used to calculate the matching degree between question difficulty and student ability; questions with strong relevance but not yet meeting the standard are prioritized (e.g., "Selection Questions on Nursing Measures for Preeclampsia"). Output: Personalized test paper (PDF / online format).

[0035] Virtual simulation practice evaluation can utilize motion capture technology to score deviations by comparing with standard operating procedures. Edge computing nodes can be used to process virtual simulation data, reducing cloud latency. The PDCA cycle engine features a closed-loop feedback design: teachers receive real-time reports on the overall weaknesses of the class, while students receive personalized learning path suggestions. Specifically, the teaching feedback loop process is as follows: student operation / answering → data collection → AI analysis → generating diagnostic reports → teacher adjusting teaching → students receiving new tasks.

[0036] The embodiments of the present invention can also be deployed on mobile devices (student and teacher terminals) using a lightweight model (such as MobileNet) to support real-time classroom feedback.

[0037] In step S400, embodiments of the present invention may employ the following methods: Figure 4 The evaluation framework shown is based on a dual-channel interpretation system to generate a multi-dimensional evaluation report: the scoring criteria are visualized using a decision tree, for example, deduction points: a 3-second delay in fetal position judgment (standard ≤ 2 seconds); and it is linked to medical guidelines to automatically generate improvement suggestions, for example, adjusting the technique 15 degrees earlier can improve the score by 12%.

[0038] Specifically, the dual-channel interpretation system includes Channel 1 and Channel 2. Channel 1 is based on the interpretation of the machine decision-making process. This channel explains the decision-making logic within the AI ​​analysis model; it typically uses models such as SHAP values ​​and LIME to output visualized contribution analysis, making the scoring process transparent and traceable. Students and teachers can immediately see the specific technical aspects of the scores and deductions. Channel 2 is based on the interpretation of clinical medical rules. This channel explains the medical principles and clinical guidelines behind the results, allowing for improvements based on medical standards; for example, it uses knowledge graphs and medical expert rule bases (i.e., digitized clinical guidelines) to output textual, pedagogically relevant improvement suggestions.

[0039] like Figure 5 As shown, another objective of this invention is to provide an AI-enabled evaluation system for obstetrics and gynecology nursing courses in higher education institutions, used to implement the aforementioned AI-enabled evaluation method for obstetrics and gynecology nursing courses in higher education institutions, comprising: Data acquisition module 10 is used to collect multi-source learning data from students; the multi-source learning data includes text data, learning behavior data, and virtual operation data; AI analysis module 20 is used to perform AI analysis on text data, learning behavior data and virtual operation data based on natural language processing model, learning behavior analysis engine and virtual operation evaluation model, and to obtain AI analysis report; The PDCA cycle module 30 is used to generate teaching optimization suggestions for teachers and trigger adaptive learning paths for students based on the preset PDCA cycle engine and AI analysis reports, and to carry out the PDCA cycle. The evaluation report generation module 40 is used to generate a multi-dimensional evaluation report based on the data from each stage of the PDCA cycle.

[0040] like Figure 6 As shown, in a preferred embodiment of the present invention, the PDCA cycle module 30 includes: Planning Unit 31 is used to generate class and / or individual learning plans based on AI analysis reports and dynamically adjust the priority of the teaching resource library; Execution unit 32 is used to push personalized learning plans to students and collect learning data through real-time classroom interaction tools; Inspection unit 33 is used to compare with preset targets, output gap reports based on students' learning data, and detect the standardization of virtual simulation operations through a virtual operation evaluation model. The processing unit 34 is used to generate teaching optimization suggestions for teachers and trigger adaptive learning paths for students based on the inspection results.

[0041] In a preferred embodiment of the present invention, the AI-enabled evaluation system for obstetrics and gynecology nursing courses in universities further includes: a virtual simulation module for constructing high-fidelity obstetrics and gynecology nursing scenarios and for evaluating students' operations in a standardized manner based on motion capture technology; mainly including the following: scenario construction: developing high-fidelity childbirth scenarios (including normal childbirth, shoulder dystocia, and other abnormal situations) using Unity 3D / Unreal Engine; operation evaluation: recording students' operations using motion capture technology (such as "hand pressing position, force and timing"); scoring based on a rule engine (such as "deducting 10 points for failing to identify umbilical cord prolapse within 30 seconds").

[0042] It should be noted that the above modules and units can be implemented as a computer program, which can run on a computer device. The computer device's memory can store the computer program that makes up the modules, enabling the processor to execute the various steps of the above method.

[0043] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0044] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Furthermore, any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory.

[0045] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for evaluating obstetrics and gynecology nursing courses in colleges and universities using AI, characterized in that... Includes the following steps: Collect students' multi-source learning data; the multi-source learning data includes text data, learning behavior data, and virtual operation data; Based on natural language processing models, learning behavior analysis engines, and virtual operation evaluation models, AI analysis is performed on text data, learning behavior data, and virtual operation data to obtain AI analysis reports. Based on the preset PDCA cycle engine, and according to the AI ​​analysis report, teaching optimization suggestions are generated for teachers and adaptive learning paths are triggered for students to carry out the PDCA cycle. A multi-dimensional evaluation report is generated based on the data from each stage of the PDCA cycle.

2. The AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities according to claim 1, characterized in that, The text data includes student attendance records, online assignments, stage test scores, theoretical test results, case analyses, and short answer questions; the learning behavior data includes assignment submission time, classroom interaction data, online learning duration, and learning video viewing progress; the virtual operation data includes virtual operation recordings and virtual simulation operation logs.

3. The AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities according to claim 1, characterized in that, The natural language processing model includes the BERT model and / or the GPT model; the learning behavior analysis engine includes a time series model; and the virtual operation evaluation model includes computer vision algorithms.

4. The AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities according to claim 3, characterized in that, The steps for AI analysis of text data, learning behavior data, and virtual operation data based on natural language processing models, learning behavior analysis engines, and virtual operation evaluation models include: Based on the BERT model and / or GPT model, AI analysis is performed on text data to automatically assess students' knowledge mastery. Based on time series models, students' learning trajectories are analyzed using learning behavior data and virtual operation data to identify learning patterns and predict students' weaknesses. Based on computer vision algorithms, AI analysis is performed on virtual operation data to compare students' operation steps, techniques, and paths with standard operation procedures frame by frame and calculate deviation scores.

5. The AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities according to claim 1, characterized in that, The preset PDCA cycle engine includes: Plan: Generate class and / or individual learning plans based on AI analysis reports, and dynamically adjust the priority of the teaching resource library; Implementation: Pushing personalized learning plans to students and collecting learning data through real-time classroom interaction tools; Inspection: Compare with preset goals, output gap reports based on students' learning data; and check the standardization of virtual simulation operations through a virtual operation evaluation model; Processing: Based on the inspection results, generate teaching optimization suggestions for teachers and trigger adaptive learning paths for students.

6. An AI-enabled evaluation system for obstetrics and gynecology nursing courses in universities, used to implement the AI-enabled evaluation method for obstetrics and gynecology nursing courses in universities as described in any one of claims 1-5, characterized in that, include: The data acquisition module is used to collect students' multi-source learning data; The multi-source learning data includes text data, learning behavior data, and virtual operation data; The AI ​​analysis module is used to perform AI analysis on text data, learning behavior data, and virtual operation data based on natural language processing models, learning behavior analysis engines, and virtual operation evaluation models, and to generate AI analysis reports. The PDCA cycle module is used to generate teaching optimization suggestions for teachers and trigger adaptive learning paths for students based on the preset PDCA cycle engine and AI analysis reports, and to carry out the PDCA cycle. The evaluation report generation module is used to generate multi-dimensional evaluation reports based on data from each stage of the PDCA cycle.

7. The AI-enabled curriculum evaluation system for obstetrics and gynecology nursing in universities according to claim 6, characterized in that, The PDCA cycle module includes: The planning unit is used to generate class and / or individual learning plans based on AI analysis reports and dynamically adjust the priority of the teaching resource library. The execution unit is used to push personalized learning plans to students and collect learning data through real-time classroom interaction tools; The inspection unit is used to compare with preset targets, output gap reports based on students' learning data, and check the standardization of virtual simulation operations through a virtual operation evaluation model. The processing unit is used to generate teaching optimization suggestions for teachers and trigger adaptive learning paths for students based on the inspection results.

8. The AI-enabled evaluation system for obstetrics and gynecology nursing courses in universities according to claim 6 or 7, characterized in that, Also includes: The virtual simulation module is used to construct high-fidelity obstetric and gynecological nursing scenarios and to conduct standardized evaluations of students' operations based on motion capture technology.

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