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已发表论文
人工智能在护理临床教育中的融合：提升迷你临床演练评估模型
Authors Wang XJ, Song LJ, Jiao XP, Chen SQ
Received 27 June 2025
Accepted for publication 23 October 2025
Published 7 November 2025 Volume 2025:18 Pages 7327—7337
DOI https://doi.org/10.2147/JMDH.S550145
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Charles V Pollack
Xiao-Jun Wang,1,* Li-Juan Song,2,* Xue-Ping Jiao,2 Su-Qing Chen3
1The Second People’s Hospital of Yangquan City, Yangquan, People’s Republic of China; 2Department of Colorectal and Anal Surgery, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, People’s Republic of China; 3Department of Gastrointestinal Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Su-Qing Chen, Department of Gastrointestinal Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, No. 99 of Longcheng Road, Xiaodian District, Taiyuan, 030032, People’s Republic of China, Tel +86 13834698844, Email suqingchencsq@126.com
Objective: This study aimed to examine the integration of artificial intelligence (AI) into nursing clinical education, with particular emphasis on enhancing the Mini-Clinical Evaluation Exercise (Mini-CEX). The study focused on the role of AI in improving the objectivity of clinical performance assessment and the quality of feedback provided.
Methods: A mixed-methods approach was used, involving 140 undergraduate nursing students randomly assigned to a control or intervention group. The intervention group received conventional clinical instruction supplemented by an AI-assisted Mini-CEX. The AI was integrated during the assessment process to provide automated performance analysis of video-recorded clinical skills and transcripts of patient interactions. This analysis generated structured, individualized feedback reports for learners, which were then used by instructors to guide the dissemination of results in post-assessment debriefing sessions.
Results: The AI-supported Mini-CEX demonstrated a significant enhancement in the consistency and objectivity of clinical evaluations. In this study, artificial intelligence was specifically applied through automated performance analysis, real-time feedback delivery, and its integration into Mini-CEX assessments, thereby clarifying the role of AI within the instructional process. Learners in the intervention group achieved more rapid acquisition of technical skills and exhibited increased engagement, which was attributed to the provision of immediate and personalized feedback.
Conclusion: The incorporation of AI into the Mini-CEX framework, specifically for automating aspects of performance analysis and feedback generation, contributed to real-time, standardized, and learner-centered assessments. This approach improved the objectivity of evaluations while maintaining the integral role of human mentorship for interpretive guidance.
Keywords: artificial intelligence, clinical competence, educational technology, mini-clinical evaluation exercise, Mini-CEX, nursing education, performance assessment, real-time feedback
1The Second People’s Hospital of Yangquan City, Yangquan, People’s Republic of China; 2Department of Colorectal and Anal Surgery, Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, People’s Republic of China; 3Department of Gastrointestinal Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Su-Qing Chen, Department of Gastrointestinal Center, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, No. 99 of Longcheng Road, Xiaodian District, Taiyuan, 030032, People’s Republic of China, Tel +86 13834698844, Email suqingchencsq@126.com
Objective: This study aimed to examine the integration of artificial intelligence (AI) into nursing clinical education, with particular emphasis on enhancing the Mini-Clinical Evaluation Exercise (Mini-CEX). The study focused on the role of AI in improving the objectivity of clinical performance assessment and the quality of feedback provided.
Methods: A mixed-methods approach was used, involving 140 undergraduate nursing students randomly assigned to a control or intervention group. The intervention group received conventional clinical instruction supplemented by an AI-assisted Mini-CEX. The AI was integrated during the assessment process to provide automated performance analysis of video-recorded clinical skills and transcripts of patient interactions. This analysis generated structured, individualized feedback reports for learners, which were then used by instructors to guide the dissemination of results in post-assessment debriefing sessions.
Results: The AI-supported Mini-CEX demonstrated a significant enhancement in the consistency and objectivity of clinical evaluations. In this study, artificial intelligence was specifically applied through automated performance analysis, real-time feedback delivery, and its integration into Mini-CEX assessments, thereby clarifying the role of AI within the instructional process. Learners in the intervention group achieved more rapid acquisition of technical skills and exhibited increased engagement, which was attributed to the provision of immediate and personalized feedback.
Conclusion: The incorporation of AI into the Mini-CEX framework, specifically for automating aspects of performance analysis and feedback generation, contributed to real-time, standardized, and learner-centered assessments. This approach improved the objectivity of evaluations while maintaining the integral role of human mentorship for interpretive guidance.
Keywords: artificial intelligence, clinical competence, educational technology, mini-clinical evaluation exercise, Mini-CEX, nursing education, performance assessment, real-time feedback