ArticleJournal of multidisciplinary healthcare2025
Integration of Artificial Intelligence in Nursing Clinical Education: Enhancing the Mini-CEX Model.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Integrating Mini-CEX into a four-stage virtual simulation platform for clinical skills training: development and student evaluation.BMC medical education · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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