Integration of Artificial Intelligence in Nursing Clinical Education: Enhancing the Mini-CEX Model

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.

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.

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.

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.