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

Area of Science:

• Nursing Education
• Artificial Intelligence in Healthcare
• Clinical Skills Assessment

Background:

• Traditional clinical education faces challenges in providing objective and timely feedback.
• The Mini-Clinical Evaluation Exercise (Mini-CEX) is a key tool for assessing nursing students' clinical performance.
• Enhancing the objectivity and feedback quality of Mini-CEX is crucial for effective nursing education.

Purpose of the Study:

• To investigate the integration of artificial intelligence (AI) into nursing clinical education.
• To evaluate AI's role in improving the objectivity and feedback quality of the Mini-Clinical Evaluation Exercise (Mini-CEX).

Main Methods:

• A mixed-methods approach with 140 undergraduate nursing students.
• Randomized control vs. intervention group design.
• Intervention group used AI-assisted Mini-CEX for automated performance analysis and feedback generation.

Main Results:

• AI-supported Mini-CEX significantly improved consistency and objectivity in clinical evaluations.
• Learners in the AI group showed faster technical skill acquisition and increased engagement.
• AI provided automated performance analysis and structured, individualized feedback reports.

Conclusions:

• AI integration into Mini-CEX enhances assessment objectivity and provides learner-centered feedback.
• AI complements human mentorship, improving the standardization and effectiveness of clinical evaluations.
• AI-driven feedback in nursing education promotes efficient skill development and engagement.