Application and Effectiveness Analysis of AI-Based Teaching Methods for Improving AI Preparedness in Healthcare Students

Abstract

Objectives This study aimed to develop AI-based teaching and learning methods to improve the AI preparedness of healthcare students and to empirically assess their effectiveness through pre- and post-intervention surveys. The goal was to examine how an integrated instructional model combining theoretical understanding and practical application of AI could enhance students’ overall competencies, including AI cognition, applied skills, ethical awareness, and legal compliance in healthcare settings. Methods AI-related instructional activities were implemented for healthcare information students in the 2023 academic year. A total of 22 items, categorized into Cognition, Ability, Vision, and Ethics, were used to measure their AI preparedness. Pre- and post-intervention surveys were conducted, and independent t-tests were performed to examine changes in scores before and after the intervention. Results The average score increased from 2.91 (pre-intervention) to 3.50 (post-intervention), showing a statistically significant improvement (p < 0.0001). Notably, greater gains were observed in the Cognition and Ability domains, while improvements were also confirmed in Vision and Ethics. These findings suggest that AI-based teaching methods effectively enhance students’ understanding of AI, practical application skills, future outlook, and ethical/legal compliance in healthcare contexts. Conclusions The AI-based teaching and learning methods applied in this study significantly increased AI preparedness among healthcare students. Future healthcare curricula should incorporate AI-focused educational activities to better equip students to utilize AI in a rapidly evolving healthcare environment.

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