Artificial intelligence in health education: teachers’ and tech experts’ views on replacing teaching competencies

Abstract

INTRODUCTION: The integration of artificial intelligence (AI) into education reflects rapid technological progress and a shift toward innovative teaching methods. Given AI’s disruptive potential, it is essential to assess its impact on teaching competencies in medical education in a comprehensive and multidisciplinary manner. OBJECTIVES: To compare the perceptions of medical educators and technology professionals regarding the potential replacement of teaching competencies by AI. MATERIALS AND METHODS: A cross-sectional, quantitative study was conducted with 82 participants (65 medical educators and 17 technology professionals) using an anonymous online survey. Participants assessed the likelihood of AI replacing 14 teaching competencies, classified by complexity, automation potential, and expected timeline. RESULTS: Agreement between groups was found in 64.3% of competencies. There was 80.0% agreement for low-complexity, high-automation tasks, 60.0% for medium-complexity, partially automatable competencies, and 50.0% for high-complexity, low-automation functions. As task complexity increased, belief in AI replacement decreased. Significant differences emerged in competencies such as rigorous content selection (p=0.029), anticipating student difficulties (p=0.018), linking theory and practice (p=0.032), reinforcing student contributions (p=0.017), and adapting teaching based on feedback (p=0.046). In these cases, technology professionals were more inclined than educators to believe in the replacement of such competencies by AI. Most participants believed replacement could occur within the next five years. CONCLUSION: Both groups foresee increasing AI adoption in medical teaching, especially in tasks of lower complexity. While agreement diminishes for more complex competencies, there is a shared expectation that AI will increasingly shape educational practices soon.

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