A New Paradigm in Learning: Examining the Interplay Between Artificial Intelligence Management and Student Learning Styles in Medical Education

Abstract

Background: Artificial intelligence (AI) is increasingly used in medical sciences education for personalized learning, clinical simulation, and decision support. However, overreliance on general-purpose AI tools may lead to superficial learning and inaccurate medical information. Therefore, AI platforms used in medical education should be specialized, evidence-based, and aligned with students' learning needs. Objectives: This study aimed to examine the relationship between AI management dimensions and experiential learning styles among medical sciences students in Qazvin. Methods: This descriptive-analytical cross-sectional study was conducted in 2025 and included 354 medical sciences students selected through multistage cluster sampling. Data were collected using a demographic questionnaire, Chen's Artificial Intelligence Questionnaire, and Kolb's Learning Style Inventory. Statistical analyses were performed using SPSS version 22, including Pearson correlation coefficients and one-way analysis of variance (ANOVA). Statistical significance was set at P < 0.05. Results: The mean age of the participants was 22.31 ± 3.4 years. Most participants (74.3%) demonstrated a dominant converging learning style. The mean total AI score was 56.78 ± 13.3. Significant associations were observed between learning style and AI-based decision-making (P < 0.001), AI skills (P = 0.006), and AI tendency (P < 0.001). AI-based decision-making was negatively correlated with concrete experience (r = -0.185, P < 0.001) and positively correlated with abstract conceptualization (r = 0.323, P < 0.001). Total AI scores were negatively correlated with concrete experience (r = -0.212, P < 0.001) and positively correlated with abstract conceptualization (r = 0.175, P = 0.001) and active experimentation (r = 0.143, P = 0.007). Conclusions: Medical students’ learning styles significantly influence their strategic management of and interactions with AI technologies. Medical curricula and AI-driven platforms should incorporate these learning-style differences into educational designs to prevent superficial learning and optimize personalized clinical training.