A New Paradigm in Learning: Examining the Interplay Between Artificial Intelligence Management and Student Learning Styles in Medical Education
| Author | Zahra Rahimi Khalifeh Kandi | en |
| Author | Zahra Arjeini | en |
| Author | Razie Alipour | en |
| Author | Ali Ahmadabadi | en |
| Orcid | Zahra Rahimi Khalifeh Kandi [0000-0001-5914-404X] | en |
| Orcid | Zahra Arjeini [0000-0001-7778-5334] | en |
| Orcid | Razie Alipour [0000-0001-8605-6571] | en |
| Orcid | Ali Ahmadabadi [0000-0002-9061-1176] | en |
| Issued Date | 2026-07-31 | en |
| 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. | en |
| DOI | https://doi.org/10.69107/cphh-173833 | en |
| URI | https://brieflands.com/journals/cphh/articles/173833 | en |
| Keyword | Artificial Intelligence | en |
| Keyword | Learning Styles | en |
| Keyword | Medical Students | en |
| Publisher | Brieflands | en |
| Title | A New Paradigm in Learning: Examining the Interplay Between Artificial Intelligence Management and Student Learning Styles in Medical Education | en |
| Type | Research Article | en |
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