AI-Driven Personalized Learning in Medical Education: Enhancing Cognitive Skills and Addressing Language Proficiency Challenges

This study examines the effectiveness of artificial intelligence-driven (AI-driven) personalized learning strategies in enhancing cognitive skills and language proficiency in medical education. By integrating AI tools into practical scenarios, such as clinical simulations and patient interactions, the research highlights AI’s potential to foster critical thinking, decision-making, and personalized learning experiences. A mixed-methods approach, combining pre- and post-intervention assessments, questionnaires, and interviews, reveals that while AI significantly improves cognitive skills, its impact on language proficiency is modest. A major contribution of this study is the proposed PCLC-AI Framework (Personalized Cognitive and Linguistic Competency Development with Artificial Intelligence), which aligns AI-driven learning with curriculum objectives to enhance both cognitive and linguistic competencies. The framework incorporates adaptive learning paths, interactive simulations, and integrated feedback mechanisms, offering a cohesive approach to skill development. Despite its potential, the study identifies challenges, including cultural adaptability, reliance on a single AI model, and the limited duration of the intervention. Findings thus underscore the need for longer-term studies and localized AI training datasets. By addressing critical gaps in the literature, this research provides actionable insights into optimizing AI integration for medical education.