Integrating Artificial Intelligence with Gamification in Medical Education: A Pedagogically Grounded Framework and Critical Review
Abstract
Digital learning technologies have transformed medical education, with artificial intelligence (AI) and gamification emerging as two of the most active areas of innovation. While each has demonstrated value independently, their integration offers distinctive potential to personalise learning, sustain engagement, and produce durable educational outcomes. Yet the convergence remains empirically disjointed and theoretically underdeveloped. This pedagogically grounded critical review synthesises the evidence at the crossroads of AI and gamification in medical and health professional education, drawing on randomised trials, scoping reviews, meta-analyses, and case studies from PubMed, DOAJ, ERIC, and Web of Science. We propose an operational definition of AI-enhanced gamification and introduce an integration matrix linking five AI methods (reinforcement learning, Bayesian learner modelling, natural language processing, computer vision, recommender systems) to specific gamification elements and to learning mechanisms grounded in Self-Determination Theory, Flow Theory, constructivism, Vygotsky's Zone of Proximal Development, connectivism, the TPACK model, and the Behaviour Change Technique taxonomy. We map applications across health literacy, mental health psychoeducation, rehabilitation, and medical education, supported by ten real-world examples. We identify challenges in theoretical grounding, outcome measurement, validation, algorithmic bias, reproducibility, equity, and…
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Evidence Hub reads this as a randomized trial. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design.
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