Integrating Artificial Intelligence with Gamification in Medical Education: A Pedagogically Grounded Framework and Critical Review

AI Summary

AI and gamification integration can personalise learning, sustain engagement, and produce durable educational outcomes across health and medical education.
Operational definition and integration matrix linking five AI methods (reinforcement learning, Bayesian learner modelling, NLP, computer vision, recommender systems) to gamification and learning theories.
Key challenges include theoretical grounding, outcome measurement, validation, algorithmic bias, reproducibility, equity, regulation; authors propose prioritised, feasibility-tagged research agenda.

AI Summary

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 identify challenges in theoretical grounding, outcome measurement, validation, algorithmic bias, reproducibility, equity, and regulation, and close with a prioritised, feasibility-tagged research agenda for advancing AI-enhanced gamification as a credible digital learning innovation in medical education.

Basic summary

Adv Med Educ Pract. 2026 Jul 14;17:612934. doi: 10.2147/AMEP.S612934. eCollection 2026.

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 regulation, and close with a prioritised, feasibility-tagged research agenda for advancing AI-enhanced gamification as a credible digital learning innovation in medical education.

PMID:42471856 | PMC:PMC13380250 | DOI:10.2147/AMEP.S612934

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