Aprendizaje adaptativo impulsado por inteligencia artificial en juegos educativos
DOI:
https://doi.org/10.32870/recibe.v14i3.468Keywords:
Keywords: Artificial Intelligence, Adaptive Learning, Educational Games, Personalization, Ethics in EdTechAbstract
Abstract. Artificial intelligence is transforming educational games by enabling adaptive learning experiences that respond to individual learners' needs in real time. This paper presents an integrative review of recent literature on AI-driven adaptivity in educational games, examining how these systems personalize instruction, enhance engagement, and improve learning outcomes. Core AI techniques—including learner modeling, reinforcement learning, procedural content generation (PCG), and affective computing—are discussed in relation to frameworks such as Flow theory and the Mechanics–Dynamics–Aesthetics (MDA) model. Representative case studies, including Math Garden and CodeCombat, illustrate how adaptive mechanics and narrative integration influence learner motivation and performance. Beyond pedagogical benefits, we analyze challenges such as algorithmic bias, data privacy, and transparency, underscoring the importance of explainable and inclusive design. Emerging trends—such as large language models, extended reality, and co-adaptive systems—are also reviewed, alongside practical recommendations for educators, developers, and policymakers. Our findings suggest that ethically designed, pedagogically grounded AI-enhanced educational games can provide scalable and engaging alternatives to traditional instruction.References
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