AI Game-Based Learning in Low-Resource Classrooms: Teachers’ Innovation Under Constraint

Abstract: The integration of artificial intelligence into game-based learning has been widely promoted as a means of enhancing engagement and personalization in education. However, existing research largely assumes well-resourced classroom conditions, offering limited insight into how such approaches are enacted in constrained contexts. This qualitative multiple case study explores how teachers design and implement AI-supported game-based learning in low-resource classrooms and how contextual constraints shape their pedagogical reasoning and innovative practices. Drawing on interviews, classroom observations, teaching artifacts, and stimulated recall, the study foregrounds teachers’ agency in mediating AI use under conditions of limited infrastructure, device access, and institutional support. The findings reveal that teachers adopt selective and hybrid approaches to AI game-based learning, combining AI-generated content with offline activities and teacher-led scaffolding. Contextual constraints function not merely as barriers but as catalysts for reflection, improvisation, and ethical decision-making related to fairness and inclusivity. This study contributes to AI in education research by reframing innovation as a situated, teacher-driven process and by highlighting the importance of context-sensitive approaches to AI-supported pedagogies, particularly in underrepresented low-resource educational settings.