Abstract

Flipped classrooms are increasingly being combined with adaptive and AI-enabled technologies, yet empirical evidence meeting a strict definition of AI-adaptive flipped learning in undergraduate engineering remains limited. This systematic review examined literature published between 2015 and 2025 and applied explicit operational criteria requiring learner-data-driven algorithmic adaptation, a structured flipped design comprising pre-class preparation and in-class application, an undergraduate engineering or computing population, and an eligible comparator or analytic contrast. Source-level verification identified five eligible peer-reviewed journal reports. These reports do not represent five independent research programmes; several arise from the same numerical-methods/adaptive-learning programme and include overlapping or sequential cohorts. Comparative reports generally found small or inconsistent differences in achievement, whereas perceived learning, classroom environment, and student experience were more consistently favourable. Kaw additionally showed that adaptive-learning-platform data could identify students at risk early, although the subsequent outcome comparison was confounded by voluntary uptake of human tutoring. No report isolated the contribution of AI adaptivity from all surrounding pedagogical and support components. The evidence therefore supports feasibility and several testable mechanisms, but not a pooled causal claim of superiority. Priorities for future research include independent multi-site trials, transparent reporting of algorithmic decisions and intervention fidelity, delayed engineering outcomes, prospective cost analysis, and predefined equity assessment.

Keywords

Flipped Classroom, Adaptive Flipped Classroom, Artificial Intelligence, Engineering Education, Personalized Learning, Pedagogical Mechanisms, Implementation Fidelity, Engineering Competencies,

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References

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