AI-powered adaptive learning interfaces: a user experience study in education platforms - Jamali, Hossein; Dascalu, Sergiu M.; Harris, Frederick C.; Wu, Rui (Frontiers in Computer Science, 2025)
Adaptive learning platforms are increasingly used to enhance online education, yet a gap exists in understanding how the design of their AI-powered features impacts user experience. This study addresses this gap by evaluating three prominent platforms–Khan Academy, Coursera, and Codecademy–in teaching HTML. Using a mixed-methods approach with 23 participants, we assessed task completion time, user satisfaction, engagement, and task accuracy. Results revealed significant performance differences: Codecademy offered the fastest task completion, while Khan Academy achieved the highest user satisfaction. A crucial finding emerged from qualitative and quantitative data: participants found the specific AI-driven adaptive features on all platforms to be subtle and minimally impactful, with core platform interactivity being a more dominant factor. This study's main contribution is the identification of a critical trade-off between learning efficiency and user engagement, which is mediated by the discoverability and perceived value of adaptive features. We conclude that for AI-powered educational tools to realize their full potential, their adaptive features must be more discoverable, intuitive, and integral to the core learning loop. The study provides actionable insights for designers and educators seeking to balance platform efficiency with a more personalized and motivating user experience.