ORIGINAL RESEARCH article
Front. Comput. Sci.
Sec. Digital Education
AI-Powered Adaptive Learning Interfaces: A User Experience Study in Education Platforms
Provisionally accepted
- 1University of Nevada, Reno, Reno, United States
- 2Kennesaw State University, Kennesaw, United States
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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.
Keywords: Adaptive Learning, User Experience, human-computer interaction, Educational Technology, Online Learning, HTML
Received: 23 Jul 2025; Accepted: 21 Oct 2025.
Copyright: © 2025 Jamali, Dascalu, Harris, Jr. and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hossein Jamali, hjamali@unr.edu
Sergiu M Dascalu, dascalus@unr.edu
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.