Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
@article{205709,
author = {Tejaswini Gunagi and Sachin. S. Kudekallu and Kishore Kumar K and Monica K P},
title = {An Adaptive AI Framework for Personalized Gamification in Online Education},
journal = {International Journal of Innovative Research in Technology},
year = {2026},
volume = {13},
number = {1},
pages = {7199-7203},
issn = {2349-6002},
url = {https://ijirt.org/article?manuscript=205709},
abstract = {Online education offers flexible, large-scale access to learning, but sustaining student engagement and motivation in virtual environments remains a persistent problem. Gamification points, badges, leaderboards, and challenges is widely used to raise participation, yet most gamified systems apply the same mechanics to every learner and ignore individual behaviour and preferences. In parallel, AI-based adaptive learning systems personalize content and difficulty but rarely personalize the gamification layer itself. This paper proposes an Adaptive AI Framework for Personalized Gamification in Online Education that targets this gap. The framework continuously collects learner interaction and performance data, builds and updates a learner profile, and uses that profile as the context for an AI adaptation engine that selects which gamification element to deliver next. We frame this selection as a contextual multi-armed bandit problem, in which the learner profile is the context, the available gamification elements are the actions, and an engagement signal derived from the learner’s response is the reward. A gamification management module delivers the chosen elements, and learner responses feed back into the loop. We describe the architecture, specify a concrete decision mechanism, and define an evaluation methodology dataset, metrics, baselines, and hypotheses through which the framework can be tested. The work is presented as a conceptual framework with a validation plan; empirical results are identified as future work},
keywords = {Adaptive learning; personalized gamification; online education; learning analytics; contextual bandits; student engagement.},
month = {June},
}
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