An Integrative Framework for the Educational Interaction Design of AI-Driven Personalized Learning Platforms

Qingwen Xu

doi:10.54097/z2fn2d44

Authors

Qingwen Xu

DOI:

https://doi.org/10.54097/z2fn2d44

Keywords:

Personalized Learning, AI in Education, Interaction Design, Learning Analytics, Cognitive Load Theory, Explainable AI.

Abstract

Personalized learning platforms powered by AI are transforming the education landscape, but the technical sophistication of the platforms creates experiences that feel disconnected, eroding user trust. This paper addresses this gap by constructing an integrative framework to guide the design and evaluation of these platforms. Informed by an interdisciplinary approach to pedagogy, cognitive science and Human-Computer Interaction (HCI), the framework is grounded in Cognitive Load Theory (CLT) to improve learning. The framework is driven by three strategic pillars: (1) personalization and Explainable AI (XAI) by providing personalized guidance in an effort to foster trust; (2) motivation and engagement enabled by meaningful gamification and immersive technologies; and (3) a balanced approach to agency and collaboration, utilizing a “Teacher-in-the-Loop” (TiL) model. Importantly, there are mechanisms within the framework to address the most pressing ethical challenges, including algorithm bias, privacy concerns and the digital divide. This study provides a systematic learner-centric framework for the design of ethically-aware platforms that facilitate more equitable and effective learning.

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