Development of A Functional Model for An AIbased Adaptive Learning Platform

초록

This study aimed to examine how experts in AI-based learning perceive the categoriesof learning platform components to which AI technologies are applied, as well as theinterrelationships among these components. It also conducted a theoretical and empiricalanalysis to derive a compositional model suitable for the ultimate purpose of learning. Toachieve this purpose, a mixed-methods approach was employed, combining quantitativesurvey data with qualitative findings from expert focus group interviews. The analysiscategorized functional components into two major contexts: a technological context,including AI algorithms, data analytics, and feedback systems, and a learning context,consisting of learning content, instructional strategies, and learner interaction. According tothe results of the quantitative research, sub-factors within each context were categorizedand analyzed to investigate whether they exist in a relationship with other sub-factors. Asa result of this study, experts recognized the necessity of dynamic integration to supportmeaningful learning processes between technology and the learning context. Experts alsohold different perceptions, depending on the group’s perspective on the interdependencyamong sub-factors of an AI-based learning platform. However, experts shared a commonopinion that AI technologies must interact with educational goals and adapt to learner’scognitive, behavioral, and affective characteristics in real-time, using data-driven insightsgenerated by the learners. Accordingly, this study developed and proposed a model in whichtechnological and pedagogical elements are functionally integrated within a data-drivencontext. The resulting learner-centered model envisions the future of AI-based adaptivelearning platforms, offering both a theoretical foundation and practical guidance for systemdesign.

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