Implementing adaptive learning is often a challenging task at higher learning institutions where the students come from diverse backgrounds and disciplines. In this work, we collected informal learning journals from learners. Using the journals, we trained two machine learning models, an automated topic alignment and a doubt detection model to identify areas of adjustment required for teaching and students who require additional attention. The models form the baseline for a quiz recommender tool to dynamically generate personalized quizzes for each learner as practices to reinforce learning. Our pilot deployment of our AI-enabled Adaptive Learning System showed that our approach delivers promising results for learner-centered teaching and personalized learning.
Tan, Kar Way; Lo, Siaw Ling; Ouh, Eng Lieh; and Neo, Wei Leng, "AI-Enabled Adaptive Learning using Automated Topic Alignment and Doubt Detection" (2022). PACIS 2022 Proceedings. 110.
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