Umeå universitets logga

Students' diverse levels of knowledge and competence—shaped by individual interests and educational debts, including structural, systemic, and institutional barriers—create substantial cognitive heterogeneity in instructional settings. Adequately addressing this heterogeneity is challenging. Emerging studies applying artificial intelligence (AI) in education claim that advanced AI techniques like machine learning (ML) can mitigate educational debts by providing adaptive support. However, previous research offers limited clarity on how learning outcomes vary following AI-based adaptive instruction and which students improve their learning outcomes. To address these issues, this article quantitatively examines the extent to which ML-based adaptivity influences students' learning outcomes over time and identifies which students, considering their intersectional identities, benefit most from this adaptive support. Specifically, we illustrate a semester-long study conducted within an undergraduate organic chemistry course, where an ML model adaptively supported 266 students across four interventions on mechanistic reasoning. We identified five learning trajectories throughout these adaptive interventions. Our findings show that students with higher prior knowledge made greater progress than their peers. Additionally, men without an underrepresented minority (URM) status majoring in chemistry benefited more than URM women who are not chemistry majors. This indicates that the adaptive support maintained and partly exacerbated educational debts. Our study contributes to the literature by analyzing how ML-based adaptivity affects educational debts in undergraduate organic chemistry. In doing so, it adopts a theoretical framework—the enhanced educational debt framework—to assess when different aims of adaptive support are most appropriate in undergraduate education, informing an equity-centered design of adaptive support.