Adaptive, but Equitable? Exploring the Impact of Machine Learning‐Based Adaptive Support on Educational Debts in Undergraduate Chemistry

Datos Bibliográficos

ID21392936
AutoresPaul P Martin (0000-0001-8648-4250, Institute of Chemistry Education Justus‐Liebig‐University Giessen Germany), Marcus Kubsch (0000-0001-5497-8336, Department of Physics Freie Universität Berlin Berlin Germany), Brandon J Yik (0000-0001-8124-8451, Department of Chemistry University of Georgia Athens Georgia USA), Benjamin T Burlingham (0009-0009-3500-8487, Department of Chemistry Indiana University Bloomington Indiana USA), Nicole Graulich (0000-0002-0444-8609, Institute of Chemistry Education Justus‐Liebig‐University Giessen Germany, autor de correspondencia)
Año2026
Volumen110
Número3
Páginas928-946
Fecha de publicación2026-05-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaScience Education (JOURNAL)
Identificadores de la revistaISSN: 0036-8326 • E-ISSN: 1098-237X
EditorialWiley (PUBLISHER • GB)
DOI10.1002/sce.70042
OpenAlexW4417291582
IdiomaEN
Referencias citadas116

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

Adaptive Learning · CLARITY · Cognition · Complex adaptive system · Debt · Higher education · Psychological intervention · Science education · Innovative Teaching and Learning Methods · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics