One size does not fit all: customizing teaching and learning strategies with Generative AI

Abstract

Introduction Higher education institutions implementing Generative Artificial Intelligence (GenAI) often assume uniform student adoption; however, evidence shows substantial variation in how learners engage with AI technologies. To address this heterogeneity, this study develops and validates a student typology framework integrating Technological Pedagogical Content Knowledge (TPACK) and the Unified Theory of Acceptance and Use of Technology (UTAUT), moving beyond generic implementation toward targeted educational interventions in business education. Methods Using a hybrid theoretical–empirical approach, we analyzed data from 252 MBA students. The integrated TPACK–UTAUT framework was applied to identify distinct patterns of GenAI engagement and adoption, enabling the empirical derivation and validation of stable student profiles. Results Three profiles emerged: Explorers (11%), younger students who actively experiment with GenAI despite limited formal training; Moderates (68%), systematic learners who favor structured approaches; and Skeptics (21%), experienced professionals who require clear educational value prior to adoption. Significant differences were observed across performance expectancy ( p < 0.001), age ( p < 0.001), and TPACK integration ( p < 0.001), with strong theoretical alignment (Cramer’s V = 0.276, p < 0.001). Discussion Rather than treating students as a homogeneous group, we propose a differentiated instructional framework comprising project-based exploration for Explorers, scaffolded training sequences for Moderates, and evidence-based case studies for Skeptics. This framework addresses the practical challenge of supporting diverse learners in GenAI-enabled business education. The typology provides a practical segmentation tool for institutional decision-making, including resource allocation and faculty development. By leveraging learning analytics, institutions may approximate student profiles to inform differentiated support strategies. The study advances theoretical understanding of GenAI adoption heterogeneity while offering a practical framework for designing differentiated educational interventions.

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