Cluster analysis identifies diverse student profiles for personalized learning support in engineering education, suggesting tailored strategies.
The integration of Generative AI in engineering education requires a deeper understanding of diverse student adoption patterns. This study applies cluster analysis grounded in the Technology Acceptance Model and extended constructs on survey data to create different user profiles. Four distinct user profiles emerged: Empowered Optimizers, Mainstream Pragmatists, Skeptical Minimalists, and Ethical Achievers. The findings challenge one-size-fits-all approaches, providing a student-centred framework for designing tailored instructional strategies, GenAI training, and ethical guidelines.
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Jin et al. (2026) studied this question.
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