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In addition to research papers, the Design Society is developing several valuable resources for those interested in the study of design. These include a repository of PhD theses, a library of case studies and transcripts of design activities, and an archive of our newsletters. Please note that these resources are accessible exclusively to Design Society members.

Generative AI adoption in engineering: a cluster analysis of student profiles for designing personalized learning support

Jin, Han; Baby, Mathew; Das, Runu Proma; Moratti, Tathyana; Pagan, Michael B.; Morkos, Beshoy W.

Type:
Papers
DS 145: Proceedings of the DESIGN2026 19th International Design Conference
Year:
2026
Editor:
Štorga, M.; Škec, S.; Martinec, T.; Marjanović, D.; Pavković, N.
Author:
Jin, Han; Baby, Mathew; Das, Runu Proma; Moratti, Tathyana; Pagan, Michael B.; Morkos, Beshoy W.
Series:
DESIGN
Institution:
College of Engineering, University of Georgia, United States of America
Page(s):
3001-3010
DOI number:
10.1017/pds.2026.10658
ISSN:
2732-527X (Online)
Abstract:
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.
Keywords:
artificial intelligence (AI), design education, technology acceptance model, student profiles, personalised learning
DESIGN2026 - The 19th International Design Conference
Conferences

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