Patterns of Perceptions and Experiences With ChatGPT Among Global Higher Education Students: A Data-Driven Approach

• In Event: AERA Poster Session Thursday 9:45am
  In Poster Session: Higher Education and Student Learning Outcomes

Abstract

To address the limited understanding of individual differences in perceptions and experiences with ChatGPT in learning, we employed a data-driven approach to explore patterns of factors related to ChatGPT use among global higher education students. Using large-scale survey data from 11,218 students across 52 countries, we conducted factor analysis to identify latent factors and latent profile analysis to uncover distinct student response patterns. Eight factors were identified—three related to academic, professional, and cognitive development; two to user experience; and three to broader societal implications. Four student profiles emerged, each characterized by unique combinations of these factors and characteristic attributes. Findings highlight the need for tailored AI-related guidance and interventions to support equitable integration of AI tools in educational contexts.

Authors

• Yuqing Zou, University of New Mexico - Gallup

• Chunrui Zou, University of Pennsylvania