International Journal of
Information and Education Technology

Abstract—As generative artificial intelligence becomes more integrated into educational environments, comprehending the psychological traits influencing users’ engagement with these technologies is crucial. This study investigated the influence of personality traits on the self-reported use of ChatGPT and the mediating role of trust in this relationship by drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework as a theoretical reference. Employing a quantitative research design, data were collected through structured questionnaires administered to a sample of 330 university students in North Cyprus using purposive/judgmental technique. The data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The findings show that extraversion and conscientiousness did not significantly influence perceived trust, whereas agreeableness, neuroticism, and openness are significant predictors of trust. In addition, perceived trust has a significant positive effect on ChatGPT use, highlighting its pivotal role as a cognitive evaluation linking dispositional traits to behavior. These results underscore how individual differences contribute to technology adoption and offer practical insights for educators and platform developers seeking to foster trustworthy Artificial Intelligence (AI) experiences. In an era of rising technological awareness, understanding the psychological antecedents of trust can help higher education institutions design interventions that encourage responsible and effective use of generative AI tools.

Keywords—big five personality traits, higher education, ChatGPT adoption, student engagement, Trust in Artificial Intelligence (AI), Unified Theory of Acceptance and Use of Technology (UTAUT)

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Cite: Damla Karagozlu, "Building Trust in Artificial Intelligence: Personality-based Strategies to Enhance Student Engagement in Higher Education," International Journal of Information and Education Technology, vol. 16, no. 7, pp. 1881-1889, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).