Author Identifier (ORCID)
Abstract
The rapid integration of Generative AI in higher education has transformed teaching and learning, yet limited research explores the factors driving its adoption and impact on academic performance. This study addresses the gap in understanding how ethical principles (fairness, accountability, transparency, accuracy, autonomy) and AI characteristics (perceived anthropomorphism, perceived intelligence) influence students’ use of Generative AI tools and their subsequent academic outcomes. The research aims to develop and test a theoretical model that integrates these factors to explain Generative AI adoption and its effect on perceived academic performance among university students. Data were collected through surveys from 318 students and analyzed via Partial Least Squares-Structural Equation Modeling (PLS-SEM). Results revealed that accountability, transparency, accuracy, autonomy, perceived anthropomorphism, and perceived intelligence significantly drive Generative AI use, while fairness does not. Generative AI use, in turn, is positively associated with academic performance, explaining 49.9% of its variance. These findings advance technology adoption and educational technology research by highlighting the interplay of ethical and technical factors in AI adoption, offering practical insights for educators and developers to optimize AI tools for equitable and effective learning.
Keywords
academic performance, AI characteristics, ethical principles, generative AI, higher education
Document Type
Journal Article
Date of Publication
1-1-2026
ISSN
07356331
Publication Title
Journal of Educational Computing Research
Publisher
Sage
School
School of Business and Law
Funding Information
This work was supported by Tenaga Nasional Berhad (TNB) and UNITEN through the BOLD Refresh Postdoctoral Fellowships under the project code of J510050002-IC-6 BOLDREFRESH2025-Centre of Excellence.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Al-Emran, M., Al-Sharafi, M. A., Foroughi, B., Al-Qaysi, N., Leung, N. K. Y., Yaseen, Z. M., & Ali, N. (2026). Role of ethical principles and AI characteristics in shaping generative AI use in higher education and its impact on academic performance. Journal of Educational Computing Research. Advance online publication. https://doi.org/10.1177/07356331261463140