Skip to content
Log in
Review Open access

Role of Ethical Principles and AI Characteristics in Shaping Generative AI Use in Higher Education and Its Impact on Academic Performance

Mostafa Al-Emran Mohammad A. Al-Sharafi Behzad Foroughi Noor Al-Qaysi Nelson Leung Z. Yaseen Nor’ashikin Ali
Jul 2026 · Journal of educational computing research · 0 citations · 73 references

TL;DR

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.

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.

DOI Semantic Scholar
Read PDF

Similar papers

Review Open access Jul 2026

Navigating generative AI in higher education: Freshers’ perceptions of ethics in AI, digital learning behavior, and institutional norms

First-year university students’ perceptions of generative AI in academic work are investigated, foregrounding student agency in a Global South context and offering pedagogical and policy implications for responsible AI adoption.

Sharifuzzaman, M. Rahman · 0 citations
Open access 2026

Integrating Generative AI in Engineering Education: A Longitudinal Framework on Personality, Roles, and Perceptions

This study investigates how engineering students’ personality traits, perceived team roles, and AI literacy influence their perceptions of generative Artificial Intelligence (Gen-AI) tools in university education. Building on previous frameworks that link psychological and behavioral variables to technology adoption, a longitudinal design was adopted across two academic years (2023–24 and 2024–25) at the University of Udine. The same validated questionnaire was administered to undergraduate and graduate engineering students, combining the Big Five personality inventory, perceived team-role selection, and five multi-item scales measuring Attitude, Trust, Social Influence, Fairness & Ethics, and Usefulness toward Gen-AI. Descriptive and inferential analyses showed stable perceptions over time, with small yet meaningful increases in Attitude and AI Literacy (p < .05). The mediation analysis indicated that AI literacy acts as a mediator between Openness and perceived Usefulness, although the effect was small and non-significant. The results suggest that continued exposure to Gen-AI fosters both greater confidence and more critical awareness among engineering students. The study provides evidence of the structural reliability of the proposed Excel-based framework and offers practical guidance for integrating AI literacy modules into design-oriented engineering curricula.

S. Filippi, E. Vaglio, Barbara Motyl · 0 citations
Open access Jul 2026

A Boon or Bane? Challenges and Opportunities of AI Adoption in Higher Education

This conceptual paper synthesizes insights from ten institutional cases across global contexts and draws on five theoretical foundations, Diffusion of Innovation, the Technology Acceptance Model, Self-Determination Theory, Social Learning Theory, and Academic Integrity frameworks, to propose a process model of AI adoption and use in higher education.

Nayyer Naseem, Yuqi Wang, Maureen Leary · 0 citations