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.
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· Asian Journal of Contemporar...· 0 citations
It is concluded that effective responses to GenAI-related integrity problems should combine policy clarity, pedagogy, AI literacy, and student support rather than relying only on prohibition or software-based surveillance.
This study examines the role of generative artificial intelligence in higher education, focusing specifically on creative degree programs and students’ perceptions of its academic and creative value. Employing a mixed-methods design, data were collected from 555 university students enrolled in communication- and design-related degrees in Spain. By combining an online survey with focus groups, the research analyzed the frequency, purposes, and meanings of AI use in academic tasks. The results show that students primarily use generative AI to clarify concepts, develop ideas, review literature, and support academic production. Although they acknowledge its utility as a learning tool, participants also raised concerns regarding overreliance, reduced creative effort, unreliable outputs, and potential threats to originality and authorship. Consequently, the study concludes that while generative AI is already influencing learning practices in higher education, its educational potential relies heavily on clear pedagogical guidance, ethical implementation, and active teacher mediation. Based on these findings, the article proposes a ten-step teaching framework for AI-supported creative interactive content design, aimed at fostering pedagogical innovation while preserving critical thinking, creativity, and student authorship.
Belén Mainer, Ana Pérez-Escoda· Education sciences· 0 citations
Objectives: The perspective on the use of Generative AI (GAI)in higher education at King Abdulaziz University is balanced yet cautious. Professors recognize GAI's potential for education but are concerned about originality, academic integrity, and ethics. Faculty believe GAI can enhance analysis and clarity in research writing, but maintaining ethical compliance and oversight is crucial to uphold originality and scholarly standards. The study aims to explore ethical concerns and their impact on the quality of graduate students' academic submissions. It seeks to promote responsible use of GAI, thereby strengthening academic integrity and fostering innovation in graduate research. The research emphasizes the importance of ethical training, clear institutional policies, and transparent guidelines to responsibly incorporate GAI into research and teaching. Increasing awareness and developing robust ethical frameworks are essential to ensure GAI serves as a tool for innovation, not academic compromise.Methods: A quantitative approach was used, with a questionnaire to collect data on professors' assessments of graduate students' research quality and their adherence to GAI ethics. 29 participants, including full professors, associate professors, assistant professors, and lecturers. Purposive sampling was used, with significant experience in evaluating research integrating GAI tools, to ensure participants are well-versed in assessing GAI-augmented academic research.Results: The findings show a cautious yet balanced view of Generative AI in higher education. Professors at the Department of Information Science at King Abdulaziz University see GAI's benefits but worry about its impact on originality, integrity, and ethics. Faculty view GAI as a helpful research tool but emphasize ethical compliance and supervision to maintain standards.Conclusions: The study highlights the need for ethical training, policies, and clear guidelines to ensure responsible use of GAI, promoting innovation while safeguarding academic values.
H. Albadi· International journal of com...· 0 citations
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· AHFE International· 0 citations
Artificial Intelligence (AI) is reshaping higher education by transforming how students learn, how faculty teach, and how institutions manage academic processes. While AI offers opportunities for autonomy, competence, collaboration, and efficiency, it also raises concerns of dependency, quasi-plagiarism, and academic dishonesty. 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. The model explains how faculty engagement, institutional policy, student motivation, peer norms, and integrity enforcement interact to shape learning outcomes, distinguishing authentic use of AI from misuse. By addressing policy gaps, governance challenges, and equity concerns, the study contributes to theory by advancing multi-framework integration and to practice by offering strategies for ethical, responsible, and sustainable AI adoption in higher education.