Adopting Generative AI in Higher Education: A Dual-Perspective Study of Students and Lecturers in Saudi Universities

Bamasoud, Doaa M. and Mohammad, Rasheed and Bilal, Sara (2025) Adopting Generative AI in Higher Education: A Dual-Perspective Study of Students and Lecturers in Saudi Universities. Big Data and Cognitive Computing, 9 (10). p. 264. ISSN 2504-2289

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Abstract

The integration of Generative Artificial Intelligence (GenAI) tools, such as ChatGPT, into higher education has introduced new opportunities and challenges for students and lecturers alike. This study investigates the psychological, ethical, and institutional factors that shape the adoption of GenAI tools in Saudi Arabian universities, drawing on an extended Technology Acceptance Model (TAM) that incorporates constructs from Self-Determination Theory (SDT) and ethical decision-making. A cross-sectional survey was administered to 578 undergraduate students and 309 university lecturers across three major institutions in Southern Saudi Arabia. Quantitative analysis using Structural Equation Modelling (SmartPLS 4) revealed that perceived usefulness, intrinsic motivation, and ethical trust significantly predicted students’ intention to use GenAI. Perceived ease of use influenced intention both directly and indirectly through usefulness, while institutional support positively shaped perceptions of GenAI’s value. Academic integrity and trust-related concerns emerged as key mediators of motivation, highlighting the ethical tensions in AI-assisted learning. Lecturer data revealed a parallel set of concerns, including fear of overreliance, diminished student effort, and erosion of assessment credibility. Although many faculty members had adapted their assessments in response to GenAI, institutional guidance was often perceived as lacking. Overall, the study offers a validated, context-sensitive model for understanding GenAI adoption in education and emphasises the importance of ethical frameworks, motivation-building, and institutional readiness. These findings offer actionable insights for policy-makers, curriculum designers, and academic leaders seeking to responsibly integrate GenAI into teaching and learning environments.

Item Type:	Article
Identification Number:	10.3390/bdcc9100264
Dates:	Date Event 11 October 2025 Accepted 18 October 2025 Published Online
Uncontrolled Keywords:	generative AI, higher education, technology acceptance, academic integrity, motivation, institutional support, Saudi Arabia, ChatGPT
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	24 Oct 2025 13:27
Last Modified:	24 Oct 2025 13:27
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16693

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