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

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.

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Publication Details

Journal
Big Data and Cognitive Computing
Published
2025-10-18
DOI
https://doi.org/10.3390/bdcc9100264
Citations
3
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
1.96

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article

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

Rasheed Mohammad Nassr, Sara Bilal, Doaa M. Bamasoud
3 citations
Big Data and Cognitive Computing
Artificial Intelligence in Healthcare and Education
1.96
article

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

Rasheed Mohammad Nassr, Sara Bilal, Doaa M. Bamasoud
article en
3 citations

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.

Big Data and Cognitive ComputingVol. 9(10)
Birmingham City University (GB), Auckland Institute of Studies (NZ), University College Birmingham (GB), University of Bisha (SA)
University of Bisha
Openalex Percentile: Top 10%
Artificial Intelligence in Healthcare and Education
1.96
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