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Faculty perceptions of ChatGPT on academic integrity and institutional roles in higher education

Jul 2026 · Scientific Reports · Vol 16 · 0 citations · 53 references
Medicine

TL;DR

Examination of faculty perceptions of ChatGPT in public universities in Saudi Arabia suggests that faculty perceptions of generative AI are shaped by professional responsibilities within specific institutional contexts, suggesting the need to develop clear institutional guidelines for AI use and redesign assessment practices to reduce misuse.

Abstract

The rapid expansion of generative artificial intelligence (GenAI) tools such as ChatGPT has raised growing concerns in higher education, particularly regarding assessment practices and academic integrity. This study examines faculty perceptions of ChatGPT in public universities in Saudi Arabia, with a focus on differences related to academic rank and institutional role. Using a sequential explanatory mixed-methods design, survey data were collected from 202 faculty members across eight public universities and analyzed using descriptive statistics and the Kruskal–Wallis test. Qualitative responses were analyzed thematically to explain and contextualize the quantitative findings. The results indicate variation in faculty perceptions. Assistant and associate professors reported higher concern regarding issues such as cheating, unequal evaluation, and the impact of AI on student performance and skills. Qualitative findings help explain these patterns by highlighting concerns about students’ reliance on AI-generated content and its effects on writing and learning processes. Faculty members in administrative roles emphasized institutional challenges, particularly the need for clearer guidelines on AI use in assessment and academic integrity procedures. The findings suggest that faculty perceptions of generative AI are shaped by professional responsibilities within specific institutional contexts. Practical implications include the need to develop clear institutional guidelines for AI use, redesign assessment practices to reduce misuse, and provide targeted faculty training on AI-aware teaching and evaluation strategies.

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