A Quality Assurance Framework for Maintaining and Enhancing Academic Standards of AI-Infused Higher Education: Insights from GCC Faculty Perspectives

Abstract

Objectives: This study addresses the gap in research on maintaining and enhancing the quality of artificial intelligence (AI)–infused higher education in a structured and systematized way by proposing a comprehensive quality assurance (QA) framework fit for that purpose. Methods: The study employs a qualitative research design approach in the form of a constructivist or interpretive investigation, exploring in depth faculty members’ different perspectives and experiences in relation to AI implementation. The study combines a textual analysis of the international literature on the topic, with insights from four focus groups involving faculty members from various disciplines and colleges across the Gulf Cooperative Council (GCC) region. Results: The literature review analysis identified key benefits and challenges of AI in education. The focus groups yielded important insights into faculty’s attitudes toward and readiness for incorporating AI in their teaching practices, along with their beliefs about students’ AI use. Collectively, these evidence-based findings informed the development of an AI-infused education QA framework comprising 10 evaluation standards, each with specific indicators and quality checks. Conclusions: The study concludes proposing a prototype of a comprehensive quality assurance framework with specific standards and indicators for regulating the implementation of AI-infused education and for evaluating and enhancing its quality as needed. Implications: The study fills an important research gap, in addition to proposing a QA framework that can provide a structured approach for higher education institutions, QA bodies, and policymakers to regulate and evaluate AI integration in higher education.

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