Abstract Book of the 8th International Academic Conference on Education, Teaching and Learning
Year: 2025
[PDF]
Integrating AI Into Course Quality Assurance Processes
Jason Myrick
ABSTRACT:
This paper explores the transformative potential of artificial intelligence (AI) in enhancing course quality review processes within higher education. Traditional course evaluation methods often rely on manual review processes that can be time-consuming, inconsistent, and prone to human bias. By leveraging machine learning algorithms, natural language processing techniques, and commercially available AI-driven tools, this study examines how automation can improve the accuracy, efficiency, and scalability of course quality assessments. The research outlines the development and implementation of an AI-driven framework designed to streamline evaluation procedures, providing more precise and timely feedback for faculty and instructional designers. This approach not only supports continuous improvement initiatives in course design but also enhances alignment with institutional learning objectives, accreditation standards, and quality assurance frameworks. Additionally, this paper addresses key challenges in integrating AI into academic review processes, including issues of data privacy, ethical considerations, and faculty perceptions of AI-assisted evaluation. It also explores strategies for effective implementation, ensuring that AI serves as a complement to expert human judgment rather than a replacement. The broader implications for academic policy, institutional accountability, and the future of AI-assisted learning design are discussed, highlighting the potential for AI to reshape course quality assurance in higher education.
Keywords: academic policy, artificial intelligence, instructional design, machine learning,quality assurance