Author Information
Yasser E. Ibrahim Mansour, Prince Sultan University, Saudi ArabiaAhmed Ibrahim, Prince Sultan University, Saudi Arabia
Abstract
Academic accreditation plays a vital role in ensuring that educational programs meet high standards of quality, foster trust among stakeholders, and enhance institutional credibility. This research leverages artificial intelligence (AI) to develop a comprehensive framework for academic program evaluation and assessment to meet the accreditation standards and to provide data-driven recommendations for continuous quality improvement. To evaluate various aspects of an academic program, five key standards are considered: program management and quality assurance, teaching and learning, students, faculty, and learning resources, facilities, and equipment. Each standard is assessed using specific criteria and a clear rubric to categorize practices effectively. Following the evaluation, strength and weakness areas of the program are depicted and tailored recommendations are provided to enhance the quality of each standard based on program scores with emphasizing relevant key performance indicators (KPIs) for consideration to monitor progress within each standard. Finally, action plans on different levels are proposed to guide the leaders of the program and help them fulfill the set goals and be better prepared for the accreditation. This AI-driven framework facilitates the creation of a detailed report on the program's accreditation assessment, which can be utilized by the program leaders for self-assessment or by external evaluators to identify areas for improvement.
Paper Information
Conference: BCE2025Stream: Assessment Theories & Methodologies
This paper is part of the BCE2025 Conference Proceedings (View)
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To cite this article:
Mansour Y., & Ibrahim A. (2025) AI-Driven Framework for Academic Program Evaluation and Accreditation Standards ISSN: 2435-9467 – The Barcelona Conference on Education 2025: Official Conference Proceedings (pp. 393-403) https://doi.org/10.22492/issn.2435-9467.2025.32
To link to this article: https://doi.org/10.22492/issn.2435-9467.2025.32
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