Artificial Intelligence for Academic Quality Assurance and Accreditation

Abstract

Academic Quality Assurance and Accreditation are fundamental mechanisms for maintaining educational standards, institutional accountability, and continuous improvement in higher education systems worldwide. Traditionally, these processes have relied on manual documentation, periodic reviews, qualitative judgment, and retrospective analysis. As higher education institutions grow in complexity, scale, and data intensity, conventional quality assurance frameworks increasingly face challenges related to efficiency, consistency, transparency, and responsiveness.
Artificial Intelligence (AI) has emerged as a transformative technology capable of addressing these limitations by enabling data-driven, automated, and predictive approaches to academic quality management. By leveraging techniques such as machine learning, natural language processing, predictive analytics, and intelligent automation, AI systems can support continuous monitoring of academic performance indicators, streamline accreditation documentation, enhance outcome-based education assessment, and facilitate evidence-based decision-making.
This chapter examines the role of Artificial Intelligence in strengthening academic quality assurance and accreditation systems. It explores how AI can be applied across key academic domains, including curriculum design, teaching–learning evaluation, assessment practices, faculty performance analysis, student progression tracking, and institutional governance. The chapter also discusses AI-enabled accreditation management, highlighting applications such as automated evidence mapping, accreditation readiness forecasting, and real-time quality dashboards.