ARTIFICIAL INTELLIGENCE APPLICATIONS IN QUALITY ASSURANCE AND ACCREDITATION PROCESSES IN EDUCATION: TOWARDS A PREDICTIVE QUALITY INTELLIGENCE FRAMEWORK FOR NIGERIAN UNIVERSITIES

Abstract

Quality assurance and accreditation in higher education have traditionally relied on periodic evaluations, retrospective reports, and labour-intensive compliance procedures, which may identify institutional weaknesses only after they have affected educational quality. This paper conceptualized Artificial Intelligence (AI) as a Predictive Quality Intelligence System (PQIS) capable of transforming quality assurance from a reactive into a proactive and predictive process. It examined how AI-driven analytics can continuously monitor institutional quality indicators, forecast accreditation risks, detect emerging performance gaps, and provide real-time decision support for university managers. Drawing on principles of educational planning and management, the paper proposed an integrated framework combining machine learning, learning analytics, institutional data systems, and intelligent dashboards to strengthen programme accreditation, academic quality monitoring, resource planning, staff productivity, student success prediction, and policy implementation. Unlike conventional quality assurance approaches that emphasize past performance, the proposed framework enables universities to anticipate quality challenges and implement timely corrective measures. The paper argued that AI can promote evidence-driven governance, continuous institutional improvement, and data-informed strategic planning. For Nigerian universities facing growing demands for accountability, competitiveness, and digital transformation, predictive quality intelligence provides a pathway towards more agile and responsive quality assurance systems. The integration of AI into quality assurance can reposition Nigerian higher education from retrospective compliance to continuous, predictive, and evidence-based quality management, thereby supporting sustainable institutional excellence. Nigerian universities should establish AI-enabled quality assurance systems, supported by reliable institutional data infrastructure and staff capacity development, to enable continuous monitoring and early intervention in emerging quality and accreditation risks.