Adaptive Ai-Driven Total Quality Management for Higher Education Excellence
DOI:
https://doi.org/10.47672/ijpm.2916Abstract
Purpose: The rapid technological evolution, student population diversity and stakeholder expectations are contemporary challenges of universities. Consequently, higher education institutions need to adopt a more flexible, data-oriented, and responsive quality management approach. TQM has been in existence for many years in the higher education industry, but the PDCA design-based quality cycles are typically back looking, not integrated and slow. The PDCA design-based quality cycles happen at the school and administrative unit levels. While learning analytics, predictive modelling, and process automation have been deployed regularly, they are mostly in piecemeal and narrow contexts.
Methodology: This review systematically examines 74 peer-reviewed studies published between 2019 and 2025. These studies are indexed by Web of Science and Scopus. Above all, it explores how AI and TQM are increasingly converging. In short, this is happening at the intersection of higher education quality management. The study primarily aims to investigate key implementation issues, delineate AI-enabled quality practices, and identify opportunities of using AI in PDCA cycle.
Findings: The lack of real-time feedback and adaptive response systems. Systemic disconnect in the data systems of the academic and administrative departments. Slow intervention due to inflexible PDCA cycles. These are the major perils identified in the review. Such challenges limit the agility of higher education institutions in responding to student needs, quality risks and institutional performance deficits. In response, this review suggests an Adaptive AI-Driven TQM Cycle which proposes to embed Real-time analytics, predictive modelling, automated interventions and continuous stakeholder feedback at every stage of PDCA.
Recommendations: According to the proposed model, higher education leaders can make use of an evidence-based framework to develop an agile, transparent, and student-centred quality management system. The future implementation should consist of integrated institutional data systems, ethical governance of AI, building the capacity of staff, and ongoing evaluation of the quality improvement practices supported by AI. Enhanced student retention, satisfaction, responsiveness to institutions, and long-term competitiveness can be supported by this approach.
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