An Empirical Study on the Integration of Artificial Intelligence into Total Quality Management: An Assessment of Benefits and Challenges in Higher Education

Alemar D. Betito, Jay A. Sario and Rene Boy R. Bacay
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Alemar D. Betito: Post Doctoral Student, Philippine Christian University, Philippines
Jay A. Sario: Post Doctoral Student, Philippine Christian University, Philippines
Rene Boy R. Bacay: Post Doctoral Student, Philippine Christian University, Philippines

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 6, 306-336

Abstract: Total Quality Management (TQM) represents a well-recognized management philosophy that underscores the importance of continuous improvement, customer satisfaction, and the pursuit of organizational excellence across diverse industries (Garcia & Patel, 2020). In recent years, there has been a notable trend among higher education institutions to adopt Total Quality Management principles with the aim of improving academic quality, administrative efficiency, and stakeholder engagement (Lopez & Kumar, 2021). Concurrently, swift progress in Artificial Intelligence (AI) has commenced the transformation of organizational processes, encompassing quality management systems. Artificial intelligence technologies empower organizations to conduct analyses of extensive datasets, automate repetitive tasks, and facilitate data-driven decision-making, thereby presenting considerable opportunities for enhancing Total Quality Management practices (Smith & Lee, 2021; Zhang et al., 2022). In the world of higher education, the applications of artificial intelligence are varied, encompassing the prediction of student performance, the personalization of learning experiences, the optimization of administrative workflows, and the monitoring of quality assurance (Nguyen et al., 2023). Nevertheless, in conjunction with these opportunities, there exist challenges, including ethical considerations, substantial implementation costs, technological complexity, and resistance from personnel who are unprepared for change (Kumar & Reddy, 2023; Patel & Garcia, 2022).

Date: 2025
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