Theoretical Logic and Implementation Path of the Transformation of Higher Education Quality Monitoring Paradigm in the Digital-Intelligent Era

Abstract

In the “digital-intelligent” era, the integration of big data and artificial intelligence technologies is profoundly reshaping the higher education ecosystem, placing traditional higher education quality monitoring paradigms in an unprecedented context of challenges and opportunities. This paper aims to systematically explore the inherent logic and future path of the transformation of university quality monitoring paradigms from a theoretical perspective. The research first analyzes the inherent limitations of the traditional quality monitoring paradigm in terms of monitoring timeliness, data dimensions, functional positioning, and evaluation orientations. Furthermore, it demonstrates the theoretical inevitability of the paradigm shift—from “small data sampling” to “full-scale data perception”, “experience-driven” to “algorithmic decision-making”, “management control” to “developmental service”, and “static closure” to “dynamic openness”—from four dimensions: data foundation, core technology, value orientation, and system ecology. On this basis, the paper constructs a theoretical model of a new digital-intelligent quality monitoring paradigm with a four-layer structure: “data foundation layer, intelligent algorithm layer, analysis and insight layer, and application service layer”. Finally, targeting theoretical dilemmas such as data ethics, algorithmic bias, and conflicts between technical rationality and educational humanity that may arise during the transformation process, it proposes clear implementation paths and policy recommendations. This study intends to provide theoretical reference for building a more forward-looking, accurate, and humanistic higher education quality assurance system in the future.