Digital Transformation and Intelligent Systems inHigher Education: An Integrative Framework forData-Driven Decision Making

Abstract

The integration of intelligent systems and data analytics in higher education has emerged as a critical driver of institutional transformation, enabling evidence-based decision making at both pedagogical and administrative levels. This paper presents an integrative framework for digital transformation in university environments, examining the convergence of machine learning, learning analytics, and process optimization technologies within educational contexts. The methodology draws on a systematic review of 47 studies published between
2019 and 2025, complemented by a case study involving the implementation of a predictive analytics dashboard across three university departments comprising 1,240 students. Results demonstrate that institutions adopting data-driven frameworks achieve a 34% reduction in administrative processing time, a 28% improvement in early academic risk detection, and statistically significant gains in student retention rates (p < 0.01). The proposed framework integrates four interdependent layers—data acquisition, intelligent processing, visualization, and governance—structured according to ISO/IEC 25012 data quality standards. The findings suggest that sustainable digital transformation requires not only technological infrastructure but also organizational readiness, faculty training, and ethical data governance policies. This work contributes a replicable blueprint for institutions seeking to leverage intelligent systems for knowledge generation and evidence-based management.