Data-Driven Learning Analytics and Artificial Intelligence in Higher Education: A Systematic Review

Technological change drives a constant cycle of adaptation in learning systems, especially within Engineering Education and ICT fields. The integration of Artificial Intelligence (AI) introduces both challenges and opportunities for improving educational processes. This article presents a Systematic Literature Review (SLR) of 51 peer-reviewed articles from the Web of Science and Scopus databases, covering the period from January 2021 to February 2025. The review investigates data-driven approaches combined with AI across four educational domains: learning, teaching, assessment, and academic administration. Results show that assessment is the most frequently targeted area, particularly through predictive modeling. However, there is a critical need for AI architectures that support Task-specific cognitive analysis. The limited adoption of mixed-methods research raises concerns about bias and restricts deeper pedagogical understanding. Advancing digital transformation in higher education requires aligning AI integration, data governance, and learning analytics with pedagogical models grounded in social justice and equity. The study concludes by highlighting the potential of Generative AI to enhance the interpretability of analytics and foster intelligent, inclusive educational ecosystems. These findings provide valuable guidance for decision-makers seeking to implement digital learning products through evidence-based, data-driven strategies.