Adoption of AI-Based Learning Analytics to Support Academic Decision-Making in Universities
Keywords:
AI-based learning analytics, academic decision-making, digital transformation, higher education management, institutional readiness, predictive analytics, educational data miningAbstract
This study examines the adoption of AI-based learning analytics and its role in supporting academic decision-making in universities. As higher education institutions face increasing demands for accountability and data-driven governance, AI-enabled analytics offer new opportunities to monitor student performance, identify risk patterns, and improve instructional quality. Using a mixed-methods design, the research collected survey responses from 261 academic staff and conducted interviews with 15 university leaders. Findings show that most respondents perceive AI-based analytics as highly useful for early identification of at-risk learners, curriculum refinement, and accreditation reporting. Institutional readiness particularly digital infrastructure, staff competence, and leadership support significantly influences the extent of analytics utilization. Qualitative insights reveal that ethical concerns, cultural resistance, and integration challenges remain major barriers to adoption. The study concludes that successful implementation requires not only technological investment but also strategic leadership and clear governance frameworks to ensure responsible and effective use of analytics. These results contribute to ongoing discussions on digital transformation in higher education and highlight the organizational conditions necessary to leverage AI-driven insights for improving academic planning and student outcomes.
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