AI-Driven Student Data Analytics: Leveraging Generative AI for Institutional Decision-Making

Abstract

Higher education institutions generate vast datasets, yet deriving actionable insights remains difficult due to the technical expertise required. This paper presents an AI-driven student analytics system that leverages large language models (LLMs) to automate SQL generation, statistical analysis, and visualization from natural language inputs. Unlike traditional tools reliant on static dashboards and manual queries, our system enables real-time exploration of student retention, graduation rates, GPA trends, and equity gaps-without requiring SQL knowledge. Em-pirical evaluation across multiple prompting strategies shows high semantic accuracy and execution efficiency. By removing technical barriers, the system supports data-driven decision-making and equity-focused interventions. Future work includes integrating learning management system (LMS) data and enhancing model interpretability.

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