A Retrieval-augmented Generation Framework for Policy-grounded Course Advising in Higher Education

Abstract

Course advising in higher education requires students and advisers to integrate information from course catalogues, programme requirements, departmental handbooks, prerequisite rules, and institutional regulations. This study presents a Retrieval-Augmented Generation (RAG) framework for policy-grounded undergraduate course advising at Babcock University, Nigeria. The prototype combines a structured academic database, an indexed institutional knowledge base, a large language model, and a separate validation layer for prerequisite and credit-load constraints. The implementation includes student-profile management, natural-language querying, knowledge-base administration, authentication, and chat-log review. Evaluation used 18 institutional source documents segmented into 246 indexed chunks, 30 simulated student profiles, 60 advisory queries, and benchmark judgements from three academic advisers. The qualitative beta evaluation examined policy retrieval, policy grounding, prerequisite and credit-limit validation, response clarity, ambiguity handling, advising usefulness, and observed failure conditions. The findings provide formative evidence that the framework can retrieve relevant institutional information, incorporate evidence into advisory responses, and apply deterministic checks to explicit academic rules. However, context fragmentation and ambiguity in source documents remained important limitations, and the evaluation did not provide statistical performance estimates. The framework is therefore positioned as a decision-support tool that retains human oversight for ambiguous or high-stakes advising cases and requires larger controlled evaluation before measurable advising performance can be established.