Generative LLM-based distance education decision design in Argentine universities

As distance education in Argentine higher education expands rapidly, decision-making systems must evolve to support personalized, fair, and scalable learning pathways. Existing recommendation tools often ignore curriculum dependencies, student goals, and the pedagogical value of recommendations. This paper proposes a generative LLM-based decision design that integrates course knowledge graphs and student profiles into a retrieval-augmented prompting framework. The system leverages large language models (LLMs), particularly GPT-4, to generate curriculum-aligned recommendations that support human-in-the-loop educational decisions. A scoring mechanism ensures graph consistency and prerequisite compliance, while experimental evaluations demonstrate improvements in recommendation accuracy, personalization, and fairness. The proposed approach offers a flexible and context-aware decision support model suitable for Latin American distance education institutions.