Fine-Tuned Large Language Models for Enhanced Automated Academic Advising

The rapid increase in student enrollment at universities across the UAE has underscored the limitations of traditional academic advising methods, including long wait times and overburdened advisors. This paper presents a finetuned Academic Advisor Chatbot leveraging the “noushermes2” large language model (LLM) to deliver real-time, context-aware guidance tailored to the university's academic policies. By integrating institution-specific data, the chatbot addresses common student queries with high accuracy and clarity, as evidenced by BLEU scores improving from 0.45 to 0.78 and ROUGE-L scores reaching 0.85 for specific academic scenarios. A user feedback study involving 50 students revealed high satisfaction levels, with an average rating of 4.6/5 for accuracy, 4.7/5 for clarity, and 4.8/5 for time efficiency. The chatbot not only enhances the course registration experience but also reduces advisor workload, offering a scalable solution to meet the needs of a growing student population. Future enhancements will focus on multilingual support and expanded program-specific datasets to further improve usability and inclusivity.