2026 ASEE Annual Conference & Exposition

A Large Language Model-Based Academic Advising Assistant for Engineering Technology Students

Presented at Engineering Technology Division (ETD) Technical Session 11

In recent years, Artificial Intelligence (AI)-based solutions, particularly Large Language Models (LLMs), have been applied to a variety of domains, such as energy, finance, transportation, healthcare, and education. Among these domains, education has become increasingly popular due to strong interest among educators and students. This study proposes an academic advising assistant system that uses LLMs to help Engineering Technology (ET) students plan their course load based on their educational history, departmental course offerings, and personal constraints, such as their preferred semester course load. The proposed LLM-based academic advising assistant system maintains a database of students' course histories and upcoming course availability to provide personalized recommendations for which courses to take based on user input. It also explains course dependencies and adapts its suggestions according to user constraints, such as course difficulty (inferred from grade distributions) and maximum credit limits per semester.

Authors
  1. Dr. Otilia Popescu Old Dominion University [biography]
  2. Adel El-Shahat Old Dominion University [biography]
  3. Ryan Cotton Old Dominion University
  4. Kayla Marie Seegers Old Dominion University
  5. William Austin Henderson Old Dominion University
Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026