AI-Augmented Advising: AI-Augmented Advising: A Comparative Study of ChatGPT-4 and Advisor-based Major Recommendations

Kasra Lekan, Zachary Pardos

Track: Innovations in AI for Education (Day 1)

Paper Length: long-paper (6 pages + references)

Keywords: Advising, major selection, ChatGPT, LLM, AI-Human collaboration, higher education, Generative AI, Experimental study

TL;DR: We investigate the utility of LLMs for supporting academic major advising and find it to be a promising area for Human-AI collaboration.

Abstract: Choosing an undergraduate major is an important decision that impacts academic and career outcomes. We investigate using GPT-4, a state-of-the-art large language model (LLM), to augment human advising for major selection. Through a 3-phase survey, we compare GPT suggestions and responses for undeclared Freshmen and Sophomore students (n=33) to expert responses from university advisors (n=25). Undeclared students were first surveyed on their interests and goals. These responses were then given to both campus advisors and to GPT to produce a major recommendation for each student. In the case of GPT, information about the majors offered on campus was added to the prompt. Advisors, overall, rated the recommendations of GPT to be highly helpful and agreed with their recommendations 33% of the time. Additionally, we observe more agreement with AI major recommendations when advisors see the AI recommendations before making their own. However, this result was not statistically significant. The results provide a first signal as to the viability of LLMs for personalized major recommendation and shed light on the promise and limitations of AI for advising support.

Submission Number: 13