2024-08-09-lekan24a.md

title	booktitle	year	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
AI-Augmented Advising: A Comparative Study of GPT-4 and Advisor-based Major Recommendations	Proceedings of the 2024 AAAI Conference on Artificial Intelligence	2024	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	lekan24a	0	AI-Augmented Advising: A Comparative Study of GPT-4 and Advisor-based Major Recommendations	85	96	85-96	85	false	Lekan, Kasra and Pardos, Zachary A.	given family Kasra Lekan given family Zachary A. Pardos	2024-08-09		Proceedings of the 2024 AAAI Conference on Artificial Intelligence	257	inproceedings	date-parts 2024 8 9	https://raw.githubusercontent.com/mlresearch/v257/main/assets/lekan24a/lekan24a.pdf