Fried, M., & Alshibli, M. (2024, October), The Impact of AI Assistance on Student Learning: A Cross-Disciplinary Study in STEM Education  Paper presented at 2024 Fall ASEE Mid-Atlantic Section Conference, Farmingdale State College, NY, New York. 10.18260/1-2--49457

\bibitem{asee_peer_49457}
  Fried, M., \& Alshibli, M. (2024, October), \emph{The Impact of AI Assistance on Student Learning: A Cross-Disciplinary Study in STEM Education}
  Paper presented at 2024 Fall ASEE Mid-Atlantic Section Conference, Farmingdale State College, NY, New York.
  10.18260/1-2--49457

Matthew Fried and Mohammad Alshibli.  "The Impact of AI Assistance on Student Learning: A Cross-Disciplinary Study in STEM Education".  2024 Fall ASEE Mid-Atlantic Section Conference, Farmingdale State College, NY, New York, 2024, October.  ASEE Conferences, 2024.  https://peer.asee.org/49457 Internet.  15 Jan, 2025

\bibitem{asee_peer_49457}
  Matthew Fried and Mohammad Alshibli.
  "The Impact of AI Assistance on Student Learning: A Cross-Disciplinary Study in STEM Education".
  \emph{2024 Fall ASEE Mid-Atlantic Section Conference, Farmingdale State College, NY, New York, 2024, October}.
  ASEE Conferences, 2024.
  https://peer.asee.org/49457 Internet.  15 Jan, 2025

@INPROCEEDINGS{asee_peer_49457
author = "Matthew Fried and Mohammad Alshibli"
title = "The Impact of AI Assistance on Student Learning: A Cross-Disciplinary Study in STEM Education"
booktitle = "2024 Fall ASEE Mid-Atlantic Section Conference"
year = "2024"
month = "October"
address = "Farmingdale State College, NY, New York"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/49457}
number = {10.18260/1-2--49457}
}

TY  - CPAPER
AB  - We examine the widespread use of AI in the form of Large Language Models (LLMs) as a tool for academic assistance. The study investigates whether students studying with AI assistance retain more information compared to those employing standard alternative approaches such as using a basic search engine, reviewing with a friend, or contemplating the material independently. The research reveals that while basic tasks and retention may benefit from AI assistance, outsized gains are lacking. Counterintuitively, specific tasks related to deep thinking and conceptual exploration are found to be better served with alternative approaches. We compare different features via hypothesis testing (p-values), ANOVA, logistic regression, and chi-square, highlighting relationships and the lack thereof. The data was collected across multiple colleges in various STEM disciplines, providing a robust cross-disciplinary perspective. Additionally, the paper discusses the influence of ethnic and cultural background, learning styles, technical talent, and other contributing factors to student success when utilizing LLMs.
AU  - Matthew Fried
AU  - Mohammad Alshibli
CY  - Farmingdale State College, NY, New York
DA  - 2024/10/25
PB  - ASEE Conferences
TI  - The Impact of AI Assistance on Student Learning: A Cross-Disciplinary Study in STEM Education
UR  - https://peer.asee.org/49457
DO  - 10.18260/1-2--49457
ER  -