Kane, D., & Goodridge, W. H., & Ahlstrom, L. D., & Lawanto, O., & Harper, M., & McCall, C. J. (2025, June), BOARD # 98: WIP: Understanding Patterns of Generative AI Use: A Study of Student Learning Across University Colleges  Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2--55915

\bibitem{asee_peer_55915}
  Kane, D., \& Goodridge, W. H., \& Ahlstrom, L. D., \& Lawanto, O., \& Harper, M., \& McCall, C. J. (2025, June), \emph{BOARD \# 98: WIP: Understanding Patterns of Generative AI Use: A Study of Student Learning Across University Colleges}
  Paper presented at 2025 ASEE Annual Conference \& Exposition , Montreal, Quebec, Canada .
  10.18260/1-2--55915

Daniel Kane, Wade H Goodridge, Linda Davis Ahlstrom, Oenardi Lawanto, Michaela Harper, and Cassandra J McCall.  "BOARD # 98: WIP: Understanding Patterns of Generative AI Use: A Study of Student Learning Across University Colleges".  2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada , 2025, June.  ASEE Conferences, 2025.  https://peer.asee.org/55915 Internet.  27 Aug, 2025

\bibitem{asee_peer_55915}
  Daniel Kane, Wade H Goodridge, Linda Davis Ahlstrom, Oenardi Lawanto, Michaela Harper, and Cassandra J McCall.
  "BOARD \# 98: WIP: Understanding Patterns of Generative AI Use: A Study of Student Learning Across University Colleges".
  \emph{2025 ASEE Annual Conference \& Exposition , Montreal, Quebec, Canada , 2025, June}.
  ASEE Conferences, 2025.
  https://peer.asee.org/55915 Internet.  27 Aug, 2025

@INPROCEEDINGS{asee_peer_55915
author = "Daniel Kane, Wade H Goodridge, Linda Davis Ahlstrom, Oenardi Lawanto, Michaela Harper, and Cassandra J McCall"
title = "BOARD \# 98: WIP: Understanding Patterns of Generative AI Use: A Study of Student Learning Across University Colleges"
booktitle = "2025 ASEE Annual Conference \& Exposition "
year = "2025"
month = "June"
address = "Montreal, Quebec, Canada "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/55915}
number = {10.18260/1-2--55915}
}

TY  - CPAPER
AB  - Generative Artificial Intelligence (GAI) has emerged in recent years as an innovative tool with promising potential for enhancing student learning across a broad spectrum of academic disciplines. GAI not only offers students personalized and adaptive learning experiences, but it is also playing an increasingly important role in various industries. As technologies evolve and society adapts to the growing AI revolution, it becomes necessary to train students of all disciplines to become proficient in using GAI. This work builds on studies that have established the effectiveness of intelligent tutoring systems, adaptive learning environments, and the use of virtual reality in education. 

This work-in-progress paper presents preliminary findings related to the relationship between university students’ area of study and the frequency at which they utilize GAI to aid their learning. Data for this study were collected using a survey distributed to students from eight different colleges at a large Western university as part of a larger ongoing project geared towards gaining insight into student perceptions and use of GAI in higher education. The goal of the overall project is to establish a foundational understanding of how disruptive technologies, like GAI, can promote learner agency. By exploring why and how students choose to engage with these technologies, the project seeks to find proactive approaches to integrate GAI technology into education, ultimately enhancing teaching and learning practices across various disciplines. This work in progress specifically examines patterns of GAI use between different colleges where the students’ program of study is housed and seeks to answer the research question: How does the use of GAI among university students vary across different academic disciplines, and what factors contribute to these variations? Preliminary results based on responses from the first 977 students indicate that student use of GAI varies significantly between colleges, with students enrolled in the school of business reporting the highest use of GAI per week and students in the college of art reporting the lowest use. This variation in GAI use may be explained through the lens of the Technology Acceptance Model (TAM) which asserts that perceived usefulness and perceived ease of use are critical factors that influence the adoption of new technology. Students from various disciplines may receive different levels of exposure to technologies such as GAI which may influence how comfortable they are with the technology or how they see it benefiting their field of study. 

These findings highlight the varying degrees of GAI integration into different academic disciplines and suggest that programs such as business may be more aligned with potential applications of GAI. By examining applications of GAI use in disciplines with students reporting higher usage, other academic programs with lower GAI use may be able to mirror some of the benefits of GAI into their own courses and programs. Results of this analysis also point to potential gaps in student exposure to GAI, with many students reporting that they have never used GAI as part of their education. Plans for ongoing research include a mixed-methods approach to determining effective uses of GAI in various academic disciplines as well as identifying reasons students do or do not choose to utilize GAI within their specific area of study. Understanding these patterns not only aids in curriculum development but also prepares students for a future where AI proficiency is crucial across all disciplines.
AU  - Daniel Kane
AU  - Wade H Goodridge
AU  - Linda Davis Ahlstrom
AU  - Oenardi Lawanto
AU  - Michaela Harper
AU  - Cassandra J McCall
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - BOARD # 98: WIP: Understanding Patterns of Generative AI Use: A Study of Student Learning Across University Colleges
UR  - https://peer.asee.org/55915
DO  - 10.18260/1-2--55915
ER  -