Ahlstrom, L. D., & Lawanto, O., & McCall, C. J., & Harper, M., & Goodridge, W. H., & Kane, D. (2025, June), BOARD #104: Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness  Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/55921

\bibitem{asee_peer_55921}
  Ahlstrom, L. D., \& Lawanto, O., \& McCall, C. J., \& Harper, M., \& Goodridge, W. H., \& Kane, D. (2025, June), \emph{BOARD \#104: Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness}
  Paper presented at 2025 ASEE Annual Conference \& Exposition , Montreal, Quebec, Canada .
  https://peer.asee.org/55921

Linda Davis Ahlstrom, Oenardi Lawanto, Cassandra J McCall, Michaela Harper, Wade H Goodridge, and Daniel Kane.  "BOARD #104: Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness".  2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada , 2025, June.  ASEE Conferences, 2025.  https://peer.asee.org/55921 Internet.  19 Aug, 2025

\bibitem{asee_peer_55921}
  Linda Davis Ahlstrom, Oenardi Lawanto, Cassandra J McCall, Michaela Harper, Wade H Goodridge, and Daniel Kane.
  "BOARD \#104: Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness".
  \emph{2025 ASEE Annual Conference \& Exposition , Montreal, Quebec, Canada , 2025, June}.
  ASEE Conferences, 2025.
  https://peer.asee.org/55921 Internet.  19 Aug, 2025

@INPROCEEDINGS{asee_peer_55921
author = "Linda Davis Ahlstrom, Oenardi Lawanto, Cassandra J McCall, Michaela Harper, Wade H Goodridge, and Daniel Kane"
title = "BOARD \#104: Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness"
booktitle = "2025 ASEE Annual Conference \& Exposition "
year = "2025"
month = "June"
address = "Montreal, Quebec, Canada "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/55921}
}

TY  - CPAPER
AB  - Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness-progress study explores self-reported data on AI use by university engineering students. The purpose of this study is to investigate how students are utilizing AI technologies and to understand their views on the role of AI in their future. The primary research question formulated was: How does the adoption of AI technologies for learning vary across demographic groups among university engineering students? Advances in technology and the emergence of AI tools have attracted attention from academia, research, and industry. The rapid growth of deep learning technologies has changed the landscape in the work environment, and universities may need to adapt to keep pace. Dynamic changes in the workplace have accelerated as these AI technologies are being leveraged to complete tasks at a high-speed rate. Research indicates that the workforce is increasingly demanding higher skill levels, including specialized AI skills. Formal education in AI basics could be crucial for future career readiness.
Over 150 engineering students reported their demographics, including age, race, gender, year in school, and if they identify as having any form of disability. Currently, the survey remains open. The final study will incorporate more responses, and additional data will come from semi-structured interviews. This research explores the ways in which undergraduate and graduate students at a major R1 land-grant university in the western United States interact with AI tools.
Students reported on using AI technologies, like ChatGPT, to aid in their learning. Preliminary findings suggest that freshman students are less likely to have used AI technologies than those later in their college careers. Encouragingly, students closest to entering the workforce are the ones with the most exposure to these technologies. Interestingly, students who identify as having any form of a disability or condition that impacts their learning (e.g., learning disability, neurodiversity, physical disability, etc.) initially reported lower usage of AI technologies compared to their classmates. The lower use by freshmen and increasing exposure to generative AI throughout students’ university experience is noteworthy.
Students were also asked for their views on the formal integration of AI technologies into the College of Engineering courses. It could be valuable for universities to explore adding formal training to help equip students for the workforce. We anticipate that this study will highlight how exposure to AI technologies may prove essential for engineering students in preparing for a rapidly evolving workplace, as AI has the potential to enhance real-world problem-solving skills and help students become more equipped for workplace demands.

AU  - Linda Davis Ahlstrom
AU  - Oenardi Lawanto
AU  - Cassandra J McCall
AU  - Michaela Harper
AU  - Wade H Goodridge
AU  - Daniel Kane
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - BOARD #104: Work-in-Progress: Uncovering AI Adoption Trends Among University Engineering Students for Learning and Career Preparedness
UR  - https://peer.asee.org/55921
ER  -