Assessing Student Adoption of Generative Artificial Intelligence across Engineering Education from 2023 to 2024

2025-03-06

AI Summary

This study investigates engineering students’ adoption behaviors, ethical perceptions, and societal impact assessments regarding generative artificial intelligence (GenAI). Drawing on two large-scale surveys conducted in May 2023 and September 2024 (N > 3,000), it employs descriptive statistics, cross-tabulation, hypothesis testing, and Likert-scale quantification of attitudes and practices. Results reveal a substantial 42% increase in GenAI adoption, primarily for deepening conceptual understanding, enhancing assignment quality, and tracking cutting-edge developments. While students largely self-report ethical usage, they express profound concerns about societal implications; notably, their “probability of doom” (P(doom)) estimates exhibit a significant bimodal distribution—indicating starkly polarized collective expectations. The study’s contributions are threefold: (1) empirically mapping the evolving GenAI adoption landscape in engineering education; (2) identifying a structural tension between ethical self-perception and pessimistic societal forecasting; and (3) providing evidence-based insights to inform AI literacy curricula and policy interventions.

Technology Category

• Philosophy and Ethics of AI: Societal Impact of AI
• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Generative Artificial Intelligence (GenAI) tools and models have the potential to re-shape educational needs, norms, practices, and policies in all sectors of engineering education. Empirical data, rather than anecdata and assumptions, on how engineering students have adopted GenAI is essential to developing a foundational understanding of students' GenAI-related behaviors and needs during academic training. This data will also help formulate effective responses to GenAI by both academic institutions and industrial employers. We collected two representative survey samples at the Colorado School of Mines, a small engineering-focused R-1 university in the USA, in May 2023 ($n_1=601$) and September 2024 ($n_2=862$) to address research questions related to (RQ1) how GenAI has been adopted by engineering students, including motivational and demographic factors contributing to GenAI use, (RQ2) students' ethical concerns about GenAI, and (RQ3) students' perceived benefits v.s. harms for themselves, science, and society. Analysis revealed a statistically significant rise in GenAI adoption rates from 2023 to 2024. Students predominantly leverage GenAI tools to deepen understanding, enhance work quality, and stay informed about emerging technologies. Although most students assess their own usage of GenAI as ethical and beneficial, they nonetheless expressed significant concerns regarding GenAI and its impacts on society. We collected student estimates of ``P(doom)'' and discovered a bimodal distribution. Thus, we show that the student body at Mines is polarized with respect to future impacts of GenAI on the engineering workforce and society, despite being increasingly willing to explore GenAI over time. We discuss implications of these findings for future research and for integrating GenAI in engineering education.

Problem

• Assessing GenAI adoption among engineering students.
• Exploring ethical concerns and societal impacts of GenAI.
• Analyzing student perceptions of GenAI benefits and harms.

Innovation

• Surveyed engineering students on GenAI adoption trends.
• Analyzed ethical concerns and societal impacts of GenAI.
• Identified polarization in student views on GenAI future.