Research Initiation: How AI Integration Shapes Undergraduate Students' Civil Engineering Professional Formation from Classroom to Career

Research Initiation: How AI Integration Shapes Undergraduate Students' Civil Engineering Professional Formation from Classroom to Career This project will examine how undergraduate civil engineering students develop as future professional engineers in an era when artificial intelligence (AI) is rapidly changing what it means for people to do engineering work. Civil engineers, like other types of engineers, need to understand and use AI-related tools and knowledge in areas including data analysis, modeling, optimization, design, construction monitoring, and infrastructure decision-making. Most undergraduate civil engineering programs are still considering how to best integrate AI into coursework to support students' technical learning and their identity shift from being a student to being a working engineering professional. This project will study how AI-integrated civil engineering coursework influences students’ professional identity, career adaptability, and perceived employability. The findings should help engineering instructors and academic programs better understand how to incorporate emerging AI technologies into courses to better prepare their graduates for AI-driven workplaces, a current national need. The project will further serve the national interest by supporting the preparation of civil engineers specifically who are technically capable, adaptable, and ready to contribute to the nation’s infrastructure development, economic competitiveness, and public welfare by using AI-enabled tools. The project will contribute to the funding program's goals expanding the community of engineering education researchers through the civil engineering research team's structured mentoring from education and workforce development researchers. This project, situated in the Department of Civil and Environmental Engineering at the University of Idaho. It will use a longitudinal mixed-methods sequential explanatory design to answer three research questions: 1) How does integrating AI content in civil engineering courses influence students’ professional identity as engineers? 2) How does integrating AI content in civil engineering courses influence students’ career adaptability in preparing for an AI-driven workforce? and 3) How does integrating AI content in civil engineering courses influence students’ perceived employability? The research team will first collect quantitative data through two online surveys administered at the beginning of Fall 2026 (pre) and the end of Spring 2027 (post) which will include validated measures of engineering identity, career adaptability, self-perceived employability, and various measures of prior experience with AI and civil engineering coursework. The team will analyze the data through descriptive statistics, internal consistency reliability estimates, and longitudinal linear mixed models to examine whether changes across the pre/post period are associated with students’ level of AI-integrated coursework exposure. The team will then collect qualitative data through individual and group interviews after the post survey, purposefully selecting participants based on variability in their survey patterns. The project will mix the quantitative and qualitative data by using survey results to guide qualitative sampling and interview questions, and by connecting statistical patterns with themes from student interviews and focus groups. The team expects project outcomes to provide empirical quantitative and qualitative evidence on AI and professional formation of engineers, which the team will translate into practical guidance for civil engineering instructors, and have relevance to engineering instructors in other disciplines. The team will further share empirically-grounded insights through developing and distributing sample teaching materials, research briefs, and workshop materials based on the outcomes, and publishing through conferences and archival publications to inform researchers and educators na NSF Award ID: 2605448 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Ruimin Feng | Institution: Regents of the University of Idaho, MOSCOW, ID | Award Amount: $200,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2605448 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2605448.html