LLMs in Civil Engineering: Education Usage Patterns, Verification Practices, and Curriculum Implications from a Taxonomy-Aligned Student Survey

Abstract

Large language models (LLMs) are rapidly entering civil engineering research and practice, yet little is known about their use in educational contexts. This study reports results from an institutional case study based on a taxonomy-aligned survey of 109 respondents (103 undergraduates, four graduate students, and two faculty) in civil engineering–related programs at a large US university. The survey examined adoption patterns, task functions, verification practices, disclosure norms, and training needs. Undergraduates primarily used LLMs for tutoring and concept explanation (83%) and design ideation (67%), with limited adoption in coding (7%) and technical reasoning (41%). Verification practices were robust: 86% recalculated manually, 52% checked against standards, and only 4% reported nonverification, yielding a median of two methods per user. Ethical orientations favored conditional disclosure for major contributions (53%) and placed primary responsibility for errors on the human user (75%). Demand for formal training was high, especially among those with greater adoption, familiarity, and verification breadth. Results reveal a developmental gap between student practices, which emphasize low-risk learning and ideation, and research and faculty practices, which emphasize technically rigorous applications. The study underscores the need for curricular pathways that guide students from exploratory uses toward responsibly verified technical tasks within similar educational contexts. By linking a civil engineering–specific taxonomy of LLM functions with educational survey data, this work offers institutionally grounded empirical evidence on artificial intelligence (AI) literacy in civil engineering education and highlights directions for curriculum and assessment design.