2026 ASEE Annual Conference & Exposition

Generative artificial intelligence (AI) tools such as ChatGPT are rapidly reshaping how engineering students learn, problem-solve, and communicate in academic settings. Although prior research has examined the cognitive impacts of AI-supported learning, less is known about how sustained student-AI interaction influences professional identity development and employability perceptions, particularly in civil engineering contexts where safety, judgment, and collaboration are central to professional practice. This work-in-progress study investigates how undergraduate civil engineering students engage with ChatGPT as a learning partner and how these interactions shape their learning experiences, professional identity, and perceived employability. Guided by Vygotsky’s Zone of Proximal Development and collaborative learning theory, we conceptualize ChatGPT as both a cognitive scaffold and a dialogic collaborator that may support students’ movement from dependence on authoritative answers toward reflective, negotiated meaning-making.
Using a qualitative case study design, we examine undergraduate students enrolled in a core Rock Mechanics course. Data are collected through three course assignments designed to capture both behavioral evidence (ChatGPT conversation transcripts) and perceptual evidence (student reflections on AI-assisted learning). All qualitative data will be analyzed using inductive thematic analysis in NVivo to identify patterns of learning behaviors, human-AI collaboration strategies, and meaning-making processes related to professional identity and employability.
Anticipated findings include diverse collaboration patterns, ranging from AI use for administrative support and concept clarification to iterative problem decomposition and reflective reasoning. We also expect to identify tensions related to AI limitations in mathematically complex and context-specific geotechnical tasks, prompting students to critically evaluate AI outputs and exercise professional judgment. This study contributes early insights into how AI-mediated learning experiences may shape students’ agency, engineering identity, and perceived career readiness, informing instructional practices that promote responsible human-AI collaboration in civil engineering education.