AI-Enhanced Learning Pathways in Geotechnical Engineering Education: Leveraging Generative AI for Active and Inquiry-Based Learning

This TLIP project (“AI-Enhanced Learning Pathways in Geotechnical Engineering Education”) targets the problem of passive, lecture-driven learning in geotechnical engineering, where students often struggle to connect soil mechanics theory with lab evidence and real-world practice; it pilots an inquiry-based pedagogy of “learning by asking” as co-thinking with AI in CIVL 3730 Fundamentals of Geotechnics (Fall 2026). The major scope is to develop and integrate a controlled course-specific AI system (GeoChat) that grounds responses in curated course materials (via RAG), supports stepwise reasoning (Guided Reasoning Framework), enables model comparison, and provides structured quizzes with interactive support, then embed these AI-supported inquiry activities across lectures, tutorials, and labs (before/during/after class) alongside light-touch assessment additions (e.g., reflection logs in lab reports). The project objectives are to (1) increase active learning and reduce rote memorisation, (2) cultivate critical AI literacy and AI-supported reasoning (evaluating and critiquing outputs against authoritative sources), (3) bridge theory and practice through guided questioning tied to lab data and cases, (4) evaluate learning impact using mixed evidence (usage logs, surveys, reflections, performance correlations, focus groups), and (5) produce transferable resources (prompt libraries, AI-enhanced modules/materials, guidelines, and an evaluation report) that can scale to other engineering courses.