Research Initiation: Building AI Literacy and Uncertainty Awareness for Digitally Competent Geotechnical Engineering Education

The safety and resilience of civil infrastructure depend on engineers who can make informed decisions despite significant uncertainty in the subsurface conditions beneath buildings, bridges, and roadways. Today, artificial intelligence (AI) is rapidly transforming engineering practice, yet many students encounter AI tools before learning how to critically evaluate their outputs, verify their accuracy, or reason about the uncertainty underlying their recommendations. This challenge is particularly significant in geotechnical engineering, where subsurface conditions are inherently variable, field data are often limited, and small changes in assumptions can substantially affect the safety, cost, and reliability of engineering designs. This project will address this gap by developing and evaluating a scalable learning framework that enables undergraduate engineering students to use AI as a disciplined partner in engineering reasoning rather than as a shortcut to answers. Students will learn to reason about uncertainty in geotechnical design, critically evaluate AI-assisted analyses, and develop, justify, and communicate engineering design decisions and recommendations for responsible professional practice. This project will serve the national interest by preparing a workforce capable of responsibly integrating emerging AI technologies into engineering practice while safeguarding the safety and resilience of the built environment. The project aligns with the Research Initiation in Engineering Formation (RIEF) program by establishing a mentored engineering education research agenda for a geotechnical engineering faculty member while investigating how undergraduate engineering students develop the professional judgment, ethical responsibility, and digital competence needed to thrive in an AI-enabled engineering workforce. The project will evaluate the effectiveness of an AI-integrated instructional framework for geotechnical engineering education and establish a scalable, evidence-based model that enables civil engineering instructors to prepare the next generation of a digitally competent engineering workforce. This project will design, implement, and study an AI-supported learning environment in the Foundation Engineering course for geotechnical engineering education at the University of Cincinnati. Guided by Kolb’s Experiential Learning Theory and the proposed AI-Supported Metacognitive Framework for Uncertainty Reasoning (AIM-UR), this project will investigate the following research questions: 1) how AI-integrated, uncertainty-explicit instruction will affect students’ ability to identify, propagate, and communicate uncertainty; 2) how structured AI modules will influence responsible AI use and geotechnical design performance; and 3) how students will use AI as a cognitive partner when making and justifying design decisions under uncertainty. The instructional approach will combine AI explanation cards, live uncertainty visualizations, lightweight Monte Carlo simulations, a Canvas-based AI co-mentor, a Prompt and Privacy Clinic, Design Dilemma Studios, and a Critical-AI Handbook with required Think-Verify-Decide checklists, all embedded within authentic geotechnical site characterization and foundation design projects. Complementary quantitative, qualitative, and mixed-methods analyses will be used to evaluate how AI-supported instruction affects students’ uncertainty reasoning, professional AI use, and design decision quality. This project advances the theory and practice of engineering education by establishing a reproducible model for AI-supported metacognitive learning and developing practical tools that help undergraduate students translate abstract concepts of uncertainty into engineering decision-making. This project will produce open, reusable educational resources, including instructional modules, the Critical-AI Handbook, assessment rubrics, and student reasoning and decision templates, which will be broadly disseminated through open repositories, conference presentations, and peer-reviewed publications. The project outcomes will contribute to workforce readiness by enhancing engineering students’ digital competence and equipping them to effectively and responsibly integrate AI into real-world geotechnical engineering design and uncertainty-informed decision-making, better preparing them for future professional practice. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.