2026 ASEE Annual Conference & Exposition

Applied Statics, a foundational course within the Construction Engineering program, often presents persistent learning challenges due to students’ insufficient mastery of prerequisite mathematical and physical concepts—particularly in trigonometry, vectors, and systems of equations. These conceptual deficiencies hinder students’ ability to apply fundamental mechanics principles in both this and subsequent courses. Addressing this issue is essential for improving student retention and academic success in construction engineering programs. This study aims to explore the pedagogical potential of generative artificial intelligence, specifically ChatGPT, as a cost-effective and scalable tool for conceptual remediation and knowledge leveling among undergraduate construction engineering students.

The purpose of this research is to evaluate the impact of a structured AI-supported leveling intervention on students’ comprehension of key prerequisite concepts for Applied Statics. The study investigates whether guided reinforcement activities using ChatGPT—designed to provide step-by-step problem solving, conceptual explanations, and adaptive feedback—can effectively reduce initial knowledge gaps and enhance students’ autonomy and engagement in their learning process. Additionally, the study analyzes students’ perceptions, acceptance, and willingness to integrate AI as an academic support tool.

This mixed-methods research involves two cohorts of Construction Engineering students: (1) a control group from the Spring 2024 semester who completed the course without AI intervention, and (2) an experimental group from the Spring 2025 semester who participated in the ChatGPT-based instructional intervention. A pre- and post-conceptual test, adapted from validated instruments in the literature, is administered to measure learning gains. The instrument includes 15 multiple-choice questions covering trigonometry, vector operations, and linear systems—core concepts for mastering Applied Statics. Quantitative data include test scores, course grades, and dispersion measures, while qualitative data derive from students’ reflections and survey responses on their experience using ChatGPT as a learning aid. The AI-mediated intervention consists of three structured reinforcement activities designed and supervised by the course instructor to ensure pedagogical coherence with the course learning outcomes.

It is anticipated that students participating in the ChatGPT intervention will exhibit measurable improvements in their comprehension of prerequisite concepts, as evidenced by higher test scores and greater consistency in course performance. Furthermore, a positive shift in students’ motivation, confidence, and self-regulated learning behaviors is expected. The results will contribute to the ongoing discourse in construction education regarding the role of artificial intelligence in enhancing learning outcomes, promoting inclusion, and supporting students at risk of academic underperformance. By focusing on conceptual bridging rather than content substitution, this research aligns with current priorities within the Construction Engineering Division related to AI-enhanced pedagogy, course assessment practices, and active learning methodologies.
This full research paper provides empirical evidence on the effectiveness of ChatGPT as a pedagogical tool for conceptual leveling in technical courses relevant to the built environment. It presents a replicable instructional framework for integrating AI into construction engineering curricula and demonstrates how such technologies can be leveraged to promote equitable and adaptive learning. The findings are expected to inform future advancements in course design, assessment practices, and sustainable implementation of emerging technologies in engineering education.