2026 ASEE Annual Conference & Exposition

Artificial intelligence (AI) platforms are increasingly used by engineering undergraduates for problem solving, technical writing, visualization, and conceptual scaffolding. However, many university policies still frame AI usage within the context of plagiarism, limiting its intentional integration into coursework. In engineering education, unmonitored AI use (e.g., generating homework or project solutions), without verifying accuracy, can undermine learning objectives and assessment validity. At the same time, engineering practice increasingly demands AI literacy and prompt-engineering skills, positioning responsible AI use as a core competency for future engineers (Cortez, 2024; Vidalis et al., 2024).

This study investigates structured AI integration across four engineering courses, spanning the freshman, sophomore, junior, and senior levels, at two institutions in the northeastern United States: one public research university with large classrooms and one private primarily undergraduate institution with smaller classes. At the public university, the study was implemented in two courses: Mechanics of Materials (enrollment ≈120 students) and Design of Reinforced Concrete Structures (enrollment ≈50 students). In the private undergraduate institution, AI was integrated into classes at the freshman-level Engineering and Design with 19 students from different engineering disciplines and senior-level Biomedical Engineering Capstone Design with 12 students. Students were encouraged to apply AI tools for diverse learning tasks such as problem solving, visualization, study-guide creation, and capstone project support. The instructional design emphasized prompt design, critical verification of AI outputs, and metacognitive reflection on the reliability and educational value of AI-generated content.

To evaluate the impact, students completed reflection assignments assessing AI capabilities and deficiencies, as well as an anonymous post-intervention survey measuring perceptions of learning, confidence in evaluating AI results, and awareness of ethical implications. The analysis will examine (a) whether academic level influences students’ perceptions of AI integration, and (b) whether institutional type (public vs. private) affects students’ attitudes and reported benefits. Preliminary results suggest that guided AI use can enhance engagement, foster self-monitoring skills, and promote a balanced understanding of both the potential and limitations of generative AI in engineering education.

This work contributes to the growing body of research on AI-inclusive pedagogy and offers a scalable framework for aligning AI integration with academic integrity and ABET-aligned learning outcomes (Bobula, 2024; National Academies of Sciences, Engineering, and Medicine, 2024; Xu et al., 2022).