2026 ASEE Annual Conference & Exposition

Recent research indicates that college students are increasingly using artificial intelligence (AI) tools to support their coursework, often without instructor permissions or institutional guidelines. This growing dependence on AI has significant implications for engineering, where conceptual understanding and ethical decision-making are central to professional competency. Educators must guide students in developing the skills to use these tools responsibly and effectively. With this aim, an assignment was developed for implementation in a Statics course. The overarching objectives of this work are to (1) help students recognize the limitations of AI in solving complex engineering problems, (2) reinforce the importance of human expertise and conceptual understanding, (3) promote reflection on the ethical and responsible use of AI in engineering, and (4) reinforce students conceptual understanding of shear and moment diagrams by engaging with the material in a different way.
This work describes a supervised learning experience, where students engage with AI tools under structured guidance. This supervised approach allows educators to ensure that AI-assisted problem solving enhances, rather than replaces, conceptual learning and professional judgment. In this activity, students were tasked with using an AI model to generate shear and moment diagrams for various beam configurations. Initially, the AI will produce incorrect solutions, therefore highlighting its current limitations. Students must then iteratively evaluate and correct the model, using their understanding of equilibrium and internal forces. This process positions students as critical evaluators of technology rather than passive users, emphasizing the central role of human judgment in verifying computational outcomes. This is an essential practice in engineering design, where errors can have significant consequences. Before and after completing the assignment, students responded to a confidential survey assessing their conceptions of AI in engineering and their confidence in their ability to perform tasks related to the construction of shear and moment diagrams. In addition, students wrote a reflective essay analyzing the strengths and weaknesses of AI tools in engineering and discussing the ethical and professional implications of AI use in practice.
Quantitative results indicate that students’ beliefs regarding AI as a replacement for human reasoning remained low and largely unchanged, while agreement that responsible AI use is an important professional skill increased modestly. Notably, students demonstrated substantial gains in confidence related to identifying and communicating errors in shear and moment diagrams and explaining the reasoning behind each step of their construction. These results suggest that the assignment was particularly effective in strengthening diagnostic and explanatory reasoning. Qualitative analysis of student reflections further reinforced these findings. Students consistently characterized generative AI as a useful but unreliable tool that requires human oversight, verification, and domain expertise, and they framed ethical and responsible use as maintaining accountability and professional judgment rather than avoiding AI altogether. These findings suggest that failure-driven, verification-centered AI assignments can support conceptual learning and ethical formation in engineering education. By positioning students as evaluators rather than consumers of AI output, this study offers an approach for integrating generative AI into foundational engineering courses while reinforcing core disciplinary values of verification, responsibility, and judgment.