The rapid integration of Generative Artificial Intelligence (GenAI) into the engineering profession demands a parallel evolution in engineering education that moves beyond tool proficiency toward professional identity formation. This “Work in Progress” (WiP) paper details a situative pedagogical initiative designed to help students practice in the classroom the professional judgment they will exercise throughout their careers. Grounded in cognitive apprenticeship theory, we position GenAI not merely as a productivity tool but as an epistemic tool that mediates how students come to know and verify engineering knowledge. Our intervention in an interdisciplinary capstone course includes a formal AI Usage Disclosure and Certifications policy to simulate the professional liability and transparency required in industry, alongside ethics instruction and scaffolded GenAI integration in technical risk identification. Preliminary findings suggest that AI-generated errors—including hallucinated standards and implausible recommendations—serve as productive “teachable moments” that make visible the verification practices experts perform routinely. We conclude that this mentorship model is effective not because it teaches use of GenAI as a tool, but because it uses the tool's limitations to teach the essential human element of engineering judgment.
Authors
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Emily H. Monroe, PE is a lecturer at the Thayer School of Engineering at Dartmouth College. She serves as the director of the Cook Engineering Design Center at Dartmouth, which connects industry, government and nonprofit sponsors with Dartmouth Engineering students to collaborate on engineering design projects. Prior to joining Dartmouth, Emily served in manufacturing and product design engineering roles. Emily is a licensed professional engineer (PE) and holds a BS degree in mechanical engineering from MIT and Master of Engineering Management from Duke University.
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William J. Scheideler is an Associate Professor of Engineering at Dartmouth College’s Thayer School of Engineering. He received his Ph.D. in Electrical Engineering and Computer Science from the University of California, Berkeley, and completed postdoctoral studies at Stanford University. His research focuses on high-performance materials and sustainable fabrication methods for flexible and printed electronics, with an emphasis on scalable manufacturing and semiconductor device physics. He teaches undergraduate and graduate courses in linear systems, semiconductor devices, and flexible electronics, and was recognized with Dartmouth’s Woodhouse Excellence in Teaching Award.
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Solomon Diamond is an Associate Professor of Engineering at Dartmouth where he teaches the capstone engineering design course sequence featuring projects solicited from industry and nonprofit partners. He also teaches mechanical engineering courses in computer-aided design, additive manufacturing, and the creative design of machines. Sol is passionate about the intersection of ethics with the practice of engineering, particularly in the context of global changes driven by AI and additive manufacturing. He also brings decades of research experience in biomedical engineering, spanning neuroscience, nanotechnology, and biosensing in the contexts of neurodegeneration and cancer. Sol holds an AB in Engineering Sciences from Dartmouth College, a BE from Thayer School of Engineering at Dartmouth, and a PhD in Engineering Sciences from Harvard University.
Note
The full paper will be available to logged in and registered conference attendees once the conference starts on
June 21, 2026, and to all visitors after the conference ends on June 24, 2026