Navigating Undergraduate Engineering Education in the Age of Generative AI

Abstract

The rapid adoption of Artificial Intelligence (AI) and the integration of Generative AI (GenAI) models present an inflection point for undergraduate engineering education, challenging traditional assessment methods that rely on well-defined problems and standard solutions.This paper addresses the critical need to preserve academic integrity while fostering deep, conceptual learning in engineering courses.Prior research demonstrated a significant challenge in content verification, showing that faculty correctly identified AI-generated text only 45% of the time.In response to this data, we detail a pedagogical framework centered on project-based learning and complex, highcontext assignment design.This paper documents the implementation of multi-stage, open-ended assignments that require personalized data, human rationale, and oral assessments.The framework also incorporates strategic use of AI detectors and faculty training.The findings suggest that shifting assessment to Bloom's highest cognitive levels is an effective and sustainable strategy for preparing engineers who are critical thinkers, not just solution-finders, in an AI-integrated workplace.While our previous work established AI detection gaps and the limitations of AI tools in solving certain engineering problems, this article documents the implementation of the multicomponent framework designed to address these findings.

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