2026 ASEE Annual Conference & Exposition

This poster presents preliminary findings from a project that aims to serve the national interest by improving undergraduate education to better prepare future engineering and computing professionals to use and develop artificial intelligence (AI). The increasing integration of AI-enabled technologies across domains creates workforce opportunities for students as well as providing mechanisms for improving human well-being. The project's significance lies in its innovative use of situated case studies to help students understand AI complexity, differing stakeholder requirements, and to develop critical reasoning about AI applications. Through early exposure in first-year courses, the project is developing transferable mindsets and skills that students can apply throughout their careers, advancing their understanding of how to prepare a workforce capable of AI innovation and supporting the nation's economic well-being. The project goals include developing and implementing six case studies that focus on familiar AI applications such as career preparedness, campus sustainability, autonomous vehicles, and mental health systems using a Situated AI Literacy framework. The scope encompasses implementation across first-year engineering and computing courses at Youngstown State University and George Mason University, serving over 500 students during the project period, with additional dissemination through faculty development workshops reaching ten external institutions. The methodology employs role-play case study discussions integrating three key competencies- complex systems cognition, perspectival understanding, and critical thinking. The project uses mixed-methods evaluation, including pre- and post-surveys, concept maps, discussion transcripts, and focus groups to assess student learning gains across these elements. It investigates how the case studies support the development of multi-level AI understanding, stakeholder perspective-taking, and critical assessment of AI benefits and limitations.

NSF Award DUE 2439460