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Background The rapid advancement of artificial intelligence (AI) is reshaping industrial workflows and workforce expectations. After its breakthrough year in 2023, AI has become ubiquitous, yet no standardized approach exists for integrating AI into engineering and computer science undergraduate curricula. Recent graduates find them- selves navigating evolving industry demands surrounding AI, often without formal preparation. The ways in which AI impacts their career decisions represent a critical perspective to support future students as graduates enter AI-friendly industries. Our work uses social cognitive career theory (SCCT) to qualitatively investigate how 14 recent engineering graduates working in a variety of industry sectors perceived the impact of AI on their careers and industries. Results Given the rapid and ongoing evolution of AI, findings suggested that SCCT may have limited applicability until AI technology has matured further. Many recent graduates lacked prior exposure to or a clear understanding of AI and its relevance to their professional roles. The timing of direct, practical exposure to AI emerged as a key influ- ence on how participants perceived AI’s impact on their career decisions. Participants emphasized a need for more customizable undergraduate curricula to align with industry trends and individual interests related to AI. While many acknowledged AI’s potential to enhance efficiency in data management and routine administrative tasks, they largely did not perceive AI as a direct threat to their core engineering functions. Instead, AI was viewed as a supplemen- tal tool requiring critical oversight. Despite interest in AI’s potential, most participants lacked the time or resources to independently pursue integrating AI into their professional roles. Broader concerns included ethical considerations, industry regulations, and the rapid pace of AI development. Conclusions This exploratory work highlights an urgent need for collaboration between higher education and industry leaders to more effectively integrate direct, hands-on experience with AI into engineering education. A personalized, context-driven approach to teaching AI that emphasizes ethical considerations and domain-specific applications would help better prepare students for evolving workforce expectations by highlighting AI’s relevance and limitations. This alignment would support more meaningful engagement with AI and empower future engineers to apply it responsibly and effectively in their fields. 

This work in progress paper we explain our process of co-sharing secondary qualitative data from separate projects funded by the National Science Foundation to better understand factors which influence faculty technology adoption in engineering education and provide a high-level presentation of preliminary results. Study A conducted 21 interviews of engineering faculty at a Midwestern US, STEM-centered university. These faculty were interviewed about the factors influencing their adoption and teaching of new engineering technologies, with a focus on programming languages, software, and instrumentation. Technology adoption models were applied as a theoretical lens for results analysis. Study B conducted 9 interviews with faculty in the College of Engineering at a Southern US university on the adoption of online laboratories in their instructional settings. The interviews focused on how faculty make use of online laboratories in electrical engineering as an essential resource. Innovation and propagation theories were applied as a theoretical lens for data analysis. The two data sets were co-shared for secondary analysis by each research group, using their own theoretical approaches. Preliminary findings lead us to believe that co-sharing of secondary data can expand qualitative data sets while providing a means for theoretical triangulation, improving data analysis.