AI and engineering careers: recent graduates’ outlook on opportunities and challenges

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 themselves 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. 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 influence 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 supplemental 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. 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.