2026 ASEE Annual Conference & Exposition

As conversations around artificial intelligence (AI) in the workforce expand globally, it is increasingly important to understand engineers’ attitudes and emotions toward the rise of AI in engineering practice. While there are growing calls for increased AI workforce development in the United States, many engineering systems in the US currently lack clear policy or guidance around AI use, whereas the European Union (EU) and the United Kingdom (UK) are actively shaping AI governance through comprehensive frameworks that balance technological advancement with ethical considerations and public safety, such as the EU AI Act. This study seeks to understand current US practicing engineers’ emotions and attitudes toward AI while exploring the thematic areas covered in AI policy in the EU and UK, with the goal of informing future guidance, training, and policy development for AI in US engineering contexts.
The research was conducted in two phases. In Phase 1, exploratory qualitative interviews with 23 practicing engineers across various disciplines in the US were analyzed using the control-value theory which suggests that emotions arise when individuals appraise an activity or outcome as personally important (value) and feel either in control or out of control of it, with lack of control often leading to negative emotions like fear [1], [2] to uncover engineers’ emotions and attitudes around AI use. Phase 2 consisted of a document analysis using qualitative coding techniques [3], [4] of AI policies in engineering education and practice in the US, EU, and UK, identifying the thematic areas in which AI policies are addressing governance, training, and practical applications.
Findings from Phase 1 reveal that early-career engineers in the US navigate mixed signals from organizational and environmental contexts, negotiate conflicting values around AI, and experience mixed emotions shaped by the absence of formal guidance. Participants reported both curiosity and optimism about AI’s benefits, such as efficiency and data analysis, while simultaneously expressing concerns about overreliance, security risks, ethical implications, and environmental impacts. As a result, they adopt a selective and cautious approach to AI use, often worrying that others may misuse these tools or over depend on them. Phase 2 presents a comparative analysis of AI policy in the EU and UK, highlighting major policy categories including regulatory approach and institutional design, risk management and safety, accountability, governance and oversight, transparency, and fairness and fundamental human rights. Together, these findings underscore the interplay between individual attitudes, emotions, and behaviors and broader institutional and regulatory frameworks, providing insight into how AI adoption and responsible use can be supported in engineering education and professional practice.
This work has implications for US engineering education and practice by highlighting the need for clear guidance, structured training, and informed policy development to support AI integration, while identifying the thematic areas where such clarity is most needed. Understanding engineers’ attitudes and emotions toward AI, alongside international policy frameworks, can help policymakers, educators, and engineering leaders foster positive perceptions of AI, promote workforce readiness, and build public trust in AI-driven engineering systems. It is our hope that these findings will spark discussion around the development of US guidance and policy that promotes safe, ethical, and effective use of AI in engineering practice and education.