Student perceptions of GenAI integration into engineering practice: How students interpret liability, safety, professional conduct and ethics across disciplines

Abstract

Generative artificial intelligence (GenAI) is rapidly entering engineering workflows, raising new questions about liability, safety, and professional ethics. This study examined how 48 students across five engineering disciplines interpreted these issues in discipline-specific GenAI failure case studies created with ChatGPT-4o. Using a deductive qualitative approach, 192 written responses were coded against a 32-item taxonomy of ethical and professional considerations. Across disciplines, students consistently prioritised control and oversight and decision-making under risk and uncertainty. In contrast, no responses addressed data ownership, power and hegemony, or language fluency. Disciplinary differences were evident: mechatronic and electrical engineering students identified a broader set of considerations, including data collection and use, equity and accessibility, and environmental impacts. These findings support aligning curriculum and assessment with explicit requirements for documenting and validating the use of GenAI, designing for appropriate human oversight, and implementing discipline-specific learning activities that address data governance, equity, and environmental implications throughout the engineering lifecycle.