Responsible integration of GenAI: Ethical and pedagogical perspectives in engineering education

Abstract

The incorporation of Generative AI (GenAI) into engineering education is progressing swiftly, fostering essential discussions about its academic utilisation. Although GenAI holds considerable promise for improving course materials and evaluations, significant issues concerning its effect on student learning, academic integrity, and the necessity for human oversight remain critical. Moreover, considerations related to data security and intellectual property present additional challenges to its seamless integration within the academic curriculum. Purpose or Goal: This pilot study examines the perspectives of engineering education academics on the integration of GenAI within academic environments. The objective is to explore the perceptions of engineering educators regarding the integration of GenAI, evaluate its proposed applications in teaching and research, and identify critical areas that necessitate institutional guidance and policy development. Approach: A quantitative research design was used, incorporating survey data and discussions from the 2024 AAEE Annual Conference. The survey aimed to capture participants' experiences and perceptions of implementation. Participants examined the ethical implications of GenAI use in academic, student, and industry contexts through four developed case studies. Outcomes: Findings indicate a high adoption rate of GenAI among academics, particularly for developing course materials (57%) and assessments (43%). However, concerns regarding data security, bias, and the erosion of critical human skills persist. While 100% of respondents agreed that GenAI should be integrated into curricula, 60% advocated for some restrictions to ensure academic integrity and responsible usage. Conclusions: This research highlights the critical need for ethical oversight and systematic policy development in the application of GenAI within engineering education. Scholars acknowledge the potential advantages of GenAI but stress the importance of human supervision, transparency, and equitable access. The conclusions advocate for specialised training, governance frameworks, and continuous dialogue to ensure that GenAI acts as a tool for improvement rather than substitution in educational settings.

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