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The introduction of generative artificial intelligence (GenAI) has been met with a mix of reactions by higher education institutions, ranging from consternation and resistance to wholehearted acceptance. Previous work has looked at the discourse and policies adopted by universities across the U.S. as well as educators, along with the inclusion of GenAI-related content and topics in higher education. Building on previous research, this study reports findings from a survey of engineering educators on their use of and perspectives toward generative AI. Specifically, we surveyed 98 educators from engineering, computer science, and education who participated in a workshop on GenAI in Engineering Education to learn about their perspectives on using these tools for teaching and research. We asked them about their use of and comfort with GenAI, their overall perspectives on GenAI, the challenges and potential harms of using it for teaching, learning, and research, and examined whether their approach to using and integrating GenAI in their classroom influenced their experiences with GenAI and perceptions of it. Consistent with other research in GenAI education, we found that while the majority of participants were somewhat familiar with GenAI, reported use varied considerably. We found that educators harbored mostly hopeful and positive views about the potential of GenAI. We also found that those who engaged more with their students on the topic of GenAI, both as communicators (those who spoke directly with their students) and as incorporators (those who included it in their syllabus), tend to be more positive about its contribution to learning, while also being more attuned to its potential abuses. These findings suggest that integrating and engaging with generative AI is essential to foster productive interactions between instructors and students around this technology. Our work ultimately contributes to the evolving discourse on GenAI use, integration, and avoidance within educational settings. Through exploratory quantitative research, we have identified specific areas for further investigation. 

Algorithms are a central component of most services we use across a range of domains. These services, platforms, and devices rely on computing and technology professionals – who work as data scientists, programmers, or artificial intelligence (AI) experts – to meet their intended goals. How do we train future professionals to have an ethical mindset in their understanding, design, and implementation of algorithms? This was the question that prompted the use of a role-playing case study, which we designed, implemented, and studied in an undergraduate engineering course. We used the Boeing Max 8 flight disaster as the scenario for this case study as it encapsulates how a software algorithm shapes decision-making in a complex scenario. Theoretically, our work is guided by the situated learning paradigm, specifically the need to learn perspectival thinking for decision-making. The ability to make ethical decisions relies to a large extent on the ability of the decision-maker to take context into account – to understand not just the immediate technical need of the work but also larger implications that might even result from unanticipated consequences. Findings from the evaluation of the role-play scenario show that students reported a higher engagement with case study material and a better understanding of the scenario due to taking on a specific role related to the scenario. Analysis of pre-and post-discussion assignments shows a shift in their perspective of the case, further supporting the overall goal of developing a more situated understanding of ethical decision-making.