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Generative artificial intelligence (GenAI) is increasingly becoming a part of work practices across the technology industry and being used across a range of industries. This has necessitated the need to better understand how GenAI is being used by professionals in the field so that we can better prepare students for the workforce. An improved understanding of the use of GenAI in practice can help provide guidance on the design of GenAI literacy efforts including how to integrate it within courses and curriculum, what aspects of GenAI to teach, and even how to teach it. This paper presents a field study that compares the use of GenAI across three different functions - product development, software engineering, and digital content creation - to identify how GenAI is currently being used in the industry. This study takes a human augmentation approach with a focus on human cognition and addresses three research questions: how is GenAI augmenting work practices; what knowledge is important and how are workers learning; and what are the implications for training the future workforce. Findings show a wide variance in the use of GenAI and in the level of computing knowledge of users. In some industries GenAI is being used in a highly technical manner with deployment of fine-tuned models across domains. Whereas in others, only off-the-shelf applications are being used for generating content. This means that the need for what to know about GenAI varies, and so does the background knowledge needed to utilize it. For the purposes of teaching and learning, our findings indicated that different levels of GenAI understanding needs to be integrated into courses. From a faculty perspective, the work has implications for training faculty so that they are aware of the advances and how students are possibly, as early adopters, already using GenAI to augment their learning practices. 

Providing rich, constructive feedback to students is essential for supporting and enhancing their learning. Recent advancements in Generative Artificial Intelligence (AI), particularly with large language models (LLMs), present new opportunities to deliver scalable, repeatable, and instant feedback, effectively making abundant a resource that has historically been scarce and costly. From a technical perspective, this approach is now feasible due to breakthroughs in AI and Natural Language Processing (NLP). While the potential educational benefits are compelling, implementing these technologies also introduces a host of ethical considerations that must be thoughtfully addressed. One of the core advantages of AI systems is their ability to automate routine and mundane tasks, potentially freeing up human educators for more nuanced work. However, the ease of automation risks a “tyranny of the majority”, where the diverse needs of minority or unique learners are overlooked, as they may be harder to systematize and less straightforward to accommodate. Ensuring inclusivity and equity in AI-generated feedback, therefore, becomes a critical aspect of responsible AI implementation in education. The process of developing machine learning models that produce valuable, personalized, and authentic feedback also requires significant input from human domain experts. Decisions around whose expertise is incorporated, how it is captured, and when it is applied have profound implications for the relevance and quality of the resulting feedback. Additionally, the maintenance and continuous refinement of these models are necessary to adapt feedback to evolving contextual, theoretical, and student-related factors. Without ongoing adaptation, feedback risks becoming obsolete or mismatched with the current needs of diverse student populations. Addressing these challenges is essential not only for ethical integrity but also for building the operational trust needed to integrate AI-driven systems as valuable tools in contemporary education. Thoughtful planning and deliberate choices are needed to ensure that these solutions truly benefit all students, allowing AI to support an inclusive and dynamic learning environment. 

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. 

This research paper is a study of the support needs of nontraditional students in engineering (NTSE). Nontraditional students in engineering are one segment of the student body that has traditionally not been a part of the conversation in engineering education– those students who do not go through a typical four-year college degree largely at a residential campus. It is only by better understanding the range of issues that NTSE face that we will be able to design interventions and support systems that can assist them. Recent work in engineering education particularly argues that co-curricular support is a critical factor in student success as it effects curricular progress but there has been no work looking specifically at co-curricular support for NTSE and their retention and persistence. The population of NTSE is increasing across campuses as more students take on jobs to support their education and as those in the workforce return to complete their education. It is imperative that higher educational systems understand how to serve the needs of these students better. Although there are a range of ways in which nontraditional students (NTS) are defined, the NCES has proposed a comprehensive definition that includes enrollment criteria, financial and family status, and high school graduation status. Overall, the seven characteristics specifically associated with NTS are: (1) Delayed enrollment by a year or more after high school, (2) attended part-time, (3) having dependents, (4) being a single parent, (5) working full time while enrolled, (6) being financially independent from parents, and (7) did not receive a standard high school diploma. We ground our research in the Model of Co-Curricular Support (MCCS) which suggests it is the role of the institution to provide the necessary support for integration. If students are aware and have access to resources, which lead to their success, then they will integrate into the university environment at higher rates than those students who are not aware and have access to those resources. This research study focuses on answering one research question: How do NTSE engage with co-curricular supports as they progress through their degree programs? To answer this question, we recruited 11 NTSE with a range of nontraditional characteristics to complete prompted reflective journaling assignments five times throughout the Fall 2021 semester. Qualitative results showcase the nuanced lives of NTSE as they pursue their engineering degrees. In particular, results indicate students interact with faculty, classmates, and friends/peers the most, and only interact with advising when required. Students rarely reach out to larger student support for help or are involved with campus or other events happening. Classmate and friend/peer interactions are the most positive, while interactions with faculty had the largest negative outcomes. 

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. 

There remains a lack of research on professional engineering work practices [1]. This deficiency is troubling because engineering education is organized and reorganized based on claims and assumptions about what professional engineering work is or will be. Without well-researched and trustworthy representations of practice, it is questionable whether engineering educators can adequately prepare future engineers for workplace realities. Although it is important that the preparation of future engineers not be tied solely to the workforce, there is a significant “disconnect between engineers in practice and engineers in academe” [2, p. 18]. If educators want to prepare students for professional success – including by assuming roles as future leaders and change agents – concrete images of engineering work are critical resources for rethinking engineering education [1]. The need for such resources is even more urgent given ongoing changes to engineering work under the forces of globalization, new organizational configurations, and new technologies of communication, design, and production. More research is needed to document images that are often discounted by students and even faculty, i.e., portrayals of engineering practice that emphasize its non-technical and non-calculative sides, as well as its non-individual aspects [3-4]. The aim of this work-in-progress paper is to introduce an exploratory project that will test innovative approaches to data collection and analysis for rapidly generating new knowledge about engineering practice. Traditionally, engineering practices have primarily been studied using in-depth ethnographic field research, requiring researchers to embed themselves as participant observers in the workplace. Yet technical work increasingly involves open workspaces and geographically distributed teams, frequent changes in job roles and team composition, and many layers of digital abstraction and collaboration. It thus may not be feasible or optimal to perform on-site research for extended periods of time. The main aim of this paper is to introduce method innovations for conducting field research which can potentially generate higher quality data more efficiently. Before doing so, we briefly overview prior research on engineering practice. 

There remains a lack of research on professional engineering work practices [1]. This deficiency is troubling because engineering education is organized and reorganized based on claims and assumptions about what professional engineering work is or will be. Without well-researched and trustworthy representations of practice, it is questionable whether engineering educators can adequately prepare future engineers for workplace realities. Although it is important that the preparation of future engineers not be tied solely to the workforce, there is a significant “disconnect between engineers in practice and engineers in academe” [2, p. 18]. If educators want to prepare students for professional success – including by assuming roles as future leaders and change agents – concrete images of engineering work are critical resources for rethinking engineering education [1]. The need for such resources is even more urgent given ongoing changes to engineering work under the forces of globalization, new organizational configurations, and new technologies of communication, design, and production. More research is needed to document images that are often discounted by students and even faculty, i.e., portrayals of engineering practice that emphasize its non-technical and non-calculative sides, as well as its non-individual aspects [3-4]. The aim of this work-in-progress paper is to introduce an exploratory project that will test innovative approaches to data collection and analysis for rapidly generating new knowledge about engineering practice. Traditionally, engineering practices have primarily been studied using in-depth ethnographic field research, requiring researchers to embed themselves as participant observers in the workplace. Yet technical work increasingly involves open workspaces and geographically distributed teams, frequent changes in job roles and team composition, and many layers of digital abstraction and collaboration. It thus may not be feasible or optimal to perform on-site research for extended periods of time. The main aim of this paper is to introduce method innovations for conducting field research which can potentially generate higher quality data more efficiently. Before doing so, we briefly overview prior research on engineering practice.