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 whole-hearted 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 communi-cators (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.

The omnipresence of software systems across all aspects of society has necessitated that future technology professionals are aware of ethical concerns raised by the design and development of software and are trained to minimize harm by undertaking responsible engineering. This need has become even more urgent with artificial intelligence (AI) driven software deployment. In this paper we present a study of an interactive pedagogical intervention –role-play case studies –designed to teach undergraduate technology students about ethics with a focus on software systems. Drawing on the situated learning perspective from the Learning Sciences, we created case studies, associated stakeholder roles, discussion scripts, and pre and post discussion assignments to guide students’ learning. Open-ended data was collected from thirty-nine students and analyzed qualitatively. Findings from the study show that by taking on different perspectives on a problem, students were able to identify a range of ethical issues and understand the role of the software system process holistically, taking context, complexity, and trade-offs into account. In their discussion and reflections, students deliberated the role of software in society and the role of humans in automation. The curricula, including case studies, are publicly available for implementation.

This research paper presents findings from a re-flective journal study of undergraduate students' everyday in-teractions with artificial intelligence (AI). As the application of AI continues to expand, students in technology programs are poised to be both producers and users of the technologies. They are also positioned to engage with AI applications within and outside the classroom. While focusing on the curriculum when examining students' AI knowledge is common, extending this connection to students' everyday interactions with AI provides a more complete picture of their learning. In this paper, we explore student's awareness and engagement with AI in the context of school and their daily lives. Over six weeks, 22 undergraduate students participated in a reflective journal study and submitted a weekly journal entry about their interactions with AI. The participants were recruited from a technology and society course that focuses on the implications of technology on people, communities, and processes. In their weekly journal entries, participants reflected on interactions with AI on campus (coursework, advertises campus events, or seminars) and beyond (social media, news, or conversations with friends and family). The journal prompts were designed to help them think through what they had read, watched, or been told and reflect on the development of their own perspectives, knowledge, and literacy on the topic. Overall, students described nine categories of interactions: coursework, news and current events, using software and applications, university events, social media related to their work, personal discussions with friends and family, interacting with content, and gaming. Students reported that completing the diaries allowed them time for reflection and made them more aware of the presence of AI in their daily lives and of its potential benefits and drawbacks. This research contributes to the ongoing work on AI awareness and literacy by bringing in perspectives from beyond a formal educational context.

In this research paper we examine undergraduate students' use of and perceptions of generative AI (GenAI). Although the initial hype around ChatGPT has subsided, GenAI applications continue to make inroads across learning activities. Like any other emerging technology, there is a lack of consensus around using GenAI within higher education. Students are early adopters of the technology, utilizing it in atypical ways and forming a range of perceptions and aspirations about it. To understand where and how students are using these tools and how they view them, we present findings from an open-ended survey response study with undergraduate students pursuing information technology degrees. Students were asked to describe 1) their understanding of GenAI; 2) their use of GenAI; 3) their opinions on the benefits, downsides, and ethical issues pertaining to its use in education; and 4) how they envision GenAI could ideally help them with their education. Thirty-seven students provided responses ranging in length from 20 to 300 words for each question. Responses were iteratively coded by researchers to uncover patterns in the data and then categorized thematically. Findings reveal that students' definitions of GenAI differed substantially and included many misconceptions - some highlight it as a technique, an application, or a tool, while others described it as a type of AI. There was a wide variation in the use of GenAI by students, with two common uses being writing and coding. They identified the ability of GenAI to summarize information and its potential to personalize learning as an advantage. Students identified two primary ethical concerns with using GenAI: plagiarism and dependency, which means that students do not learn independently. They also cautioned that responses from GenAI applications are often untrustworthy and need verification. Overall, they appreciated that they could do things quickly with GenAI but were cautious as using the technology was not necessarily in their best long-term as it interfered with the learning process. In terms of aspirations for GenAI, students expressed both practical advantages and idealistic and improbable visions. They said it could serve as a tutor or coach and allow them to understand the material better. We discuss the implications of the findings for student learning and instruction.

Questioning who has access to knowledge, skills, tools, and data becomes paramount as algorithms and the artificial intelligence (AI) systems they support find widespread applications. To address these concerns, "AI democratization" has become a prominent goal. In broad strokes, democratization allows more people to understand and work with AI, but a central question remains: for whom is AI being democratized? As the phrase can represent different meanings and stakeholders, grounding the concept of democratization for undergraduate students can be challenging. This ongoing work explores student engagement with the definitions of democratizing AI through a case study highlighting a fictional (but realistic) isolated community at risk of losing its last local grocery store and the potential for technology to address the supply chain fallout. Using role-play as the instructional activity for participants to engage in a collaborative peer-learning environment, students were immersed in a verisimilar discussion, envisioning the forms democratization can take. Seventy students participated in a course focused on technology's global and social impact, and their responses were analyzed before and after participating in the role-play activity. Overall, by being primed to the discussion on democratization through pre-assigned resources, students highlighted a nuanced understanding for whom democratization was relevant even before participating in the role-play. Results from statistical analysis showed significant improvement in recognizing the democratization of AI development, profits, and governance. Additionally, most students highlighted the role-play discussion as having strengthened the concepts they highlighted initially, but some described a broader view and recognition of other levels of democratization.

This Full Research paper presents a comparison of two codebook generation methods using natural language processing (NLP): a human and NLP collaboration method and a fully automated NLP method (referred to as Human-NLP and Auto-NLP, respectively). Codebook generation serves as a preliminary step in most qualitative projects, and using NLP as a tool can help support the analysis and efficiency of the researcher. By utilizing NLP in the early stages of codebook generation, there are opportunities for detailed and productive gains when working with large corpora of textual data. Using NLP at this stage also allows the researcher to make sense of any outputs generated through automated means rather than simply accepting the output as it is. The outcome of both methods tested in this work will be used to evaluate and apply the codes across a large dataset. The Human-NLP method involves generating the initial themes using a large-language model (LLM), and the researcher revises the codebook further. The Auto-NLP method involves generating three rounds of codes, summarizing the codes in each until a saturation level has been reached through the overarching themes. The dataset used for this study comes from an analysis of students' perception and recognition of ethical concepts after participating in a semester-long course focused on ethics, society, and technology. The course introduced students to traditional ethics topics, such as those around engineering disasters, but also explored developing topics, such as facial recognition, dataset bias, and the impact of technology on the global food supply. We collected data between fall 2020 and 2022 from six (6) iterations of a semester-long course. A total of 210 student responses to the question - what did this course teach you about ethics - were analyzed. The results from both Human-NLP and Auto-NLP methods were promising in the level of detail summarized and the similarity of themes across the data. Eight (8) themes were finalized through the Human-NLP method, and twelve (12) were generated through the Auto-NLP method. We present a discussion exploring these themes and the limitations of using these methods.

In this research-to-practice paper, we present a study examining how role-play case studies can teach technology students multidimensional ethical thinking on broad, everyday uses of algorithms and technology. Given the complexity of most engineering objects and systems, students need to be able to think not only from a technical or social perspective when it comes to ethical and responsible development and use but also to incorporate an awareness of environmental and sustainability concerns. To enable students to think in this multidimensional manner, we developed a role-play case study that explores different aspects of using e-scooters on a college campus. E-scooters are commonly available across the US on many college campuses, and it is not uncommon for students to have experienced using them or being in an environment where they are frequently used. The case study explores aspects of using e-scooters on campuses considering the technical, social, policy, and sustainability issues. The role-play activity encourages students to work collaboratively with their peers to understand the case study, raise concerns, and ultimately reach a consensus on the future use of the technology. The goal is to get students to recognize ethics in play in everyday spaces. To support this goal, open-ended data were collected from six student teams (6–7 students per team) with a total of 39 students and analyzed using a thematic analysis approach. Students learning about the case were supported through the use of academic and industry resources and frameworks. We found that students expressed a range of ethical concerns and were able to identify dilemmas and value tensions that are inherent in technology use. Thematic analysis of student discussions demonstrated students' uptake of ideas about dignity, well-being, sustainability, regulation, and convenience with reflection, among others. The data highlights students developing empathy and ethical perspectives by taking on a role, “being,” and negotiating from a character's viewpoint and motivation.

In this paper we provide an overview of theoretical and conceptual frameworks used in engineering education research (EER). First, we define the considerations for how the terminology around frameworks is used and how they are applied in the discipline. Then, we conduct a mapping review of different theoretical and conceptual frameworks used in EER that have been identified from grey literature publications in the discipline. We specifically use grey literature for this analysis to explore how frameworks are being used beyond their representation in traditional venues such as journal or conference publications, which are often post-hoc. Given the growth of EER during the past decade, many of the novel ideas and frameworks are likely to be first discussed and available in these venues. Furthermore, within the grey literature we analyze, frameworks are likely to play a broader role, including guiding the research design. The grey literature we analyze includes NSF-funded proposals, and engineering education Ph.D. dissertations from US institutions over the past decade (2011-2021). We examined a total of 194 dissertations and 106 NSF proposal abstracts uncovered using a set of keywords, academic discipline, and research areas. Our analysis approach utilizes and expands on existing categorizations, such as the Engineering Education Research Taxonomy and other categorical approaches within both the engineering education field and adjacent STEM and technology-related disciplines. Finally, we recognize the limitation of examining research situated only in the US but hopefully this study provides a roadmap to extend the analysis across other geographical areas with EER presence.

In this Research-to-Practice paper, we present findings from a study of role-play discussions for teaching technology ethics. In recent years there has been an increased emphasis on preparing students who are not only aware of microlevel aspects of ethics but are cognizant of broader ethical obligations at organizational and societal levels. In order to prepare engineers who can contribute towards addressing the grand challenges facing the world, such as sustainability, poverty, and social justice, among others, this approach is necessary. Students have to develop the ability to look beyond a narrow problem or issue, e.g., ethical design choice related to the use of a certain chemical compound and harms to users, to the larger environmental implications of this choice. Ethics is taught in a variety of ways in engineering, including diverse pedagogical approaches and topics, but one core technique is the use of case studies. Although case studies have been used by engineering ethics educators for decades, their efficacy for teaching issues beyond micro-ethics and linking different levels of ethical concerns is unclear. In this paper, we present a research study examining the efficacy of one genre of case studies, role-play discussion, in allowing participants to link ethical concerns at multiple levels. As exemplars, we discuss two cases we designed and used. We collected data from 20 groups of 4-6 students each that discussed a specific case and find evidence that when designed with appropriate roles and related narrative, role-play case studies can be an effective pedagogical intervention. This paper presents findings from qualitative analysis of student discussions and other pre/post assessment measures to show how students link micro, meso, and macro ethical concerns through role-play discussions.

We present a Research-to-Practice paper where we used role-play case studies to improve student understanding of the ethics of algorithms. As the use of algorithmic decision-making continues to grow across areas of society, there is a need to prepare future technology workforce for ethical thinking related. Our work was informed by the situated learning paradigm, and our goal was to improve perspectival thinking among students. Recognizing an issue from multiple perspectives and taking on different perspectives to examine it leads to increased understanding. Drawing on this work, we created and implemented a role-play case study in an undergraduate computing data mining course. The role-play case study focused on the use of algorithms for facial recognition. Data were collected from pre-and post-discussion assignments, and a student survey. Thirty-one students enrolled in the course and completed the ethics module. The data collected in the assignments focused on student's recognition of ethical dilemmas, the change in student's perspective on the case due to creating a collaborative consensus and understanding the complexity of algorithmic decision making. To formally analyze the data, we created a coding schema drawing on the literature and preliminary qualitative analysis of our data. The data were independently coded by multiple coders. The findings indicate that through their participation in collaborative role-play scenarios, students were able to recognize a wide range of issues and offer potential solutions. We discuss the implications of the work. Curriculum material created as part of this work is available as an open education resource.