publications

2025

1. AI Ethics and Governance in the Job Market: Trends, Skills, and Sectoral Demand

   Lucas Wiese, Sonali Subbu Rathinam, Matthias Oschinski, Bryan DeWitt, and Daniel S. Schiff

   IEEE Transactions on Technology and Society, 2025

   Abs DOI

   Demand for an AI-literate workforce has surged, in large part to counter a growing skills gap. Meanwhile, expertise in ethical and governance dimensions of AI is increasingly deemed crucial to handle various organizational, regulatory, and social concerns. However, the focus of AI literacy efforts to date has been primarily technical. This paper helps close this gap by providing the first large-scale analysis of AI ethics and governance skills sought by employers in the labor market. Drawing on more than four million job postings for AI-related professions over the years 2018-2023, we provide an empirically-grounded characterization of AI ethics and governance competencies and perform associated descriptive analyses. We find that professionals with AI ethics and governance competencies are requested by employers to hold diverse skill sets, covering technical, managerial, and regulatory domains, though the two professions remain distinct. Moreover, the demand for expertise in these domains has grown rapidly, both in absolute terms and as a proportion of AI-related job postings. More than 100,000 professionals with expertise in AI ethics and governance are now requested annually, with the concentration highest in the financial and information sectors. These findings can help individuals, employers, and institutions of higher education better design job requirements, educational programs, and individual learning pathways, closing the career competency gap in AI ethics and governance.

2. Discourse Before Doctrine: The Category Error at the Heart of AI Ethics Education

   Lucas J. Wiese and Daniel S. Schiff

   In Handbook of Critical Studies of Artificial Intelligence and Education, 2025

   Forthcoming

   Abs

   Centring students in Artificial Intelligence (AI) ethics education is necessary to achieve commonly stated societal desires for responsible, safe, and trustworthy AI. The problem is that AI ethics material is not enough for an educational program to train ethical behaviour in future professionals working with AI systems. Education needs to forego the traditional models of depositing knowledge and skills into students and instead account for the real cognitive-behavioural dispositions of students’ lives. We argue that discourse offers a practical method and observable measure for instruction that effectively impacts precursors for behavioural change. Drawing on evidence in cognitive-behavioural psychology, discourse improves the efficacy of decision-making, is normatively tractable, promotes student confidence, and directly qualifies students for AI ethics work. By reframing AI ethics education around discursive practice, we encourage scholars, practitioners, and policymakers to consider the students at the heart of the sociological change expected by the AI ethics field.

3. AI ethics education: A systematic literature review

   Lucas J. Wiese, Indira Patil, Daniel S. Schiff, and Alejandra J. Magana

   Computers and Education: Artificial Intelligence, 2025

   Abs DOI

   The potential of AI technology to transform human life, well-being, and daily work is faced with numerous risks and challenges yet to be fully accounted for. However, the complexity of AI ethics makes it hard to pin down what to teach, how to teach it, and how to assess its effectiveness. Drawing on an educational perspective, this paper presents a systematic literature review and qualitative analysis of the early years of AI ethics education as a formalized field to analyze whether its future trajectory is aligned with educational best practices. Our review highlights core challenges in AI ethics education and the content, assessment, and pedagogy used in real interventions over recent years. We find that efforts to teach AI ethics do helpfully draw on a holistic view (as opposed to a narrow view), and utilize progressive pedagogies like case studies and group projects that aim to meaningfully challenge students’ ethical reasoning skills in applied practices. However, many real- world AI ethics teaching interventions do not leverage well-supported assessment techniques known to support student learning; rather, assessment is conducted primarily for research evaluative purposes. This gap in rigorous assessment raises implications for researchers and practitioners, as responsible development and use of AI will be stymied if educators cannot successfully determine whether students have truly learned relevant AI ethics content or skills.

4. Manufacturing Stakeholders’ Perceptions of Factors That Promote and Inhibit Advanced Technology Adoption

   Lucas J. Wiese, Alejandra J. Magana, Khalil El Breidi, and Ali Shakouri

   Sustainability, 2025

   Abs DOI

   This study explores factors promoting and inhibiting advanced technology adoption in small- and medium-sized manufacturing firms (SMEs). With AI’s rapid advancement impacting productivity and efficiency across industries, understanding the challenges that SMEs face to remain competitive is crucial. Utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) model as a theoretical framework, we analyzed managers, engineers, and line workers’ observations on workforce challenges, training needs, and opportunities faced by SMEs to provide insights into their smart manufacturing deployment experiences. Our findings highlight social influence’s role in promoting technology adoption, emphasizing community, shared experiences, and collaborative networks. Conversely, effort expectancy emerged as the largest inhibitor, with concerns about the complexity, time, and resources required for implementation. Individuals were also influenced by factors of facilitating conditions (organizational buy-in, infrastructure, etc.) and performance expectancy on their propensity to adopt advanced technology. By fostering positive organizational environments and communities that share success stories and challenges, we suggest this can mitigate the perceived effort expected to implement new technology. In turn, SMEs can better leverage AI and other advanced technologies to maintain global competitiveness. The research contributes to understanding technology adoption dynamics in manufacturing, providing a foundation for future workforce development and policy initiatives.

5. Reliable LLM Analysis Workflow as a Topic Modeling Alternative: Case Study on the Norms of AI Professionals

   Lucas J. Wiese, Daniel S. Schiff, Tatiana Ringenberg, and Alejandra J. Magana

   2025

   Under Review

2024

1. Undergraduate and graduate students’ conceptual understanding of model classification outcomes under the lens of scientific argumentation

   Lucas Wiese, Hector E. Will Pinto, and Alejandra J. Magana

   Computer Applications in Engineering Education, 2024

   Abs DOI

   Abstract Recent advancements in artificial intelligence (AI) and machine learning (ML) have driven research and development across multiple industries to meet national economic and technological demands. Consequently, companies are investing in AI, ML, and data analytics workforce development efforts to digitalize operations and enhance global competitiveness. As such, evidence-based educational research around ML is essential to provide a foundation for the future workforce as they face complex AI challenges. This study explored students’ conceptual ML understanding through a scientific argumentation framework, where we examined how they used evidence and reasoning to support claims about their ML models. This framework lets us gain insight into students’ conceptualizations and helped scaffold student learning via a cognitive apprenticeship model. Thirty students in a mechanical engineering classroom at Purdue University experimented with neural network ML models within a computational notebook to create visual claims (ML models) with textual explanations of their evidence and reasoning. Accordingly, we qualitatively analyzed their learning artifacts to examine their underfit, fit, and overfit models and explanations. It was found that some students tended toward technical explanations while others used visual explanations. Students with technically dominant explanations had higher proficiency in generating correctly fit models but lacked explanatory evidence. Conversely, students with visually dominant explanations provided evidence but lacked technical reasoning and were less accurate in identifying fit models. We discuss implications for both groups of students and offer future research directions to examine how positive pedagogical elements of learning design can optimize ML educational material and AI workforce development.

2. Applied Ethics via Encouraging Intuitive Reflection and Deliberate Discourse

   Lucas J. Wiese and Alejandra J. Magana

   In 2024 ASEE Annual Conference & Exposition, 2024

   Abs DOI PDF

   Artificial intelligence’s (AI) widespread societal impact means that students of all disciplines will be working in roles adjacent to this new technology. As a result, they need to understand how to appropriately navigate and behave ethically in practice. The purpose of this paper is to introduce and detail a learning intervention intended to enhance the ethical behavior of future AI developers and engineers. The SIMDE conceptual framework was developed to offer a basis for understanding the pre-rational aspects of ethical decision-making as they are carried out into deliberate discourse in a social space amongst peers. To investigate the SIMDE framework, students were asked to solve a professional AI ethics problem in a dilemma-based seven-step learning activity. The qualitative results of this paper examine how constructs in the SIMDE conceptual framework were present in student responses, and what students learned from peer discourse that led them to either justify their gut-reaction decision or change their mind. We found that students are impacted by perspective-taking, they use reasoning to defend their position rather than seek and appraise truth, and moral self-reflection helps them learn more about themselves. Moreover, even when students learn new information and improve their reasoning, they are not inclined to change their minds from their initial intuitive judgment. This finding supports literature that suggests ‘reasoning’ can only go so far in the ethics curriculum if behavioral change is the goal. More interdisciplinary educational research is necessary to design an ethics curriculum that can appropriately prepare future AI professionals for the demands of industry.

3. A Department’s Syllabi Review for LLM Considerations Prior to University-standard Guidance

   Lucas J. Wiese and Alejandra J. Magana

   In 2024 ASEE Annual Conference & Exposition, 2024

   Abs DOI PDF

   The release and widespread use of generative artificial intelligence causes concern for the future of teaching and learning. Since the release of ChatGPT, some institutions released guidance on its use in education, while other institutions waited for the technology to mature. This study is contextually situated during the Fall 2023 semester at a single university; Unique because the university had not published LLM guidance yet, but the technology had been out long enough for students to become familiar with its use. Through the conceptual lens of Teacher Noticing This study examined (a) whether faculty saw the potential use of LLMs for teaching and learning, and (b) how they responded to the rapid impact of LLMs in the classroom before university-standard guidance. Via document analysis, we found that despite LLM chatbots being widespread for roughly 9 months before the Fall semester, only a third of faculty acknowledged its use in the classroom. Faculty took three positions toward it: encouraged, discouraged, and prohibited. As found in qualitative analysis, most of the language was precautionary and discouraging. Through the lens of Teacher Noticing, we suggest that this is worrisome since faculty beliefs seemed to be mismatched with the enthusiasm and excitement of AI from students. Only a few months later, the university encouraged the use of creatively incorporating LLMs in the classroom to foster learning and increase students’ awareness of the limitations of the tools. In a technology department especially, instructors falling behind the curve of digital literacy may impact students’ satisfaction with their education. Future work should be done to understand how university guidance impacts faculty beliefs and how that translates to pedagogical techniques and learning outcomes.

2023

1. An Experiential Learning Approach to Industrial IoT Implementation of Smart Manufacturing through Coursework and University-Industry Partnerships

   Eunseob Kim, Lucas J. Wiese, Hector Will Pinto, Alejandra J. Magana, and Martin Jun

   Journal of Engineering Technology, 2023

   Abs

   As the Internet of Things (IoT) and artificial intelligence (AI) continue to reshape the industrial landscape, the US manufacturing sector faces pressing challenges in bridging the skills gap. This issue is not merely about filling vacancies but adapting to the demands of AI-centric manufacturing roles. Traditional engineering curricula often lag behind in catering to the requirements of contemporary AI and IoT paradigms. Recognizing this shortfall, Purdue University, in partnership with local industries, launched an innovative graduate course. This initiative is crafted to equip engineering graduate students with a comprehensive grasp of data, from IoT sensor and machine connectivity to its interpretation through AI-driven analytics. An integral part of the course was the lab sessions, structured around hands-on activities. Through these labs, students had the opportunity to immerse in IoT and AI-related technologies, gaining practical experience and insights. Building on the knowledge acquired in the lectures and labs, students performed on semester-term projects in collaboration with regional manufacturing companies. Beyond academic advancement, the course offers a unique opportunity for regional firms to harness the transformative potential of IoT and AI, helping them navigate through their operational challenges. This study delves into the course’s pioneering design, rooted in the experiential learning theory (ELT), highlighting the significant outcomes and showcasing the collaborative projects that seamlessly integrated classroom learning with practical, real-world applications.

2. Exploring Machine Learning Methods to Identify Patterns in Students’ Solutions to Programming Assignments

   Xiaojin Liu, Hugo Castellanos, Lucas Wiese, and Alejandra J. Magana

   In 2023 IEEE Frontiers in Education Conference (FIE), 2023

   Abs DOI

   Technology and automation have become increasingly critical for organizations today, and programming has become an essential skill for all STEM majors to meet this demand. Graduates are expected to possess programming skills to meet the needs of the modern workforce. Acquiring programming skills is a challenging task, and institutions often struggle to provide adequate resources to meet the industry’s demand for proficient computer programmers. To take steps toward better understanding programming challenges among undergraduate students in science disciplines, this study aims to characterize patterns in students’ solutions to programming assignments over the course of a semester. With this, the goal is to characterize students’ most common challenges and take steps toward providing automated feedback. Specifically, this study applies machine learning (ML) classification algorithms to analyze student artifacts from a college-level introductory Python programming course for science majors, including source code from labs, homework assignments, projects, and two live-coding exams. Data was collected as part of a semester-long course and pre-processed and de-identified. The researchers labeled the data, and relevant features were selected to prepare the data for training the ML algorithms. Various classification algorithms were trained, and the resulting ML models were evaluated for their accuracy. Then, the study deployed a quantitative research method to evaluate both the effectiveness of various ML models and the quality of the feedback the model could provide, such as efficiency and accuracy. The research results are expected to inform the development of machine-learning algorithms to provide higher-quality feedback mechanisms for students in introductory programming courses. In that manner, this study contributes to improving the quality of programming education.

3. Data Analytics Short Courses for Reskilling and Upskilling Indiana’s Manufacturing Workforce

   In 2023 ASEE Annual Conference & Exposition, 2023

   Abs DOI PDF

   The power of ML and AI has not been fully realized in the manufacturing sector. One of the major challenges is that the small and medium manufacturers, which account for 98% of the industry, lack the dedicated data analytic workforce. In Indiana, to address this need, partnerships have been established between industry and academia through Wabash Heartland Innovation Network (WHIN) at Purdue University. In collaboration with Ivy Tech Community College, a series of workshops were developed to introduce data analytics, the internet of things, and basic machine learning concepts to local small and large manufacturing companies. This study will describe the first of three short courses geared toward industry workers and professionals. The first short course was on the topic of energy savings and data analytics for Variable Frequency Drives (VFDs). The attendees consisted of 44 participants from 17 manufacturing companies. A final evaluation of the course reports on participants’ levels of satisfaction with the course, the major learnings and takeaways, and their institutional support. The evaluation results indicate that the course was a good introduction to VFDs and the large number of applications where VFD data can be used for energy savings, diagnostics, and operations. However, workshop participants wanted more hands-on opportunities to see how the VFD data can be extracted for new motors and many legacy equipment still in use and how various settings can be adjusted.

4. Being Proactive for Responsible AI: Analyzing Multiple Sectors for Innovation via Systematic Literature Review

   Lucas J. Wiese, Daniel S. Schiff, and Alejandra J. Magana

   In 2023 IEEE International Symposium on Ethics in Engineering, Science, and Technology (ETHICS), 2023

   DOI