2024 ASEE Annual Conference & Exposition

This Complete Evidence-based Practice paper describes first-year engineering students’ perceptions, and specifically their shifts in those perspectives, towards the role of automation and data science in society as well as the racial implications of how those human-made systems are implemented and deployed. As part of a larger curricular change being made to a first-year engineering course in computation, this paper specifically examines two reflection assignments where students wrote, at different points in in the semester (week 2 and week 12), regarding their own personal questions and understandings related to the role of machine learning, artificial intelligence, and automation in society and its relationship to systemic racism and racial impact of engineering and technological systems. For analysis, the submissions were compiled, and comparisons of the two moments in the semester were coded and analyzed for thematic commonalities seen in student written responses and the overall progression of students’ thinking. Results showed commonalities amongst students' initial reactions to the video such as questions surrounding who is responsible for the impact of designed technologies along with a strong ideological separation between humans and machines. Juxtaposed with the week 2 assignments, week 12 findings showed commonalities in students’ progress such as an increased awareness of the complexity of racialized sociotechnical problems, stronger emotional responses, more refined ideas about potential solutions, and realizing the systemic nature of racism. Findings suggest that the students met learning goals regarding an awareness of sociotechnical problems, and catalyzed (early) critical thinking on how to address them through engineering. Implications from this work demonstrate that first-year students are capable of wrestling with difficult topics such as racism in technology, while still meeting ABET requirements within the course for data science and coding.