Perception, Psychological Distance, and Career Choice in Artificial Intelligence and High-Performance Computing among Engineering Students

High-performance computing (HPC) uses powerful computing systems to solve large and complex problems. Together with artificial intelligence (AI), HPC is transforming engineering in fields ranging from medicine and transportation to energy and advanced manufacturing. Students who lack opportunities to learn about these technologies, or who view them as too difficult, unfamiliar, or unrelated to their futures, may miss out on important educational and career pathways. These challenges may be especially consequential for students whose colleges offer limited AI/HPC courses, computing resources, faculty expertise, mentors, or industry partnerships. This project will examine why some undergraduate engineering students view AI and HPC as relevant and approachable while others experience greater psychological distance, a sense that these technologies and careers are remote from their lives and goals. The study will investigate how students’ backgrounds, educational settings, experiences, and beliefs shape opportunities to engage with AI and HPC, how they perceive these technologies, whether they see AI- and HPC-related careers as attainable, and how these relationships vary across student groups and educational settings. Findings will guide the design of courses, advising, and other educational supports that expand pathways into AI- and HPC-related careers. Faculty workshops, student briefs, practitioner reports, and public communications will carry the findings to educators, students, institutions, industry, and policymakers. By strengthening the foundation for broad participation in these rapidly evolving areas of engineering, the project will help prepare an AI- and HPC-skilled workforce while reducing the risk that unequal access and perceptions create a two-tiered engineering profession. The four-year Level II project will advance the National Science Foundation's EDU Core Research (ECR:Core) program through a sequential explanatory mixed-methods study conducted across eight institutions representing diverse U.S. regions, institution types, program sizes, and levels of AI and HPC resources. Guided by a newly developed Technology Perception and Distance Social Cognitive Career Framework, the study will integrate surveys and interviews to examine perceptions of and experiences with AI and HPC, psychological distance, career interests and choices, and the individual and educational factors that shape these outcomes. In Phase I, the team will develop and refine survey instruments before surveying at least 5,000 engineering students. Quantitative analyses will identify distinct student profiles and show how perceptions, psychological distance, and career interests vary across institutions and student groups, providing the foundation for the interviews conducted in Phase II. In Phase II, interviews with approximately 70 students will explain why these patterns develop and how they influence students' career decisions. In Phase III, the team will integrate quantitative and qualitative findings to develop a more complete understanding of the pathways that encourage or discourage participation in AI- and HPC-related careers. The project will produce survey instruments supported by evidence of validity and reliability, student profiles, an extended career-development framework, and guidance for educational interventions that broaden participation in AI- and HPC-related careers. Instruments, protocols, analytic approaches, and synthesized findings will be made freely available through the Open Science Framework. This project is supported by NSF’s EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.