2026 ASEE Annual Conference & Exposition

Employer Expectations: The Impact of Artificial Intelligence Tools and Competencies Needed for Computing Positions

Presented at CIT Technical Session 10: Workforce and Research Ecosystem.

The rapid rise and integration of artificial intelligence (AI) are reshaping the labor market. There is an increasing expectation that workers who enter industry will pair domain-specific knowledge with AI-related competencies to thrive in their roles. Yet, it remains unclear: 1) what applications are being utilized in varying computing positions (e.g., software engineers, data scientists, AI or machine learning engineers); 2) whether upskilling or training is being offered for current employees; and 3) how job applicants may be assessed for these competencies during the hiring process. We sought to address these areas through a qualitative investigation of the perspectives of hiring decision makers, applying the framework of Social Cognitive Career Theory. We conducted semi-structured interviews with (n = 18) industry professionals involved in workforce recruitment decisions across various sectors, including healthcare, finance, logistics, technology, and consulting, and we analyzed the transcripts using reflexive thematic analysis. Our results highlighted that all employees, both current and future, will be expected to integrate AI in some capacity. Despite strong expectations for employees to possess AI skills and wanting them to integrate them into operations, only a small subset of companies reported offering in-house training or resources to learn. In addition, most employers expected baseline AI literacy, and some pointed out that certificates and coursework could indicate sufficient competence. However, some employers, who possessed high domain-specific expertise, also stressed that working knowledge and applied skills were of the utmost value. Accordingly, portfolios were considered more demonstrative of how the candidates used AI to build and deploy projects than credentials listed on a resume and were suggested as a better approach to displaying evidence of AI readiness. The findings from this investigation can serve to inform as well as offer insight on how faculty, administrators, policymakers, and other stakeholders could integrate AI-related competencies into academic preparation to meet evolving industry needs and enhance graduate employability.

Authors

Sandeep Varry http://orcid.org/0009-0009-5177-4755 Florida International University [biography]

Sandeep Varry is a Senior Full Stack Engineer at Florida International University’s College of Engineering and Computing, where he leads the design and implementation of scalable digital systems that support academic programs, workforce development initiatives, and industry engagement. With nearly two decades of experience at FIU, his work spans web platforms, automation, CRM systems, and data-driven infrastructure that connect students, institutions, and employers. He holds master’s degrees in Computer Science and Mass Communications, and is currently pursuing a Ph.D. focused on the intersection of artificial intelligence, workforce development, and higher education. His research explores the implications for curriculum design and industry–academic collaboration. As a scholar-practitioner, he aims to integrate his research directly into applied systems and programs, bridging technical innovation with human-centered educational outcomes.
Stephanie Jill Lunn http://orcid.org/0000-0003-3840-1822 Florida International University [biography]

Stephanie Lunn is an Assistant Professor in the Multidisciplinary Engineering and Computing Education, Systems and Management (ESM) department and the STEM Transformation Institute at Florida International University (FIU). She also has a secondary appointment in the Knight Foundation School of Computing and Information Sciences (KFSCIS). Previously, Dr. Lunn earned her doctorate in computer science from the KFSCIS at FIU, with a focus on computing education. She also holds B.S. and M.S. degrees in computer science from FIU and B.S. and M.S. degrees in neuroscience from the University of Miami. In addition, she served as a postdoctoral fellow in the Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology, with a focus on engineering education. Her research interests span the fields of computing and engineering education, human-computer interaction, data science, and machine learning.
Nimmi Arunachalam Florida International University [biography]

Nimmi Arunachalam is presently a Ph.D. student in the Multidisciplinary Engineering and Computing Education, Systems and Management department at Florida International University (FIU). Nimmi is a computing educator, practitioner, and researcher with two decades of experience spanning higher education, K-12 Computer Science (CS) instruction, and industry. She currently leads computing education initiatives at FIU, where she serves as Program Director for the Sprinternship™ program — a micro-internship initiative that has placed over 400 computing students in their first experiential learning roles in industry, many of whom have received full-time offers as a direct result.

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