Artificial Intelligence (AI) and the Future of the Engineering and Computing Workforce: A Systematic Review of Grey Literature and Document Analysis of U.S. Reports (2020–2025)

Synthesis: Fletcher and colleagues (2026) conduct a systematic review of U.S. grey literature and document analysis of 22 reports (2020–2025) to understand how AI is reshaping the engineering and computing workforce and what this means for higher education. Framing the tension between rapid technological change and urgent policy decisions as the "Dual Train Problem," the review synthesizes projections from sources such as the Bureau of Labor Statistics, MIT's Shaping Work Project (1.6–3.2 million U.S. jobs displaced over two decades; up to 30% automatable by the mid-2030s), and the World Economic Forum. The findings imply that while some engineering and computing fields will grow, AI will replace many tasks (reducing jobs and working hours) even as new AI-oriented roles emerge, leading the authors to recommend that higher education prioritize durable AI competencies, Ethics and Governance, and skill-based credentials aligned with emerging roles (e.g., Prompt Engineering, AI auditing, AI policy) to sustain human-centered engineering.

The Dual Train Problem

The paper opens with the "Dual Train Problem": the rapid advancement of AI and automation runs in tension with the urgency of policy and institutional decisions, and higher education must adapt proactively. The authors note that AI is now believed to affect white-collar jobs — not only the middle- and low-skilled occupations that prior automation targeted — and that attitudes toward AI's impact diverge sharply between those who foresee productivity and new roles (data scientists, AI programmers) and those who perceive displacement, inequality, and polarization.

Workforce projections and scenarios

Drawing on economic and labor-market forecasts, the review surfaces a range of projections:

• Bureau of Labor Statistics (2023–2033) projections now account for AI exposure across several computer and engineering occupations, acknowledging heightened automation risk.
• MIT's Shaping Work Project estimates 1.6–3.2 million U.S. jobs could be displaced by AI-driven automation over two decades; more ambitious projections suggest up to 30% of jobs may be automatable by the mid-2030s.
• Four scenarios for 2030 (after Ellis 2025): supercharged progress (exponential AI advance outpacing governance), age of displacement (automation outpacing reskilling, generating social fractures), co-pilot economy (gradual AI-augmented work through human–AI collaboration), and stalled progress (uneven skill diffusion widening adoption gaps).
• Microsoft (2025) finds skills in analytical thinking, resilience, Ethics, and digital literacy are twice as likely to be required in job postings.

The review's central conclusion from these projections is that engineering and computing face a transformation — some growth in selected fields, but AI replacing tasks and reducing jobs and working hours, alongside the emergence of new AI-oriented roles requiring generative AI upskilling.

Implications and responsibilities for higher education

The authors translate these workforce projections into recommendations for faculty, deans, and executive academic leadership:

• Prioritize durable AI competencies — competencies that transfer across evolving roles, rather than tool-specific skills.
• Embed ethics and governance — as essential components of engineering formation, not add-ons.
• Adopt skill-based credentials aligned with emerging roles such as prompt engineering, AI auditing, and AI policy.
• Coordinate policy, resources, and partnerships — institutional leaders must orchestrate curriculum, industry partnerships, and resource allocation to manage workforce-transition risk and sustain human-centered engineering.

Connections to the wiki

The paper extends the wiki's coverage of AI and work, complementing empirical frameworks like the Workforce Readiness Level and connecting to Professional Training, AI Literacy, and Governance concepts. It frames Curriculum Design for engineering and computing as a strategic response to labor-market transformation, and positions Higher Ed institutions as key actors in workforce preparation.

Connected Concepts

• Engineering Education
• Professional Training
• AI Literacy
• Governance
• Curriculum Design
• Prompt Engineering
• Ethics
• Higher Ed
• STEM Education
• CS Education
• AI Education

Connected Articles

• Workforce Readiness Smart Manufacturing Wrl 2026 — Workforce Readiness Level Framework for Smart Manufacturing
• AI Higher Ed Workforce Survey — The Impact of AI on Work in Higher Education
• GenAI Expertise Pathways Sysadmin — GenAI and Expertise Pathways in System Administration
• Skill Diversity Worker Resilience — Skill Diversity and Worker Resilience
• AI Education Global Capacity — What AI in Education Needs Next: Lessons from Youth Leaders
• AI Higher Ed Bridge Gap — Higher Education Must Bridge the AI Gap
• AI Vocational Education Training Review — AI in Vocational Education and Training: A Systematic Review
• Credential Cognitive Stewardship AI Assessment — What Does the Credential Still Certify?

Citation

Fletcher, T. L., Webb, M. E., Alharbi, A., & Fletcher, T. (2026). Artificial Intelligence (AI) and the Future of the Engineering and Computing Workforce: A Systematic Review of Grey Literature and Document Analysis of U.S. Reports (2020–2025). ASEE Annual Conference & Exposition, Paper ID #53492.