2026 ASEE Annual Conference & Exposition

Background. Manufacturing and engineering sectors rapidly integrating artificial intelligence technologies face critical shortages of qualified technicians, with forty percent of manufacturing competency requirements projected to change within five years. Systematic analysis of regional workforce needs and educational responses remains limited, particularly for rural and semi-rural regions positioned to benefit from AI-driven economic development.
Purpose. Grounded in Human Capital Theory, Regional Innovation Systems Theory, and Skills-Based Technological Change Theory, this study examines workforce demand and associated competency requirements for AI-skilled technicians across national, state, and regional levels, with primary focus on Maryland’s Eastern Shore, to identify implications for community college curriculum development.
Method. We employ a convergent mixed-methods secondary analysis synthesizing 32 high-quality sources through systematic content analysis and quantitative comparison. Sources underwent quality assessment using a six-dimension framework (18-point scale, 66.7 percent inclusion threshold), achieving a 91.4 percent inclusion rate and 94 percent inter-rater agreement. Analysis applied structured coding across eight a priori themes, multi-source triangulation across four source types, and geographic-level comparisons.
Results. Findings indicate substantial and growing demand for AI-skilled technicians nationally, with 87.5 percent source convergence documenting 1.9 to 3.8 million unfilled manufacturing positions by 2033. The Baltimore-Washington capital region is the nation’s second-largest AI jobs hub, trailing only California. Eastern Shore documentation shows active Industry 4.0 investments and growing employer-education coordination. Three-quarters of sources identify community colleges as primary pathways, with technical competency convergence around machine learning (23 sources), data analysis (21 sources), and AI programming (19 sources).
Conclusions. Evidence supports investment in AI technician education on Maryland’s Eastern Shore through modular credential pathways, deep industry partnerships, and sustained faculty development. The systematic multi-source analysis approach offers a replicable model for regional workforce assessment in emerging technology fields.