Back in 1985, about 4 percent of bachelor’s degrees were in computer science and just under 2 percent were in math. Fast forward almost 40 years and the percentages are nearly the same. Despite an explosion of technology, automation, and AI, these degree shares have stayed flat.
Why?
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Other majors grew faster: Health, business, and social sciences surged, keeping CS and Math from growing in relative share.
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Industry skills gap: Employers value portfolios, certifications, and self-taught coding just as much as formal degrees.
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AI and automation: Entry-level coding jobs are shrinking. Employers now seek integrators and problem solvers, not just coders.
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Math’s role: Math has always been foundational, but remains a springboard into teaching or grad school rather than a career endpoint.
This is exactly why I am working on AI-Augmented Exploratory Learning (AAEL) in my doctoral research.
AAEL Framework in three steps:
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Scaffolded exploration – start with structured but open-ended real world tasks.
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AI as coach and co-creator – learners use AI to collaborate, debug, and adapt solutions.
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Iterative refinement with judgment – professionals refine results and apply critical thinking.
Degrees may look the same as they did in 1985, but the workforce does not. To thrive in the era of AI, we need new models like AAEL that empower learners to adapt quickly, work alongside AI, and build judgment that automation cannot replace.
Robert Foreman
Doctoral Student in Educational Technology
Central Michigan University
📧 forem1r@cmich.edu | 🌐 nhancedata.com | 📱 480-415-0783