How Students and Recent Grads are Responding to the Rise of AI

2026-02-09 · Source: Agglomerations · Field: Finance & Economics — Economic Analysis & Policy, Labor Economics · Depth: Intermediate, long

Summary

A recent Lumina Foundation-Gallup poll revealed that 42 percent of bachelor's degree students have reconsidered their major due to Artificial Intelligence, with 16 percent already changing their field of study. Despite concerns about AI's impact on jobs, undergraduates are increasingly pursuing degrees with high AI exposure, showing an 8 percent enrollment increase last year compared to 2017. This trend persists even with a decline in Computer Science degrees, which is counterbalanced by rising enrollment in other AI-exposed fields like Engineering. The primary driver appears to be higher wages associated with these degrees, a finding consistent with research indicating that highly-exposed students experienced increased salaries and quicker job placement post-ChatGPT. Furthermore, data from the American Community Survey indicates that recent graduates with AI-exposed degrees are not significantly shifting out of their fields, with their occupational choices remaining stable over time.

Key takeaway

For university administrators and career counselors advising students, recognize that the perceived value of AI-exposed degrees is currently high, driven by higher wages and stable graduate employment. You should emphasize skill development that complements AI tools, rather than avoiding these fields, as the long-term impact of AI on specific job tasks is still evolving. Focus curricula on adaptive skills and AI integration to prepare students for an augmenting, not solely automating, future.

Key insights

Students are increasingly pursuing AI-exposed degrees, driven by higher wages and stable career outcomes, rather than avoiding them.

Principles

• Student degree choices are responsive to perceived labor market conditions.
• Degrees leading to AI-exposed occupations generally command higher wages.
• AI's long-term labor market impact remains largely undefined.

Method

AI exposure for degrees is quantified by crosswalking occupational AI-exposure scores (e.g., Eloundou et al. 2024 GPT-4 beta) with historical degree-to-occupation mappings from ACS data. Enrollment trends are monitored via the National Student Clearinghouse.

In practice

• Use occupational AI exposure metrics to assess degree program relevance.
• Monitor graduate employment stability in AI-exposed fields.

Topics

• AI Exposure Measurement
• Student Enrollment Trends
• Labor Market Dynamics
• Graduate Employment
• Wage Premiums
• Higher Education

Code references

• EIG-Research/ai-degree-exposure

Best for: Research Scientist, Policy Maker, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Agglomerations.