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Will O'Kane, Auburn University, United StatesAbstract
Generative Artificial Intelligence (AI) stands to transform and disrupt labor markets at an unprecedented pace and scale. Estimates suggest that 80% of United States workers may see at least 10% of tasks affected by large language models, with some leading opinion leaders predicting widespread job displacement to a degree that necessitates government-sponsored universal basic income. Higher-income knowledge workers appear especially vulnerable. Generation Alpha will make post-secondary decisions during this transition, often under the same “college equals security” heuristic that contributed to Millennial misalignment: 52% underemployment among recent graduates and negative lifetime Return on Investment (ROI) in 23% of bachelor's programs. This study conducts an integrative analysis of the forces behind adverse Millennial higher-education outcomes using federal labor data (Federal Reserve Bank of New York, Bureau of Labor Statistics, Census Bureau/American Community Survey), institutional datasets (Georgetown University Center on Education and the Workforce, Opportunity Insights), and behavioral economics literature. We identify overlapping domains including stigmatizing vocational pathways, assumptions about inflated credentials, cognitive biases of teen decision making, parents with obsolete priors about labor, and gaps in the K-12 guidance programs. The paper proposes a practical decision framework to help Generation Alpha families evaluate ROI, incorporate AI task exposure, and consider alternative credentials to avoid repeating the errors that shaped Millennial outcomes.
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