To Gen-AI or not to Gen-AI: Sensory-Economic Framework for Aligning Post-secondary Education with an AI-Transformed Labor Market
DOI:
https://doi.org/10.54097/2xxwdp57Keywords:
Generative AI, Task Digitalization, Sensory Resistance, Career Segments, Educational Policy.Abstract
Generative AI is restructuring labor markets, challenging post-secondary institutions to adapt curricula. This paper develops the GRASP Framework, predicting occupation-specific AI penetration and generating curriculum optimization strategies. Grounded in O*NET data, PULSE forecasts labor demand evolution via a Sensory Resistance Coefficient establishing automation ceilings; CALIBER employs constrained Cobb-Douglas optimization for curriculum transitions. Three career segments are examined: Library Music Producers (Berklee), Gameplay Programmers (USC), and Full-Service Chefs (CIA). Extending beyond employment, COMPASS maps eighteen occupations onto a strategy space of sensory resistance and demand elasticity, classifying careers into Sanctuary, Augmentation, and Pivot zones. The primary innovation disaggregates occupation-level analysis into career segments, revealing within-occupation variation obscured by aggregate assessments.
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