Exploring the Institutionalization of Industry-Linked AI Education Policy: Policy Frame Redesign through the Case of Gwangju Metropolitan City

This study aims to empirically examine the institutional potential of a local government-led AI talent development policy, using the Gwangju AI Academy as a case study.Drawing on Lucas's (1988) theory of Human Capital Spillover, the research adopts a mixed methods approach, combining quantitative indicators (e.g., completion rate, employment rate, local retention) with qualitative data (e.g., interviews with graduates and industry partners).The findings are as follows.First, the Gwangju AI Academy enhanced digital skills among local youth and fostered employment and entrepreneurship through industry-collaborative, project-based education, demonstrating measurable spillover effects.Second, the program's outcomes correlated positively with regional economic indicators, such as youth employment rates and AI sector growth, and led to indirect impacts, including startup formation and the creation of AI-related academic programs.Third, structural constraints remain in talent retention, curriculum-industry alignment, and post-graduation support systems.To address these gaps, this study proposes the 'AI Academy 2.0' as an integrated talent development framework.The model includes industry-linked recruitment systems, tiered job tiered curriculum track, alumni career support platforms, and a comprehensive performance feedback system.This research contributes theoretically and practically by demonstrating how AI education policies, led by local governments, can serve as structural platforms for regional innovation ecosystems and offer sustainable, decentralized models for digital transformation.