Policy Implications of Experiential Programs for Expanding the AI Talent Base

• In Event: Algorithmic Governance: Machine Learning and Spatial Analytics in Public Policy

Abstract

This study aims to analyze the role and diffusion mechanisms of AI experiential programs as specific policy instruments for expanding the AI talent base, proposing strategic policy design directions for broadening AI capabilities across the general public. As artificial intelligence becomes a core component of national competitiveness, understanding how to transition citizens from passive observers to active participants is a critical administrative challenge.To this end, this study examines the “Online Coding Party,” a representative AI and software experiential program operated in South Korea over the past decade, as a primary case study. Drawing on a robust longitudinal dataset of operational data accumulated from 2015 to 2025, the research applies the Diffusion of Innovations framework to analyze the program’s expansion patterns and evolving characteristics. By utilizing this framework, the study evaluates how the program’s attributes—such as relative advantage, complexity, and observability—influenced its adoption across diverse demographic groups.The findings demonstrate that the program, as a policy instrument, successfully achieved large-scale mass participation by leveraging high accessibility and trialability. These features effectively lowered the entry barrier for the general public, contributing significantly to the early-stage objective of expanding the national AI talent base. However, the longitudinal analysis also identified several critical structural limitations that threaten long-term efficacy. These include a notable lack of interconnection among various content components, a high concentration in content provision that limits diversity, and insufficient institutional infrastructure to support sustained, progressive learning experiences. These factors collectively constrain the continuity and deepening of policy outcomes, often resulting in "one-off" engagements rather than cumulative skill acquisition.Based on these findings, the study reinterprets AI diffusion policies grounded in experiential approaches through the lens of non-formal education. We suggest policy design directions that move beyond mere participation expansion toward strengthening structural linkages among sustained engagement, the accumulation of learning experiences, and tangible capability development. This study contributes to the field by providing a structured analysis of the functional limitations of experiential policies and offering an analytical framework to inform future design for expanding the global AI talent base.

Author

• Seung Hyun Lee, Korea Foundation for Science and Creativity
  Presenting Author