City to City of Learning in Thailand: From Research Synthesis by Large Language Models

Abstract

This research synthesizes findings from studies on the Learning Cities (LC) Program in Thailand, based on the utilization of Large Language Models (LLM) technology, combined with the analysis of research project outcomes and expert opinions. The objectives are to extract key issues, synthesize the mechanisms of emerging Learning Cities, and propose future development models for Learning Cities. The research methodology includes extracting data from 36 projects supported by the Program Management Unit on Area Based Development (PMU A) during 2021-2022 in 29 cities. Using Text Network Analysis and processing it with LLM, the study identifies the connections between critical factors used by researchers, such as city component structures and driving mechanisms in areas connected to synthesized knowledge. The study identified six models and mechanisms of LC projects as follows: 1) City for urban development, 2) City for the development of learning and innovation spaces, 3) City for fostering participation and developing Learning City networks, 4) City for developing personnel in Learning City innovation, 5) City for research and development, and 6) City for knowledge exchange, learning, and cooperation among Learning Cities. This research demonstrates that using Large Language Models (LLM) can jointly explain human opinions and large-scale data systems, elucidating the Learning City phenomenon from various situations. This leads to conclusions and recommendations for implementation steps that can inform future policies.