Cultivating Participatory Learning Ecologies: Social Network Analysis of Peer-Driven Learning Network

Aim/Purpose: This study examines the role of peer networks in promoting social learning to address Veo3 (a web-based artificial intelligence (AI) video generation tool) rather than common top-down formal learning interventions. Background: In a top-down formal learning intervention where the instructor is only present in a workshop, the teaching methods only outline how to write prompts. The instructor has little idea about the product or culture. This limitation hinders micro-entrepreneurs’ ability to create contextually appropriate advertising content while experimenting with or implementing Veo3 prompts. Methodology: A mixed-methods approach was utilized, integrating post-workshop retrospective interviews with SNA facilitated by Neo4j. The research included halal food craftsmen, participants from Kuningan, Indonesia, who participated in the Veo3 advertising workshop. Coding reliability was delivered through inter-coder agreement of α = 0.928. We used SNA metrics, such as betweenness centrality, eigenvector centrality, and Louvain community detection, to create a quantitative map of the network structure before and after the formal top-down workshop. Contribution: The primary contribution of this study lies in social learning theory, which provides empirical evidence that individuals acquire influence and knowledge through participation and collaboration in a peer-driven, participatory learning ecology rather than through a top-down pre-workshop learning ecology. Findings: The post-workshop peer-driven learning ecology encountered significant transformation in comparison to the pre-workshop learning ecology. The results support the following main hypothesis: (1) influence shifted from hierarchical figures (high betweenness) to active collaborators (high eigenvector centrality); (2) a core participatory sub-community emerged, while non-active participants were peripheral; and (3) this restructured network directly enabled sophisticated, iterative, and culturally grounded AI creation workflows among artisans. Recommendations for Practitioners: Learning designers must prioritize making “community share abilities” participatory design that requires peer interaction before central instruction. Practitioners should design collaborative tasks that generate practice-based network edges, connect learners directly to institutional resources, and monitor network health using centrality metrics to identify structural vulnerabilities. Recommendation for Researchers: Researchers need to examine the long-term impacts of these network structures on the resilience and cultural preservation of individual learners, as well as how this peer-driven learning ecology may enhance the workflows of advanced culturally grounded AI creation. Impact on Society: When micro-entrepreneurs use Veo3 as an AI tool to preserve their culture and promote products relevant to their audience, this approach encourages individuals’ computational empowerment. Future Research: In the future, researchers should (1) use longitudinal SNA to determine out how long peer-driven learning ecologies last and how they affect business results and cultural preservation by examining how incremental individuals’ computational empowerment affects them, (2) create more detailed SNA edge definitions to tell the difference between types of interaction (such as “help-seeking” and “co-creation”), and (3) set up standard procedures for turning qualitative data into network parameters so that studies can be compared.