Collaborative Artificial Intelligence Literacy Framework for Sustainable Regional Digital Transformation
Year: 2026
Type: conference-paper
Abstract: Sustainable regional digital transformation requires cross-sector collaboration to build enduring artificial intelligence capabilities across diverse communities. This study investigates how multi-stakeholder initiatives translate local artificial intelligence education into regional capability through the AI Ignition Road program in North Sulawesi, Indonesia. Using a qualitative case study design, data were collected from eight semi-structured interviews with ecosystem actors, trainers, high school students, and micro, small, and medium enterprise representatives, alongside two official statements from national and provincial government leaders. Data were processed through reflexive thematic analysis using ATLAS.ti version 26 to code participant experiences and establish relationships across governance, learning, and empowerment dimensions. The analysis identified five structural themes: educational leadership and artificial intelligence capacity building, collaborative governance, multi-stakeholder engagement, community empowerment, and regional innovation. These empirical relationships ground the proposed Collaborative AI Literacy Ecosystem framework, which structures multi-sector governance into distinct learning and application pathways. The results demonstrate how practice-based artificial intelligence instruction supports tailored outcomes including future-skills readiness for students, community knowledge diffusion, and enhanced enterprise productivity for local businesses. Regional policy administrators and educational stakeholders can implement this four-layer framework to coordinate public-private resources, align educational outreach with regional economic needs, and sustain long-term digital capacity building.
Institutions: Institut Pemerintahan Dalam Negeri, Politeknik Negeri Manado
Language: English
Subfield: Education
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7225201508