Aliyeva, Lale Rahman
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AI-Driven Economic Capacity Building and Digital Skills Development for Community Groups in Azerbaijan Ibrahimova, Konul; Aliyeva, Lale Rahman; Garayeva , Valida Togrul
SocietalServe: Journal of Community Engagement and Services Vol 2 No 2 (2025): Societal Serve: Journal of Community Engagement and Services
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/societalserve.v2i2.131

Abstract

This community engagement program aims to strengthen the economic capacity of community groups in Azerbaijan through an integrated model combining artificial intelligence (AI)–supported learning, digital skills enhancement, and practical economic training. Although Azerbaijan has demonstrated rapid digital progress, micro-entrepreneurs still face challenges related to limited digital literacy, insufficient financial planning capabilities, and the absence of structured mentorship that integrates emerging technologies. This program utilizes a participatory development approach involving baseline assessment, AI-driven training modules, intensive workshops, and mentoring over two months. A total of 45 participants from three community clusters were included. Quantitative findings reveal a 63% increase in digital literacy scores and a 67% adoption rate of AI-supported tools for marketing and financial planning. Qualitative insights show improved confidence in business decision-making, better understanding of market dynamics, and enhanced practical skills in budgeting and content creation. Participants also reported early indicators of improved sales performance and customer engagement. The integration of AI tools into community-based capacity-building initiatives demonstrates high effectiveness in accelerating digital readiness and entrepreneurial resilience. The program concludes that structured AI-based interventions can significantly empower grassroots communities in emerging economies. Limitations include the small sample size and short mentoring duration. Future research should explore larger populations and long-term economic outcomes.