Model Pembelajaran Ekonomi Berbasis Artificial Intelligence dalam Keterbatasan Akses Digital: Tinjauan Literatur

Khofidatus Sukriya, Firman Firansya, Eka Naily Zakiyah, Lisa Rokhmani

Abstract


The advancement of artificial intelligence (AI) in education presents significant opportunities for adaptive and personalized learning. However, the digital divide remains a substantial barrier to equitable technology adoption, particularly in Indonesia's rural and remote areas. This study employs a Systematic Literature Review (SLR) approach to examine AI-based economics learning models and evaluate their applicability in contexts with limited digital access. A total of 20 peer-reviewed articles published between 2015 and 2025 were analyzed through thematic synthesis. Findings indicate three main AI-based learning models relevant to economics education: adaptive learning systems, learning analytics, and recommendation systems. Of these, blended and low-tech-compatible models demonstrate the greatest potential for implementation under infrastructure constraints. The study proposes an adaptation framework comprising four strategies: blended learning integration, low-tech AI utilization, alternative interactive media development, and context-sensitive policy alignment. These findings suggest that AI-enhanced economics education remains feasible without full digital infrastructure, provided that pedagogical adaptation and teacher capacity are prioritized.

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References


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