Opportunities and barriers of artificial intelligence in transforming education in developing regions: a systematic scoping review

Artificial Intelligence (AI) is revolutionizing education globally, offering innovative solutions to persistent challenges while driving transformative practices. In developing regions, educational systems often grapple with limited resources, overcrowded classrooms, and teacher shortages. This systematic scoping review synthesizes evidence from 29 eligible studies (drawn from an initial pool of 2,102 records) to assess AI’s effectiveness, barriers, emerging innovations, ethical implications, and best practices for AI integration in developing region education systems. AI-powered tools, particularly adaptive learning systems, intelligent tutoring platforms, and generative AI (e.g., ChatGPT), demonstrate positive effects on personalized learning, teacher efficiency, and student engagement in 18 of 29 studies (62%). However, technical barriers (infrastructure deficits, high costs, limited internet access) were reported in 26% of studies, ethical and human challenges (data privacy, algorithmic bias, resistance to change) in 22%, and educational system challenges (lack of teacher training, outdated curricula) in 19%. Only 5 of 29 studies (17%) reported outcomes beyond a single academic term, and none employed randomized controlled designs, indicating a weak evidence base for long-term effectiveness. Emerging innovations such as generative AI for culturally relevant content creation and virtual labs for experiential STEM education show promise for addressing resource constraints, but remain underutilized (reported in only 14.8% of studies). Region-specific insights reveal that low-resource settings prioritize basic access tools (voice assistants, mobile platforms), while higher-resource developing regions focus on personalized learning systems. AI holds transformative potential for education in developing regions, but realizing this potential requires substantial investments in infrastructure, teacher training, culturally localized AI tools, and robust ethical frameworks. Without context-specific, inclusive strategies, AI risks deepening existing educational inequities rather than alleviating them.