A Systematic Review of AI Usage for Educational Content in Medium and Low Resource Contexts: Global Overview and Case of Madagascar

Background: Artificial intelligence (AI), particularly natural language processing (NLP) and deep learning, is increasingly used in education. However, its application to educational content generation in low-resource contexts remains insufficiently studied, especially in environments characterized by data scarcity, low-resource languages, and limited computational infrastructure, such as Madagascar. Objective: This systematic review provides a global overview of research on AI usage for educational content in low-resource contexts and examines Madagascar as a focused case study to identify dominant approaches, contextual constraints, and research gaps. Methods: Following PRISMA guidelines, studies published between 2010 and 2024 were retrieved from major scientific databases, including IEEE Xplore, ACM Digital Library, SpringerLink, Elsevier, and arXiv. Selected works focus on AI-driven educational content creation, adaptation, or personalization in low-resource environments. A qualitative synthesis was conducted to categorize methods, use cases, and contextual challenges. Results: The results indicate a predominance of NLP and deep learning methods, particularly transformer-based and large language models. Most studies target high-resource settings, with limited consideration of low-resource languages and infrastructures. Key challenges include data scarcity, high computational costs, and insufficient pedagogical evaluation. Lightweight models, prompt-based learning, black-box LLMs, and human-in-the-loop strategies emerge as promising directions. Conclusions: This review reveals a clear gap in AI solutions tailored to low-resource educational contexts. It highlights the need for frugal, adaptable, and pedagogically grounded AI approaches. By combining a global overview with a case analysis of Madagascar, this review provides a structured foundation for developing context-aware AI strategies tailored to low-resource educational systems.