A Systematic Review of AI Usage for Educational Content in Low Resource Contexts: The 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 settings characterized by data scarcity, lowresource languages, and limited computational infrastructure, such as Madagascar.

Objective: This systematic review analyzes existing research on AI-based educational content in low-resource contexts, with a focus on Madagascar, in order to identify dominant approaches, application domains, limitations, and research gaps under realworld constraints.

Methods: Following PRISMA guidelines, studies published between 2015 and 2025 were retrieved from major scientific databases. including IEEE Xplore, ACM Digital Library, SpringerLink, Elsevier, and arXiv. Selected works focus on AIdriven 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-theloop 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, positioning Madagascar as a representative case for future research on inclusive and scalable AI-driven education.