Speech Technology and Machine Learning for Inclusive Education in Developing Regions

This mixed-methods study investigated the potential of speech technology and machine learning to enhance inclusive education across rural India, sub-Saharan Africa, and Southeast Asia. A voice-based adaptive learning platform was developed and implemented, revealing significant improvements in language learning (d = 0.58) and basic numeracy (d = 0.42). While speech recognition accuracy varied across languages (WER 26.9%-47.2%), qualitative findings highlighted increased accessibility and cultural relevance. Challenges in infrastructure, teacher training, and policy integration underscore the need for continued investment in low-resource language technologies and supportive frameworks to fully realize the potential of these tools for democratizing education in developing regions. Ethical considerations, including data privacy and AI transparency, were addressed through a culturally sensitive framework. The study contributes to the growing body of literature on technology-enhanced learning in resource-constrained environments, demonstrating the efficacy of speech-based interfaces and adaptive algorithms in multilingual settings. Our findings indicate significant improvements in learning outcomes, with language learning showing the highest effect size. It also suggests that thoughtful implementation of these technologies can significantly democratize access to quality education in developing regions, while emphasizing the need for continued investment in low-resource language technologies and supportive policy frameworks.