Digital Progress and Trends Report 2025 : Exploring AI’s Disruptive Promise for Education Systems in Low- and Middle-Income Countries - Case Study 1

Overview & Synopsis

Realizing developing countries’ demographic dividend requires investments in human capital through the provision of quality education. The benefits of demographic change for economic growth are mediated by educational attainment, with low-income countries standing to benefit the most. However, education systems across developing countries are constrained by poor infrastructure and resources, and, as a result, they often struggle to ensure they have enough trained teachers and quality teaching and learning materials. Qualitative research methods can shed light on how Artificial Intelligence (AI) is being adopted in education systems and its potential effects, given the rapidly evolving state of play. Through in-depth interviews and focus groups, the deep dive presented in this case study aims to uncover the implementation of, barriers to, and perceived effects of AI - especially GenAI tools - in education systems in India and Nigeria. This deep dive relies on in-depth interviews and focus-group-based, country-level consultations with a wide range of stakeholders - government officials, private sector individuals, civil society members, philanthropists, teachers, and school administrators. Consultations occurred between October 30, 2024, and February 2025 and lasted 45 to 120 minutes for in-depth interviews and 2 to 4 hours for focus groups and larger stakeholder consultations. In addition, 3 focus group discussions (6 to 12 people each) and 1 larger stakeholder consultation at an Indian educational institution with approximately 150 people were conducted. In India, interviews and focus groups occurred in Bengaluru, Gujarat (Gandhinagar), New Delhi, and Telangana (Hyderabad), with virtual consultations for Indian organizations based outside of these cities. In Nigeria, consultations centered on Abuja and Lagos, with virtual consultations for organizations based outside these cities. The AI tools described in this case study reflect strategic selection and practical constraints and surfaced through our consultations for the case study. This deep dive is structured in three sections. The first section offers a taxonomy of AI tools being implemented at scale across two countries, India and Nigeria, with an emphasis on the differences and nuances in the development and implementation of these tools in the context of developing countries. The second section surfaces key insights from the consultations in India and Nigeria focused on three key aspects: infrastructure; the policy and regulatory environment; and the perceptions of developers, educators, policy makers, and students regarding these tools in the AI ecosystem. Finally, considering these findings, the last section provides recommendations for policy makers across developing countries.