Introduction
In the past decade, especially since COVID-19, interest in education technology (EdTech) has surged to expand learning, boost access, and improve education.
Despite rising investment, little evidence exists on how governments choose and scale digital innovations, the forces influencing decisions, and how scalability, political economy, and sustainability are considered. Filling this gap is vital for equitable, lasting learning improvements.
This brief addresses this gap by bringing together existing research on scaling the impact of promising or proven innovations (Brock 2026; Cooley and Linn 2024; Fullan and Pomfret 1977) with analysis on government decisionmaking around education reform in low- and middle-income countries (Carney and Klerides 2020; Kucirkova et al. 2026; Mundy 2007; Olsen 2023). Drawing on this work and interviews conducted with key stakeholders, the brief examines the forces informing decisions about EdTech in South and Southeast Asia and the constraints and enabling factors that shape the scaling process. This brief is written for decisionmakers in central and subnational government, local officials, funding representatives, private-sector EdTech providers, and researchers. It offers analysis organized around three dimensions—motivation, feasibility, and sustainability—and proposes recommendations for making clearer, better-informed decisions around scaling EdTech.
After reviewing key literature specific to the focal regions, conducting a dozen interviews with key stakeholders, and triangulating findings against a decade of our own research on scaling, the central takeaway is that decisionmaking about EdTech in these contexts often prioritizes motivation for and feasibility of scaling over sustainability and evidence. This runs a high risk of worsening existing inequities, contributing to policy and implementation fragmentation, and hindering the impact of scaling. But with greater intentionality around evidence and sustainability, scaling efforts can be designed so that the benefits of EdTech outweigh the risks.
Recommendations for decisionmakers
- Start with a focus on the hardest-to-reach locations and users. Design, select, and test innovations with low-resource, low-connectivity settings in mind from the beginning. Scale and equity are not tradeoffs; they complement each other.
- Focus on the purpose, not the product. Before adopting an EdTech innovation, clearly identify the intended change, how it will occur, and how the impact will be measured. Do not be afraid to decline innovations that do not fit this vision—especially popular ones.
- Strengthen institutional capacity as a core scaling strategy. Prioritize effective, ongoing professional development for teachers and local/middle-tier education officials. Foster in-house expertise across all levels of the system.
- Invest in contextualization of innovations. Collaborate with teachers, students, and parent organizations in the piloting, adaptation, and scaling of EdTech solutions. Encourage the development of innovations tailored specifically to the local context.
- Prioritize interoperability and system coherence at all levels. Ensure up–to–date national guidance exists on safe, effective, and inclusive EdTech adoption in schools, and that policies are regularly updated in response to new evidence and emerging technologies. Create clear structures for coordination across teams and departments.
- Fund, support, and demand data on the impact of EdTech, including its effectiveness, relevance, inclusion, and sustainability. Recognize when it is time to move on from an innovation that is not showing impact.
Authors
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Acknowledgements and disclosures
First and foremost, the authors would like to thank the government representatives, education specialists, and other interview participants who generously offered their time and perspectives on the topics covered in this report. The authors also wish to thank Eden Foster and Junjie Ren at the Brookings Institution for their contributions to this report. Appreciation is given to Emily Morris, Charlie Radman, and Rebecca Winthrop for thoughtful comments on earlier drafts.
This project is supported by the Asian Development Bank (ADB) through a Knowledge Partnership Agreement. The views expressed herein do not necessarily represent those of the ADB, its country teams, or its board of directors. Brookings is committed to quality, independence, and impact in all of its work. Activities supported by its donors reflect this commitment, and the analysis and recommendations are solely determined by the scholar.
Throughout the drafting of this report, the authors used generative AI tools such as Anthropic’s Claude and Microsoft’s Copilot. These tools were used, in limited cases, to summarize research articles, locate sources, visualize findings, as well as lightly edit and streamline earlier drafts. All outputs were reviewed by humans, revised for factual accuracy, and checked for plagiarism by the authors prior to publication. The AI tools did not contribute any original ideas or writing to the report.
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