Millions Learning’s latest research and commentary examine government decisionmaking on scaling education innovations, with a particular focus on education technology (edtech).
Research
From access to impact: Government decisionmaking and scaling of edtech in South and Southeast Asia
This research brief examines how governments in South and Southeast Asia make decisions about adopting and scaling edtech, based on a literature review and a dozen stakeholder interviews. It finds that decisionmaking in the region tends to prioritize motivation and feasibility over sustainability and evidence, risking greater inequity and fragmented implementation. The brief closes with six recommendations for decisionmakers, from designing for hardest-to-reach users to demanding impact data before scaling further.
Unlocking the potential of middle-tier education governance for scaling impact in low- and middle-income countries (French, Nepali, Russian)
This report, based on 90 interviews across El Salvador, the Kyrgyz Republic, Malawi, and Nepal, finds that mid-level education officials are typically excluded from decisionmaking and confined to compliance and data-reporting work, despite their being well-positioned to shape scaling decisions. It calls on central governments to formally share decisionmaking authority with the middle tier, so the unique positioning of these actors can better be leveraged to support identifying, adapting, and scaling innovations.
Read the report (also available in French, Nepali, and Russian).
Government decisionmaking on education in low- and middle-income countries: Understanding the fit among innovation, scaling strategy, and broader environment
This report identifies five dimensions shaping government decisionmaking in low- and middle-income countries—national politics, donor priorities, educational transfer and contextualization, the rise of edtech, and the lack of meaningful data—and analyzes how they shape demand, feasibility, and sustainability for scaling. It contends that successful scaling depends not just on an innovation’s design or the surrounding environment separately, but on the fit between the innovation, the scaling strategy, and the broader ecosystem.
Commentary
What do governments need to scale ed tech? Contextualization, evidence, and the middle tier. This commentary contends that government decisionmakers scaling ed tech often skip proper contextualization and lack useful evidence, leaving them vulnerable to global pressures, politics, and vendor marketing. It makes the case that district officials and pedagogical specialists—an underused layer of education systems that sits between central government and schools—are well positioned to gather relevant local evidence, tailor innovations to context, and support teachers during rollout.
Will AI in education succeed? This commentary argues that AI in education will succeed or fail based on the same conditions that shaped two decades of prior edtech: whether it supports rather than replaces teachers, whether infrastructure and equity gaps are addressed, and whether rollouts are rigorously evaluated rather than scaled on hype. Drawing on studies from India, Peru, Nigeria, and Ghana, the piece contends AI is a tool, not a standalone fix.
How to avoid past edtech pitfalls as we begin using AI to scale impact in education. This commentary draws on a decade of research to caution that AI in education risks repeating past edtech mistakes: overpromising quick fixes, skipping contextualization, and scaling without evidence. It outlines four pitfalls of over-relying on AI and suggests AI may hold more value supporting the work of scaling (like analyzing data or aiding contextualization) than as a solution to be scaled itself.
How to improve government decisionmaking around edtech innovations. This commentary, drawing on interviews with 10 government decisionmakers and 10 edtech academics and industry experts, finds that decisionmakers’ interest in edtech is driven mainly by pressure to “go digital,” donor priorities, signaling, and vendor marketing, not evidence of impact. It introduces a three-part framework to help decisionmakers evaluate innovations more systematically, arguing that data-informed decisions become possible only when all three factors are addressed together.