Education Technology

Education technology has the potential to be a driving force for learning and productivity in developing countries, but the evidence also makes clear that realising this potential depends much more on how tools are used than on which tools are procured. Across settings, promising results occur when EdTech improves the clarity of instruction, enables more timely communication and support for teachers and parents, and creates regular opportunities for students to practice at the right level with feedback. Where these channels are weak, average impacts are small and highly variable, even when using state-of-the-art technologies.

Several areas stand out as especially promising. First, short, tightly scripted videos and worked examples can clarify exposition and model step-by-step problem solving. In multiple settings, complementing teacher instruction with such materials has raised test performance. High-quality examples reduce ambiguity, lower cognitive load, and—when immediately followed by guided practice—turn passive viewing into active processing.

A second area of promising EdTech is low-cost communication that makes coordination with teachers and parents easier. Messaging tools and simple dashboards can help administrators push lesson plans and bite-sized coaching to teachers, surface problems quickly, and keep classes focused on a small number of priority skills. The same channels allow schools to communicate with parents about attendance, assessment results, and weekly goals. In early childhood, phone-based outreach and messaging have encouraged more frequent caregiver-child interactions and, in some cases, reduced caregiver stress, suggesting that simple technologies can complement more intensive services where face-to-face capacity is limited.

The most consistent gains in EdTech appear to come from computer-assisted learning (CAL) that personalises practice. CAL systems that diagnose a student’s starting point, assign practice at that level, provide immediate feedback, and require mastery before advancing can approximate key features of one-to-one tutoring at much lower marginal cost. Students progress at their own pace, repeat items until mastered, and work on prerequisite skills that whole-class instruction may skip. Impacts are greatest when students spend sufficient time in the system and when tasks are well matched to the student’s current proficiency—conditions that are feasible to create but not automatic.

AI is best viewed, at least for now, as an extension of CAL rather than a separate category. Large-language-model-based assistants can offer richer explanations, interactive hinting, and adaptive questioning, while authoring practice items or summaries aligned to a student’s recent errors. Properly constrained, these capabilities could increase the quality of practice (deeper feedback, better targeting) and the quantity (more engaging, lower setup costs for teachers). But the same features carry risks: hallucinated or subtly wrong explanations, over-scaffolding that reduces productive struggle, and a shift from deliberate practice to conversational shortcuts. Without guardrails on content accuracy, alignment to curricular objectives, and metrics that prioritise mastery rather than mere interaction time, AI-augmented tools could plausibly reduce learning even as measured engagement rises. Thus, the policy question is not ‘AI or not’, but rather ‘what additional governance, assessment, and usage norms are required for AI to engage students productively and enhance learning rather than reduce it’.

A central policy challenge is implementation at scale. Many studies document that, absent direct oversight, teachers under-use or unevenly integrate CAL. Buy-in can be limited when programmes feel externally imposed, teacher training is brief or generic, devices are shared across grades, or schedules lack protected practice time. Average treatment effects often mask enormous heterogeneity across classrooms that maps closely to differences in exposure (minutes on task) and fit (working at the right level). Notably, some of the largest and most consistent impacts come from programmes that deploy ‘lab-in-charges’ or external facilitators who ensure fidelity: they timetable sessions, troubleshoot logins and hardware, keep students on task, and verify that students practice at their level. These models are more expensive, but they make transparent a key lesson for policy design: most of what drives impact is not the content license per se but the organisational capacity to secure regular, level-appropriate practice with feedback.

For governments and implementers aiming to scale using existing resources, without an external lab-in-charge, more research is needed to determine how to generate effective CAL usage. Some possibilities include: continuous training and feedback rather than one-off training; ensuring administrators and principals endorse and protect CAL time; and aligning incentives around dosage and mastery. It would also be useful to identify ways to reward teachers and schools that meet practice targets.

Encouragingly, much of the work to strengthen these levers can be tested quickly by focusing on ‘first-stage’ outcomes. A large share of the variation in downstream learning gains is explained by dosage and quality of practice. Programmes and researchers need not always run a year-long randomised trial to learn whether a change is promising. Within a few weeks, one can measure: (i) the share of scheduled sessions that actually occurred; (ii) minutes per student in the platform; (iii) the proportion of practice at an appropriate difficulty (neither trivial nor impossible); and (iv) simple progression metrics (e.g. items mastered, error rates falling on focal skills). When an intervention materially improves these first-stage metrics – e.g. reliably achieving at least 30 minutes per week or one dedicated math session, with most items at the student’s level – then a larger randomised evaluation of learning effects is justified. Conversely, if first-stage metrics do not move, it is unlikely that test scores will.

EdTech is not a substitute for teachers, but it can extend what teachers and schools can achieve when it strengthens clarity of instruction, feedback, and level-appropriate practice – and when systems protect time and attention for those processes. AI may accelerate progress on the same dimensions if deployed with guardrails that keep students thinking rather than merely interacting. The research pathway is equally clear: test whether implementation bundles raise exposure and progression in the short run; scale those that do into longer trials focused on learning; and continuously simplify supports so they can be sustained with existing resources. With that architecture in place, EdTech can move from sporadic success to a consistent contributor to learning and productivity in developing countries.

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