What the screen time debate in schools gets wrong about EdTech

The screen-time debate has reached the classroom. Global policies might appear to be moving in opposite directions, because we still haven't agreed how much screen time in schools is appropriate for children. Look closer, however, and debates about this topic reveal a deeper transition – and a changing relationship with educational technology (EdTech).

This development can be understood as a shift from "EdTech 1.0", to "EdTech 2.0", and now "EdTech 3.0".

EdTech 1.0 prioritized engagement like clicks. EdTech 2.0 accounted for evidence before deploying tools. EdTech 3.0 is using AI to deliver personalized education for better outcomes. Countries, districts and institutions are starting from very different evidence baselines, research capacities and digital infrastructures, so the transition from 1.0 to 3.0 has been uneven.

France, with the framework of deliberate digital use, has extended its prohibition on mobile phones and other connected devices through the age of 18. Similarly, Sweden has moved towards mobile-free schools and pursued “more reading time and less screen time”.

Meanwhile, in Malawi, the government is scaling up technology use through BEFIT (Building Education Foundations through Innovation and Technology) across approximately 6,000 public primary schools, targeting around 3.8 million children. And Colombia’s Conexión Total strategy treats internet connectivity as essential for equitable access to educational resources including e-books, educational video and interactive platforms.

These contrasting positions can be seen as different responses to “EdTech 1.0” approaches.

EdTech 1.0 imported into education much of the logic of the attention economy developed around social media and consumer technology. It prioritized scale, access and engagement, with clicks, taps and time-on-screen too often treated as proxies for meaningful participation or learning.

An EdTech 1.0 product may be as simple as an e-book or as sophisticated as a teacher-directed AI application; what defines it is not the technology itself, but the absence of robust evidence that its use adds educational value. The governmental push against EdTech in some contexts is not simply a backlash against screens, but a response to the limitations of allowing technologies to scale in schools before establishing whether, for whom, and under what conditions they improve learning.

In August 2026, the US Department of Education issued new guidance urging states, districts and schools to distinguish between technologies that distract from learning and those that demonstrably support it. Procurement teams were compelled to ask: does the tool serve a clear instructional purpose, and is that purpose backed by independent evidence? Is its use transparent to parents, minimised where unnecessary, and reviewed over time against student outcomes?

What may look like new guidance in the US is closer to business as usual in many lower-resource education systems, where digitization without a documented pathway to improved learning is difficult to justify in the first place.

Malawi is a clear example of this. Onebillion, an adaptive literacy and numeracy programme for Standards 1–4 that includes instruction in the national language Chichewa, began working in Malawi in 2013. It has since been supported by evidence from randomized controlled trials, broader impact evaluations and research involving children with special educational needs. The government is thus scaling what works.

Meanwhile Colombian investment in digital learning is accompanied by building stronger mechanisms for monitoring and research through the Colombian Observatory of Educational Management, whose explicit mandate is to strengthen the capacity to design, implement and evaluate education policy with innovations.

Some school systems build on established research infrastructures and shared definitions of evidence. Others are constructing these foundations in real time, often through locally developed frameworks and external consultancy. The progression from EdTech 1.0 to EdTech 2.0 – where it's crucial that each technology has an evidence-based approach to positive education outcomes before it is adopted – has therefore not been linear. It has been made up of multiple transitions occurring at different speeds, and with very different levels of methodological maturity.

When the EdTech evidence ecosystem is immature, there is a risk that evidence models evolve towards pay-to-participate systems – where comparable products are evaluated through hurried pilots, and rigorous evaluation often begins only after technologies have already been designed, procured through long-term contracts, and deployed in classrooms. As demand for evidence grows, so is the proliferation of evidence labels, frameworks and certifications – sometimes grounded in robust independent evaluation, but at other times generated by developers themselves or through methodologies of uncertain quality.

The result is a crowded and difficult-to-navigate market where distinguishing between marketing claims, self-described EdTech expertise, product-level research and genuinely robust evidence of impact remains unnecessarily difficult.

Indeed, in the EdTech 2.0 era of evidence-based solutions, the challenge has become building the research, development and evaluation infrastructure that makes evidence substantive and consequential.

The discussions at this year's UNESCO Digital Learning Week revealed that countries are moving towards EdTech 3.0 – an AI-powered, personalized and outcome-driven form of learning. The technologies involved are increasingly global, potentially scaling across borders far faster than local capacity to evaluate their educational value. The countries best placed to benefit are therefore those with strong public capacity for judging that value.

UNESCO’s framing of educational technology as a public good also raises a deeper question: what ecosystem is being built around EdTech, and in whose interests does it operate?

As private AI investment grows and scientific capacity shifts into firms that both study and commercialize the technologies, independent, public-interest research is becoming more important just as it is becoming more constrained. Under these conditions, building a robust evidence base becomes harder, particularly as evaluation methodologies must also evolve while demand for independent research continues to grow.

The shift from EdTech 1.0 to EdTech 2.0 and 3.0 is a story of competing logics coexisting in the same system. A technology may embody EdTech 3.0 principles, while still being procured through EdTech 2.0 structures.

The task now is to move EdTech 3.0 from isolated practice to a system architecture that aligns the incentives, standards and institutions that determine which technologies get designed, tested, purchased and allowed to scale.

EdTech 3.0 requires a stronger ecosystem where learning scientists, educators, developers and policymakers work together from the outset, designing around children's holistic outcomes rather than test scores alone – and testing anticipated impact before large-scale deployment.

No country is there yet.