AI in Education is not the Revolution We Think It Is: A View from Latin American Classrooms

Publication Date: 22/04/2026

Publication Number: 42026 - Type: Academic Journalism

AI is often framed as a global educational revolution, a rising tide expected to transform classrooms everywhere (UNESCO, 2019). Personalised learning, instant feedback, adaptive systems: the promise sounds universal. Tools like Knewton, DreamBox and Khan Academy have become shorthand for that promise, designed to adjust to each learner’s pace and close educational gaps at scale. In practice, that sense of universality rests more on assumption than on evidence. A closer look at public schools across Latin America reveals a more uneven and complex reality.

The global discourse on AI in education is substantial. Organisations such as UNESCO (2023) describe their potential to expand access to quality learning, reduce teacher workload, and personalise instruction at scale. EdTech companies and policymakers echo similar expectations (Pedro et al., 2019), presenting AI as a way to close longstanding educational gaps. Those gaps are not abstract: they include the persistent divide between students who reach higher education and those who drop out before secondary school ends, the distance between well-resourced urban schools and underfunded rural ones, and the unequal outcomes that consistently follow socioeconomic lines. The contrast is important because it shapes how we evaluate any tool that claims to address them. 

Governments are drafting national strategies, and universities are beginning to adjust their curricula to include AI literacy, data skills, and computational thinking. Whether this constitutes genuine adaptation is debatable. In many cases, the institutional response resembles rebranding more than structural reform: the same content, delivered through newer platforms, without addressing the conditions that produce inequality in the first place.  The momentum reflects a real possibility, and one that makes institutional investment feel urgent.

A different picture emerges when the focus shifts to who can actually participate in this transformation. In many Latin American public schools, the conditions are limited from the start. Internet access is often unstable, devices are shared, and funding constraints shape everyday practice (Pedro et al., 2019). Research mapping the obstacles to AI adoption in developing countries identifies hardware availability, internet reliability, and data costs as primary barriers, none of which are incidental or easily resolved (Nye, 2015, as cited in Pedro et al., 2019).  In a classroom where five students work around a single device, tools designed for individual, high-connectivity use lose much of their intended value.

Infrastructure is only part of the issue. AI tools evolve faster than professional development systems can respond, and many teachers are expected to adapt without sufficient training, time, or institutional support. This raises a question that neither governments nor EdTech companies have answered adequately: who is actually responsible for closing that gap? 

Public education systems in Latin America have historically underfunded teacher development, and the arrival of AI has not changed that pattern. Governments tend to frame digital transformation as a priority while allocating resources that reflect the opposite. Meanwhile, the companies producing these tools design them for contexts that bear little resemblance to an Argentine or Colombian public school, and rarely invest in the pedagogical scaffolding that would make adoption meaningful.  

The result produces a dynamic that is rarely discussed openly: students often arrive in the classroom with more practical familiarity with an AI tool than their teachers. A teenager who uses ChatGPT daily to draft assignments or translate text operates with a fluency that no institutional training programme has yet equipped their teacher to match or contextualise. This does not empower the student. It creates a gap in authority and guidance at precisely the moment when critical, informed use of these tools matters most. When a teacher cannot evaluate what a tool does or how it shapes the output, they cannot teach students to question it either. The classroom, rather than becoming a space of critical engagement with AI, risks becoming one where the technology simply runs unchecked, normalised but not understood by anyone responsible for the learning that takes place there. 

 These expectations tend to fall, in the end, on educators who already work across multiple schools and carry significant workloads across multiple schools, with little time and even less institutional acknowledgement of what is being asked of them. 

This does not suggest that teachers in the region are resistant to change. It points instead to a gap in how the global conversation is structured. Tools are often designed in one context and introduced into another, with limited attention to how they function in practice. As Selwyn (2016) argues, discussions around educational technology tend to reflect the priorities of those who design it rather than those who use it.

What remains difficult to reproduce through technology is the interpretive work that defines teaching in practice. Noticing when a student needs encouragement before they ask for it, adjusting pacing in response to the atmosphere of the classroom, or recognising the difference between memorisation and understanding. These are not marginal aspects of teaching; they are central to it. AI cannot replace this dimension of the profession, and attempts to frame it as an inefficiency to be automated misunderstand what teaching actually is (UNESCO, 2023). 

AI tools can support this work when they are implemented carefully. They can reduce administrative tasks and highlight patterns in student performance (UNESCO, 2023). Their value, however, depends on the presence of a critically engaged teacher. In contexts where that teacher is working with limited resources and time, even well-designed tools can become an additional burden rather than meaningful support.

A more useful question, then, is whether the conditions exist for AI to contribute to education equitably. Pedro et al. (2019) frame this directly: the least developed countries risk suffering new technological and social divides precisely because the infrastruccture and policy conditions that would make AI benefiial are not yet in place.  Before settling on any dominant narrative, it is worth considering which classrooms are being centred and which are left out. Educational innovation has always been shaped by context and relationships. AI does not change that premise. It simply exposes it.