Unequal Beginnings: Artificial Intelligence and Latin America’s Educational Divide
This essay from Harvard’s DRCLAS ReVista makes a straightforward claim: in a region where educational inequality is already structural, AI is more likely to accelerate existing gaps than magically erase them. The author frames AI as a “multiplier” — adaptive tutors, automated feedback, and language tools could help teachers and students, but only for the schools that can actually access them.
The piece argues the bigger risk isn’t just hardware or bandwidth. It’s an “epistemic divide”: who learns the new skills that matter in an AI-mediated world (questioning outputs, spotting model failure modes, refining prompts, and turning ideas into working digital artifacts). Without those skills, communities don’t just fall behind economically — they lose agency over how information is created and contested.
From there, the recommendations are intentionally practical: treat AI literacy as a basic educational right; invest first in the most under-resourced schools; train teachers who are currently being asked to adopt tools without support; and build more local AI ecosystems so the region isn’t only consuming imported tech. It’s a useful lens for evaluating any “AI in education” rollout: if it doesn’t start with equity, it can end up institutionalizing inequality.