AI in Education: Why We Need Transformation, Not Just Improvement
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Highlights
We are educating for a world that no longer exists: AI is being layered onto systems designed for a pre-digital era, even as evidence shows technology is already reshaping cognition, learning patterns, and mental health.
Incremental improvement misses the scale of the challenge: using AI to optimize existing practices cannot address the learning crisis, inequality, or relevance; only system-level transformation can.
Future value depends on strong foundations plus human judgment: education must prioritize general skills that enable complex, “messy” work—where execution, reasoning, and adaptability remain beyond AI.
We stand at a rare moment in history: a chance to fundamentally reimagine education for a world that technology has already transformed.
This is not a distant future: it is the reality we are living now and there is no way back. Recent work, including the book Artificial Intelligence and Education in the Global South, brings this urgency into sharp focus, reminding us that the central question is no longer whether AI belongs in education.
Technology has already reshaped how we learn, think, and work. The real opportunity lies in understanding what this moment demands of us: not incremental improvements to existing systems, but genuine transformation—one that prepares students for work and life in an AI-augmented world. Here's what the latest research tells us about getting this right. We're not simply adding AI to education. We're introducing it into a world already fundamentally reshaped by technology—and that distinction matters profoundly.
The Reality We're Already Living
Consider how dramatically our world has shifted. In the 1930s, we spent most of our time with family and friends. Today, we spend 60% of our time online. Misha Rubin’s animation illustrates the evolution of how humans spend their time between 1930 and 2024. Recent research reveals that infant screen exposure (children 0-2) has lasting impacts on brain development and adolescent mental health, with higher infant screen time showing accelerated maturation of brain networks. This acceleration isn't beneficial—certain brain networks develop too quickly, before establishing the efficient connections needed for complex thinking, potentially limiting flexibility and resilience later in life.
For adolescents navigating critical prefrontal cortex development, the effects of social media, cyberbullying, and isolation are equally concerning. And a 2025 MIT study demonstrated that over-reliance on AI tools for cognitive tasks creates what researchers call "cognitive debt": reduced neural engagement, impaired memory recall, and weaker sense of essay ownership among students who used AI support to develop essays.
Improvement vs. Transformation
This context demands more than incremental change. Educational improvement—using AI to make existing practices slightly more efficient or support teachers in current frameworks—is insufficient. These improvements are localized, difficult to scale, and fundamentally maintain the status quo.
What we need is educational transformation: systemic change that addresses our most fundamental challenges—the learning crisis, inequality, and relevance—at scale and sustainably. Transformation requires complete systemic alignment across curriculum, teaching methods, assessment, and governance.
Simply put: improvement optimizes parts of the system. Transformation changes how the system works.
Balancing Foundations and Futures
The key lies in understanding how human capital actually develops. A 2023 paper, "Deconstructing Human Capital to Construct Hierarchical Nestedness" analyzed U.S. occupational data and revealed that human capital is hierarchically structured, not flat. The research identifies two types of specialized skills:
- Un-nested specialized skills can be acquired without a strong general foundation, but they offer limited economic returns.
- Nested specialized skills build upon robust general capabilities. These are associated with career progression and significant wage premiums. Premium workers don't simply pile up narrow skills; they deepen general competencies with strategic specializations.
The conclusion is clear: to construct valuable specialized skills, workers and economies must first invest in strong general skills. Specialization without this foundation delivers weaker economic returns.
The Choice Between Single-Task and Messy Jobs
Professor Luis Garicano offers a complementary insight about the future of work. We face a fundamental choice between two job paths:
Single-task jobs are increasingly vulnerable. AI excels at automating well-defined, single tasks. While humans remain in the loop today due to error rates that still persist in many fields (preventing unsupervised AI), those errors are decreasing rapidly.
Messy jobs, those combining multiple tasks, judgment, local knowledge, relationships, and real-world execution, are far more resilient. AI doesn't thrive at these kinds of tasks.
Garicano's conclusion: take the messy job, where learning, judgment, and execution matter, because that's where humans will retain value and gain leverage in an AI-driven economy.
What This Means for Education
If we want AI to augment humans rather than replace them, we must balance foundational learning and core competencies with emerging specialized skills. Students need strong general skills as the foundation for developing nested specialized capabilities that lead to resilient, complex work.
AI commoditizes codified knowledge, but it doesn't replace execution, coordination, empathy, political navigation, or tacit knowledge. These uniquely human capabilities flourish in environments that demand judgment, synthesis, and adaptive thinking.
The Path Forward
This isn't about making our current education system work slightly better. It's about fundamentally rethinking what education means in a world where technology has already transformed how we think, learn, and work.
The question isn't whether to use AI in education. It's whether we'll transform our educational systems to prepare students for a world where their value lies not in what they know, but in how they think, connect, adapt, and execute in messy, complex, human contexts.
That's the transformation we need.
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We invite you to explore the IDB’s report on Artificial Intelligence and Education, that examines the role of artificial intelligence through the lens of what we already know from decades of digital education. Across Latin America and the Caribbean, teachers and school leaders are asking the same question: How can we use artificial intelligence to help every student learn better? In this blog we share how 193 real AI initiatives are already transforming teaching, inclusion, and school management, and what it means for the future of education.
Also, discover how teachers across the region are already integrating AI into their classrooms — based on new data from CIMA Note #37, drawn from the international TALIS 2024 survey.
