Gain leverage in an AI-driven economy

AI in Education: Why We Need Transformation, Not Just Improvement

By Maria Mercedes Mateo-Berganza Diaz

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.

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The Opportunities and Challenges of Artificial Intelligence (AI) for Latin America and the Caribbean

Can Latin America and the Caribbean Unlock AI’s Potential While Mitigating Its Perils?

To fully capitalize on AI’s transformative power for Latin America and the Caribbean, policymakers, businesses, and societies must proactively address challenges related to ethics, workforce adaptation, cybersecurity, and global collaboration

By Eric Parrado

Artificial Intelligence (AI) has emerged as a disruptive force that promises to reshape economies across the globe.  With its ability to process vast amounts of data, learn from patterns, and make intelligent decisions, AI holds immense potential to revolutionize various sectors, increase productivity, and drive economic growth.  However, as we navigate this transformative era, it is crucial to carefully consider both the opportunities and challenges of AI for Latin America and the Caribbean.

AI technology offers several key features that can significantly impact our economies:

Enhanced Productivity and Efficiency.  AI’s ability to automate repetitive tasks and augment human capabilities can unlock substantial productivity gains.  By streamlining processes, optimizing resource allocation, and improving decision-making, AI has the potential to boost overall economic output.  Recent studies validates this empirically, showing that AI adoption is boosting firms’ productivity.

Job Creation and Skill Enhancement.  AI, when properly managed and regulated, has the potential to stimulate the growth of certain job types that complement technology.  While automation may render some roles obsolete, new opportunities will emerge in the fields of AI research, development, implementation, and maintenance.  In fact, recent evidence suggest that AI adoption correlates with growing vacancies in AI-related jobs.  Moreover, AI could allow workers to concentrate on tasks that require human interaction and emotional intelligence, where humans have a clear comparative advantage.

Improved Decision-Making.  AI-powered algorithms can process vast amounts of data and generate valuable insights for better decision-making.  From financial markets to supply chain management, AI’s ability to analyze complex patterns and predict outcomes can help businesses and governments optimize strategies, reduce risks, and allocate resources more effectively.

Innovation and Economic Growth.  AI constitutes not only an innovation by itself, but also has the potential of being “a new method of invention”.  It can spur innovation by enabling breakthroughs in various industries.  From healthcare and transportation to agriculture and energy, AI-driven advancements can lead to the development of new products, services, and business models, driving economic growth and competitiveness.

While the potential benefits are significant, there are crucial considerations to address to harness AI’s transformative power responsibly:

Ethical and Legal Concerns.  The use of AI raises ethical questions around privacy, bias, accountability, and transparency.  Clear guidelines and regulations must be established to ensure that AI systems are fair, transparent, and accountable, protecting individuals’ rights and fostering trust.

Cognitive Automation and the Decline of Labor.  As AI improves its ability to perform a wide range of cognitive tasks, many types of cognitive labor may be automated in the coming decades, particularly as the cost of implementing and maintaining algorithms continues to decrease.  This trend could have severe consequences on employment.  In fact, evidence already shows that recent technology innovations, for example robots, have reduced employment and wages.  If AI follows a similar trend, this could have profound consequences, especially in developing countries where labor is the primary factor of production.

Inequality.  Technological innovations do not automatically translate into shared prosperity.  Instead, they can sometimes lead to increased inequality, particularly if they show high automation potential.  This is partly because automation can increase returns to capital while reducing wages.  Therefore, it’s crucial for civil society to advocate for equitable regulations, including the taxation of AI firms, to ensure that technological innovations benefit everyone.

Governments and businesses should also prioritize training programs, so workers are equipped with the necessary skills for the AI-driven economy.  Efforts should be made to ensure a just, inclusive transition, minimizing the impact on vulnerable populations.

Bias and Inequities.  AI systems are only as unbiased as the data they are trained on.  If biased data is used to train AI algorithms, those can amplify and perpetuate existing social, economic, and racial biases.  This can result in unfair outcomes, such as discriminatory hiring practices or biased lending decisions, affecting marginalized groups and exacerbating inequality.  Ensuring fairness, transparency, and accountability in AI systems is crucial to mitigating these risks.

Concentration of Economic Power.  AI-driven advancements may concentrate economic power in the hands of a few dominant players.  Large technology companies that possess the resources to develop and deploy AI systems could gain a competitive advantage, potentially exacerbating existing inequalities.  Smaller businesses or industries without access to AI capabilities may struggle to keep up, leading to a consolidation of economic power in favor of those who can harness AI effectively.

Influence on Democratic Processes.  AI can potentially impact democratic processes, including elections and public discourse.  The use of AI-powered algorithms in targeted advertising or social media platforms can shape public opinion, potentially leading to echo chambers and the spread of misinformation.  This raises concerns about the integrity of democratic decision-making, the influence of AI-driven algorithms on political discourse, and the need for transparency and accountability in AI applications that affect public opinion.

Cybersecurity and Data Privacy.  As AI systems rely on vast amounts of data, safeguarding privacy and preventing unauthorized access become paramount. Protecting data infrastructure, implementing robust cybersecurity measures, and establishing comprehensive data protection frameworks are crucial to maintain public trust and mitigate risks.

Other recommendations, which should be tailored to specific national contexts, will also be crucial to maximizing AI’s advantages for Latin America and the Caribbean.

Foster Digital Infrastructure.  Invest in robust digital infrastructure, including high-speed internet access, to support the adoption and utilization of AI technologies.  This will enable businesses, organizations, and individuals to leverage the benefits of AI and participate in the digital economy effectively.

Promote AI Research and Development.  Allocate resources to support AI research and development in collaboration with academic institutions, private sector entities, and research centers.  Encouraging innovation and knowledge creation will contribute to the development of AI expertise within the countries of Latin America and the Caribbean and foster homegrown AI solutions.

Enhance Education and Skills Development.  Prioritize educational programs that equip individuals with the necessary skills for the AI-driven economy.  Foster partnerships between educational institutions and industry to develop AI-related curricula and provide training opportunities, ensuring a workforce capable of leveraging AI technologies effectively.

Support Entrepreneurship and Startups.  Create a conducive environment for AI startups and entrepreneurs by offering financial incentives, access to funding, and mentorship programs.  Foster innovation ecosystems and facilitate collaboration between startups, established businesses, and research institutions to drive AI-driven entrepreneurship.

Encourage Collaboration and Knowledge Sharing.  Promote collaboration among governments, academia, private sector entities, and civil society organizations at regional and international levels.  Facilitate knowledge sharing, exchange of experiences, and collaborative initiatives to address common challenges and leverage shared expertise in AI.  Establishing common ethical standards, sharing best practices, and promoting international cooperation can help address challenges such as data governance, intellectual property rights, and algorithmic transparency.

Invest in AI for Social Impact.  Encourage the application of AI in addressing social challenges, such as those in education, healthcare, food security, and poverty alleviation, among others.  Support AI-driven solutions that have a positive impact on marginalized communities, enhance public services, and promote inclusive development.

Artificial Intelligence has the potential to revolutionize economies, enhancing productivity, driving innovation, and creating new opportunities.  To fully capitalize on AI’s transformative power for Latin America and the Caribbean, policymakers, businesses, and societies must proactively address challenges related to ethics, workforce adaptation, cybersecurity, and global collaboration.  By doing so, they can shape a future where AI serves as a catalyst for inclusive economic growth, while upholding human values and safeguarding societal well-being.

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