A persistent idea in education and development is that access leads to opportunity.

For decades, discussions about the digital divide largely revolved around access. Who had a computer? Who had Internet? Who was connected and who was not? The solution seemed straightforward: build infrastructure, expand connectivity, and place technology into people’s hands.

These questions still matter. Around the world, access to digital technologies remains uneven and deeply shaped by geography, wealth, language, and politics. Yet, as artificial intelligence dominates educational debate, I wonder whether we are once again misdiagnosing the problem.

What if the digital divide was never primarily about technology?

Having worked at the intersection of media literacy, educational technology, and international development, I have watched the divide evolve from computers to broadband, mobile phones, and now AI. Each shift arrived accompanied by familiar promises that connectivity would expand opportunity, participation, and empowerment.

Those promises were not entirely wrong. New technologies have created meaningful opportunities for learning, creativity, and connection.

But access alone has never been enough.

Years ago, while teaching in Sri Lanka, I watched students learn to navigate digital platforms and social media environments. Yet, what struck me was how unevenly they interpreted what they encountered. Access to information did not automatically become critical engagement. The challenge was never simply technological. It was social, cultural, and educational.

Looking back, I cared more about how students made sense of technology.

That concern resurfaced while listening across FreshEd conversations on AI, datafication, digital infrastructure, and higher education. Though these discussions differed, they shared a common thread: the most important educational questions were rarely about technology itself. They were about how people understand, interpret, and respond to the systems increasingly shaping educational life.

In her discussion of students in an AI world, Mary Burns ultimately raises questions about literacy. The issue is not simply whether students can use AI systems but whether they can understand their limitations, assumptions, and consequences.

Other FreshEd conversations reinforce this concern. Discussions with Nelli Piattoeva, Ezekiel Dixon-Román, Noah W. Sobe, Ben Williamson, and Janja Komljenovic all point toward the same conclusion: educational institutions increasingly operate through systems of measurement, prediction, digital infrastructure, and analytics that shape educational experience.

Taken together, these conversations suggest that the central educational challenge before us may not be technological access. It may be interpretation.

By interpretation, I mean the ability to make sense of the systems that increasingly shape how we learn, what we know, and whose knowledge counts.

Definitions of literacy have never been fixed. Over time, literacy has expanded beyond reading and writing to include the ability to evaluate information, navigate media environments, and create meaning across increasingly complex systems. AI pushes this evolution even further. If literacy once meant learning to read texts, literacy today increasingly requires learning to read systems.

Literacy has always been more than decoding symbols on a page. It is about making meaning, asking questions, and recognizing that every text reflects particular choices, values, and assumptions.

A student may know how to generate text with an AI chatbot. That does not necessarily mean they understand how training data shape responses, how algorithms privilege certain forms of knowledge, or how commercial interests influence technological development. Using a tool is not the same as understanding the environment in which it operates.

The danger is not that students will use AI. The danger is that educational systems increasingly normalize forms of automated authority that students are never taught to question. Access without interpretive capacity increasingly produces a different form of exclusion.

Interpretation begins with a habit that schools too often neglect: wondering.

Why did this system produce this result?

What information is missing?

Whose voices are absent?

Who benefits from this arrangement?

These are not merely technical questions. They are educational ones.

Interpretation is a form of agency. People who can interpret systems are better positioned to question them, recognize bias, challenge assumptions, understand how decisions are made, and shape the environments they inhabit rather than merely adapting to them. Others are increasingly asked to trust systems they do not understand

Ultimately, this raises a larger question about the purpose of education. If education is primarily about preparing students to use new tools, then AI becomes another competency to master. But if education is also about democratic participation, critical citizenship, and judgment, then students need opportunities not merely to use systems but to question them.

This is not an argument against AI or technological innovation. New technologies create new possibilities alongside new challenges. The problem arises when discussions of technological change eclipse discussions of power, governance, and literacy.

For decades, we have often treated technological access as synonymous with progress. Yet, access alone has never guaranteed empowerment.

If literacy today increasingly requires learning to read systems, then educational institutions have a responsibility to respond accordingly. Critical digital literacy, media literacy, and meaningful inquiry are becoming foundational.

The digital divide has not disappeared. It has shifted. Increasingly, it lives in the distance between using a system and understanding it—between accepting an output and questioning its assumptions, between participation and agency.

August 1, 2026