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Artificial Intelligence (AI), fueled by recent advances in large language models, is now moving at a breakneck pace and transforming into a multi-billion-dollar industry. In education specifically, the global AI market is estimated at around 7 billion dollars in 2025, with an expected annual growth rate of more than 36% over the next decade. The challenge, then, is how education systems will harness these technologies in ways that truly serve students and teachers. Studies show that about 30% of teachers use Generative AI (GAI) on a weekly basis. In Greece, the situation looks different. When asked whether they use GAI in preparing their lessons, nearly half of Greek teachers reported that they never use it, while around 13% said they use it weekly or more often (see Figure 1). This suggests that we are still at the beginning. Technology promises a lot, but time, appropriate tools, and training are needed for it to become a meaningful part of everyday school life. 

One application of GAI is assisting teachers in grading. The rationale is that having an initial evaluation from AI can help a teacher reach accurate and consistent grading in less time. Such tools already exist. A recent study of ours, however, shows that teachers should be cautious since the tools are not necessarily error-free. In research we conducted in Greece with Professor Rigissa Megalokonomou from Monash University and Dr. Panagiotis Sotirakopoulos from Curtin University, we found that teachers were more likely to leave grading mistakes uncorrected when these appeared as recommendations from an AI system, compared to identical mistakes presented as human-made. These failures of oversight demonstrate that while AI promises to help teachers, training and awareness are also required so that human oversight of the technology ensures that the pitfalls of uncritical acceptance are avoided. 

Another area of AI application in education is academic and career guidance. Together with Professor Faidra Monachou and Ph.D. candidate Hemanshu Das from Yale University, we carried out an experiment with Greek high school students, examining how they respond to academic counseling advice presented either as coming from a professional advisor or from AI. Results showed that 73% of students report being willing to use algorithmic recommendation systems for their college application. However, students differ considerably in how they approach advice from such tools. The critical factor for adopting recommendations is not the perceived ability of the system but trust in its intentions. 

This shows that leveraging AI to guide young people is not simply a matter of technological advancement, but equally a matter of trust and presentation. Our studies teach us that AI in education is not only about technical ability but primarily about human trust and the perception of what the “machine” is doing. Students follow advice when they trust the intentions of the one giving it, while teachers often show excessive tolerance of mistakes from a system they consider objective. In other words, what is needed is not only better algorithms, but an understanding of how humans interact with them. And this interaction, as both international studies and our experience in Greece show, is shaped by the social and cultural context. Concepts like creativity, trust, or fairness are not universal but differ from country to country. Therefore, the use of AI in Greek education cannot simply be copied from other systems but must be based on Greek data and take into account our culture, so that it reflects our own needs and priorities. For this to happen, continued research on Greek ground is essential. 

Furthermore, a regulatory framework based on local experience is more likely to foster citizens’ trust and ensure that the new technology operates fairly and with respect for our. Greece has a wealth of human capital working and conducting research on AI, both within the country and in the international academic and business community. This “knowledge reservoir” can support public dialogue and contribute to shaping rules and practices that reflect the needs of Greek education. If properly utilized, it can serve as a bridge between technological innovation and our cultural specificities, ensuring that AI develops in ways that reinforce rather than undermine the work of teachers. 

The challenge—and the invitation—is not to fear AI, but to tailor it to our needs so that it becomes a true ally of our schools. 

Sofoklis Goulas is an economist and works as an Associate Research Scholar at Yale University in the United States. He has held research positions at the Brookings Institution, Stanford University, and the World Bank. He is a Research Affiliate of the Institute for the Study of Labor (IZA) in Bonn and a Research Fellow of the Foundation for Economic and Industrial Research (ΙΟΒΕ) in Athens. He earned his Ph.D. from the University of North Carolina at Chapel Hill on a Fulbright Scholarship. 

Everywhere you look, someone is telling students and workers to “learn AI.” 

It’s become the go-to advice for staying employable, relevant and prepared for the future. But here’s the problem: While definitions of artificial intelligence literacy are starting to emerge, we still lack a consistent, measurable framework to know whether someone is truly ready to use AI effectively and responsibly. 

And that is becoming a serious issue for education and workforce systems already being reshaped by AI. Schools and colleges are redesigning their entire curriculums. Companies are rewriting job descriptions. States are launching AI-focused initiatives.  

Yet we’re missing a foundational step: agreeing not only on what we mean by AI literacy, but on how we assess it in practice. 

Two major recent developments underscore why this step matters, and why it is important that we find a way to take it before urging students to use AI. First, the U.S. Department of Education released its proposed priorities for advancing AI in education, guidance that will ultimately shape how federal grants will support K-12 and higher education. For the first time, we now have a proposed federal definition of AI literacy: the technical knowledge, durable skills and future-ready attitudes required to thrive in a world influenced by AI. Such literacy will enable learners to engage and create with, manage and design AI, while critically evaluating its benefits, risks and implications. 

Second, we now have the White House’s American AI Action Plan, a broader national strategy aimed at strengthening the country’s leadership in artificial intelligence. Education and workforce development are central to the plan. 

Related: A lot goes on in classrooms from kindergarten to high school. Keep up with our free weekly newsletter on K-12 education. 

What both efforts share is a recognition that AI is not just a technological shift, it’s a human one. In many ways, the most important AI literacy skills are not about AI itself, but about the human capacities needed to use AI wisely. 

Sadly, the consequences of shallow AI education are already visible in workplaces. Some 55 percent of managers believe their employees are AI-proficient, while only 43 percent of employees share that confidence, according to the 2025 ETS Human Progress Report.  

One can say that the same perception gap exists between school administrators and teachers. The disconnect creates risks for organizations and reveals how assumptions about AI literacy can diverge sharply from reality. 

But if we’re going to build AI literacy into every level of learning, we have to ask the harder question: How do we both determine when someone is truly AI literate and assess it in ways that are fair, useful and scalable? 

AI literacy may be new, but we don’t have to start from scratch to measure it. We’ve tackled challenges like this before, moving beyond check-the-box tests in digital literacy to capture deeper, real-world skills. Building on those lessons will help define and measure this next evolution of 21st-century skills. 

Right now, we often treat AI literacy as a binary: You either “have it” or you don’t. But real AI literacy and readiness is more nuanced. It includes understanding how AI works, being able to use it effectively in real-world settings and knowing when to trust it. It includes writing effective prompts, spotting bias, asking hard questions and applying judgment. 

This isn’t just about teaching coding or issuing a certificate. It’s about making sure that students, educators and workers can collaborate in and navigate a world in which AI is increasingly involved in how we learn, hire, communicate and make decisions.  

Without a way to measure AI literacy, we can’t identify who needs support. We can’t track progress. And we risk letting a new kind of unfairness take root, in which some communities build real capacity with AI and others are left with shallow exposure and no feedback. 

Related: To employers, AI skills aren’t just for tech majors anymore 

What can education leaders do right now to address this issue? I have a few ideas.  

First, we need a working definition of AI literacy that goes beyond tool usage. The Department of Education’s proposed definition is a good start, combining technical fluency, applied reasoning and ethical awareness.  

Second, assessments of AI literacy should be integrated into curriculum design. Schools and colleges incorporating AI into coursework need clear definitions of proficiency. TeachAI’s AI Literacy Framework for Primary and Secondary Education is a great resource. 

Third, AI proficiency must be defined and measured consistently, or we risk a mismatched state of literacy. Without consistent measurements and standards, one district may see AI literacy as just using ChatGPT, while another defines it far more broadly, leaving students unevenly ready for the next generation of jobs. 

To prepare for an AI-driven future, defining and measuring AI literacy must be a priority. Every student will be graduating into a world in which AI literacy is essential. Human resources leaders confirmed in the 2025 ETS Human Progress Report that the No. 1 skill employers are demanding today is AI literacy. Without measurement, we risk building the future on assumptions, not readiness.  

And that’s too shaky a foundation for the stakes ahead. 

Amit Sevak is CEO of ETS, the largest private educational assessment organization in the world. 

Contact the opinion editor at opinion@hechingerreport.org. 

This story about AI literacy was produced by The Hechinger Report, a nonprofit, independent news organization focused on inequality and innovation in education. Sign up for Hechinger’s weekly newsletter.