Everywhere you see, someone is telling students and workers to “learn AI.”
It became a go-to advice for being employable, relevant and ready for the future. But here's the problem. The definition of artificial intelligence literacy is beginning to emerge, but there is still no consistent, measurable framework for knowing whether someone is really ready to use AI responsibly.
And it is becoming a serious problem for the education and workforce systems already being reshaped by AI. Schools and universities are redesigning their entire curriculum. Companies are rewriting job descriptions. The state is launching an AI-centric initiative.
But we're missing out on the basic steps: not just agreeing what It means AI literacy, how we evaluation that actually.
Two recent major developments highlight why this step is important and why it is important to find ways to take it before encouraging students to use AI. First, the US Department of Education has announced proposed priorities to advance AI in education. This is the guidance that will ultimately shape how federal grants support K-12 and higher education. For the first time, a federal definition of AI literacy has been proposed. Technical knowledge, durable skills, and the future attitude needed to thrive in an AI-influenced world. This literacy allows learners to engage and create, manage and design with AI, critically assessing its benefits, risks and meaning.
Secondly, we currently have the White House American AI Action Plan. This is a broader national strategy aimed at strengthening national leadership in artificial intelligence. Education and workforce development are central to planning.
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What both efforts share is the perception that AI is human, not merely technological change. In many ways, the most important AI literacy skills are not about AI itself, but about the human abilities needed to use AI wisely.
Sadly, the results of shallow AI education are already seen in the workplace. According to the 2025 ETS Human Progress Report, around 55% of managers think their employees are rich in AI, but only 43% of employees share that trust.
It can be said that there is a similar perception gap between school administrators and teachers. This disconnect creates risk for the organization and reveals how assumptions about AI literacy can change dramatically from reality.
However, if you are trying to build AI literacy for all levels of learning, you have to ask more difficult questions. How do you determine that someone is actually literate in AI and evaluate it in a fair, useful and scalable way?
AI literacy may be new, but you don't need to start from scratch to measure it. We have previously tackled these challenges. Beyond the digital literacy checkbox test, we captured deeper real-world skills. Based on these lessons, we will help you define and measure this next evolution of 21st century skills.
Now we often treat AI literacy as binary. You “have” or not. But true AI literacy and preparation are even more subtle. This includes understanding how AI works, knowing how effective it can be used in a real setting, and when to trust it. It includes creating effective prompts, finding biases, asking tough questions and applying judgements.
This is not just about coding guidance and certificates. It's about enabling students, educators and workers to collaborate and navigate a world where AI is increasingly involved in the ways we learn, hire, communicate and make decisions.
Without a way to measure AI literacy, it is not possible to identify who needs support. I can't track progress. And we risk rooting a new kind of injustice. There, some communities build real capabilities with AI, while others leave no shallow exposure and feedback.
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What can education leaders do now to address this issue? There are a few ideas.
First, there is a practical definition of AI literacy beyond the use of tools. The Ministry of Education's proposed definition is a good start, combining technical flow ency, applied reasoning and ethical awareness.
Second, the evaluation of AI literacy needs to be integrated into curriculum design. Schools and universities that incorporate AI into their coursework require a clear definition of proficiency. Teachai's AI literacy framework for primary and secondary education is a great resource.
Third, AI proficiency needs to be consistently defined and measured. Alternatively, you should risk a state of literacy inconsistency. Without consistent measurements and standards, one district might consider AI literacy to be merely using ChatGpt, while another district defines it much broadly, with students unevenly prepared for the next generation of work.
Defining and measuring AI literacy must be a priority to prepare for an AI-driven future. All students graduate into a world where AI literacy is essential. Human Resources leader confirmed in the 2025 ETS Human Progress Report what one skilled employer today is asking for today. Without measurement, we risk building our future with assumptions rather than preparation.
And it's too unstable and the foundation of future stakes.
Amit Sevak is the CEO of ETS, the world's largest private education assessment organization.
Please contact the opinion editor at Opinion@hechingerreport.org.
This story about AI literacy was created by Hekinger Reporta nonprofit, independent news organization focusing on education inequality and innovation. Sign up for Hechinger's Weekly Newsletter.
