Artificial intelligence has introduced an unusual problem into education.
Schools need to prepare students to use a technology that is becoming increasingly common outside the classroom. But preparing students to use AI well may also require teaching them not to use it automatically.
That distinction matters.
Educators profiled by Education Week this summer described introducing elementary students to AI under close supervision while emphasizing its limits as well as its capabilities. One teacher offered a particularly simple rule: AI can help, but students need to think first.
That may be one of the more useful ways to understand AI literacy.
The first wave of generative AI discussion in education frequently centered on access. Could students use ChatGPT? Should teachers use AI to generate lessons? What policies should schools write?
The next questions are harder.
When does AI strengthen learning? When does it remove productive struggle? When does convenience begin replacing cognition?
Education Week recently framed a similar distinction through questions students can ask themselves before turning to AI: Is its use permitted for the assignment? Is this something they would reasonably ask a teacher for help with, or are they asking the system to do the work for them? And can they verify what it produces?
The underlying principle is less about mastering a tool than developing judgment about when and how to use one.
That distinction is also appearing in emerging AI literacy frameworks.
The OECD and European Commission’s 2026 AI Literacy Framework for Primary and Secondary Education describes AI literacy as more than knowing how to operate AI systems. It encompasses understanding how AI works, critically evaluating its outputs, using it ethically and creatively, and making informed decisions about the risks and opportunities associated with its use.
The framework also emphasizes critical judgment, learner agency, and the ability to determine whether, when and how AI should be used, including recognizing situations in which not using AI is the more responsible choice.
Prompting still matters. It is part of learning how to communicate effectively with an AI system.
But prompting is an operational skill.
Judgment determines when that skill should be used at all.
What Happens When AI Enters the Instructional Process?
Early research suggests those distinctions cannot be ignored.
The Hechinger Report recently examined one of the first randomized classroom studies of an AI teaching assistant. The experiment involved 193 teachers and more than 2,800 middle and high school students in a private school network in Turkey. Some teachers received access to a customized ChatGPT-based assistant aligned with the national curriculum. They primarily used the system to create materials such as lecture notes, assignments, and exams.
The results were more complicated than a simple story about AI helping or hurting instruction.
Students whose teachers had access to the customized AI assistant rated their classes as slightly less enjoyable, interesting, and important. Average academic performance did not decline overall. However, among students of teachers whose classes had posted lower performance before the experiment, achievement and confidence declined. The researchers used those prior results as a proxy for teacher performance.
The researchers could not determine exactly why.
One possibility raised by lead author Alp Sungu is that educators may differ in how much they revise, contextualize, and build upon AI-generated materials before using them with students. A teacher might treat generated content as a first draft requiring substantial professional judgment, for example, while another might rely more heavily on the initial output.
The experiment did not directly observe that process, however, so it remains a possible explanation rather than a demonstrated cause.
There are other important limitations.
The study is dated June 25, 2026 and had not yet been published in a peer-reviewed journal when Hechinger reported on it. It also was not a pure comparison between AI-assisted teaching and AI-free teaching. Educators in the control group remained free to use other AI tools as part of their normal practice.
Because the experiment took place within one private-school network in Turkey, the findings also should not be generalized directly to other education systems or classroom settings without further research.
One study should not determine the future of AI in education.
But it raises an important question: Does easier access to generated instructional material necessarily translate into better instruction?
The ability to produce material quickly and the ability to improve learning are not necessarily the same thing.
AI Use Is Moving Faster Than Formal Preparation
Higher education is confronting the issue from another direction.
An Instructure survey reported by Higher Ed Dive found that 90% of surveyed higher education students said they use AI in class at least occasionally, compared with 61% of surveyed higher education instructors.
Among higher education educators who answered the training question, 11% reported receiving comprehensive AI training, while 41% reported receiving no formal training.
Nearly two-thirds of surveyed higher education students and educators also expressed concern about AI systems confidently producing inaccurate information.
In other words, the survey suggests that AI use is moving faster than formal educator preparation, while respondents continue to call for clearer institutional guidance and support.
That creates an opportunity for schools, universities and education companies to think differently about what AI literacy requires.
Students certainly need to understand how to interact with AI systems, construct useful prompts, evaluate outputs, protect personal information and identify hallucinations or bias.
But they also need something less technological: judgment.
They need to know when to ask AI for assistance and when the struggle of solving something themselves is the learning.
They need to recognize when a system is helping them communicate an idea and when it is generating the idea for them.
They need enough understanding of the technology to question its authority rather than mistake fluent language for accurate information.
And they need to recognize that choosing not to use AI can itself be a responsible and informed decision.
Educators Need the Same Judgment
Those expectations cannot fall entirely on students.
Educators need parallel skills.
AI training cannot stop at showing teachers which buttons to press or which prompts generate lesson plans. It also has to help them evaluate when automation supports instructional quality, when human revision is necessary and when a task should remain human from the beginning.
The distinction becomes particularly important as AI capabilities are incorporated into broader education technology workflows.
Teachers may not always encounter AI through a standalone chatbot. AI capabilities can be embedded within the education platforms and workflows they already use, including tools that support content creation, feedback and other instructional tasks.
In those environments, AI literacy becomes partly a question of professional judgment: What has the system generated? What evidence supports it? What assumptions shaped the output? And what still requires a teacher’s expertise?
That is a different kind of technological fluency.
For education providers, it may become increasingly important as well.
Products incorporating AI may be judged not simply by how much work they automate, but by whether their design preserves meaningful opportunities for reasoning, verification and educator decision-making.
For providers, making the boundaries between human and machine contributions visible may therefore become an increasingly important design, trust and implementation consideration.
Assistance Is Not the Same as Substitution
Perhaps the most important AI skill schools can teach, then, is not fluency with a particular platform.
Platforms will change.
The more durable skill is understanding the boundary between assistance and substitution.
Education has encountered new tools before.
Calculators did not eliminate mathematics. Search engines did not eliminate research. Word processors did not eliminate writing.
But each changed what educators needed students to understand about the intellectual work surrounding the tool.
AI will likely do the same.
The goal should not be to produce students who never use artificial intelligence. Nor should it be to produce students who instinctively reach for it whenever a task becomes difficult.
The goal is to develop students who can recognize the difference between the moments when AI extends their thinking and the moments when it begins doing the thinking for them.
That may be the real foundation of AI literacy.
Simba Information continues to track these developments across the education and instructional technology landscape through our Education Market Advisor newsletter. Request access today.
About the Author
Martha Scharping is Senior Education Analyst and Content Strategist at Simba Information, where she leads coverage of K–12, higher education, and workforce-aligned learning markets. She brings prior experience as an educator into her analysis, grounding market intelligence and policy signals in firsthand classroom and instructional practice. Her work integrates market data, policy analysis, and teaching-informed insight to translate system-level shifts, including AI adoption, credential evolution, and work-based learning expansion, into clear strategic guidance for education publishers, EdTech companies, and institutional leaders. She focuses on how trends reshape incentives, operating models, and measurable outcomes over time.
