From copier to tutor: how teachers can turn AI into a real learning tool

By Zendoric · June 28, 2026.

After years in which the conversation about AI in the classroom has swung between prohibition and permissiveness, a more interesting third way is beginning to make headway: deliberate pedagogical integration. An article published by The Hindu gathers a series of concrete strategies that teachers can apply so that their students not only use tools like ChatGPT, but use them well, with judgment and genuinely learning in the process.

The starting point is significant: the text does not debate whether students should or should not turn to AI. It takes for granted that they already do, and proposes that the teacher's role be to shape that practice. This is, in fact, the most realistic stance an education system can adopt in 2026. The question that deserves an answer is not "do I ban or allow?" but "how do I teach people to use this intelligently?"

The strategies described orbit around six axes. The first is prompt engineering, that is, teaching students to formulate precise questions: instructing the model to act as a senior developer, iteratively refining responses instead of accepting the first output, and understanding that a good result depends largely on the quality of the initial instruction. This is not a trick; it is a meta-competency transferable to any domain where communication with language systems is relevant, which in the coming decade will be practically everything.

The second axis is the use of AI for code debugging. Pasting an error message into ChatGPT and asking it to explain what went wrong and why is not the same as copying a solution: it amounts to having an interlocutor available 24 hours a day who can make explicit the logic behind the failure. If the teacher guides the exercise correctly, the student does not get an answer, they get an understanding of the error mechanism. The difference is pedagogically enormous.

The third and fourth axes, code explanation and optimization and Socratic interaction, go in the same direction: treating the model not as an oracle but as an interlocutor. Asking for line-by-line explanations, requesting pseudocode before final code to avoid "blank page syndrome," or generating practice quizzes on a concept like "scope in JavaScript" or "recursion" are uses that reinforce understanding rather than short-circuit it.

Where the approach gains greater depth is in the last two axes: critical evaluation and ethical use. The proposal that students generate a response with AI and then look for evidence to confirm or refute it through official documentation is, in essence, an exercise in epistemic literacy. Likewise, comparing a solution written by a human with one generated by AI to identify nuances, subtle errors or absent creativity trains precisely the critical eye that we most need to develop in the face of these systems.

Regarding ethical use, the proposed approach is pragmatic and not moralistic: clearly delimiting when AI is allowed (brainstorming, debugging) and when it is not (assessments of core competencies), and requiring students to keep a record of their interactions with the model, including the prompts used and the modifications made. The latter has a twofold pedagogical value: it fosters transparency and forces the student to reflect on their own learning process.

As sector context, this type of pedagogical framework is gaining traction in education systems around the world, but its actual implementation remains uneven. Many institutions have updated their acceptable-use policies without updating teacher training, which creates a gap between the written rule and actual classroom practice. The most successful approaches are those that, like the one described here, equip the teacher with concrete tools and not just abstract guidelines.

There is something structurally relevant in all this: the skills this approach trains—formulating precise questions, evaluating sources, iterating on results, documenting processes—are exactly the ones the technology labor market will value in the coming years. Not because AI is going to disappear, but because it is going to be everywhere and the ability to work with it critically and productively will become a real differentiator.

Teaching prompt engineering in a school context may sound like a minor detail, but it points to something bigger: redefining what it means to know something in a world where content generation is within anyone's reach. To know is no longer just to remember; it is to know how to ask, verify, contextualize and improve. Teachers who internalize this are not ceding ground to AI; they are teaching something that AI alone cannot teach.