Every day, students arrive in class with summaries compiled by ChatGPT or ideas outlined by a virtual assistant . Given this reality, an awkward question arises: what’s the point of meeting in class? The traditional classroom is losing its meaning as a place where what a machine can generate in seconds is repeated.

The challenge , then, is to stop asking ourselves “what am I going to teach today” and start asking ourselves “what are we going to build together?” How can we achieve this? Here are four concrete ways to transform the classroom into a laboratory where knowledge is built.

The hypothesis office

Let’s imagine that before class we ask students to use artificial intelligence (AI) to generate a thesis on the day’s topic. The task is not simply to accept it as valid, but to challenge it.

The dialogic challenge begins in the classroom. Class time is dedicated to students sharing what AI delivers and evaluating their interactions with these tools. Students discuss their results and evaluate together: Was this technology really useful? Which questions worked best? How did it help us correct errors and advance new questions?

The teacher invites the group to identify the weak points in the argument, the strongest counterarguments, potential logical fallacies, or information gaps. For example, if the AI generated a thesis that states: “The use of virtual reality accelerates students’ learning curve by 30% across all disciplines,” the students’ task is to question that claim. What evidence is presented for that 30%? Does it apply equally to all subjects? And to all ages?

Classroom work becomes a dialogue about meaning and bias, using an automatic response to go beyond it, questioning it, and exploring alternative sources.

The sessions are divided into 20-30 minute blocks: first, they share what they brought from home, then the group work challenge begins in the classroom, then they critique and reconstruct, and finally, they synthesize what they learned.

The fallacies workshop

The teacher launches a provocation on a controversial topic. For example: “Should recreational cannabis be legalized?” He asks students to use AI to generate arguments for and against. But here’s the interesting part: the classwork is to analyze the logic of those arguments and identify logical errors.

Students learn to detect ad hominem fallacies , undue appeals, or false causality. They contrast the artificial voice with their own critical judgment and construct more ethical and solid arguments based on scientific evidence.

The classroom ceases to be a space to receive answers to questions that the student has not asked, and becomes an environment to test and formulate the right questions .

The class becomes a rhetorical laboratory where logic, argumentative ethics, and the ability to not only analyze the way arguments are constructed, but also to dismantle artificial persuasions and discern between a well-constructed argument and simple persuasion, are trained.

Let’s consider the case of a Digital Law class. The teacher asks the AI the question: “Should facial recognition be allowed in public spaces to prevent crime?” The AI answers both in favor (“it increases security and deters crime”) and against (“it invades privacy and encourages mass surveillance”).

The teacher divides the class into small groups: while half analyzes the arguments for AI and reasons whether it is a hasty generalization (assuming that more surveillance always equals more security), the other half analyzes whether the argument against it commits a false dichotomy by suggesting that the only option is total surveillance.

They then collaboratively reconstruct both discourses, nuancing the arguments with key theoretical concepts such as the need for clear regulation, transparency, and the implementation of citizen audits. In this way, the classroom becomes a space to enrich critical thinking, using AI as a simple starting point.

The simulation room

AI allows complex scenarios to be created in real time, turning the classroom into a space for collective experimentation.

Let’s suppose the teacher proposes a real-life case study: an economic crisis, a bioethical dilemma, a political conflict. In small groups, students interact with the AI to simulate responses or develop alternative solutions. Meanwhile, the teacher guides the discussion with questions that delve into the complexity of the scenario: How does culture influence it? What ethical risks are being ignored? What does the artificial intelligence say that the real-life context doesn’t?

For example, in a marketing class , a group uses AI to simulate a product launch in an emerging market. What if the price is low? What if there’s local competition? The challenge is for students to discover that AI ignores crucial cultural factors, such as purchasing habits, the composition of the business community, or the cultural significance of certain colors.

Physically, the traditional front-facing row arrangement is eliminated, and the classroom is reorganized into small circles of 4-6 people. Ideally, each group shares a screen to show their interactions with the AI.

The creativity laboratory

AI is excellent at generating initial ideas, but the value emerges when those ideas are refined and evaluated collectively. In this scenario, we ask students to elicit several creative proposals from AI (e.g., for a public health campaign). In class, they must present the best option and justify their choice.

Then comes the challenge: they must defend an idea they didn’t choose, forcing them to shift perspectives and leave their comfort zones. This exercise reinforces skills that no AI can automate: argumentative empathy, collective judgment, and lateral thinking .

For example, for a water-saving campaign, the AI generates five proposals. A student chooses the most creative one, but must then defend the most practical one. The teacher leads the discussion, offering arguments about the originality, feasibility, or impact of each proposal.

A classroom that recovers its value

In all these formats, the work with AI begins before entering the classroom but continues in the classroom, reserving time for what is most valuable: dialogue, defending ideas, questioning, and creating together. The classroom becomes a place where students go not to “know,” but to “learn to want to know.”

Thus, the classroom not only survives artificial intelligence , but also recovers its value as a social space of collective intelligence. A place where knowledge is exchanged, ideas are challenged, and profoundly human capacities are trained.

This technological revolution reminds us of something fundamental: knowledge isn’t received, it’s built. And it isn’t built in isolation.

Author Bio: Rosa M. Rodríguez-Izquierdo is Professor of Education in the Department of Education and Social Psychology at Pablo de Olavide University