{"id":52435,"date":"2026-10-06T13:51:27","date_gmt":"2026-10-06T11:51:27","guid":{"rendered":"https:\/\/impulseducacio.org\/?p=52435"},"modified":"2026-10-06T13:51:52","modified_gmt":"2026-10-06T11:51:52","slug":"critical-thinking-artificial-intelligence-classroom","status":"publish","type":"post","link":"https:\/\/impulseducacio.org\/en\/critical-thinking-artificial-intelligence-classroom\/","title":{"rendered":"From critical thinking to lateral reading: doubting is a procedure"},"content":{"rendered":"

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Nobody Doubts What They Do Not Know<\/h1><\/h1><\/div>
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6 October 2026<\/p>\n<\/div>

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From critical thinking to lateral reading: doubting is a procedure<\/h2>\n<\/div>
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<\/i>MORE INFO.<\/span><\/a><\/div><\/div><\/div>

Judgement Cannot Be Delegated \u00b7 6 of 12<\/p>\n<\/div><\/div><\/div><\/div>

It is commonly accepted that teenagers, who have grown up with a screen in their hand, notice straight away when a text has been written by a machine, and that it is enough to teach them to be a little more distrustful. It would be worth observing, however, what happens when they are really taught.<\/h5>\n<\/div>
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At a state secondary school in Catalonia, thirteen students in the first year of Bachillerato (Year 12) spent twelve sessions working on what generative artificial intelligence does and does not do. At the end, their teacher, Miquel \u00c0ngel Fuentes Arjona, a trainer in teachers\u2019 digital competence, asked them to write down what they thought they knew before and what they knew now. Before, they said that AI \u2018always told the truth\u2019 and that it was neutral \u2018because it is a machine\u2019. Afterwards, that it sounds convincing, that it is not capable of verifying what it says, and that it is no longer so easy to tell what it has generated. It was a retrospective task, in which each student recounts their own change, and not a measure of learning. But what had changed, by their own account, was not their distrust, it was their repertoire. (1)<\/p>\n<\/div>

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Distrusting is not a method<\/h3>\n

At the Colegio Juan de Lanuza in Zaragoza, students in Years 4 and 5 of primary school do an activity called \u2018Sentence Detectives\u2019: they are given twelve cards, six generated by an AI and six attributed to authors or historical figures, they classify them and explain which clues they are relying on. The assessment criterion set by its authors, a team of teachers coordinated by Cristian Ruiz Reinales, the school\u2019s head of technology, is what gives the whole activity its meaning: justifying matters more than getting it right. (2)<\/p>\n

This is not a kindly concession. It is a decision about what is being taught. Guessing the origin of a text is not a competence, because success does not depend on the student, but on how well or badly the forgery has been made. Justifying is a competence, and it can also be assessed.<\/p>\n

It is worth defining what we are talking about when we say critical thinking, since the term has become so broad that it serves for everything and therefore for nothing. A systematic review of fifty-two studies, coordinated by Iliana Guaranda-Arias at the State University of Milagro, describes it as a set of skills rather than as dispositions of character towards thinking well. Some of those operational skills are: analysing, evaluating evidence and arguing, to which are added identifying bias and justifying the pedagogical decision. (3) They are verbs, and together with the necessary attitudes of having an open mind, curiosity or the intention to seek the truth, they look more like intellectual virtues than mere dispositions. And verbs, and virtues, are educated and taught.<\/p>\n

An international panel of experts convened by researchers at Link\u00f6ping University, in Sweden, reached a consensus pointing in the same direction: to use AI responsibly students need to learn critical thinking, and critical thinking is the most important skill for examining bias, skewed data and deep fakes. (4)<\/p>\n

In the second article<\/a><\/span><\/span> in this series, \u2018No Warning Light\u2019, we saw that the fluency of a generated text does not guarantee its truth, because the system predicts words and does not check facts. In the fourth<\/a><\/span><\/span>, \u2018The Debt You Cannot See\u2019, that the more confidence the user places in the tool, the less scrutiny they deploy. Both left open the question this article asks: what exactly is to be done on Monday morning.<\/p>\n<\/div><\/div><\/div><\/div>

Guessing the origin of a text is not an assessable skill; justifying it with evidence is.<\/h6>\n<\/div><\/div><\/div>
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Doubt has foundations<\/h3>\n

Evaluating an AI answer about the Spanish Civil War requires knowing about the Spanish Civil War. It seems obvious, doesn\u2019t it? Well, the consequences are rather less so.<\/p>\n

In the discussion of their study, the Link\u00f6ping researchers offer a reading of that consensus that deserves attention. They take up the argument of the cognitive psychologist Daniel Willingham, according to whom critical thinking requires sufficient content knowledge, and they hold that the two categories into which their panel\u2019s consensus is grouped, foundational knowledge about AI and critical perspectives, reflect that interdependence: knowledge is the foundation upon which reasoning and critical perspectives are built.<\/p>\n

Alejandro Espeso-Garc\u00eda, of the Catholic University of Murcia, reaches the same conclusion by another route. Without a base of knowledge established in long-term memory that allows the algorithm to be audited and corrected, there is no real oversight, only the appearance of oversight. That is why he proposes leaving behind the model in which the person merely validates what the machine produces, and moving towards another in which they retain final authority. (5)
\nAnd the practical consequence is already set down, in so many words, in the AI literacy framework of Digital Promise, led by Kelly Mills: generative AI systems should not be used to learn new information about a topic, because they tend to include subtly wrong information. (6) They are useful for working on what is already known. Not for finding out what is not yet known.<\/p>\n

The conclusion that follows is ours, and it is worth saying so. AI does not make content knowledge dispensable. It makes it more necessary and changes its function: until now it served above all to produce answers, and now it also serves to audit them. A curriculum emptied of content because \u2018the machine looks that up now\u2019 leaves students without the instrument with which they might argue back at the machine.<\/p>\n<\/div><\/div><\/div><\/div>

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Prior knowledge in long-term memory is the only foundation capable of auditing the algorithm.<\/h6>\n<\/div><\/div><\/div><\/div>

Leaving the page<\/h3>\n

In 2019, Sam Wineburg, professor of education at Stanford University, and Sarah McGrew, then a doctoral student at the same school, published a study that every teaching staff should know. They sat forty-five expert internet users in front of a computer, ten historians with doctorates, ten professional fact checkers and twenty-five first-year students, and asked them to think aloud, with the screen recorded, while they evaluated websites on controversial issues. (7)<\/p>\n

One of the tasks consisted in assessing two articles on bullying from two organisations with almost identical names: the American Academy of Pediatrics, the largest association of paediatricians in the world, with sixty-six thousand members; and the American College of Pediatricians, a group that split off in 2002, with a few hundred members and a single full-time employee.<\/p>\n

Every fact checker considered the Academy\u2019s site more reliable. Sixty per cent of the students chose the splinter group\u2019s. The historians equivocated.<\/p>\n

What separated one group from the other was not reading better. It was reading less. The fact checkers left the page in about half a minute, opened new tabs and found out from outside who was behind what they had in front of them. The historians took almost three times as long to leave. The students, more than a minute and a half, and nine of the twenty-five never left at all. They learned more about a site precisely by leaving it early.<\/p>\n

One detail gives the measure of the problem. Seven of the ten historians pointed to the references at the end of the article as proof that the article was reliable. Among those ten references there was one to an online dictionary and two to news blogs.<\/p>\n

Wineburg and McGrew name three practices that explain the difference, and all three can be taught. Taking bearings: getting your orientation before going into unfamiliar content, like the walker who checks the compass before heading into the woods. Lateral reading: leaving the page and checking the source against what other sources say about it. And click restraint: reading the whole page of search results before clicking on the first one.<\/p>\n<\/div><\/div><\/div><\/div>

Verification requires leaving the page: opening external tabs to uncover who funds the source.<\/h6>\n<\/div><\/div><\/div>
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Practical application<\/h3>\n

In the classroom: the three-voices routine<\/strong>. The Colegio Juan de Lanuza project builds all its activities on a template that students repeat from primary school to Bachillerato: \u2018AI says, the source says, I conclude\u2019. It separates what the machine proposes from what can be checked and from what the person decides, and it leaves an assessable trail. In primary they discover and question; in the first and second years of ESO (Years 7 and 8) they experiment, compare and validate; in the third and fourth years (Years 9 and 10) they analyse and redesign; in Bachillerato they research and compare tools. (2)<\/p>\n

In the classroom: asking for explanations, not answers<\/strong>. The same team trains students to ask the machine for local explanations, about a specific case; selective ones, a single reason that helps them improve; and, in secondary, comparative ones, why this answer and not another. In primary it starts with the question the Year 5 students put to an AI that has solved a problem badly: at which step did you go wrong. (2)<\/p>\n

In the classroom: a lateral reading session<\/strong>. The CRAFT project at Stanford University, co-designed with teachers from several disciplines, publishes a sixty-minute lesson on how to fact-check what an AI answers, with a teacher\u2019s guide and a student worksheet, introducing lateral reading on the basis of Wineburg and McGrew\u2019s work. It is designed for Bachillerato and social sciences, and can be adapted to the fourth year of ESO (Year 10). (8)<\/p>\n

For the school: a whole-school verification standard<\/strong>. The indicators of the Digital Promise framework are written as observable behaviours and can be turned into a stage-wide agreement: analysing and synthesising multiple perspectives to support lateral reading, citing valid, reliable data and evidence, and evaluating the credibility of an answer. (6) Formulating them as a standard is our own proposal based on the framework, not an instrument taken from it.<\/p>\n

For the school: the teaching staff first.<\/strong> The Guaranda-Arias review indicates what teacher training should include here: case analysis, source verification, identification of bias and evaluation of AI-generated answers. With one caveat that its authors underline and that this blog is not going to hide: the relationship between AI literacy and teachers\u2019 critical thinking appears as a conceptual one, supported by studies that are mostly documentary or cross-sectional, and does not allow causality to be asserted. (3) The ninth article, \u2018What No One Can Learn on Their Own\u2019, will deal with this.<\/p>\n<\/div><\/div><\/div><\/div>

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The ‘three voices’ routine separates the algorithmic response from the student’s own conclusion.<\/h6>\n<\/div><\/div><\/div><\/div>

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To conclude<\/h3>\n

Schools do not have to teach students to distrust artificial intelligence. Distrust is learned on one\u2019s own, as soon as you have your first unpleasant surprise, and distrust without a method leads nowhere, because whoever suspects everything ends up believing anything. What schools have to teach is how to check. And checking requires knowing.<\/p>\n

Food for thought<\/strong>. If tomorrow a student hands in a piece of work that is correct, well written and false, what specific procedure have they been taught in order to notice? And in which subject were they taught it?<\/p>\n<\/div><\/div><\/div><\/div>

Distrust without structure is useless; education must provide rigorous verification methods.<\/h6>\n<\/div><\/div><\/div>
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Glossary<\/h4>\n