About the author: José Luis Granados Mateo is a former secondary-school and sixth-form STEM teacher, having taught physics, chemistry and mathematics. He is currently a postdoctoral researcher in the epistemology of science at the University of the Basque Country (UPV/EHU).
When I taught physics, chemistry and mathematics to secondary-school and sixth-form students, a correct answer was sometimes where the interesting part began. I would ask why they had used a particular equation. A solution that had looked perfectly sound could become much less convincing; occasionally the reverse happened, and a student whose algebra had gone wrong could explain the physics rather well. Nothing on the page had changed. My estimate of what the student understood had.
I have been thinking about those exchanges while reading the research on generative AI in education. Students can now submit a convincing explanation, solve a numerical problem or produce working code with considerable help from a language model. It is easy to see the achievement and much harder to work out what the student has learnt along the way.
That difficulty cannot be settled by appealing to the virtues of an unaided mind. Scientific work depends on instruments, notation, diagrams, software and other people. Delegating part of a calculation may be entirely sensible. Yet teaching has a complication that is easy to overlook from the perspective of an expert: the operation being delegated may be one of the things through which a beginner is meant to acquire expertise.
An experiment by Hamsa Bastani and colleagues brings this problem into unusually sharp focus.1 Nearly a thousand pupils at a secondary school in Turkey took part in a study comparing two GPT-4 systems with a control condition without AI. GPT Base offered assistance resembling a general-purpose chatbot. GPT Tutor had additional material from teachers, including solutions and common mistakes, and instructions to guide pupils with hints rather than give away the answer.
During practice, both systems helped. The estimated scores were 48% higher than the control mean with GPT Base and 127% higher with GPT Tutor. These are relative differences, not percentage-point gains. On the tests that followed, however, the pupils had to work without assistance. The GPT Base group then scored an estimated 17% below the control mean. GPT Tutor largely mitigated that penalty, but its users showed no statistically significant advantage over the control group. The tests took place within the study sessions, so they tell us about immediate independent performance rather than retention months later.
Had the researchers stopped at the practice exercises, they would have had an encouraging result for both systems. They did not stop there, and the educational interpretation changed. A pupil working with AI had become more successful at the task. Whether that success belonged to the pupil in any lasting sense was another matter.
Psychologists have long distinguished performance during acquisition from learning: a relatively durable change in knowledge or skill. Soderstrom and Bjork review the evidence for that distinction, including cases in which fluent practice leads to disappointing retention and others in which slower, more error-prone practice produces better results later.2 Watching someone learn, we inevitably see performance. To find out what remains, we need to look again after a delay, withdraw some of the support or ask for an application in a different context. Even then, what we have is evidence to interpret, not a direct view of understanding.
In STEM, much of the important work happens before the calculation. A mechanics problem requires a choice of system, a judgement about the relevant interactions and some representation of the situation. Chemistry involves moving between observable phenomena, molecular accounts and symbols on a page. A program that runs is the endpoint of decisions about what to separate, what to abstract and how to test it. Nersessian’s work on model-based reasoning gives representations a central place in scientific thought: constructing and revising a model can be part of arriving at an explanation, rather than merely a way of presenting one.3
A language model can help at almost any of these points. Perhaps the student has made the difficult modelling decisions and wants an algebraic check. Perhaps the model has chosen the principle, drawn up the representation, done the algebra and supplied an explanation before the student has formed a view of the problem. A questionnaire asking whether both students “used AI” would record the same answer.
There is an obvious temptation here to defend struggle. I am wary of it. Spending half an hour looking for a formula can be frustrating without being especially instructive, and a teacher who withholds every useful clue has not necessarily protected the student’s learning.
Worked examples offer a familiar counterexample. A novice studying a solution can attend to the structure of a procedure without having to search blindly for it. Barbieri and colleagues’ meta-analysis, covering 43 articles, 55 studies and 181 effect sizes, found a medium benefit for mathematics performance (g = 0.48).4 Removing some work can help a learner do other work more productively. The judgement lies in deciding what to remove.
Nor will the right decision remain right indefinitely. In a 2025 meta-analysis of the expertise reversal effect, Tetzlaff and colleagues brought together 60 experiments involving 5,924 participants and 176 effect sizes. Learners with little prior knowledge benefited from greater instructional assistance (d = 0.505); for those with high prior knowledge, lower-assistance conditions were preferable (d = −0.428).5 These are heterogeneous results across educational contexts, not estimates specifically for generative AI. Still, they help explain a familiar experience: an explanation that once made a procedure possible can later get in the way of carrying it out.
This is part of what Koedinger and colleagues call the assistance dilemma: when should instruction provide information, and when should it require the learner to generate it?6 With a conversational model, the practical difficulty is that help need never run out. Another prompt can produce another hint, or an answer, or a complete solution to a problem the student has scarcely begun to consider.
Pablo Rodríguez Sánchez raised a related question in Mapping Ignorance, in Why teach calculus in the age of AI. Software can compute many standard derivatives and integrals almost immediately, and AI interfaces make such tools easier to consult. That does change the case for teaching manual procedures. It does not dispose of it. Working through integration by parts can make its relation to the product rule visible; substitution can bring the chain rule into focus from the other direction. Some of the value of a procedure lies in the relationships a student encounters while carrying it out. Requiring the procedure without regard to those relationships would miss the point just as thoroughly as abandoning it because a machine is faster.
We already move parts of our thinking into the world. We write a number down so that we do not have to remember it, or set a reminder for something we expect to forget. Risko and Gilbert describe such actions as cognitive offloading: changing the processing demands of a task through physical action and external resources.7 Its consequences are worth studying, but the fact that someone has offloaded a task is not in itself evidence of cognitive decline.
For history and philosophy of science (HPS), this should be familiar ground. Giere and Moffatt describe scientific cognition as distributed across people and artefacts.8 On this account, instruments, representations and social arrangements can be parts of the system through which scientific knowledge is produced. A physicist who uses a computer to integrate a differential equation has not thereby revealed a failure to understand physics.
She does, though, need some grounds for trusting the result. She must know what calculation is appropriate, which assumptions it involves and when the output ought to make her suspicious. A beginner may still be acquiring those resources. If every mechanics problem arrives already represented and ready for calculation, there may be very little occasion to learn how to represent one.
“Which formula do I use?” was a question I heard often. Usually, in the cases I remember, the difficulty was not simply a forgotten equation. The student had not yet made sense of the situation in a way that gave any equation a purpose. Supplying the formula could rescue the exercise while leaving that difficulty untouched. A question about the forces, or about what was being treated as the system, could sometimes help much more.
The records from Bastani’s experiment are suggestive in this respect. GPT Base users more often requested answers, and the authors interpreted their interaction and error analyses as consistent with copying solutions. GPT Tutor elicited more attempted answers and requests for help. The surveys, administered at the end of the sessions, revealed a further complication. GPT Base users did not perceive the disadvantage visible in their unassisted scores; GPT Tutor users rated their performance more favourably even though no significant exam advantage had been detected.1 Students’ impressions were not a dependable guide to what the assistance had done for them.
But the evidence does not all point towards disappointment. In an introductory physics course at Harvard, Kestin and colleagues compared a purpose-built AI tutor with an established active-learning class.9 Their randomised crossover study covered two lessons, with students experiencing both conditions. The tutor incorporated scaffolding, targeted feedback and self-pacing. Median post-test scores were 4.5 in the AI condition and 3.5 in the classroom condition. Measured against the pooled pre-test median, the gain in the AI condition was more than twice as large. Seventy per cent of students spent less than the 60 minutes allocated to classroom learning on the AI activity; the median was 49 minutes. They also reported greater engagement and motivation.
That is an interesting result on its own terms. It concerns particular content, a carefully prepared tutor and tests taken after the lessons. The intervention also combined several differences, including the setting, pace and prerecorded introductions, so the study cannot tell us how much of the benefit came from the language model alone. Nor does it establish long-term retention. Within those limits, it is good reason to take the design of AI-supported instruction seriously. Efficiency and learning need not be enemies.
Programming provides a less reassuring case. Bassner and colleagues reported a three-arm randomised trial at the Technical University of Munich, with 275 introductory programming students in the reported sample.10 During a 90-minute exercise, students used either the scaffolded tutor Iris, unrestricted ChatGPT or conventional web resources without AI. Both AI groups achieved higher scores on automated tests of their code and reported less frustration. Neither showed a statistically significant advantage in knowledge gains or code comprehension. Only Iris showed an increase in intrinsic motivation. There was clearer evidence of better task performance than of better understanding, although the absence of a significant learning difference does not establish that the groups learnt exactly the same amount.
Three studies cannot carry a general theory of AI in education. Their students, tasks and measurements are too different, and their time horizons too short. What they do make difficult to ignore is the need to decide whose competence an assessment is measuring. If a tool remains available throughout, successful performance may tell us a great deal about the person–tool combination and rather less about a change in the person.
The larger reviews do not make this much easier. Boolzen and colleagues’ 2026 review identified 85 eligible controlled studies of cognitive outcomes in STEM; 49 studies, yielding 59 effect sizes, entered the meta-analysis.11 A conventional random-effects model gave a positive mean effect, alongside extraordinary heterogeneity (I² = 96.32%). Its prediction interval ran from g = −1.52 to g = 3.20. For comparable settings, the model therefore allows for effects extending from substantial harm to substantial benefit. It would be a mistake to read the positive average as a reliable forecast for a new classroom intervention.
The authors also found funnel-plot asymmetry, consistent with possible publication bias. Using a robust Bayesian adjustment, they obtained an overall estimate close to zero (μ = 0.076), with substantial uncertainty. The heterogeneity remained. Asymmetry need not be caused by publication bias, and the adjusted estimate depends on modelling assumptions; it is not a final answer about whether AI helps students learn.
Their moderator analysis is more helpful for thinking about instruction. They coded the learning outcome and the change in students’ activity relative to the control condition. The patterns favoured augmentation over substitution, but these were comparisons across studies, not randomised tests of those design choices. Much variation remained unexplained, and no combination of the examined moderators reliably supplied a sufficient condition for a large effect.11 The distinction is promising as a research question. We do not yet have a rule for applying it to every lesson.
Wu and colleagues’ 2026 meta-analysis sounds more optimistic: 35 studies, 4,193 participants and a positive mean effect of g = 0.670, with a 95% confidence interval of 0.495–0.844.12 The two reviews are not estimating quite the same thing. Wu’s synthesis ranges across disciplines and includes motivation, engagement and self-efficacy alongside cognitive outcomes. It also reports very high heterogeneity (I² = 91.444%). Its tests did not detect publication bias, which is different from establishing its absence. Most included studies concerned higher education.
I would hesitate to choose between those reviews on the strength of their averages. An answer-giving chatbot, a tutor that asks for explanations and a debugging assistant may use closely related models while organising the student’s activity very differently. Asking for “the effect of ChatGPT on learning” begins to sound like asking for the effect of books. We would immediately want to know which books, who was reading them and what they were being asked to do.
Meanwhile, students are getting on with using the tools. PISA 2025 cannot resolve the causal questions raised by the experiments, but it gives some sense of their scale. On average across OECD countries, 46% of 15-year-olds reported using AI chatbots at least weekly to help them learn. In Spain, the figure was 54%. The Spanish percentages reported for preliminary research, summarising assigned texts and drafting written work were 42%, 40% and 33%, respectively. Only 8% reported never or almost never using AI for any of the schoolwork purposes examined.13,14
The achievement patterns deserve attention, though they are easily overread. After adjustment for students’ socioeconomic profile, the OECD-average results generally favoured non-users over those reporting AI use for specific tasks such as drafting or summarising. Weekly use for the broader purpose of helping with learning was associated with roughly similar science performance to non-use. For some specific tasks, moderate users did better than very frequent users.13
A pupil who is struggling may seek more help. Schools may encourage AI use in response to difficulties that already exist. And frequency tells us very little about the encounter itself: requesting an answer, interrogating a hint and challenging an explanation can all count as using a chatbot. Those possibilities are not findings that explain away PISA’s associations; they are reasons why the associations cannot establish what AI caused.
PISA also examines opportunities to evaluate AI-generated information at school. Some descriptive patterns look favourable, but the overall positive association with that activity is not observed, on average, after adjustment for students’ and schools’ socioeconomic profiles.13 The report gives us no simple causal claim about the benefits of teaching students to check AI output.
There is nevertheless an educational question here that deserves more than a slogan. Mapping Ignorance has discussed it in Critical thinking for the unstoppable use of artificial intelligence. In STEM, asking a student to check an answer presupposes that something is available with which to check it. Does the student know what order of magnitude to expect? Which assumption matters? What evidence would bear on the explanation? A fluent paragraph can be physically impossible, but fluency alone will not tell a beginner where it goes wrong.
It is also worth keeping the chronology straight. Generative AI cannot be the sole explanation for declines in educational achievement that began before ChatGPT was publicly available. What PISA establishes more usefully here is that the practices under discussion are already part of ordinary schoolwork. Teachers have to make decisions while the evidence is still developing.
Some of those decisions will be quite small. A worked solution may be exactly what a pupil needs on Monday. A few weeks later, providing the whole solution may remove an opportunity to reconstruct the method. AI could make it easier to vary the support: ask for a representation before proposing an equation, invite a prediction before showing a simulation, or offer a flawed argument for the student to diagnose. These are plausible uses, not capabilities we should assume a system can deliver reliably simply because it holds a convincing conversation.
Assessment poses a related problem. Students will need to work competently with tools, as scientists and engineers do, and that competence deserves to be assessed. But if we also want to know whether they can perform a particular operation themselves, there must be an occasion when the tool is not doing it for them. We have to say what belongs in the learner’s own repertoire, and then find an assessment that gives us reasonable evidence of it.
For someone working in History and Philosophy of Science, the interest extends beyond the ownership of a correct answer. Scientific competence involves judgement about models, evidence, instruments and the reach of a procedure. A student may know a formula and still not know when it applies. An experienced scientist may quite reasonably delegate its numerical solution while retaining responsibility for the assumptions and interpretation. Learning to make those distinctions is part of becoming competent.
That brings me back to the classroom question. When I asked why a student had used an equation, I wanted access to the reasoning behind the calculation. With generative AI, the visible work may belong to a temporary partnership, and locating that reasoning becomes harder. The partnership itself need not be objectionable. What matters educationally is what repeated participation in it does to the student.
Over time, can the learner represent a problem more confidently, choose a method, recognise an implausible result or decide when help is needed? If so, the tool may be contributing to the acquisition of expertise. If those capacities scarcely change while the submitted answers become more polished, the same outward success has a different meaning.
I do not think the most useful question is whether students should use AI in STEM. It is what they should still be learning to do, how the assistance bears on that learning, and when it ought to recede. A correct answer was never a guarantee of understanding. We now have to be more deliberate about finding out what lies behind it.
References
The post AI, problem solving, and the future of STEM education appeared first on Mapping Ignorance.
Palliative care has long been framed as a specialist discipline reserved for hospices and oncology wards, but a growing body of evidence insists it belongs everywhere people approach the end of life, including nursing homes, where a large share of deaths in ageing societies now occur. A new qualitative study from Sweden, published in BMC Nursing, offers an unusually candid look at what happens inside care teams after they receive structured palliative care education, and the central finding is striking: training changes how staff think, but thinking alone does not change care. The researchers, Camilla Ekeblom, Carina Lundh Hagelin and Jane Österlind of Marie Cederschiöld University in Stockholm, found that the value of an educational intervention depends on an intricate alignment between individual emotions, relational dynamics and organisational realities, and that when any one of these dimensions falls out of step, the benefits of education can quietly dissolve.
The study set out to explore how nursing home staff reason about providing palliative care after participating in an interprofessional educational intervention, abbreviated in the literature as IPEI. Rather than measuring outcomes with questionnaires, the team chose a descriptive qualitative design, interviewing eighteen staff members drawn from five Swedish nursing homes. The participants represented the full spectrum of people who actually deliver and manage care: registered nurses, assistant nurses, a physician, first-line managers and designated palliative care champions. This breadth matters, because palliative care in nursing homes is not delivered by any single profession; it emerges, or fails to emerge, from the daily interplay of people with different training levels, responsibilities and emotional exposures to dying residents.
Data were collected through semi-structured individual interviews conducted between March and April 2024, and analysed using inductive qualitative content analysis, a method that allows themes to rise from the material itself rather than being forced into pre-existing theoretical boxes. From this analysis, three main categories emerged, and together they map the terrain on which palliative care is actually practised. The first concerns managing one’s own emotions, thoughts and attitudes. The second addresses the significance of relationships, communication and interactions in care. The third captures how staff navigate norms, assumptions and organisational structures in everyday palliative care. The categories correspond, respectively, to the individual, relational and organisational dimensions of the work, and the authors argue that it is the interplay among all three, not any single factor, that determines whether good palliative care becomes possible.
The individual dimension is perhaps the most human part of the findings. Staff described the ongoing internal work of confronting their own feelings about death, ageing and suffering, and of examining assumptions they carried about what palliative care even means. A recurring tension in the literature, echoed here, is the tendency to equate palliative care narrowly with end-of-life care, the final days and hours, rather than understanding it as an approach that can and should be integrated into everyday care over a longer trajectory. The interviews suggested that the educational intervention gave participants opportunities for personal as well as professional development, creating space to reflect on attitudes that had previously gone unexamined. In this sense, education functioned less like a knowledge transfer and more like a mirror, prompting staff to notice how their own emotional responses shaped their clinical decisions.
The relational dimension proved equally consequential. Palliative care in a nursing home is saturated with communication: conversations with residents about what matters to them, exchanges with distressed relatives, coordination between assistant nurses who know the residents intimately and nurses and physicians who carry clinical responsibility. The participants’ reasoning placed relationships and interactions at the heart of care provision, indicating that no amount of theoretical knowledge substitutes for the ability to build trust, listen and communicate across professional boundaries. This is where the interprofessional design of the intervention appears to have paid off. By bringing different professions into a shared educational space, the programme created a common language and, implicitly, a shared understanding of what the team was trying to achieve together, something that fragmented, profession-specific training often fails to produce.
The third dimension, the organisational, is where the study’s message becomes most uncomfortable for policymakers and care home leadership. Staff described navigating norms and assumptions embedded in their workplaces, along with structural conditions that could either support or obstruct a palliative approach. Staffing levels, routines, leadership priorities and workplace culture all shape what is practically possible on a shift where residents are dying, families are anxious and time is scarce. The authors’ conclusion is explicit on this point: providing palliative care was not solely a matter of enhancing staff knowledge. Knowledge and the conditions for care are shaped through the interplay between staff attitudes, their reasoning about care, relational dynamics and organisational conditions. When these dimensions align and mutually reinforce one another, favourable conditions for palliative care may be supported; when they do not, even well-trained staff may find their intentions frustrated.
Why does this matter on a societal scale? Nursing homes accommodate older people with complex needs and account for a large proportion of deaths, which makes palliative care a central component of care in these settings rather than an occasional emergency. Yet access to palliative care remains unequal, particularly for older people, and staff in long-term care often lack sufficient training. The demographic arithmetic is unforgiving: as populations age, the number of people who will die in nursing homes rises, and the quality of their final months depends heavily on the competence and confidence of the workforce that cares for them. Studies like this one move the conversation beyond the simplistic assumption that a training course fixes the problem, and toward a systems view in which education is necessary but insufficient on its own.
The study also carries a methodological lesson for how we evaluate educational interventions in care settings. Much of the existing research measures knowledge gains or self-reported confidence before and after training. Those metrics are useful, but they cannot reveal how staff actually reason about applying what they have learned in the messy reality of daily care. By using inductive content analysis of interviews, the Swedish team surfaced the texture of that reasoning: the hesitations, the emotional labour, the negotiations with colleagues and managers, the quiet adjustments between ideal practice and what the shift allows. The findings suggest that interprofessional educational initiatives work best when they combine theoretical knowledge with opportunities for reflection and practical application, allowing staff to translate concepts into their own working context rather than leaving translation to chance.
There are limits worth noting. The study involved eighteen participants in five Swedish nursing homes, interviewed at a single point in time after the intervention, and qualitative findings are not designed to be statistically generalised. The researchers themselves submitted the work for ethical review, receiving approval from the Swedish Ethical Review Authority, and obtained written informed consent from all participants, but the transferability of the results to other health systems will depend on how similar those systems’ nursing homes and educational programmes are to the Swedish context. Still, the three-dimensional framework that emerged, individual, relational and organisational, offers a portable lens that other teams can use to examine their own settings, and it aligns with a broader international consensus that palliative care competence must be built at every level of an organisation simultaneously.
The most durable insight from this research may be its refusal to locate the solution in any single place. Staff who completed the intervention perceived it as an opportunity for growth, and their reasoning showed genuine movement in how they understood palliative care as an ongoing approach rather than a final act. But their accounts also made clear that attitudes, relationships and structures must pull in the same direction for that understanding to reach residents’ bedsides. For nursing homes everywhere, the implication is a practical checklist of a different kind: train your staff together, give them room to reflect, examine the norms your workplace quietly enforces, and make sure the organisation’s daily conditions match what the education teaches. Where those elements align, palliative care stops being an aspiration and becomes, simply, the way care is done.
Subject of Research: Nursing home staff's reasoning about providing palliative care after an interprofessional educational intervention
Article Title: “Nursing home staff’s reasoning about providing palliative care following an educational intervention – a qualitative interview study”
Article References: Ekeblom, C., Lundh Hagelin, C., & Österlind, J. (2026). “Nursing home staff’s reasoning about providing palliative care following an educational intervention – a qualitative interview study”. BMC Nursing, 25(1), Article 956. https://doi.org/10.1186/s12912-026-05480-4
Image Credits: AI Generated
DOI: 10.1186/s12912-026-05480-4
Keywords: palliative care, nursing homes, older people, interprofessional education, qualitative research, nursing education, end-of-life care, care staff attitudes, organisational conditions, Sweden, BMC Nursing, healthcare workforce
Meet Hilary Cremin: the Cambridge Professor of Education (yes, the one who appointed Jason Arday) who wants to abolish all secondary schools because they're "fascist", among other mad ideas, says Dr Nicholas Tate.
The post Meet Hilary Cremin: The Cambridge Professor of Education Who Wants to Abolish Secondary Schools, Among Other Mad Ideas appeared first on The Daily Sceptic.
White British pupils have become a minority in England's selective grammar schools, making up just 44% of pupils in 2026 compared to 66% in 2016, with one Headmaster describing the demographic change as "spectacular".
The post Why Are White British Pupils a Minority at Grammar Schools? appeared first on The Daily Sceptic.
Humans will have to compete with AI-powered virtual employees in the near future. Here are our tips for the last human holdouts
Futureproofing your career with an AI-resistant degree is a tough ask, says Charlie Ball, an expert on graduate employment for Jisc, the UK’s higher education digital, data and technology agency.
“What you’re trying to do is futureproof a 45-year career in a time of rapid technological change – and that’s really quite hard to do. What’s likely is that jobs that hinge a lot on human, face-to-face interaction are unlikely to be replaced.”
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© Photograph: sturti/Getty Images
© Photograph: sturti/Getty Images
© Photograph: sturti/Getty Images
You wouldn’t be remiss to accuse college students using AI to cheat of being lazy. But some of the wilier ones are putting in the hard work to make sure their LLM-generated answers avoid detection from their nosy pedagogues.
New York Magazine interviewed some of these clever cheats in a recent piece, who are well aware that by by now, many professors are wise to students’ tricks and are changing the assignments they give and deploying increasingly powerful tech to weed out any illicit AI assistance.
One student named Jared attending New York University took his introductory calculus class’s use of WebAssign, a platform that’s designed to make cheating “far more difficult,” as a challenge. He and his friend used an AI coding tool to build a bot that does all their homework for them, but at the sluggish pace of a struggling student.
“The bot moves slowly, leaving a professor checking time stamps none the wiser,” according to the piece. Jared, in his munificence, gave the tool to his friends, and now he’s not even sure how many classmates have his automatic cheating machine tech in their hands.
It’s a demonstration of the power of AI agents — AI models that can perform complex tasks on a human’s behalf without any intervention, which range from writing entire pieces of software to conducting research. Perhaps it’s also a response to the punitive, detection-focused approach that beleaguered professors have adopted amid a deluge of AI cheating.
That seemed to be the argument made by Will, a recent graduate of Carleton College. By making a big deal of obvious AI tell-tales like em-dashes, Will figured, faculty were actually spotlighting exactly what the cheaters need to disguise.
“People are going to find a way to cover up those little stylistic quirks,” Will told NYMag. “If the only way you’re trying to discourage cheating is by saying ‘Well, I have a detector,’ then you’re not going to succeed.”
In Will’s case, his professors banned AI-generated writing, with one of them bragging that they had a “battery” of tools to enforce the ban. Determined to have AI crank out his work anyway, Will used Claude to analyze and write extensive passages for an essay, then went back and manually rewrote the AI-generated paragraphs.
The best cases of AI cheating, it seems, requires a human touch. Theo, a UMass Amherst sophomore and self-proclaimed autodidact disinterested in his college’s gen-ed courses, boasted to NYMag about how he created an astronomy presentation by using OpenAI’s AI coding agent Codex and combining it with a plugin that improved its web-search capabilities. But because the resulting presentation looked too polished, he spent four hours hand-degrading the slides to make it look more like the work of a mediocre college sophmore, according to NYMag.
“It took a lot of work,” he told the magazine.
Danielle Carr, a historian and anthropologist at UCLA, thinks that professors relying on surveilling and detection methods to weed out cheating will only keep them stuck in a game of cat and mouse. AI cheating, she told the magazine, isn’t “a technical problem that has a technical fix.”
More on education: Zohran Mamdani Bans AI for NYC Public School Students Up to Eighth Grade
The post College Students Using AI to Cheat Are Going to Absurd Lengths to Avoid Detection appeared first on Futurism.
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