Bristol-based illustrator and designer Adam Birch was one of the first to make a public statement. He was cautious because he wanted to be, indicating that he didn’t believe the choice was malevolent but rather misguided. However, he kept returning to a question that was more difficult to ignore than the anatomical errors on the cover: why learn creative skills if the organization endorsing those classes doesn’t value them enough to bother commissioning even a photograph, let alone a real human illustration? “Something devoid of creativity is being used to promote creativity,” he stated. It was the type of sentence that didn’t require embellishment.

In addition to the cover, the booklet included about ten other AI-generated images. And there was a big backdrop to it all. In 2023–2024, Bristol’s cultural sector had an economic impact of £892.9 million. Nearly 90% of the council’s £134 million fine art collection is hidden away in storage. Just weeks prior to the backlash over the pamphlet, the council’s Head of Culture, Philip Walker, had publicly stated that Bristol’s creative community was a “vital part of how we live, connect, and grow.” There was enough space for a bus to pass between that language and a four-fingered AI illustration on a pamphlet about painting classes.

Wick, South Gloucestershire-based illustrator and artist Luke Oram expressed the worry that elevates this beyond a local embarrassment. He described the 22-year-old recent art school graduate who is attempting to establish himself in a field that is already challenging under typical conditions. Careers start with those entry-level commissions, such as the booklet cover, the small council project, or the community publication. They don’t have any prestige. They are frequently underpaid. However, their existence is important, and when organizations choose to use an AI tool instead, the claim that the work isn’t worth paying for becomes self-fulfilling in a way that intensifies over time.

Speaking to the BBC, an unnamed artist from Leamington Spa claimed that his CEO had instructed them to incorporate AI into their work. “We’re being told to bring our heads out of the sand,” he replied. However, he had a different opinion about who really gains from that training. The people who will benefit from AI are not the ones being advised to use it. Additionally, his portrayal of the technology as “fast-food”—never pausing to consider whether we should, only whether we could—landed more forcefully than the majority of the official statements made by both sides of the dispute.

Reading the Bristol response gives the impression that the city recognized the decision’s symbolic significance in a way that the council did not at first. Eventually, council leader Tony Dyer acknowledged the “strong feelings expressed by residents” and announced that AI guidelines had been revised. For his part, Birch kept going back to a straightforward solution: use pictures from the real classes. Showcase the artwork created by the students who participate. When people show up, let the cover be a celebration of their creations. It would have been practically free. It would have made the appropriate statement. Additionally, it would have had the right number of fingers.

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For many years, the question of how physical space influences creative thought has been growing in both research and practice, but Harvard’s involvement with it has taken on a new degree of seriousness. The Harvard Joint Center for Housing Studies held a half-day event at the GSD in April 2026 to examine how design, from a single shared kitchen to an entire neighborhood, can reorganize how individuals of various ages, backgrounds, and disciplines interact with one another. The topic of discussion at the event, which was organized by Jennifer Molinsky, Jenny French, and Tim Love, ranged from the legalization of mid-rise single-stair construction in Massachusetts to multigenerational housing models. Beneath all of this, however, was a single, unifying argument: most of our existing spaces make decisions that we haven’t consciously considered, and how we design shared space is a cultural decision rather than merely a technical one.

French, an assistant professor in practice of architecture whose firm French 2D designed Bay State Cohousing in Malden, Massachusetts, frequently brought up the term “setting up preconditions” because it touches on a point that most architectural thinking ignores. Interaction is not forced in a good collaborative space. It increases the likelihood of low-friction interaction. With its cozy wood cabinets and a layout that makes it genuinely hard to avoid the neighbors, the shared kitchen at Bay State Cohousing is anything but decorative generosity. It is a thoughtful debate about what occurs when individuals of different ages are given an excuse to be in the same space at the same time.

Ethan Bernstein’s seminal study on open office architecture from Harvard Business School, which has been referenced almost 550 times, discovered something that defies the logic of most office redesigns. Face-to-face interaction actually decreased, sometimes by as much as 70%, when businesses removed walls and made their spaces completely open. People put on their headphones. They withdrew behind screens. The architecture that was meant to foster unplanned cooperation instead created a learned avoidance and social defensive posture. Since then, research published in the Harvard Business Review has made the counterargument that dynamic shared space—rooms, hallways, and common areas that serve multiple purposes and invite people in without requiring them to collaborate on cue—is what actually fosters spontaneous creative interaction rather than openness per se.

How much of what emerged from the GSD event maps onto that finding is difficult to ignore. James Stockard, who has spent more than 50 years living in Common Place, a twelve-unit cooperative housing community, explained how the kids there were raised to view shared living as normal, not radical or ideological, just something people do. He claims that one of the most important results of the architectural decision his group made fifty years ago was that normalization. The culture was shaped by the space. The expectations of the following generation were shaped by the culture. A few of those kids now reside in comparable situations. Some people are open to it, but they don’t. The design process is still ongoing.

The same issue is addressed from the perspective of organizations rather than housing in the GSD’s executive education program on creating innovative and cooperative workplaces, which is taught by Jacob Reidel and Carly Tortorelli. The physical environment has always played a major cultural role in the creative industries, which is why they have struggled more than most with the shift to hybrid work. The table covered in real models, the shared wall of drawings, and the studio are more than just tools. They are the circumstances that make it possible to think in certain ways and initiate conversations that would not otherwise take place.

Observing Harvard’s architects, housing researchers, and workplace designers come to the same conclusion from various angles gives the impression that the field is coming together around something it has always understood but has seldom intentionally created. Despite their surroundings, good people do not create spontaneous creative collaboration by cultural accident. One shared kitchen, one hallway, one pentagonal void at a time—spaces can either make it easy or difficult.

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For Black Panther in 2018 and Wakanda Forever in 2022, Carter became the first Black person to win two Academy Awards for Best Costume Design. She began her career with Spike Lee’s School Daze in 1988 while attending Hampton University, an HBCU in Virginia. Since then, she has costumed over 70 movies and TV shows, creating what Robertson refers to as a “Afrohistoricist” body of work, which is based on the notion that authentic design starts with historical excavation rather than trend research. Malcolm X’s oversized, patterned Zoot suits were specifically chosen for their symbolic meaning in the 1940s: racialized politics of fabric during wartime rationing, cultural assertion, and defiance. Carter was aware of all of that prior to selecting a single swatch.

This methodology—research first, design later, always in service of the story—is beginning to permeate design education in ways that are actually changing what the field views as fundamental. According to Robertson, Carter functions as a “fashion griot,” referencing the West African custom of the storyteller who uses art to preserve and transmit history. The art form in her case is clothing. She has stated that the non-negotiable first step in working on Black Panther was comprehending the various African tribes, not the superhero suit, the box office forecasts, or the pre-existing sketches from the Marvel visual development team. The tribes. Their jewelry, colors, and attitudes toward silver versus gold, as well as how they used clothing to express their social standing and sense of belonging. The design didn’t start until that research was finished.

The pedagogical shift is noteworthy for young creatives who are absorbing this philosophy through her exhibitions, her virtual keynote at the Costume Society of America Conference, and her structured digital course created in collaboration with PLC Detroit. Technical skills such as draping, pattern-making, textile knowledge, and construction have long been the focus of design education. None of that is being disregarded by Carter. She is introducing the notion that a garment’s authority stems from its knowledge, which most curricula have tended to overlook. Queen Ramonda’s 3D-printed Isicholo crowns in Wakanda Forever were not ornamental. They were constructed using in-depth research on traditional headwear worn by Zulu married women in South Africa, filtered through the logic of Afrofuturism, which honors ancestral aesthetics while envisioning what they might become in a civilization that has never been colonized. A stylist doesn’t make that choice. It is a historian’s choice conveyed through the artistry of a fabricator.

As Carter’s influence grows through design schools, exhibitions, and online learning environments, it seems as though she is actually imparting a self-authorization philosophy. She has stated unequivocally that she always wanted to be the first, not out of a sense of competition but rather out of a quiet conviction that the stories she was sharing were important and worthy of praise. She advised graduating students at Suffolk University’s 2019 commencement to take risks, be unpredictable, and find validation within themselves rather than waiting for it from traditional industry structures. Black students entering design fields that have historically marginalized their perspectives and histories are especially affected by this message, which effectively tells them that having a thorough understanding of their own culture is not a niche interest but rather their greatest competitive advantage.

It’s still unclear if educational institutions will take Carter’s framework seriously enough to alter the way their curricula are actually organized, or if her influence will continue to be inspirational rather than structural. However, the case for teaching design the way Carter practices it is already complete in the Indianapolis Children’s Museum, those five dresses, the young child who noticed something lovely, and the researcher who saw everything beneath it.

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Puutio took a different approach than outlawing the instruments that made it. He now mandates that students use AI in all of their assignments. This decision, which he described in an essay for Business Insider in March 2026, is based on a set of guidelines intended to do something that most university AI policies aren’t yet capable of: differentiate between AI that does the thinking and AI that supports the thinking.

The distinction seems straightforward. In reality, it is the whole issue. Undergraduate students at Harvard are as aware of this as their professors are, and in some ways, they have been more openly dealing with its complexity. Developed through interviews with seven faculty members and 27 HGSE graduate students, a guide released by the Harvard Graduate School of Education in January 2025 paints a picture of students who are considering AI in ways that aren’t always reflected in institution-wide policy documents. These students aren’t attempting to get away with anything. They are students who have already incorporated AI into their work processes and are now, quite understandably, requesting that their teachers catch up.

Even though it seems hard to deliver, what they want is not complicated. Instead of the ambiguous language about “responsible experimentation,” which Harvard’s general AI policy currently offers, they want explicit policies that specify exactly what constitutes authorized use in each course. Instead, they want actual guidance from the instructor who is grading their work. Professor Karen Brennan, who co-wrote the guide and oversees the Creative Computing Lab at HGSE, compares the fear surrounding AI to the “moral panic” that followed the development of the pocket calculator. This fear proved to be partially justified, partially misguided, and primarily resolvable through careful integration rather than outright prohibition. Similar statements were made by the students in her study. When instructors explained why they were assigning a certain task, they said it was actually helpful because it made it easier for them to decide what to give to AI and what to keep for themselves.

The HGSE guide quotes a student who said, “Really think about what you want at this moment,” which seems to have more wisdom than most institutional policy statements. Do you want to learn or just finish the task at hand? Perhaps that question ought to be at the top of every American course syllabus at the moment. The students who are inquiring are aware of the limitations of AI. They cautioned one another about the challenging learning curve, the need for several rounds of quick refinement to produce meaningful results, and the moment one student, six hours into a session that should have taken two, realized that sometimes using your own skills is just more efficient than battling the machine.

One example of this in action is the framework Puutio employs in his own classroom. AI for research and synthesis, AI as an editor and critic once the argument has been developed, but never AI creating the argument itself. The student must be the one who thinks. When that line is clearly drawn, the assignment’s nature is altered without giving up on technology. It’s difficult to ignore how different that is from the binary decisions that the majority of universities continue to make: either completely prohibit it or say nothing helpful and hope for the best.

Fundamentally, what Harvard students are requesting is honesty. They are aware of the existence of AI. Regardless of policy, the majority of them use it in every course. They want academics who are prepared to confront this reality head-on, not act as though it will disappear or treat every discussion of AI as a disciplinary issue, but rather sit with the true complexity of a technology that, depending on how it is applied, can either enhance or detract from learning. This is already being figured out by the students. Whether their institutions will advance fast enough to be beneficial is the question.

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Students, trainees, and apprentices have the opportunity to test their skills in competitions that are in line with WorldSkills international standards thanks to Skills Competition Wales, which is supported by the Welsh Government and offered through a network of colleges and work-based learning providers throughout the nation. The competitions have been going on for years, but something about their recent trajectory suggests the program has found a second gear. This includes the participants’ direct advancement into higher education, the aspirations of the students they are producing, and the expansion of disciplines in 2026 to include Textiles and Surface Pattern, Ceramics, and Sustainable Agriculture.

Ellie Vaughan, a Swansea native, was introduced to UWTSD through the competition prior to her official enrollment. She had finished her Foundation Year in Art and Design at Gower College Swansea, and the competition provided her with access to the resources and educational setting that she would eventually decide to pursue. Before she arrived, she was aware of the live briefs, the international travel, and the studio spaces. That is not a minor issue. The discrepancy between what universities describe in their prospectuses and what students actually find when they arrive is one of the recurring problems in higher education recruitment. Honestly and practically, the competition closes that gap.

Eva Kingston-Gharaati recounts a similar tale, pointing out that the briefs she was given at UWTSD were sufficiently expansive to permit real experimentation while still requiring in-depth investigation of global artists and designers. This combination of freedom and rigor is more difficult to attain in a curriculum than it may seem, and it indicates a deliberate aspect of the programs’ design. Another competition pathway alumna, Thea Wakeford, went one step further and, with university support, started a small business in addition to her studies. This is either an early indication of an exceptionally entrepreneurial student or proof that her surroundings made it seem reasonable to try. Most likely both.

The competition program is framed by Dr. Mark Cocks, Dean of UWTSD’s Wales Institute of Science and Art, in terms of confidence and access, two things that are often lacking for students making the transition from further education to university, especially those who are the first in their family to consider it. Institutional leaders frequently use the term “widening access” in ways that are stubbornly abstract. The pathway is made tangible, repeatable, and concrete by the Skills Competition Wales model. As competitors, students enter the studio once. A few of them choose to return as students. It appears that some emerge as professionals.

Here, the larger context is important. It is easy for outsiders to underestimate the consistency with which Wales has been developing its infrastructure for creative education. For many years, the Creative Learning Through the Arts program of the Arts Council of Wales has assisted schools in creating innovative approaches to curriculum design, integrating artistic practice into subjects ranging from citizenship to mathematics. The Welsh Government’s Future Wales 2025 initiative, a collaboration between Pact and Creative Wales, is expanding that reach into new creative sectors and the screen industry. These are not stand-alone projects. Wales seems to have been putting together something cohesive in the field of creative education, albeit slowly, quietly, and increasingly successfully.

It’s still unclear if students enrolled in the Skills Competition Wales pathway will compete at the top international levels or if the program’s goals will expand beyond its current scope to include all of Wales’ colleges. Something is clearly working, as evidenced by the Swansea studios and the students’ own accounts of how they got there.

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The course, which has been offered at Montclair State since 2012, started out as a direct reaction to an unsettling finding in American educational statistics. A seminal study conducted in 2010 found that children’s creative thinking test scores had been steadily declining for the previous twenty years. The results were quiet in the same way that some uncomfortable facts are often quiet; they were acknowledged, briefly mentioned, and then mainly ignored while academic institutions continued to teach in the same manner. Montclair State took a different approach. It created a course centered on the issue and asked a few genuinely unlikely partners to teach it together.

Professor of physics Ashuwin Vaidya, who led sessions in 2012 and 2014 and contributed to the curriculum’s development, talks about wanting students to think and ask questions independently rather than just absorb information. That seems simple until you consider how infrequently it occurs in a traditional lecture hall, where the room’s layout—rows of seats facing a single speaker—has been subtly arguing the same point about authority and knowledge transfer for 200 years. That architecture is upended by the Creative Thinking course. Professors and visiting artists are added to the curriculum. The next day, students sit in the same room as the artists and ask questions after attending rehearsals for performances by visiting choreographers. Liz Queler, a Grammy-nominated singer-songwriter, stopped by and talked about her process. In 2013, musician and performer Laurie Anderson shared her thoughts on creativity. These are not guest lectures added to an already-existing curriculum. They are the framework.

Mika Munakata, a math professor, asked students to draw parallels between math and a dance performance by the Brazilian group Companhia Urbana de Dança. Her goal was to help students see mathematics as a means of observing pattern, proportion, and movement in the world rather than as an end in and of itself. Kirk McDermid, a philosophy professor, describes creativity in terms of exploration, making it clear that this process is dangerous, prone to failure, and difficult to measure. Just this recognition distinguishes the course from the majority of university programs, where the grading system tends to subtly discourage the kind of unrestricted intellectual risk-taking that results in truly novel ideas.

Reading about this course and the faculty’s descriptions of their own teaching experiences gives me the impression that modeling is more crucial than actual instruction. By working with individuals who have different perspectives, venturing outside of their fields, and letting their students witness their uncertainty, the professors themselves are taking chances. In higher education, where expertise is frequently performed as confidence and admitting ignorance can feel costly from a professional standpoint, that is less common than it should be.

It is worthwhile to trace the intellectual lineage that runs beneath the course. Paul Baker, a 1950s educator and artist whose curriculum thinking was decades ahead of most current discussions about interdisciplinary learning, planted the seed. Furthermore, the link to Matthew Lipman’s Philosophy for Children program at Montclair State College—where Australian philosopher Philip Cam received his training and went on to create inquiry-based teaching strategies that are currently employed in classrooms throughout several nations—indicates that Montclair has been quietly considering how to teach thinking for a longer period of time than the current discourse tends to recognize.

It’s still unclear if the Creative Thinking course can be replicated on a large scale or if it requires a certain mix of institutional support, willing faculty, and the unique creative energy that seems to congregate around programs that have found their purpose. The idea that a student who learns to view a tree through the simultaneous lenses of language, mathematics, dance, philosophy, and science has gained knowledge that no single discipline, taught in isolation, could have imparted seems more difficult to dispute.

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The physical division between the arts wing and the science corridor is intentional. It reveals a deeper aspect of the way American schools have been set up for many years and how they have subtly taught pupils how to organize themselves. You may or may not be a STEM person. You are either analytical or creative. Students are affected for years after they graduate from school, and the categories solidify early.

Ashley Labodda, a Ph.D. candidate in philosophy at the University of Rochester with a degree in biochemistry, has witnessed this unfold in real time from both sides for years. As a teaching assistant for ethics and chemistry classes, she has witnessed biology majors enter introductory philosophy classes believing they will fail before they have written a single sentence. She has witnessed journalism students approach a logic course, which is offered as a substitute for a math requirement, with the express purpose of identifying the least mathematical option. The defeatism is not sporadic. According to her observations, it is persistent, pervasive, and subtly harmful.

Teachers have long been aware of what Labodda describes, but they have struggled to institutionally address it. The documented history of the STEM-humanities divide dates at least to 1959, when British scientist and novelist C.P. Snow published The Two Cultures and the Scientific Revolution, cautioning that the humanities and sciences were becoming so dissimilar that they could no longer meaningfully communicate with one another. This seemed risky to Snow. The argument is still very much relevant sixty-six years later, and the school day schedule has changed remarkably little in response to it.

As a workable solution to this issue, the STEAM movement—the intentional addition of Arts to the STEM acronym—has been gaining traction for years. According to the Institute for Arts Integration and STEAM, it uses science, technology, engineering, the arts, and mathematics as interconnected access points for directing student inquiry rather than being a straightforward combination of subjects. The difference is important. A biology lesson followed by a drawing exercise is not what STEAM is all about. In this type of project-based, interdisciplinary learning, students must simultaneously comprehend engineering principles and aesthetic choices when designing a structure; data visualization necessitates both mathematical literacy and true design thinking; and the question under investigation cannot be resolved by a single discipline.

Nyiramukama Diana Kashaka of Kampala International University conducted research in October 2024 that looked at the historical connection between STEM and arts education and consistently found evidence that integrating artistic processes into scientific learning enhances student motivation, engagement, and creative thinking. Anyone who has witnessed a student come to life in a project that required them to use more of their brain simultaneously will not be surprised by the findings. The institutional machinery’s slow reaction to that evidence is a little more startling.

As the STEAM discourse spreads throughout university departments and school districts, there’s a sense that the true challenge was cultural rather than pedagogical. Standardized testing, tracking systems, college admissions pressures, and career counseling that reduces the diversity of human potential to a single employability metric all contribute to the deeply ingrained belief in American education that a serious student chooses a lane and stays in it. Labodda compares general skills, such as critical thinking, problem-solving, and the capacity to formulate and evaluate an argument, to a tool belt, with specific skills serving as the tools. She contends that when students are asked to use their tools in unfamiliar situations, across the divide rather than safely within one side of it, that belt becomes stronger.

It’s still unclear if schools that sincerely commit to STEAM integration are creating something long-lasting or if the term will be absorbed into current structures with little to no underlying change. With regard to education reform, that has previously occurred. However, when a student who described herself as “completely STEM-brained” finds that writing a rigorous argument and testing a hypothesis require more of the same thinking than she anticipated, something truly changes in the classrooms where it is actually working. It turns out that the gap is smaller than claimed. It was always the case.

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The program, known as mGene, was created at Children’s National by Marius George Linguraru and his colleagues. As of right now, it has accuracy rates well over 90% for identifying four serious genetic syndromes: Down, DiGeorge, Williams, and Noonan. The fact that it was trained on infants from twenty different nations is more significant than it may first appear. As Linguraru has pointed out, the majority of geneticists receive their training from textbooks that mostly feature cases from northern Europe. Theoretically, the algorithm has a wider perspective than the expert who trained it.

Particularly in the diagnosis of learning disabilities and neurodevelopmental conditions, this concept—the algorithm seeing what humans miss and doing it faster—is increasingly making its way from research papers into real clinical discussions. According to a study released by Duke Health in April 2026, AI can predict a child’s likelihood of developing ADHD years before a traditional diagnosis is usually made by evaluating routine electronic health records. By capturing gaze pauses during reading, AI-powered eye-tracking systems are also identifying dyslexia with up to 90% accuracy. Traditionally, this task required lengthy assessment periods and specialist referrals, which many families, especially in under-resourced communities, simply never access.

This has a strong attraction. In many states in the United States, waiting lists for evaluations of autism and ADHD can last for months or even more than a year. Instead, children who could have benefited from early intervention support are sitting in classrooms where their needs are ignored and their struggles are misinterpreted as behavioral issues, a lack of effort, or something in between. The question of whether to implement an AI system that analyzes motor patterns and can diagnose ASD or ADHD with 86% accuracy in 15 minutes seems less philosophical and more pressing.

Siham Mohamed and colleagues conducted a thorough 2026 review that was published in PubMed Central and looked at the use of AI in neurodevelopmental disorders such as autism, ADHD, dyslexia, and developmental language disorder. According to the review, machine learning and deep learning techniques are actually increasing diagnostic accuracy, especially when it comes to their capacity to incorporate what scientists refer to as multimodal data, which includes behavioral observations, neuroimaging, genetic profiles, and written language samples examined using natural language processing. A single clinician working in a 30-minute appointment is structurally unable to accomplish what the combination of data types does.

However, the review is cautious about something that is sometimes overlooked in the excitement: algorithmic bias is not a small technical detail. It is a major issue. When applied to children from other demographic backgrounds, AI systems that were primarily trained on white, middle-class pediatric populations clearly perform worse. In a situation where the stakes are not abstract, the same pattern that has been repeatedly seen in radiology—models trained on small datasets perform poorly when deployed outside of their original conditions—applies here as well. An eight-year-old who is not diagnosed with dyslexia is not a data point. It is years of a child thinking they are just not intelligent enough.

This story has a familiar shape that is difficult to ignore. Despite repeated predictions of obsolescence, the field of radiology has spent the better part of a decade learning that AI’s benchmark performance and its real-world performance are not the same thing, and that human radiologists are still as important and busy as ever. The lesson is that integrating AI into complex human systems results in complex human outcomes that pure accuracy metrics cannot predict. This lesson was painstakingly learned through mammography computer-aided detection programs, which ultimately increased biopsies without catching more cancer.

The idea of the human-in-the-loop appears to be what researchers working on learning disability AI are coming to, albeit reluctantly considering how alluring full automation sounds. AI as a diagnostic tool, highlighting patterns and risk that a teacher, pediatrician, or school psychologist can then look into with the right context and expertise. Not a substitute. An extremely knowledgeable assistant. It’s still unclear if insurance, healthcare, and educational systems are built to effectively use that kind of tool, or if, as has happened in the past, the technology will arrive before the infrastructure necessary to use it responsibly is constructed.

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A growing number of child development researchers are spending a lot of time worrying about that transaction, which is quick, seamless, and becoming more frequent. It’s not because the image is poor; rather, it’s because of all the things that didn’t go as planned.

Twenty empirical studies on the impact of AI-based painting tools on children’s creative thinking were reviewed in a 2025 systematic review published in Frontiers in Psychology, which was headed by Anna Wang at Jiangxi Science and Technology Normal University in China. Both enthusiasts and skeptics should find the results unsettling because they are genuinely conflicting. On the one hand, AI tools seem to improve engagement and creative expression, especially in kids who find traditional art-making intimidating. However, the same standardized interfaces that make these tools accessible also carry a real risk of what the paper refers to as cognitive homogenization: a gradual flattening of the diversity of creative output as children start producing work that looks, feels, and thinks alike. This is the section that researchers keep coming back to.

That might seem abstract until you think about its practical implications. When a child draws with pencil and paper, thousands of tiny decisions are made, such as where to begin the line, how hard to press, what to leave out, and how to correct the mistake that turns into the best part. The series of decisions—including the mistakes—is not incidental to the process of creative growth. For the most part, it’s creative development. Most of it is ignored by AI. The outcome is already finished, resolved, and devoid of the awkwardness that imparts invaluable knowledge about creating things to a young mind.

Assistant professor Qiao Jin of North Carolina State University oversaw a different study that looked at how parents and kids aged four to eight reacted to AI-generated pictures in children’s books. The results showed that children were more sensitive than their parents to the emotional content of the pictures, which seems both obvious in hindsight and genuinely surprising in its details. Children noticed when an illustration’s emotions didn’t match the text’s emotions, and it turns out that AI still has a lot of trouble deciphering emotional cues. In ways that their parents frequently overlooked completely, they noticed and were disturbed. The youngest readers were the most sensitive to the emotional void left by AI, which is something to think about.

Researchers’ discussions have started to lean toward what some are referring to as the “AI Second” approach, which is a framework that requires kids to work with tangible materials first, using paint, clay, pencils, and mess to develop fundamental skills and personal meaning before introducing AI as a tool for iteration rather than origination. It’s a sensible notion. Additionally, it calls for a degree of deliberate, structured guidance that most homes don’t naturally provide and most classrooms aren’t currently equipped to provide.

By creating child-safe AI art environments with age-appropriate filters and a stated goal of assisting children in creating with AI rather than being replaced by it, platforms like LittleLit are attempting to find a middle ground. The distinction is genuine and deserving of preservation. However, it still depends on someone—a parent, a teacher, or someone who is paying attention—making sure the tool continues to support the child’s imagination rather than subtly replacing it. That is a more difficult problem than any algorithm has been able to solve so far.

Watching all of this take place in classrooms, living rooms, and tablet screens, researchers seem to agree most of the time that technology shouldn’t be used to solve the inefficiency of learning to imagine, which is the slow, frustrating, and fundamentally human task of conjuring something from nothing. It is the actual object.

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Located in the heart of Shrewsbury’s town centre, The Hive is housed in a building that seems to have been discovered rather than planned. Its programming is so varied that it may hold a film screening, a jazz concert, a life drawing class, or a toddler music session every week. It has a charity registration. Its TripAdvisor rating places it comfortably in the top forty things to do in the town, and it has about 7,400 Facebook followers. That doesn’t adequately describe what it does.

The Hive introduced its Alternative Provision program in November of last year. The program’s name is purposefully low-key, but it actually represents a real rethinking of what education can be for young people who have fallen out of the traditional system. The program is intended for kids and teenagers who have trouble going to school, who bring anxiety into every classroom they attend, who are in foster care or have special education plans, or who have just reached the point where the conventional model is no longer effective for them. The duration of sessions is two to four hours. Groups can consist of just one young person and a mentor. Music, DJing, songwriting, graphic design, photography, and printmaking are among the activities. Exams don’t exist. No bells are present.

The CEO, Katie Jennings, put it this way: to make every young person feel like they have a place in the world. That may sound like what charities say in press releases, but there is proof that this goes beyond words. During the organization’s Save The Hive campaign last summer, Mary Keith, who oversees Buzzy Beats, the Hive’s interactive music program for children under five and their caregivers, stated that the venue matters in ways that are genuinely hard to replace. Although she was discussing the building’s survival, the sentiment encompasses all of its contents.

Observing what The Hive does and hearing the language people use around it gives me the impression that alternative education is no longer the exception in Britain; rather, it is subtly evolving into a parallel system. The majority of parents only partially comprehend the pressure that schools are under. National attendance statistics have not returned to their pre-pandemic levels. The number of kids receiving home education has significantly increased. Local authorities like Shropshire have SEND waiting lists that can last for months or even years. Organizations like The Hive are filling that gap with something that government programs frequently lack completely: flexibility and genuine warmth, rather than the resources of such programs.

The only multi-artform alternative facility in Shropshire is The Hive. Sitting with that for a while is worthwhile. The work that the formal education system hasn’t been able to accomplish is being carried out by one charity in a town center building in a county with about 330,000 residents. It’s difficult not to find that both admirable and subtly troubling—admirable because of what The Hive has created, troubling because of what its necessity makes clear. Most of the young people who end up at 5 Belmont are ones that the system has already attempted but failed to retain. It’s noteworthy that The Hive can keep them engaged, whether it’s during a DJing session, a printmaking afternoon, or a discussion with a mentor who isn’t keeping track of their attendance on a spreadsheet.

It is still genuinely unclear if this model can expand or if it will continue to rely on the unstable funding environment that almost put an end to it last summer. Standing outside this specific building on a weekday morning while the rest of the town goes about its business, it seems more difficult to dispute that something genuine is taking place here. Silently. Without much fanfare. Just as educational revolutions usually do.

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