The Struggle Paradox: How AI Tutors Rewrite the Rules of Academic Resilience
In a gleaming classroom at Carnegie Mellon University, Vincent Aleven watches as a student wrestles with a particularly thorny calculus problem. The student's tutor—an AI system refined over decades of research—notices the struggle immediately. But instead of swooping in with the answer, it does something unexpected: it waits. Then, with surgical precision, it offers just enough guidance to keep the student moving forward without removing the productive difficulty entirely.
This scene encapsulates one of education's most pressing questions in 2025: As artificial intelligence becomes increasingly sophisticated at adapting to individual learning styles, are we inadvertently robbing students of something essential—the valuable experience of struggling with difficult concepts and developing resilience through academic challenges?
The debate has never been more urgent. With AI tutoring systems now reaching over 24 million students globally and the education AI market projected to surpass $20 billion by 2027, we're witnessing a fundamental shift in how humans learn. But beneath the impressive statistics and technological prowess lies a deeper question about the nature of learning itself: Can we preserve the benefits of productive struggle whilst harnessing AI's personalisation power?
The Great Learning Paradox
The concept of “productive struggle” isn't just educational jargon—it's backed by decades of cognitive science. When students grapple with challenging material just beyond their current understanding, something remarkable happens in their brains. Neural pathways strengthen, myelin sheaths thicken around axons, and the hard-won knowledge becomes deeply embedded in ways that easy victories never achieve.
Carol Dweck, Stanford's pioneering psychologist whose growth mindset research has shaped modern education, puts it bluntly: “We have to really send the right messages, that taking on a challenging task is what I admire. Sticking to something and trying many strategies, that's what I admire. That struggling means you're committed to something and are willing to work hard.”
But here's where the plot thickens. Recent research from 2024 and 2025 reveals that AI tutoring systems, when properly designed, don't necessarily eliminate struggle—they transform it. A landmark study published in Scientific Reports found that students using AI-powered tutors actually learned significantly more in less time compared to traditional active learning classes, whilst also feeling more engaged and motivated. The key? These systems weren't removing difficulty; they were optimising it.
Inside the Algorithm's Classroom
To understand this transformation, we need to peek inside the black box of modern AI tutoring. Take Squirrel AI Learning, China's educational technology juggernaut that launched the world's first all-discipline Large Adaptive Model in January 2024. Drawing on 10 billion learning behaviour data points from 24 million students, the system doesn't just track what students know—it maps how they struggle.
“AI education should prioritise educational needs rather than just the technology itself,” explains Dr Joleen Liang, Squirrel AI's co-founder, speaking at the Cambridge Generative AI in Education Conference. “In K-12 education, it's crucial for students to engage in problem-solving through active thinking and learning processes, rather than simply looking for direct answers.”
The company's approach represents a radical departure from the “answer machine” model that many feared AI would become. Instead of providing instant solutions, Squirrel AI's system breaks down knowledge into nano-level components—transforming hundreds of traditional knowledge points into tens of thousands of precise, granular concepts. When a student struggles, the AI doesn't eliminate the challenge; it recalibrates it, finding the exact level of difficulty that keeps the student in what psychologists call the “zone of proximal development”—that sweet spot where learning happens most effectively.
This granular approach yielded striking results in 2024. Mathematics students using the platform showed a 37.2% improvement in academic performance, with problem-solving abilities increasing significantly after just eight weeks of use. But perhaps more importantly, these students weren't just memorising answers—they were developing deeper conceptual understanding through carefully calibrated challenges.
The Khan Academy Experiment
Meanwhile, in Silicon Valley, Khan Academy's AI tutor Khanmigo is conducting its own experiment in preserving productive struggle. Unlike ChatGPT or other general-purpose AI tools, Khanmigo refuses to simply provide answers. Instead, with what the company describes as “limitless patience,” it guides learners to find solutions themselves.
“If it's wrong, it'll tell you that's wrong but in a nice way,” reports a tenth-grade maths student participating in one of the 266 school district pilots currently underway. “Before a test or quiz, I ask Khanmigo to give me practice problems, and I feel more prepared—and my score increases.”
The numbers back up these anecdotal reports. Students who engage with Khan Academy and Khanmigo for the recommended 30 minutes per week achieve approximately 20% higher gains on state tests. When implemented as part of district partnerships, the platform becomes 8 to 14 times more effective at driving learning outcomes compared to independent study.
But Sal Khan, the organisation's founder, is careful to emphasise that Khanmigo isn't about making learning easier—it's about making struggle more productive. The AI acts more like a Socratic tutor than an answer key, asking probing questions, offering hints rather than solutions, and encouraging students to explain their reasoning.
The Neuroscience of Struggle
To understand why this matters, we need to dive into what's happening inside students' brains when they struggle. Research published in Trends in Neuroscience reveals that exposing children to challenges in productive struggle settings increases the volume of key neural structures. The process of myelination—the formation of protective sheaths around nerve fibres that speed up electrical impulses—requires specific elements to develop properly.
“Newness, challenge, exercise, diet, and love” are essential for basic motor and cognitive functions, researchers found. Remove the challenge, and you remove a critical component of brain development. It's like trying to build muscle without resistance—the system simply doesn't strengthen in the same way.
This neurological reality creates a fundamental tension with AI's capability to smooth out every bump in the learning journey. If an AI system becomes too effective at eliminating frustration, it might inadvertently prevent the very neural changes that constitute deep learning.
Kenneth Koedinger, professor of Human-Computer Interaction and Psychology at Carnegie Mellon University, has spent decades wrestling with this balance. His team's research on hybrid human-AI tutoring systems suggests that the future isn't about choosing between human struggle and AI assistance—it's about combining them strategically.
“We're creating a hybrid human-AI tutoring system that gives each student the necessary amount of tutoring based on their individual needs,” Koedinger explains. The key word here is “necessary”—not maximum, not minimum, but precisely calibrated to maintain productive struggle whilst preventing destructive frustration.
The Chinese Laboratory
Perhaps nowhere is this experiment playing out more dramatically than in China, where Squirrel AI has established over 2,000 learning centres across 1,500 cities. The scale is staggering: 24 million registered students, 10 million free accounts provided to impoverished families, and over 2 billion yuan invested in research and development.
But what makes the Chinese approach particularly fascinating is its explicit goal of reaching what researchers call “L5” education—fully intelligent adaptive education where AI assumes the primary instructional role. This isn't about supplementing human teachers; it's about potentially replacing them, at least for certain types of learning.
The results so far challenge our assumptions about the necessity of human struggle. In controlled studies, students using Squirrel AI's system not only matched but often exceeded the performance of those in traditional classrooms. More surprisingly, they reported higher levels of engagement and satisfaction, despite—or perhaps because of—the AI's refusal to simply hand over answers.
Wei Zhou, Squirrel AI's CEO, made a bold claim at the 2024 World AI Conference in Shanghai: their AI tutor could make humans “10 times smarter.” But smartness, in this context, doesn't mean avoiding difficulty. Instead, it means encountering the right difficulties at the right time, with the right support—something human teachers, constrained by time and class sizes, struggle to provide consistently.
The Resistance Movement
Not everyone is convinced. A growing chorus of educators and psychologists warns that we're conducting a massive, uncontrolled experiment on an entire generation of learners. Their concerns aren't merely Luddite resistance to technology—they're grounded in legitimate questions about what we might be losing.
“There has been little research on whether such tools are effective in helping students regain lost ground,” notes a 2024 research review. Schools have limited resources and “need to choose something that has the best shot of helping the most students,” but the evidence base remains frustratingly incomplete.
The critics point to several potential pitfalls. First, there's the risk of creating what some call “algorithmic learned helplessness”—students become so accustomed to AI support that they lose the ability to struggle independently. Second, there's concern about the metacognitive skills developed through unassisted struggle: learning how to learn, recognising when you're stuck, developing strategies for getting unstuck.
Chris Piech, assistant professor of computer science at Stanford, discovered an unexpected example of this in his own research. When ChatGPT-4 was introduced to a large online programming course, student engagement actually decreased—contrary to expectations. The AI was too helpful, removing the productive friction that kept students engaged with the material.
The Middle Path
Emma Brunskill, another Stanford computer science professor, suggests that the answer lies not in choosing sides but in reconceptualising the role of struggle in AI-enhanced education. “AI invites revisiting what productive struggle should look like in a technology-rich world,” she argues. “Not all friction may be inherently beneficial, nor all ease harmful.”
This nuanced view is gaining traction. AI might reduce surface-level barriers—like organising ideas or decoding complex instructions—whilst preserving or even enhancing deeper cognitive challenges. It's the difference between struggling to understand what a maths problem is asking (often unproductive) and struggling to solve it once you understand the question (potentially very productive).
The latest research supports this differentiated approach. A 2024 systematic review examining 28 studies with nearly 4,600 students found that intelligent tutoring systems' effects were “generally positive” but varied significantly based on implementation. The most successful systems weren't those that eliminated difficulty entirely, but those that redistributed it more effectively.
Real Students, Real Struggles
To understand what this means in practice, consider the experience of students in Newark, New Jersey, where the school district is piloting Khanmigo across multiple schools. The AI doesn't replace teachers or eliminate homework struggles. Instead, it acts as an always-available study partner that refuses to do the work for students.
“Sometimes I want it to just give me the answer,” admits one frustrated student. “But then when I finally figure it out myself, with its help, I actually remember it better.”
This tension—between the desire for easy answers and the recognition that struggle produces better learning—captures the essence of the debate. Students simultaneously appreciate and resent the AI's refusal to simply solve their problems.
Teachers, too, are navigating this new landscape with mixed feelings. Many report that AI tutors free them from repetitive tasks like grading basic exercises, allowing more time for the kind of deep, Socratic dialogue that no algorithm can replicate. But others worry about losing touch with their students' learning processes, missing those moments of struggle that often provide the most valuable teaching opportunities.
The Writing Revolution
One particularly illuminating case study comes from Khan Academy's Writing Coach, launched in 2024 and featured on 60 Minutes. Rather than writing essays for students—a common fear about AI—the system provides iterative feedback throughout the writing process. It's the difference between having someone write your essay and having an infinitely patient editor who helps you improve your own work.
For educators, Writing Coach handles time-intensive early feedback whilst providing transparency into students' writing processes. Teachers can see not just the final product but the journey—where students struggled, what revisions they made, how they responded to feedback. This visibility into the struggle process might actually enhance rather than diminish teachers' ability to support student learning.
The data suggests this approach works. Students using Writing Coach show marked improvements not just in writing quality but in writing confidence and willingness to revise—key indicators of developing writers. They're still struggling with writing, but the struggle has become more productive, more focused on higher-order concerns like argumentation and evidence rather than lower-level issues like grammar and spelling.
The Resilience Question
But what about resilience—that ineffable quality developed through overcoming challenges? Can an AI-supported struggle build the same character as wrestling alone with a difficult problem?
The research here is surprisingly optimistic. A 2024 study on academic resilience found that it's not struggle alone that builds resilience, but rather the combination of challenge and support. Students need to experience difficulty, yes, but they also need to believe they can overcome it. AI tutors, by providing consistent, patient support without removing challenge entirely, might actually create ideal conditions for resilience development.
The key insight from recent psychological research is that resilience isn't built through suffering—it's built through supported struggle that leads to success. An AI tutor that helps students work through challenges, rather than avoiding them, might paradoxically build more resilience than traditional “sink or swim” approaches.
Cultural Considerations
The global nature of AI education raises fascinating questions about cultural attitudes toward struggle and learning. In East Asian educational contexts, where struggle has traditionally been viewed as essential to learning, AI tutoring systems are being designed differently than in Western contexts.
Squirrel AI's approach, rooted in Chinese educational philosophy, maintains higher difficulty levels than many Western counterparts. The system embodies the Confucian belief that effort and struggle are inherent to the learning process, not obstacles to be minimised.
Meanwhile, in Silicon Valley, the emphasis tends toward “optimal challenge”—finding the Goldilocks zone where difficulty is neither too easy nor too hard. This cultural difference in how we conceptualise productive struggle might lead to divergent AI tutoring philosophies, each optimised for different cultural contexts and learning goals.
The Teacher's Dilemma
For educators, the rise of AI tutoring presents both opportunity and existential challenge. On one hand, AI can handle the repetitive aspects of teaching—drilling multiplication tables, providing grammar feedback, checking problem sets—freeing teachers to focus on higher-order thinking, creativity, and social-emotional learning.
On the other hand, many teachers worry about losing their connection to students' learning processes. “When I grade homework, I see where students struggle,” explains a veteran maths teacher. “That tells me what to emphasise in tomorrow's lesson. If an AI handles all that, how do I know what my students need?”
The most successful implementations seem to be those that position AI as a teaching assistant rather than a replacement. Teachers receive dashboards showing where students struggled, how long they spent on problems, what hints they needed. This data-rich environment potentially gives teachers more insight into student learning, not less.
The Creativity Conundrum
One area where the struggle debate becomes particularly complex is creative work. Can AI support creative struggle without undermining the creative process itself? Early experiments suggest a nuanced answer.
Students using AI tools for creative writing or artistic projects report a paradoxical experience. The AI removes certain technical barriers—suggesting rhyme schemes, offering colour palette options, providing structural templates—whilst potentially opening up space for deeper creative challenges. It's like giving a painter better brushes; the fundamental challenge of creating meaningful art remains.
But critics worry about homogenisation. If every student has access to the same AI creative assistant, will we see a convergence toward AI-optimised mediocrity? Will the strange, difficult, breakthrough ideas that come from struggling alone with a blank page become extinct?
The Equity Equation
Perhaps the most compelling argument for AI tutoring comes from its potential to democratise access to quality education. Squirrel AI's provision of 10 million free accounts to impoverished Chinese families represents a massive experiment in educational equity.
For students without access to expensive human tutors or high-quality schools, AI tutoring might not be removing valuable struggle—it might be providing the first opportunity for supported, productive struggle. The choice isn't between AI-assisted learning and traditional human instruction; it's between AI-assisted learning and no assistance at all.
This equity dimension complicates simplistic narratives about AI removing valuable difficulties. For privileged students with access to excellent teachers and tutors, AI might indeed risk over-smoothing the learning journey. But for millions of underserved students globally, AI tutoring might provide their first experience of the kind of calibrated, supported challenge that builds both knowledge and resilience.
The Motivation Matrix
One surprising finding from recent research is that AI tutoring might actually increase student motivation to tackle difficult problems. The 2025 study showing students felt more engaged with AI tutors than traditional instruction challenges assumptions about human connection being essential for motivation.
The key seems to be the AI's infinite patience and non-judgmental responses. Students report feeling less anxious about making mistakes with an AI tutor, more willing to attempt difficult problems they might avoid in a classroom setting. The removal of social anxiety doesn't eliminate struggle—it might actually enable students to engage with more challenging material.
“Before, I'd pretend to understand rather than ask my teacher to explain again,” admits a student in the Khanmigo pilot programme. “But with the AI, I can ask the same question ten different ways until I really get it.”
The Future Learning Landscape
As we peer into education's future, it's becoming clear that the question isn't whether AI will transform learning—it's how we'll shape that transformation. The binary choice between human struggle and AI assistance is giving way to a more sophisticated understanding of how these elements can work together.
Emerging research suggests several principles for preserving productive struggle in an AI-enhanced learning environment:
First, AI should provide scaffolding, not solutions. The best systems guide students toward answers rather than providing them directly, maintaining the cognitive work that produces deep learning.
Second, difficulty should be personalised, not eliminated. What's productively challenging for one student might be destructively frustrating for another. AI's ability to calibrate difficulty to individual learners might actually increase the amount of productive struggle students experience.
Third, metacognition matters more than ever. Students need to understand not just what they're learning but how they're learning, developing awareness of their own cognitive processes that will serve them long after any specific content knowledge becomes obsolete.
Fourth, human connection remains irreplaceable for certain types of learning. AI can support skill acquisition and knowledge building, but the deeply human aspects of education—inspiration, mentorship, ethical development—still require human teachers.
The Neuroplasticity Factor
Recent neuroscience research adds another dimension to this debate. The brain's plasticity—its ability to form new neural connections—is enhanced by novelty and challenge. But there's a catch: too much stress inhibits neuroplasticity, whilst too little stimulation fails to trigger it.
AI tutoring systems, with their ability to maintain challenge within optimal bounds, might actually enhance neuroplasticity more effectively than traditional instruction. By preventing both overwhelming frustration and underwhelming ease, AI could keep students in the neurological sweet spot for brain development.
This has particular implications for younger learners, whose brains are still developing. The concern that AI might prevent crucial neural development through struggle reduction might be backwards—properly designed AI systems might optimise the conditions for neural growth.
The Assessment Revolution
One often-overlooked aspect of the AI tutoring revolution is how it's changing assessment. Traditional testing creates artificial, high-stakes struggles that often measure test-taking ability more than subject mastery. AI's continuous, low-stakes assessment might provide more accurate measures of learning whilst reducing destructive test anxiety.
Students using AI tutors are assessed constantly but invisibly, through their interactions with the system. Every problem attempted, every hint requested, every explanation viewed becomes data about their learning. This ongoing assessment can identify struggling students earlier and more accurately than periodic high-stakes tests.
But this raises new questions about privacy, data ownership, and the psychological effects of constant monitoring. Are we creating a panopticon of learning, where students' every cognitive move is tracked and analysed? What are the long-term effects of such comprehensive surveillance on student psychology and autonomy?
The Pandemic Acceleration
The COVID-19 pandemic dramatically accelerated AI tutoring adoption, compressed years of gradual change into months. This rapid shift provided an unintended natural experiment in AI-assisted learning at scale. The results, still being analysed, offer crucial insights into what happens when AI suddenly becomes central to education.
Initial findings suggest that students who had access to high-quality AI tutoring during remote learning maintained or even improved their academic performance, whilst those without such tools fell behind. This disparity highlights both AI's potential to support learning during disruption and the digital divide's educational implications.
Post-pandemic, many schools have maintained their AI tutoring programmes, finding that the benefits extend beyond emergency remote learning. The forced experiment of 2020-2021 might have permanently shifted educational paradigms around the role of AI in supporting student struggle and success.
The Global Experiment
We're witnessing a massive, uncoordinated global experiment in AI-enhanced education. Different countries, cultures, and educational systems are implementing AI tutoring in vastly different ways, creating a natural laboratory for understanding what works.
In South Korea, AI tutors are being integrated into the hagwon (cram school) system, intensifying rather than reducing academic pressure. In Finland, AI is being used to support student-directed learning, emphasising autonomy over achievement. In India, AI tutoring is reaching rural students who previously had no access to quality education.
These varied approaches will likely yield different outcomes, shaped by cultural values, educational philosophies, and economic realities. The global diversity of AI tutoring implementations might ultimately teach us that there's no one-size-fits-all answer to the struggle question.
The Economic Imperative
The economics of education are pushing AI tutoring adoption regardless of pedagogical concerns. With global education facing a shortage of 69 million teachers by 2030, according to UNESCO, AI tutoring isn't just an enhancement—it might be a necessity.
The cost-effectiveness of AI tutoring is compelling. Once developed, an AI tutor can serve millions of students simultaneously, providing personalised instruction at a fraction of human tutoring costs. For cash-strapped educational systems worldwide, this economic reality might override concerns about productive struggle.
But this economic pressure raises ethical questions. Are we accepting second-best education for economic reasons? Or might AI tutoring, even if imperfect, be better than the alternative of overcrowded classrooms and overworked teachers?
The Philosophical Core
At its heart, the debate about AI tutoring and struggle reflects deeper philosophical questions about the purpose of education. Is education primarily about knowledge acquisition, skill development, character building, or social preparation? How we answer shapes how we evaluate AI's role.
If education is primarily about efficient knowledge transfer, AI tutoring seems unambiguously positive. But if education is about developing resilience, creativity, and critical thinking through struggle, the picture becomes more complex. The challenge is that education serves all these purposes simultaneously, and AI might enhance some whilst diminishing others.
The Hybrid Future
The emerging consensus among researchers and practitioners points toward a hybrid future where AI and human instruction complement each other. AI handles the aspects of learning that benefit from infinite patience and personalisation—drilling facts, practising skills, providing immediate feedback. Humans focus on inspiration, creativity, ethical development, and the deeply social aspects of learning.
In this hybrid model, struggle isn't eliminated but transformed. Students still wrestle with difficult concepts, but with AI support that keeps struggle productive rather than destructive. Teachers still guide learning journeys, but with AI-provided insights into where each student needs help.
This isn't a compromise or middle ground—it's potentially a synthesis that surpasses either pure human or pure AI instruction. By combining AI's personalisation and patience with human creativity and connection, we might create educational experiences that preserve struggle's benefits whilst eliminating its unnecessary suffering.
The Call to Action
As we stand at this educational crossroads, the choices we make now will shape how humanity learns for generations. The question isn't whether to embrace or reject AI tutoring—that ship has sailed. The question is how to shape its development and implementation to preserve what matters most about human learning.
This requires active engagement from all stakeholders. Educators need to articulate what aspects of struggle are genuinely valuable versus merely traditional. Technologists need to design systems that support rather than supplant productive difficulty. Policymakers need to ensure equitable access whilst protecting student privacy and autonomy. Parents and students need to understand both AI's capabilities and limitations.
Most importantly, we need ongoing research to understand AI tutoring's long-term effects. The current generation of students is inadvertently participating in a massive experiment. We owe them rigorous study of the outcomes, honest assessment of trade-offs, and willingness to adjust course based on evidence.
The Struggle Continues
The debate over AI tutoring and productive struggle isn't ending anytime soon—nor should it. As AI capabilities expand and our understanding of learning deepens, we'll need to continuously reassess this balance. What seems like concerning struggle reduction today might prove to be beneficial cognitive load optimisation tomorrow. What appears to be helpful AI support might reveal unexpected negative consequences years hence.
The irony is that we're struggling with the question of struggle itself. Wrestling with how to preserve wrestling with difficult concepts. This meta-struggle might be the most productive of all, forcing us to examine fundamental assumptions about learning, challenge, and human development.
Perhaps that's the ultimate lesson. The rise of AI tutoring isn't eliminating struggle—it's transforming it. Instead of struggling alone with mathematical concepts or grammatical rules, we're now struggling collectively with profound questions about education's purpose and process. This new struggle might be harder than any calculus problem or essay assignment, but it's arguably more important.
As Vincent Aleven watches his students work with AI tutors at Carnegie Mellon, he sees not the end of academic struggle but its evolution. The students are still wrestling with difficult concepts, still experiencing frustration and breakthrough. But now they're doing so with an infinitely patient partner that knows exactly when to help and when to step back.
The future of education won't be struggle-free. It will be a future where struggle is more precise, more productive, and more personalised than ever before. The challenge isn't to preserve struggle for its own sake but to ensure that the difficulties students face are the ones that genuinely promote learning and growth.
In this brave new world of AI-enhanced education, the most important lesson might be that struggle itself is evolving. Just as calculators didn't eliminate mathematical thinking but shifted it to higher levels, AI tutoring might not eliminate productive struggle but elevate it to new cognitive territories we're only beginning to explore.
The students of 2025 aren't avoiding difficulty—they're encountering new kinds of challenges that previous generations never faced. Learning how to learn with AI, developing metacognitive awareness in an algorithm-assisted environment, maintaining human creativity in a world of artificial intelligence—these are the productive struggles of our time.
And perhaps that's the most hopeful conclusion of all. Each generation faces its own challenges, develops resilience in its own way. The students growing up with AI tutors aren't missing out on struggle—they're pioneering new forms of it. The question isn't whether they'll develop resilience, but what kind of resilience they'll need for the AI-augmented world they're inheriting.
The debate continues, the experiment proceeds, and the struggle—in all its evolving forms—endures. That might be the most human thing about this whole artificial intelligence revolution: no matter how smart our machines become, learning remains hard work. And maybe, just maybe, that's exactly as it should be.
References and Further Information
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“AI tutoring outperforms in-class active learning: an RCT introducing a novel research-based design in an authentic educational setting.” Scientific Reports, Nature, 2025.
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Khasawneh, M. “High school mathematics education study with intelligent tutoring systems.” Educational Research Journal, 2024.
“How Productive Is the Productive Struggle? Lessons Learned from a Scoping Review.” International Journal of Education in Mathematics, Science and Technology, 2024.
Warshauer, H. “The role of productive struggle in mathematics learning.” Second Handbook of Research on Mathematics Teaching and Learning, 2011.
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Dweck, Carol. “Mindset: The New Psychology of Success.” Random House, 2006.
Stanford Teaching Commons. “Growth Mindset and Enhanced Learning.” Stanford University, 2024.
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Zhou, Wei. Presentation at World AI Conference & High-Level Meeting on Global AI Governance, Shanghai, 2024.
Liang, Joleen. Cambridge Generative AI in Education Conference presentation, 2024.
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“Khanmigo AI tutor pilot programme results.” Newark School District, 2024.
Common Sense Media. “AI Tools for Learning Rating Report.” 2024.
Aleven, Vincent and Koedinger, Kenneth. “Towards the Future of AI-Augmented Human Tutoring in Math Learning.” International Conference on Artificial Intelligence in Education, 2023-2024.
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Piech, Chris. “ChatGPT-4 Impact on Student Engagement in Programming Courses.” Stanford University research, 2024.
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Tim Green UK-based Systems Theorist & Independent Technology Writer
Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.
His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.
ORCID: 0000-0002-0156-9795 Email: tim@smarterarticles.co.uk