Connection & Belonging in the Age of AI: Why the Human Brain Still Leads Learning

Artificial intelligence (AI) technologies are rapidly advancing and transforming many aspects of education, from intelligent tutoring systems to automated grading. Yet amid these innovations, the human brain possesses unique qualities that AI cannot reproduce, qualities that are pivotal in education, such as conscious understanding, emotional connection, and value-driven judgment. This article contends that the human brain will always outpace AI where it matters most in education: connection and belonging. Grounded in neuroscience and educational research, we explore how human cognitive and emotional capacities, consciousness and intentional meaning-making, creativity and abstract transfer of knowledge, metacognitive error awareness, genuine empathy and social intelligence, and ethical agency, give educators and learners an irreplaceable edge. We also discuss how AI, when used wisely and ethically, can complement these human strengths to enhance connection and belonging in classrooms. The goal is to frame AI not as a competitor in education but as a tool that, when guided by human insight and values, can support deeper learning.

Consciousness, Intentionality, and Meaning

Human brains are not just information processors; they generate conscious experience and intentionality, the sense of purpose behind our thoughts and actions. In neuroscience and philosophy, this subjective quality of mind (sometimes called qualia) underpins how we derive meaning from our world.

By contrast, AI systems, including advanced language models, operate by manipulating symbols and patterns without any intrinsic understanding of their meaning. As John Searle famously argued in the Chinese Room thought experiment, a computer can appear to understand Chinese by shuffling symbols, but it has no genuine grasp of what those symbols mean, there is syntax but no semantics (Searle, 1980).

In other words, today’s AI lacks the conscious awareness or intentional states that permeate human thinking with real meaning and context (Butlin et al., 2023; Yeager et al., 2014). This distinction is highly relevant in education, where meaning-making is central to learning. Students are not passive recipients of information; their motivation and deep comprehension flourish when learning connects to personal meaning, values, or purposes. Indeed, research shows that when students see learning as purposeful and connected to a larger pro-social goal, their self-regulation and persistence increase significantly (Yeager et al., 2014). In a series of studies with over 2,000 adolescents, promoting a “self-transcendent purpose” for learning (for example, learning science to help others or improve the world) led to better diligence on tedious tasks and even improved academic grades months later (Yeager et al., 2014). Such findings underscore that education is not just about information transmission but about meaningful engagement. Humans, with our conscious minds, excel at deriving and imparting meaning, a caring teacher can inspire a student by connecting content to that student’s personal interests or cultural background, something no AI tutor can truly understand how to do. Moreover, qualities like empathy and ethical reasoning rely on conscious intentionality: a teacher senses a student’s frustration and chooses to encourage them; a student ponders the moral implications of a historical event. These arise from the human capacity to attribute meaning and value to experiences. AI can simulate empathetic language or provide ethical decision trees, but it does not feel empathy or hold ethical convictions. In education, a fundamentally human endeavour, the lack of genuine understanding in AI means it cannot replace the authentic human connection and meaning-making that drive transformative learning.

Creativity, Transfer, and Abstraction

Human brains are remarkably creative and adaptable, capable of forming abstract connections across very different domains.

A hallmark of human learning is transfer, we can apply knowledge from one context to solve problems in another. For instance, a student might transfer patterns learned in music to grasp mathematical concepts, or use lessons from gardening to illuminate ideas in leadership. This kind of ‘distant’ transfer involves abstract thinking and analogy, rooted in our ability to reimagine experiences. Our creativity is not merely recombining known snippets; it is often imaginative and generative, producing genuinely novel ideas grounded in our lived experience, emotions, and tacit knowledge. Neuroscience research shows that when people generate original, creative ideas, their brains engage widespread networks that integrate memory, emotion, and executive control. For example, an fMRI study by Benedek and colleagues found that coming up with creative new ideas (as opposed to simply recalling information) activates distinct neural mechanisms, including interplay between frontal executive regions and the brain’s “default mode” network associated with internal thought (Butlin et al., 2023). Creative cognition draws on personal memory stores and emotional valuation, essentially the brain “reaching beyond” the available data or ‘memories’ to imagine possibilities. AI, in contrast, is fundamentally constrained by its training data and algorithms. Machine learning models excel at pattern recognition and can generate outputs by recombining elements from their vast datasets. This can certainly produce impressive results that seem novel (for example, a language model writing a new poem). However, AI’s “creativity” remains bounded by the scope of its input data and programmed objectives. It cannot truly step outside of those bounds or inject the kind of personal meaning and emotion that human creativity can. As a result, AI-generated ideas, while sometimes useful or surprising, lack the open-ended imaginative spark that characterises human innovation. Moreover, humans are experts at analogical reasoning, drawing parallels between superficially unrelated situations, which is crucial for deep learning and transfer. We form abstract schemas and metaphors (e.g. “the atom is like a solar system”) that help us understand new concepts. AI systems do not form such analogies unless explicitly trained to, and even then, they do not experience the insight. In educational settings, a teacher’s creativity in adapting a lesson on the fly, or a student’s ability to connect a story from history to a present-day personal experience, exemplify the kind of domain-flexible, context-rich thinking where humans shine.

While AI can generate multiple solutions quickly, it does so without genuine insight or cross-domain understanding, its “novelty” is ultimately an echo, or even ‘hallucinations’ of data. Thus, human creativity, with its neural basis in dynamic, emotionally-informed brain networks, remains steps ahead, enabling the imaginative leaps and meaningful analogies that drive transformative learning experiences.

Error Awareness and Metacognition

One of the brain’s powerful tools for learning is its ability to recognise errors and adapt, not just automatically, but with self-awareness.

Neuroscience research has identified specific signals and regions in the brain that monitor performance and flag mistakes. For instance, when a person makes an error, within milliseconds the brain’s anterior cingulate cortex (ACC) generates an “uh-oh” signal known as the error-related negativity (ERN), detectable in EEG readings (Johnson, Pinar et al. 2015; Orr & Hester, 2012). This reflects the brain’s early, automatic registration of something going wrong. Almost immediately, a host of cognitive control processes kick in, our attention heightens, and we often slow down and analyse what happened to avoid repeating the mistake.

In effect, the human brain has a built-in performance monitoring system that not only reacts to errors but can also learn from them by adjusting behaviour. Research consistently implicates the ACC and connected prefrontal regions in this error monitoring and adaptive control. Notably, if this neural network is underactive or impaired (for example, in certain clinical conditions), individuals show deficits in learning from mistakes. Beyond the automatic neural response, humans possess metacognition, awareness of our own thinking and learning processes. We can reflect on what we know and don’t know, evaluate our strategies, and make deliberate changes. After making an error, a learner can experience an emotional realisation (“I got it wrong and I see why”) and then consciously adjust their approach. This conscious awareness of mistakes is fundamental to learning and development. Put simply, effective goal-directed behaviour depends on recognising when a response is inaccurate and flexibly adapting actions to correct it (Orr & Hester, 2012).In fact, bringing errors to conscious awareness has been linked to better motivation and strategic adjustments, when we know we got something wrong and care about it, we are more likely to improve next time. Children develop these skills over time, and supportive educational experiences can foster better error awareness. For example, a recent fMRI study compared children in Montessori vs. traditional schools on an error-based learning task. It found that while accuracy was similar, Montessori students showed different brain connectivity patterns, more ACC–frontal connectivity after mistakes, suggesting they were engaging brain networks to self-correct and learn from errors (Denervaud et al., 2020). How does AI compare? Current AI systems lack genuine self-awareness or metacognition. An AI algorithm will certainly respond to errors in the sense of adjusting weights or updating parameters if programmed to do so (as in machine learning training), but it has no conscious sense of “I was wrong, let me reflect why.” AI does not truly know what it doesn’t know, it can be supremely confident in a wrong answer because it cannot feel uncertainty the way a human student can. It will not strategise about how to avoid a mistake in the future beyond what its optimisation function dictates. This is evident in AI language models that sometimes hallucinate false information with complete confidence. They have no internal alarm bell equivalent to the human ACC saying “this doesn’t seem right.” In classrooms, this difference is critical, underscoring the need to embed digital literacy more deeply into curricula. A student with good metacognitive skills will notice a misunderstanding and seek help or adjust approach, a key to mastery. An AI tutoring system, in contrast, might flag a wrong answer but it cannot empathise with the student’s confusion or dynamically change its pedagogy with insightful reflection. Thus, the human capacity for error awareness and reflection ensures that learning is an active, self-correcting journey, rather than a mere output of pre-programmed responses. Educators can explicitly teach this students, for example, by normalising mistakes as opportunities and guiding reflection. And when students use AI, they will need those metacognitive skills more than ever to critically evaluate the accuracy of AI-generated outputs.

Emotions and Social Intelligence

Education is fundamentally a social and emotional enterprise. Viewed from a neuroscientific lens, emotions are not just additions to thinking; they are deeply integrated regulators of attention, memory, and decision-making. The limbic system, including structures like the amygdala, interacts continuously with cognitive regions to modulate what we notice and what we remember. Simply put, we tend to learn and recall information that we care about, that we assign relevance and emotion to. When students are curious or engaged, their attention sharpens and memory retention improves, whereas stressful or threatening experiences can leave lasting imprints on memory through hormone-driven consolidation processes. Emotions also guide moral judgments and interpersonal understanding through complex neural networks. Humans have evolved a sophisticated social brain that enables empathy, theory of mind (understanding others’ perspectives), and moral reasoning. Neuroscience has uncovered mechanisms like the mirror neuron system, brain cells that fire both when we perform an action and when we see someone else perform it, which may underlie our ability to intuit others’ intentions and feelings.

For example, when we watch a peer struggle to answer a difficult question, our own brain may mirror their tension, giving us an intrinsic sense of their effort. An elegant study by Singer et al. (2004) demonstrated this empathic mirroring in the context of pain: volunteers’ brains were scanned while they received a painful stimulus and while they saw a loved one in pain.

The results showed that the affective pain centres (like the anterior insula and rostral ACC) lit up in both cases, when feeling pain themselves and when empathising with another’s pain, whereas the sensory pain regions only activated for one’s own pain. Moreover, the degree of ACC/insular activation correlated with individuals’ self-reported empathy levels (Singer et al., 2004). This neural evidence reinforces what educators know: empathy is real and biologically rooted, and it enables the trust and rapport that make learning environments feel safe and meaningful. In the classroom, social intelligence allows teachers to read the room, noticing the confused frown on a student’s face or the excitement in another’s eyes, and respond supportively. It allows students to collaborate, to consider classmates’ viewpoints, and to build a community of learning. AI, no matter how well it is programmed to recognise sentiment or generate polite responses, does not experience emotion or empathy. An AI teaching assistant might be able to say, “I’m sorry you’re having trouble, let’s work through this,” but it does not actually feel concern or patience. Students (especially younger ones) are astute at sensing the difference between genuine affection and a scripted response. While AI can mimic some empathetic behaviours through sophisticated natural language processing, it lacks the authentic social cognition that humans have via our mirror neuron networks and emotional brain circuits. This limitation affects trust and relationship-building. Research on social robots and AI tutors in education finds that although these tools can be engaging, many students still prefer human interaction for emotional support. There is a depth of relational connection, the feeling of belonging, that arises from human-to-human empathy, which AI currently cannot replicate. This is why roles like mentor, coach, and nurturer remain uniquely human. A teacher can inspire with a passionate story, share a spontaneous laugh with students, or detect a child’s anxiety and adapt the lesson, all leveraging emotional awareness. Learning is enhanced when students feel emotionally connected and supported, because positive emotions open up neural pathways for exploration, whereas fear and alienation impede curiosity.

Ethics, Values, and Agency

Education is not only about the transfer of knowledge and skills; it is profoundly about values, ethics, and the development of agency in young people. Teachers don’t just teach math or literature, they impart fairness, encourage inclusion, model kindness, and cultivate students’ sense of responsibility.

Humans approach decisions and behaviours in education within rich cultural and ethical contexts. Our brains integrate cognitive and affective evaluations with an intrinsic (if sometimes subconscious) sense of right and wrong. We make judgments informed by empathy, societal norms, and personal principles. This capacity is tied to frontal lobe networks and socio-emotional circuitry that evaluate consequences in terms of values, not just utility. AI systems, on the other hand, have no innate values or moral compass. They operate based on the objectives set by their programmers and the data they are trained on. If an AI’s training data or reward function embeds a bias or a harmful assumption, the AI will perpetuate it, without any understanding that it is doing harm. This has been seen in various domains: for instance, machine learning models in facial recognition that were trained on unrepresentative datasets showed accuracy disparities across racial groups, reflecting biases with potentially unethical outcomes. Unlike a human teacher, who can consciously strive to be fair and correct for bias, an AI has no intent to be fair unless fairness is explicitly defined in its code. In educational uses, this is critical. For instance, an AI system might disproportionately recommend advanced coursework to male students due to hidden gender biases in its training data, not from intent, but from replicating patterns it has learned. It takes human oversight and ethical agency to catch and correct such issues.

Moreover, humans can make context-dependent ethical decisions that AI would struggle with. For example, consider an AI tutoring system that detects a student has cheated on an assignment. A human teacher might discern why, perhaps the student is under extreme stress or didn’t understand the material, and choose a compassionate response (like a second chance and a talk about integrity). An AI might only see a rule broken and issue a penalty, not understanding the broader context or the opportunity for a character-building conversation. Because of this lack of intrinsic values, we must be cautious about how much authority and autonomy we give AI in educational settings. It is vital that educators remain in the loop to ensure decisions align with human values like equity, privacy, and well-being. Digital and AI literacy education for students become crucial as well. Students need to learn that AI outputs are not infallible or value-free, they should be taught to critically appraise AI-provided information and recognise potential biases. Encouragingly, awareness of this need is growing.

A recent study of university students in Austria found that improving students’ understanding of how AI works (its technical underpinnings and limitations) and their ability to critically evaluate AI outputs was associated with better use of AI tools and greater academic self-efficacy (Bećirović et al., 2025).

In other words, educating students about AI’s strengths and weaknesses can empower them to use these tools responsibly and effectively. The study underlines that AI literacy, including ethical and critical thinking about AI, should be part of modern education so that the next generation can navigate an AI-influenced world conscientiously.

Finally, it’s worth noting that AI’s lack of agency also means it will reflect whatever values (or lack thereof) are embedded in its design and training. This puts a great responsibility on the designers and users of educational AI. We must actively embed humanistic values into educational technologies, transparency, inclusivity, respect for learner autonomy, rather than assume the technology is neutral. In essence, the human brain’s capacity to care about why something is done, not just how, ensures that education is guided by purpose and principle. AI can assist with the how (efficiently optimising tasks, analysing data at scale), but only humans can truly ask whether we should do something and shape education according to core values and ethical goals.

Embracing Complementarity: Humans and AI in Partnership

Given the human advantages in meaning-making, creativity, metacognition, emotional intelligence, and ethical reasoning, it is clear that AI will not replace the human brain where it matters most in education. The compassionate mentor, the imaginative coach, the wise role model, these roles require a mind and heart that AI lacks the capacity to embody.

However, this does not mean AI has no place in the classroom. On the contrary, when perceived as a tool used for good, AI holds significant potential to enhance connection and belonging rather than diminish them. The key is to frame AI as a complement to human educators and learners, not a competitor. Neuroscience and education research together suggest an ideal synergy: let AI handle what it excels at, speed, scale, and pattern recognition, to support and amplify the human strengths of meaning, empathy, and creativity. For example, AI-driven tools can quickly analyse which skills a student is struggling with and provide targeted practice, freeing up the teacher’s time. That teacher can then spend more time on rich human interactions that provide relevance, connection and a sense of belonging: discussing the meaning of a story, encouraging a love of learning, or facilitating a collaborative project. AI can assist in personalisation, ensuring each student gets exercises at the right level, which, if guided by teachers, can lead to greater student engagement and a sense of being supported.

Recent studies have noted that well-implemented AI tutoring systems can modestly improve performance and motivation, especially when they allow individualised pacing and feedback (Bećirović et al., 2025). Students often report appreciating AI tools for quick help or practice.

But importantly, those positive outcomes occur when human guidance is present: a teacher orchestrating the use of AI, interpreting its suggestions, and maintaining the relational climate of the classroom. On the other hand, research also warns of potential downsides if AI is integrated poorly. Overreliance on AI without human mediation could lead to reduced development of students’ own critical thinking and creativity, and, as one study highlighted, even a loss of human interaction between teachers and students. This would erode the very connection and belonging that we seek to strengthen. Therefore, schools should integrate AI thoughtfully, using it to augment human connection rather than replace it. For example, an AI-powered learning management system (LMS) could manage administrative tasks or initial grading, freeing teachers to focus on building relationships with students. Similarly, AI-supported discussion forums can draw in every learner, including quieter voices, and provide insights that teachers can then weave into in-person conversations that strengthen classroom community. When teachers and students are trained in AI literacy, they can critically evaluate AI outputs together, turning it into a learning opportunity that also reinforces trust (students see the teacher as a guide through the AI’s strengths and flaws).

Conclusion

The human brain remains the seat of what makes education truly transformative: the spark that ignites curiosity, the shared laughter and wonder of discovery, and the moral conviction that shapes a school’s ethos.

Neuroscience reminds us that conscious understanding, creativity, metacognition, empathy, and ethical judgment are deeply rooted in our biology and development. These capacities ensure that humans will always lead education where it matters most, nurturing whole individuals and communities.

AI is undeniably powerful at processing vast amounts of information, yet it remains a tool shaped and guided by us. When used wisely, it can help identify learning gaps, deliver adaptive support, and even foster belonging through personalised attention. Still, in matters of meaning, trust, and inspiration, nothing rivals the human brain. The future of education is not a contest between AI and humans, but a partnership, one where educators harness technology to empower learners while ensuring the heart and humanity of teaching remain at the centre.

Implications for Future Generations

Students entering school today will never know a world without AI. This reality makes it essential to teach them, not just through theory, but through practice and reflection, how to navigate AI responsibly. Digital and AI literacy must become core competencies, equipping young people to evaluate outputs critically, use tools ethically, and adapt confidently.

At the same time, human connection remains paramount. Children learn through relationships and belonging, not content delivery alone. In an AI-rich world, ensuring genuine connection in classrooms is more important than ever. A balanced approach, embedding digital literacy while intentionally cultivating empathy, ethics, and belonging, will prepare the next generation to thrive as competent, caring, and socially grounded individuals in an AI-driven future.

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