Flow, Creativity and Dialogue with AI

One of my strongest memories from school is watching the classroom clock, willing the hands to move faster so break would finally arrive. Time felt like an enemy, something to be endured. Years later, when we introduced Thinking Together lessons in classrooms and evaluated the impact with talk around reasoning tests something different happened. Children became so absorbed in solving problems together that they ignored the bell and carried on talking while other classes streamed out. They didn’t want to stop. Time had somehow disappeared.

What happened in those moments? And what happens when we add AI to the conversation?

The Puzzle of Dialogic Flow

The problems children solved required genuine insight—after initial bafflement, a pattern suddenly appears and everything clicks into place. But the satisfaction came from more than just solving puzzles. In genuine dialogue, people don’t think separately and then pool their ideas; they think together. The experience isn’t “I found the answer” or “you found the answer” but “we found the answer.” When dialogue really works, participants become fully engaged in a shared flow of meaning, and awareness of time fades.

Mihaly Csikszentmihalyi called this phenomenon “flow”—deep absorption in which people are fully involved in an activity for its own sake. In flow, the ego falls away, time flies, and actions follow one another with an inner logic, like improvising jazz. He developed the concept through interviews with highly creative people who had transformed their fields, and they consistently described their most creative moments as intrinsically rewarding, joyful, and marked by a striking change in the experience of time.

What’s particularly relevant now is what Keith Sawyer, Csikszentmihalyi’s student, discovered: the most common place people report experiencing flow is in conversation with others. Creativity often arises between people, not just inside individual minds. This matters because it means we can’t understand creativity by studying isolated individuals—we need to understand the dynamics of dialogue.

This is where things get interesting for how we think about AI.

Not All Flows Are Created Equal

Flow describes what creativity feels like from the inside, but it’s not yet a full theory of creativity. After all, losing track of time isn’t enough. Certain video games can absorb attention and generate intense engagement, yet they may channel creative energy into short-term loops rather than anything larger. Scroll through social media and you might experience a kind of flow, but it’s qualitatively different from the flow of writing a novel or solving a mathematical proof.

What distinguishes creative flow from mere absorption? It seems to be about scale and direction. A mobile puzzle game involves insight and satisfaction, but it operates within a closed system designed for quick resolution. The creativity we most value in education and culture emerges from longer-term, open-ended dialogues—dialogues that stretch across disciplines, traditions, and generations.

This distinction matters because we’re now facing a new kind of conversational partner: artificial intelligence. Large language models can engage in extended dialogue, offer perspectives, push back on ideas, and help us think through problems. So which category do they belong to? Is interacting with ChatGPT more like playing Candy Crush or more like reading Freeman Dyson? More like scrolling social media or more like having a conversation with a thoughtful colleague? The answer isn’t obvious, and it isn’t determined by the technology alone—it depends on how we frame the interaction.

The Pattern of Creative Emergence

Looking closely at creative flow, a recurring pattern appears. First, there’s division: the world appears solid and external, and you feel confronted by something you don’t understand. Freeman Dyson described first encountering quantum field theory as something outside himself, resistant and opaque—a wall of mathematics that refused to make sense.

Then comes internalisation. Dyson immersed himself in reading, absorbing the literature of his field. What was once fixed and external became fluid and dialogic, explored from multiple angles in imagination. He held conversations with the ideas, turned them over, saw them from different perspectives.

Finally, externalisation: insight arrives, often unexpectedly, and seems to write itself into the world. Dyson famously remarked that when he was writing equations, his fingers seemed to do the work rather than his conscious mind. The boundary between inside and outside had shifted.

Flow often accompanies the moment when this tension between inside and outside temporarily resolves. But what exactly is shifting?

The Boundary That Thinks

To understand why time disappears in flow, we need to shift focus from time itself to boundaries. The philosopher who perhaps more than any other focussed on unpacking the nature of experience, Maurice Merleau-Ponty, pointed out that perception isn’t simply a subject observing an object, but an interaction in which we’re always, in some sense, on both sides. When you touch your left arm with your right hand, you experience yourself both as touching and as being touched. This interaction can’t be neatly located inside space and time; it’s one of the processes through which our sense of space and time is continually constructed.

We experience space and time as constraints when we identify with a bounded self located within them—waiting, watching the clock, feeling trapped. In flow, something shifts. Identification moves from a fixed image of the self toward the boundary where self and world meet and co-create each other. Flow isn’t just immersion in activity but participation in the dynamic process that generates meaning, space, and time.

In dialogue, this shift takes a distinctive form. Participants stop identifying primarily with themselves or even with the group as a bounded entity. Instead, they identify with the flow of dialogue itself. This is what I mean by dialogic flow.

And here’s the provocative question: can this happen with AI?

AI as Dialogic Partner or Creative Dead-End?

At first glance, interacting with AI seems closer to playing a game on a screen than participating in genuine dialogue. AI can easily short-circuit creativity by offering quick answers, closing down questions, and rewarding efficiency rather than exploration. Used this way, it becomes another technology that absorbs attention while diverting creative energy away from larger cultural dialogues. The concern is legitimate—we’ve seen how recommendation algorithms can create compulsive loops that feel engaging but lead nowhere.

But this isn’t the only possible framing.

Consider a researcher wrestling with a vague tension—an idea that doesn’t quite work, a model that predicts most of the data but fails mysteriously in certain conditions. Rather than asking AI for the answer, they use it to hold the tension open. They describe their model; the AI reformulates it, revealing assumptions they hadn’t noticed. They explain the anomaly; the AI suggests counter-examples from different domains. They push back; the AI adjusts. Many suggestions are rejected; some are taken up and transformed.

Over time, something strange happens. The thinking doesn’t feel like it’s coming from them alone, nor from the AI alone. It emerges in the interaction. The familiar signs of dialogic flow appear: time passes unnoticed, individual authorship loosens, the dialogue takes on a life of its own. The AI functions like the literature Dyson absorbed, or like a thoughtful conversational partner—it helps turn what was initially external and fixed into something fluid and dialogic.

This isn’t because the AI is intelligent in itself. It’s because it helps sustain a space in which identities, ideas, and meanings remain open and negotiable. The AI becomes part of the dialogic gap — not so much an absence as a responsive surface that pushes back, surprises, and sustains creative tension.

The Critical Difference: Scale and Continuity

But there’s a crucial test: does the interaction with AI connect to longer-term, larger-scale dialogues? This is where many AI interactions fail. If you use AI to quickly generate a blog post, tick a box, and move on, you’ve created a closed loop—satisfying in the moment but disconnected from any larger conversation. The flow might feel real, but it’s more like the video game flow: absorbing but ultimately self-contained.

The question isn’t whether AI can support dialogic flow—clearly it can, under the right conditions. The question is whether we can structure our interactions with AI to support participation in ongoing, open-ended cultural dialogues about questions that matter: scientific understanding, ethical complexity, educational practice, artistic meaning.

When a physicist uses AI to explore different mathematical frameworks, testing intuitions and discovering unexpected connections, and those insights eventually feed into papers, collaborations, and further research—that’s AI supporting genuine dialogic flow. When a teacher uses AI to brainstorm different ways of explaining a difficult concept, then brings those ideas into conversation with students and colleagues, refining them through actual classroom experience—that’s dialogic.

When someone uses AI to generate content that mimics these activities but remains disconnected from any real dialogue or ongoing inquiry—that’s the closed loop we should worry about.

Implications for Education

This distinction has profound implications for how we think about AI in education. The dominant concern remains about cheating or plagiarism: students using AI to generate essays they didn’t write. But this frames AI purely as a tool for producing outputs, which guarantees it will short-circuit genuine learning.

The deeper challenge is whether we can frame AI as a participant in dialogic flow—a way to enter into more complex, sustained inquiry rather than a way to avoid it. This would mean:

Teaching students to use AI dialogically rather than instrumentally. Not “give me an essay on Shakespeare” but “help me understand why Hamlet’s delay matters by exploring different critical perspectives, pushing back on my interpretations, and helping me see what I’m missing.”

Designing assignments that require ongoing dialogue rather than final products. If the assignment is a single essay, AI can short-circuit the process. If the assignment involves developing understanding through iterative dialogue—with AI, with texts, with peers—that might be harder to fake.

Recognizing that dialogic flow has always required learned skills. We don’t assume students naturally know how to have productive conversations with peers, with texts, or with teachers. Why would we assume they know how to have productive conversations with AI?

Acknowledging that some uses of AI will create genuine creative flow and others won’t. The technology itself doesn’t determine this—the framing does.

A Shift in How We Think About Creativity

The introduction of AI into creative and educational practice forces us to clarify something we might have taken for granted: creativity isn’t a property of individuals acting in isolation, but a property of dialogic processes. It emerges at boundaries—between people, between person and tradition, between question and response.

AI can participate in these boundaries. It can help sustain the kind of open-ended dialogue from which genuine creativity emerges. But it can also create closed loops that feel engaging while actually cutting us off from larger dialogues.

The challenge isn’t whether to use AI—for most of us that question is already settled. The challenge is how we frame it: as an answer machine that closes questions down, or as a partner that helps hold questions open. As a shortcut past the hard work of thinking, or as a tool for sustaining the kind of sustained inquiry where real creativity happens.

The difference between these framings isn’t technical. It’s about how we understand creativity itself, and what kinds of educational and cultural practices we want to preserve and develop. In the end, the question of AI and creativity is also a question about what kinds of human beings we want to become, and what kinds of dialogues we want to participate in.

Flow, understood dialogically, isn’t about losing yourself in distraction. It’s about finding yourself at the boundary where meaning emerges—in the space between self and other, question and answer, known and unknown. Under the right conditions, AI can become part of that boundary. The task for education is to create those conditions deliberately, rather than letting them emerge by accident or not at all.