My previous essay, Building a Generative Artwork That Can Change Its Maker, ended with the thought that the artwork had answered back. This essay begins where that formulation became insufficient. “Answering back” still suggests two relatively stable partners: a maker on one side and a completed work on the other. What actually developed was less orderly. A visual system generated forms; the forms changed my questions; another person’s response changed how I understood the forms; an old theoretical vocabulary became newly legible; AI helped externalize the change; the website preserved it; and I later returned to my own AI-assisted writing as a reader. By then, neither the work nor the person encountering it was quite the same as before.
The beginning was much less serious. I wanted my personal website to look cooler. Its brutalist and experimental design needed a visual background that felt alive without competing with the articles and photographs in the foreground. I did not begin with Romano Guardini, Bildung, metanoia, second-order cybernetics, distributed cognition, or a theory of AI-assisted authorship. I began with tiling. Tiling became visually repetitive. Several mosaic forms were more interesting, but they still required me to imagine and describe too many arrangements one by one. Mathematical structures then offered something verbal instruction could not provide efficiently: rules capable of producing families of compositions that I had not individually designed. The generator engine began by generating layouts. Only later did it begin generating questions.
Even that summary is retrospective. At no point did the next stage feel guaranteed. Sometimes an output was merely ugly. Sometimes AI misunderstood the aesthetic objective. Sometimes I accepted an explanation because it sounded coherent and then rejected it when the visible work contradicted it. Sometimes a technical solution disclosed an artistic possibility I had not requested. The project moved through dissatisfaction, correction, accident, resistance and delayed recognition. Its later significance cannot be used to pretend that the first intention secretly contained the whole development.
My first complete draft of this essay nevertheless reproduced that tendency in a subtler form. It placed art and Prof.ssa Dohna at the centre, kept the technical material proportionate and included the concepts I had reached. Yet when I reread it, I recognized the conclusions more readily than the path by which I had become able to think them. “Recursive conservation of insight,” “diachronic relational authorship” and “metanoia of epistemology” appeared as well-formed results. The draft said that I had hesitated, but it did not always let the reader inhabit the hesitation. It preserved the map and compressed the journey. That recognition changed the objective of revision. I objected to the implicit model of knowledge in the draft: it made understanding look like a possession displayed after the struggle was over. I asked instead for the genealogy—the early answers that seemed sufficient, the objections that unsettled them, the evidence that forced another distinction and the questions that remained after a satisfying formulation. This second pass therefore becomes part of the case it describes. I encountered an AI-assisted text that was substantially mine in direction but no longer adequate to my experience; that encounter changed the instructions; the changed instructions altered the text that a later reader, including me, will encounter.
The technical reality can be stated briefly. The work is a browser-based visual environment integrated into WordPress. A curated archive of images and animations is arranged by pseudorandom selection and mathematical structures into changing compositions. Navigation or refresh can activate another manifestation. AI substantially assisted the development of the system, but a conventional programmer could have built its runtime generativity without AI. The mechanism matters because the philosophical claims arose from something that actually ran, produced visible relations, failed in particular ways and could be encountered by other people. The subject here is what happened when making exceeded the reason for which I had begun making.
The new question is therefore not simply whether the browser compositions count as art. It is whether the relevant artistic unit gradually expanded because each earlier boundary could no longer describe the evidence. At first, the artwork seemed to be one generated composition. Then it seemed to be the system plus its many possible manifestations. Then the visitor’s action and situated encounter became relevant. Finally, interpretation, writing, another person’s response, later rereading and revised intention began returning causally into the practice. If those returns alter what is made next, are they external commentary on the artwork, or have they become part of its material history?
I do not want to answer by declaring that everything is art. That would make the category too generous to explain anything. The criterion I arrived at is narrower: an event becomes artistically integrated when it is causally incorporated back into the practice. A passing comment is not automatically part of the artwork. Prof.ssa Yvonne Dohna-Schlobitten’s response matters artistically because it changed my interpretation; that interpretation changed the questions I brought to theory and AI; those questions changed how I understood future design; and those changes can alter later encounters. The return, not mere proximity, is decisive. This essay follows that return. It is the transformation of creation through an actual human encounter: the artwork gave me a new point of entry into questions running through Prof.ssa Dohna’s work, while those questions gave me new ways of seeing the artwork. Around that encounter, AI, writing and the website became unusual participants in a longer process through which the artist could become the beholder of their own work, and the later beholder could change the person who would continue creating.
I did not set out to make a theory
The temptation in a polished essay is to make the past look intelligent. Once an artwork has acquired a philosophical vocabulary, the writer can quietly move that vocabulary backward until every early decision appears to have anticipated the conclusion. In my case, that would falsify the most important evidence. The ordinary motive—the wish for a cooler website—is not an embarrassing detail to be removed after the project becomes serious. It shows that the theory emerged through making rather than being illustrated by it.
The first changes were aesthetic and practical. A single background could become monotonous. Random selection introduced variety but not necessarily composition. Tiling multiplied an image without creating sufficiently rich relations. Mosaics allowed contrast among records, yet hand-specifying more and more arrangements soon became a poor way to explore a large visual possibility space. Mathematical structures changed the problem. Instead of describing each desired layout, I could establish procedures that distributed scale, territory, adjacency, repetition and interruption. The work moved from choosing a picture to constructing conditions under which pictures could meet. That sequence contained several temporary solutions. At first, I thought variation itself would solve the aesthetic problem: if the background changed, it would remain interesting. The actual page contradicted that assumption. Randomness could prevent repetition while producing no relation worth seeing. Tiling then seemed attractive because it transformed one image into a field, but repetition quickly became another kind of monotony. Mosaics appeared to solve that problem by placing several records together. For a while, this felt like the answer. Then each new arrangement required another verbal description, another special case and another correction. I was accumulating layouts without yet creating a visual language.
I initially responded by describing more precisely what I wanted to AI. That helped at the local level: one gap could be reduced, one panel could be enlarged, one collision could be avoided. But the improvement exposed the limitation of the method. I could continue asking for individual compositions, or I could ask what kind of rule would generate a family of compositions. The original question—“How should these images be arranged?”—became “What relations of scale, adjacency and interruption should be capable of arranging themselves differently?” The move toward mathematical generators did not arrive as a theoretical commitment to procedural art. It arrived because verbal micromanagement had become aesthetically and conceptually exhausted.
AI helped translate that frustration into candidate procedures. Some proposals were mathematically neat and visually lifeless. Others were technically correct but too dense, too regular or too eager to display their own geometry. I rejected them not because their code failed, but because the visible work failed to sustain the tension I wanted between the foreground article and the background field. At other moments, an output I had not predicted produced a relation stronger than my prompt. Those cases forced me to revise my intention rather than merely the implementation. The evidence was not AI’s assurance that a procedure should work; it was what happened when the procedure met the images and the page.
That move belongs recognizably to generative art. Philip Galanter’s influential definition centres on an artist setting a system in motion with a degree of autonomy, and it explicitly refuses to tie generative art to one technology (Galanter, 2003). This protects an essential distinction: AI did not invent generative art, and AI was not required for the browser to produce varied manifestations. Random procedures, instruction-based art, mathematical composition and autonomous systems long predate current language models. The distinctive role of AI in my case emerged in the development and interpretation surrounding the runtime system, not in the bare fact that an algorithm could vary a layout.
At first, I treated each new output as evidence about the implementation. Did it fill the screen? Did the images remain legible? Did the foreground still dominate? Those questions were necessary, but actual outputs repeatedly made the brief itself unstable. A technically correct arrangement could be aesthetically dead. A failure could reveal that my description of the objective was too narrow. An unintended relation could make me want a capacity I had not known to request. The design process began to resemble Donald Schön’s account of a reflective conversation with a situation: the maker acts, encounters consequences, notices what the move has disclosed and reconstructs the problem (Schön, 1992). Schön’s sequence of seeing, moving and seeing again explains more of this history than the fantasy of a complete specification followed by execution. AI accelerated that conversation. I could move from a vague visual intuition to a candidate rule, from the rule to a running manifestation, from the manifestation to an objection, and from the objection to a revised concept in a short span of time. Yet speed did not make the development inevitable or automatically insightful. The useful unit was not “prompt followed by answer.” It was a recurrent encounter with resistance:
ordinary visual wish → first arrangement → dissatisfaction → new rule → unexpected manifestation → aesthetic or conceptual objection → changed understanding of the problem → further making
Only after this sequence had repeated did I begin to see that the system was not merely producing backgrounds. It was externalizing possibilities that could address the person who had initiated them. The old description—“a feature that makes varied layouts”—was still technically true, but artistically insufficient. The work had begun to teach me what I had been trying to make. This is also why the speed of the later reflection must not be confused with the age of the thought. Some of the strongest connections appeared over a few hours. But those hours depended on years of technical practice, humanistic study, prior writing, my relationship with Prof.ssa Dohna, familiarity with her vocabulary, and the existence of a live artwork capable of making that vocabulary concrete. Composition time is not formation time. AI compressed the time needed to formulate and connect; it did not manufacture the whole history that made recognition possible.
The boundary of the artwork kept failing
The first candidate boundary was simple: artwork = visible composition. It failed because no single composition exhausted the work. Each manifestation depended on a rule system, a curated archive, a viewport, a moment of execution and the visitor action that made this particular state actual. A screenshot could preserve one result, but it could not contain the possibility of another result. The artwork therefore seemed better described as generative system + manifestations.
I did not reject the first boundary through theory alone. I tried to preserve compositions as images and noticed what disappeared. The screenshot retained the arrangement but removed the possibility of refresh, the expectation of change and the visitor’s memory of what had been there before. It documented an appearance while omitting the conditions that made the appearance one event among others. That was the evidence that moved the system itself inside the artistic unit. For a time, system + manifestations seemed complete.
That second boundary also failed. A manifestation did not appear in an empty laboratory. It appeared behind an article, to a visitor with a history, perhaps after earlier visits and remembered combinations. Refresh was not authorship in the full sense, but neither was it irrelevant. The visitor performed a modest act of actualization: this possibility, now. The work had become system + activation + manifestation + encounter. Its aesthetic life included recurrence, disappearance, surprise, boredom and memory. My language overshot before it became more precise. I first described the visitor as a co-creator. The phrase captured something important—the visitor’s action helped determine which potential state became actual—but it also attributed too much control. The visitor could refresh, attend, remember or leave; they could not specify the geometry, records or relation that would appear. I therefore retreated from “co-creator” to a more modest performer of selection. What survived from the first phrase was participation. What had to be abandoned was the suggestion of equal authorship or complete control.
Art history already provides families of thought that prevent me from presenting this as unprecedented. Generative art treats the system as part of the work. Process and systems-oriented practices move attention away from a self-contained object toward operations, conditions and changing relations. Relational aesthetics made human relations and their social contexts central artistic materials (Bourriaud, 2002). Second-order cybernetics asks what changes when the observer cannot be treated as external to the observed system (von Foerster, 2003). My case belongs near these traditions, but it is not identical to any one of them. The distinctive problem that arose for me was the causal return from encounter into the ongoing construction of the work and, at the same time, into the formation of the maker.
This is where the third boundary became unstable. The article I wrote about the compositions changed how I presented them. That changed how Prof.ssa Dohna encountered the work. Her response changed what I reread and what I asked. The new interpretation began suggesting different artistic possibilities. Writing was no longer simply a label placed beside a finished object. Theory had entered the causal loop of practice. At that point, an expansive explanation became tempting. If code, output, visitor, memory, writing and response all mattered, perhaps the artwork was the entire surrounding ecology. AI could formulate that enlargement persuasively. I found the account exciting because it explained why the work felt larger than the screen. Then I objected: if every circumstance could be absorbed into the artwork after the fact, the boundary had not been expanded but dissolved. An incidental event and a transforming encounter would become indistinguishable. The explanation needed a test.
It would still be a mistake to count every surrounding event as part of the artwork. An unrelated administrative event, an incidental conversation or a stranger’s passing glance may belong to the work’s circumstances without becoming artistic material. The causal-incorporation criterion is stricter. Prof.ssa Dohna’s reaction becomes artistically consequential because the practice after that reaction is not the same practice as before it. If the encounter changes the artist’s understanding but never returns to selection, form, participation, preservation, presentation or future design, it may remain biographically important without yet becoming part of this artwork.
This was how the criterion emerged rather than how it began. Mere contact was too weak; “everything is connected” was too broad. I needed to ask what happened next. Did the event alter interpretation? Did the altered interpretation change an artistic decision, a question, a condition of participation or the future possibility space? An event becomes artistically integrated when it is causally incorporated back into the practice. The formulation did not prove that every return is good art. It distinguished a consequential return from an adjacent occurrence.
I therefore began using two provisional descriptions. Formative generative practice names a practice in which the system generates forms while the larger process participates in forming the people who continue it. Reflexive processual generative artwork emphasizes that the work’s history includes returns through interpretation and redesigned conditions. Neither phrase announces a new movement. They are attempts to name why “browser composition” had become too small and why “everything around it” would be too large.
The resulting artistic material can include code, algorithms, mathematical structures, images, visitor actions, writing and human response—but not merely because they are nearby. They become integrated when one alters the conditions under which another can occur. The artwork is not an infinitely expanding bag of associations. It is a trajectory of consequential returns.
Prof.ssa Dohna’s question reopened an old vocabulary
Prof.ssa Yvonne Dohna-Schlobitten has been part of my intellectual world for years. Our exchanges have often moved between very practical things—technical assistance, computer problems, translation and work on drafts—and the theoretical questions running through her work, including contemplation, encounter, figuration, Guardini, Bildung and metanoia. I had therefore been familiar with many of these themes long before this project. What changed through the generative artwork was the way I encountered them: ideas I had previously met through conversation, reading and translation suddenly acquired another kind of immediacy because they were now connected to something unfolding inside my own artistic practice. What changed was not simply how much I understood, but the form that understanding took. Concepts that had already been intellectually available to me became experientially concrete, and this new point of contact allowed me to notice relations that I had not previously had reason to see in quite the same way. That distinction matters relationally.
The immediate encounter with Prof.ssa Dohna had already been described in my previous essay, so I do not need to reconstruct the exchange in detail here. What matters for the present argument is what happened afterward. When I shared the generative work with her, her response unexpectedly reopened the relationship between the questions she had been developing around encounter, artistic creation and metanoia and the computational practice I had approached from a very different direction. What had initially seemed to me like two neighbouring intellectual worlds—her theoretical work and my technical experimentation—suddenly began to illuminate one another.
Her reaction did not provide me with a ready-made interpretation of the artwork. It did something more valuable: it interrupted the interpretation I already had. I began asking whether a generative system that could produce forms without itself undergoing the human experience of encounter actually weakened the importance of her questions, or whether it made those questions more urgent. The more I reflected, the more the second possibility seemed worth pursuing. The artwork was no longer interesting to me simply because it could generate unexpected compositions; it had become a concrete situation in which questions about the relation among making, beholding, meaning and transformation could be experienced rather than discussed only in abstraction.
This was also the point at which I returned to Prof.ssa Dohna’s theoretical work with a different kind of attention. I had encountered many of these ideas before, but the artistic experience now gave them another point of contact. At the same time, those concepts gave me a vocabulary for dimensions of the artwork that I had not previously known how to describe. The movement therefore became reciprocal: the artwork made her theory newly intelligible to me, while her theory made the artwork newly intelligible to me.
The AI dialogue reinforced that temptation by producing elegant variations of a comforting answer: the computational artwork had not displaced her inquiry but confirmed its relevance. I recognized something true in that formulation. Yet it seemed too easy. If the artwork merely “proved” that Prof.ssa Dohna had been right all along, then her destabilizing question had been converted into a device for validating a theory. The account would flatter both the project and my artwork while leaving the disturbance untouched. I asked a harder question: what, exactly, had become more intelligible, and what evidence showed that this was more than a retrospective intellectual decoration? That objection changed the next action. Instead of continuing to generate reassuring interpretations, I returned to her published work. The rereading was not background research added to strengthen an already finished thesis. It was a test of the thesis. I wanted to know whether the concepts I was invoking actually described the process that had occurred, whether I had remembered them accurately and whether they resisted my desire for a satisfying symmetry.
In “What we see looks back at us,” she links seeing, love and artistic creation through a space in which a person or thing may disclose itself rather than be reduced to use (Dohna Schlobitten, 2022). Verso nuovi occhi explicitly moves among theoretical, experiential and contemplative knowing and connects artistic form with spiritual discernment (Dohna Schlobitten, 2023). Her study of Guardini’s Weltanschauung asks how thought becomes form and how artistic theory may arise from artistic practice rather than merely being applied to it from outside (Dohna-Schlobitten, 2024).
These were not new words to me. What changed was the structure of relevance. I had been treating theory largely as a source of statements to understand. The artwork made me experience theory as a new capacity of perception. A programmer who learns the concept of a race condition can suddenly see a problem that was physically present in the code before the concept was available. In another register, encounter and figuration allowed me to see that the generated image was not the only object of artistic attention. The relation among manifestation, beholder, response and renewed making had also become visible.
This was the evidence that the first reassurance had lacked. Her texts did not simply provide the word “encounter” for a visitor seeing an image. They redirected attention toward the transformation of seeing itself and toward the way form can open a relation without exhausting the other. That returned me to the exchange with a different question. Perhaps her project and my artwork were not competing explanations of creation. Perhaps each had disclosed something the other had not yet made visible to me:
A powerful theory does not merely answer visible questions. It renders previously invisible questions askable.
Delayed recognition should not be romanticized. A theoretical framework does not become more convincing merely because its relevance becomes apparent only later, and a powerful experience does not exempt an interpretation from criticism. The correction is more modest: my inability to perceive significance may be evidence about the encounter between me and the theory, not decisive evidence about the theory itself. Sometimes additional information is needed. Sometimes criticism is justified. But sometimes the missing condition is an experience capable of reorganizing what the existing words can disclose.
Guardini’s description of a work of art as opening a space in which people may move and encounter what becomes open before them acquired a new concreteness for me through the browser work (Francis, 2023). I do not claim that a generative background proves Guardini, or that my website has the spiritual or artistic stature of the works he discussed. The important event was reciprocal illumination:
The artwork gave me a new point of entry into questions running through her work, while those questions gave me new ways of seeing the artwork.
The dialogue eventually produced that sentence, and I felt an immediate sense of recognition. I did not accept it only because it was beautiful. I tested its two directions against the chronology. The artwork had indeed changed what I could perceive in her texts; the reread texts had indeed changed what I considered part of the artwork. Neither simply produced the other. The theory was not an intellectual ornament borrowed after the fact to elevate some moving images. The artwork was not a demonstration programmed to confirm a prior framework. They met through Prof.ssa Dohna’s response, and the disturbance between them altered how I could see both.
The experience also gave me a new way of seeing our long-standing intellectual exchange. Practical collaboration and theoretical conversation had often existed alongside one another, but through this project they unexpectedly began to converge. Questions arising from my own artistic practice resonated with themes she had been exploring for years, while her thought gave me new ways of attending to what was unfolding in the work. Whatever the encounter may mean from another perspective, for me it opened a dimension of our intellectual relationship that I had not previously experienced in quite this form.
Encounter became the operation of transformation
Before this sequence, encounter seemed to name what happened between a visitor and a generated composition. That account was not wrong; it was simply the first instance. I then encountered outputs I had helped make but had not individually composed. Prof.ssa Dohna encountered the artwork. I encountered her reaction. Her reaction led me to encounter her theory differently. That rereading led me to encounter my earlier judgments differently. Later, I encountered my own AI-assisted article as a reader. At each stage, something entered the next stage in a changed form.
An earlier AI question had been useful: “Where exactly is sense?” My first answer located it mainly in the human interpretation of a generated juxtaposition. The geometry could place two images beside one another; the beholder could recognize comedy, memory or theology in the relation. Prof.ssa Dohna’s response made that answer too narrow. Sense was not occurring only at the moment of looking. Her question changed what I later read, and the rereading changed what the work could become. Meaning had acquired a temporal and causal path. Another AI formulation then became important: “The person can become implicated.” I initially accepted it because it named the difference between observing an interesting output and discovering that the output makes a claim upon one’s own understanding. But I had to ask what “implicated” meant in evidence rather than rhetoric. In my case, it meant that I could no longer preserve the same account of Prof.ssa Dohna’s theory, our intellectual relation or my own role as maker. The phrase became useful only when tied to those observable changes.
state₁ → encounter → transformation → state₂ state₂ → encounter → transformation → state₃ state₃ → changed making → another possible encounter
I use “transformation operator” only as a conceptual image (but not as established scientific terminology): an encounter can confuse, wound, flatter, narrow or mislead. Nor is every change metanoia. The repeated structure matters because the same operation—meeting something not fully controlled by the present self—kept changing the state of the practice and of the person continuing it.
The exchange with Prof.ssa Dohna is the clearest human instance in this case. Her response interrupted my initial interpretation and changed what I subsequently read and asked. That change then returned into the artistic interpretation, giving the exchange formative as well as artistic consequence.
At first, “feedback” seemed to name the whole structure. Output returned as input; a later state differed because of an earlier response. I accepted the cybernetic description provisionally because it made the circularity visible. Then the comparison exposed its own limit. Feedback can adjust a parameter while leaving the objective untouched. Encounter can introduce a new category of relevance. A visitor clicking “like” might change the probability assigned to an image; a human response can instead make the artist reconsider what kind of problem the work has created. The first changes a value inside a given model. The second may change the model.
This is also where the observer moved inside the history being observed. Second-order cybernetics became relevant because it directs attention to systems in which observation and description cannot be treated as external, neutral additions (von Foerster, 2003). I initially treated the comparison almost as an explanation. On reflection, it was better understood as a diagnostic aid. Cybernetics made the recursive position of the observer visible; it did not by itself explain the aesthetic, relational or theological meaning of the transformation. I built the generator, observed it, changed because of what I observed, wrote about the change and allowed the writing to influence subsequent intentions. Prof.ssa Dohna encountered the work, and her response changed the way I, as its maker, would continue it. AI helped describe the loop and thereby became another causal participant in the loop it described.
The artwork’s boundary consequently expanded once more:
generative system → manifestation → human encounter → response → another encounter with that response → changed interpretation → changed artistic intention → future system and manifestation
The causal-incorporation criterion prevents this from becoming metaphysical inflation. What enters the work is the response she actually gave, my documented reaction to it, the interpretive changes that followed and whatever future practice those changes genuinely affect. The criterion preserves both relational openness and respect for the other person’s independence. Encounter thus ceased to be merely a theme represented by the artwork. It became the recurring operation through which the practice changed state. That is aesthetically significant because the work’s form now includes not only arrangements on a screen but the temporal pattern by which arrangements, responses and revised conditions become consequential to one another. The artwork no longer ends where the pixels end.
The artist returned as a beholder
A further instability appeared when I returned to the article I had written with AI about the project. During its production I had been an originator, witness, questioner and editor. I supplied the lived events, the project, the quotations, the objections and the direction of inquiry. AI generated a great deal of the verbal surface. At the time, I was close enough to the process that the text still felt like an extension of an active conversation. After publication, temporal distance changed its status. It became an object I could meet.
On rereading, I sometimes found an argument whose consequence I had not fully absorbed when I approved the sentences. This is not the romantic myth of an autonomous machine secretly placing messages in my work. It is a more ordinary and, for me, more consequential fact: a person can help cause a text without being simultaneously present to every implication that the text makes available. Writers have always discovered things in their own drafts, and artists have always encountered consequences they did not consciously plan. AI intensified the separation among initiating, articulating, selecting, publishing and later understanding.
My first explanation was less generous: perhaps I had simply failed to read carefully enough. That remained a legitimate possibility, especially when AI had produced a long passage quickly. It also fed the discomfort I later discuss about publishing words whose implications I had not fully mastered. Yet the explanation did not cover everything. I had read, selected and approved the text. What changed during the later encounter was not only my attention to the same sentence but the history I brought to it. Events and further reflection had made an implication available that the earlier reader could not yet organize in the same way.
The AI dialogue proposed a formulation close to “the creator can become the beholder of their own AI-assisted creation.” I recognized the experience immediately, but I resisted the suggestion that this was entirely new or uniquely artificial. Writers have long been surprised by their drafts; painters encounter forms they did not plan; temporal distance changes every rereading. The stronger revised claim was comparative rather than absolute: AI can greatly widen the interval between originating a problem, producing its verbal surface and fully encountering what that surface makes thinkable. It intensifies an old artistic possibility and distributes it across more agents and times.
me as originator and witness → AI-assisted articulation → selected and published text → temporal distance → me as later reader → renewed encounter → changed understanding → further writing and making
I had become a beholder of my own AI-assisted creation, and the beholder could now change the creator. This mattered artistically because the return did not stop at private appreciation. What I understood later changed the questions I asked about the visual system, Prof.ssa Dohna’s project and the meaning of authorship. The article joined the causal history of the artwork. It was simultaneously a record of one state of understanding and an instrument capable of altering a later state.
That altered how I understood the website itself. I had treated it mainly as a publishing platform: a place where an article became available after it was written. In practice, it also behaved as a temporal cognitive apparatus. At one moment, a fleeting intuition could be articulated with AI and preserved. At a later moment, a changed self could reread that preserved object and recognize something that the earlier self had not yet been able to hold. A new discussion could then revise the recognition and produce another object for a still later return.
“Archive” was my first better word. Unlike “publication,” it emphasized preservation across time. But an archive still sounded passive, as though the website merely stored finished states. My own rereading supplied contrary evidence: an old article could reorganize a current question, and the current question could send me back to Prof.ssa Dohna’s work with new attention. Storage had become an active relation among differently situated versions of myself.
t1 fleeting intuition → AI-assisted articulation → archive t2 changed self → rereading → new recognition t3 renewed discussion → revised understanding → new archive
Calling this a cognitive apparatus does not require the extravagant claim that my website literally thinks. I was briefly attracted to the stronger language of an extended or distributed mind because it captured how much work the external archive was doing. Then I narrowed the claim. Work on distributed cognition and the extended mind has long challenged the idea that every cognitively important operation must remain inside an individual skull. Hutchins showed cognition coordinated across people and material structures in real practices; Clark and Chalmers argued that external resources can sometimes participate in cognitive processes rather than merely report their results (Hutchins, 1995) (Clark and Chalmers, 1998). Those theories situate the problem, but I do not need to prove that the website is literally part of my mind. My narrower claim is functional: the archive changes what I can remember, compare, revisit and develop. When its preserved formulations later alter my judgment, it is causally active in the history of my thinking.
The larger circuit is therefore not simply human → AI → text. It looks more like human memory ↔ AI ↔ writing ↔ public archive ↔ later self ↔ other people ↔ new experience. The arrows are not equal. The archive does not possess a life history; AI does not inherit my responsibility; other people are not components to be absorbed into my private cognitive machinery. The notation only marks that thinking occurred through several differently situated participants and media, and that what passed through them could return changed.
I began calling one function of this circuit recursive conservation of insight. The phrase became necessary because “note-taking” did not describe the whole sequence. Many intuitions arrive with more force than clarity. If I do not record them, they vanish; if I record only a fragment, I may later recover the words but not the relation that made them alive. AI can help turn a fragile intuition into an external object: it can articulate, expand, question, compare and preserve the thought quickly enough that the original energy is not entirely lost. After temporal distance, that object can be encountered again, interpreted by a changed person and returned to the process in another form.
The phrase did not arrive all at once. “Memory aid” captured the prevention of forgetting but not the later return. “Externalized thought” captured the existence of an inspectable object but not its persistence through time. “Conservation” then seemed closer, until I objected that conservation normally suggests keeping something unchanged. In practice, the preserved formulation became productive precisely because a later encounter transformed it. “Recursive conservation” retained both movements: enough stability for the intuition to survive, and enough openness for it to re-enter thought as a new cause:
Recursive conservation of insight is not merely the prevention of forgetting. It is the preservation of a thought in a form durable enough to act back upon the person from whom it began.
The recursion matters. A preserved insight that never returns is an archive entry. A preserved insight that later changes the reader who changes the practice has acquired formative force. In this case, the website preserved an AI-assisted interpretation of the artwork; rereading it altered my understanding of encounter; that changed how I reread Prof.ssa Dohna; her framework then changed how I saw the artwork. The conserved thought did not remain identical while travelling through time. Preservation made transformation possible.
There is also a serious danger. AI can inflate a weak intuition into an impressive miniature theory before the intuition has survived contact with evidence. Linguistic completeness can disguise conceptual prematurity. A paragraph may look as though years of thought have already occurred because it has the cadence, distinctions and references of mature reflection. Recursive conservation can then become recursive self-confirmation: I preserve a rhetorically enlarged claim, reread it as if its polished form were evidence, and build further claims upon it.
I noticed this risk while developing the phrase itself. Once AI could unfold “recursive conservation of insight” into an elegant account of memory, archives and later selves, the concept felt more established than it was. My excitement was evidence that the formulation had gathered something important, but not evidence that the gathering was accurate. I had to return to the concrete sequence—fleeting intuition, AI-assisted articulation, publication, temporal distance, rereading and changed action—and ask which links had actually occurred. The concept survived that test as a provisional description, not as a proven theory of cognition.
The archive therefore needs visible epistemic differences. A published exploration is not automatically a certified conclusion (strangely!). Dates, sources, corrections, explicit uncertainty and later revisions matter because the website is public even when it functions as a notebook. Its openness allows another person to encounter the thinking, as Prof.ssa Dohna did, but that same openness creates obligations. A temporal mirror can reveal change only if I do not repaint every earlier reflection to resemble the present one.
Authorship separated into times and responsibilities
Becoming the reader of my own AI-assisted prose made the authorship question more difficult, not less. In some articles, AI may have generated most of the final sentences—perhaps a very large majority of the visible language. Concealing that would falsify the process. But treating sentence count as a complete measure would falsify it differently. The problem is not solved by assigning a percentage to “me” and another to “AI,” because several things normally compressed into the word author had come apart. My first impulse was nevertheless quantitative. If AI had produced most of the sentences, perhaps it had produced most of the article; if I could estimate the proportion, perhaps the moral unease would become manageable. The arithmetic had the advantage of honesty about the surface form. It also concealed an assumption: that every authorial contribution is commensurable with sentence production. A percentage could count words while assigning no place to the experience that created the problem, the evidence that constrained it, the rejection of a persuasive but false interpretation or the responsibility incurred by publication.
AI could answer that objection with another simplification: I remained the “real author” because I supplied the vision and directed the system. I found that reassuring for a moment. Then I challenged it. “Direction” can become an honorific that protects human prestige without examining what the human actually did. If I had not read the result, could not explain it, supplied no decisive evidence and accepted every generated paragraph, the word “director” would not rescue the practice. I needed a description that neither erased AI’s enormous verbal contribution nor converted my intention into magical ownership.
Who originated the problem? Who underwent the experience? Who recognized that the exchange with Prof.ssa Dohna mattered? Who supplied the evidence and objected when a formulation exceeded it? Who proposed a connection, generated a sentence, selected a passage, rejected an interpretation, implemented a consequence, published the result, accepted responsibility for the claims and returned later to continue the inquiry? In a conventional account these functions may belong mostly to one person and one period of work. Here they were distributed across a human, an AI system, other people’s scholarship, another person’s response, several artifacts and more than one version of myself.
The revision of this essay supplied a particularly clear instance of that distribution. AI could produce a coherent first draft and most of its polished language. I was the one who recognized that coherence had hidden the epistemic path, objected to the result and redefined the objective from “state the theory clearly” to “show how the theory became thinkable.” That intervention was not a cosmetic preference and did not require me to type every replacement sentence. It changed the model of what the article was. Human agency appeared as diagnosis, resistance and redefinition of the work’s criterion.
Research on human–AI writing increasingly examines this distribution through process rather than final-text inspection. The CoAuthor dataset, for example, records detailed interaction histories from human–AI writing sessions, making visible cycles of requesting, accepting, editing and rejecting that a finished page conceals (Lee, Liang and Yang, 2022). A recent provisional framework for creative ownership similarly separates the person, the process and the generative system, and asks about intention, control, effort, embodiment, production and interdependence rather than relying on a single measure (Polimetla and Gero, 2025). These approaches do not decide my case, but they confirm why a word percentage is analytically poor.
“Co-authorship” initially seemed the obvious alternative to sole authorship, but it implied a symmetry the case did not support. “Distributed authorship” captured dispersion but made the relations sound spatial and static. I tentatively arrived at relational authorship for work whose formation depends on contributions that cannot be adequately understood in isolation, and diachronic relational authorship when the relevant relations include later versions of the human author. The adjectives do not erase asymmetry. Prof.ssa Dohna is not a co-author of my article merely because her response changed it. The cited thinkers are not interchangeable with a language model. The model’s verbal production is not the same activity as my lived experience or editorial responsibility. My later self’s interpretation is not what my earlier self knew. The point is precisely to preserve these differences while acknowledging their causal relation.
Nor can the issue be dissolved by saying that AI is “just a tool.” A brush does not normally propose a conceptual distinction, draft several paragraphs, retrieve a rhetorical structure or respond to a changed question in language. Yet the opposite slogan—“AI wrote it, therefore the human contribution is fraudulent”—also collapses what happened. It makes the final verbal surface the sole site of intellectual causation. In this case, the experience, ethical stakes, selection, resistance, publication and continuation remain human even where sentence production is heavily machine-assisted.
Legal categories help define one boundary but not the whole philosophical problem. The United States Copyright Office’s 2025 report retains human authorship as the basis of copyright and evaluates human selection, arrangement and modification case by case; it does not treat prompting alone as automatically sufficient (U.S. Copyright Office, 2025). That is relevant to rights in a particular jurisdiction. It does not tell me whether I understand what I publish, whether another person has been represented fairly, whether the work is artistically mine, or what kinds of dependency I should disclose. Legal authorship, creative ownership, intellectual contribution and moral responsibility overlap without becoming identical.
This is where guilt enters. When I know that AI has generated much of the prose, I sometimes feel discomfort about placing my name above it. It would be too easy to answer that feeling with blanket reassurance. Some of it may be inherited from an older image of authorship in which the named author personally produces every significant sentence. Some may be intellectual unease rather than evidence of a moral wrong. But some of it asks legitimate questions. Am I receiving credit for linguistic labour I did not perform? Have I allowed eloquence to outrun my understanding? Did AI accelerate thinking, or did it substitute for the moment in which I should have struggled? Can I defend the claims to a reader—and to Prof.ssa Dohna—without returning to the model for an explanation of what appears under my name?
I had to separate several feelings that the word “guilt” compressed. There may be moral guilt if I deceive readers, misrepresent another person, publish claims I know to be false or claim unaided production that did not occur. There may instead be intellectual unease when the prose exceeds my present mastery, even if no deliberate deception has occurred. There is also responsibility, which remains whether or not I feel guilty. The distinctions do not remove the discomfort. They prevent discomfort itself from deciding the moral diagnosis. Those questions cannot be answered globally. They have to be tested in the conduct of the practice. Did I read the final text carefully? Did I verify documentary claims and scholarship? Did I reject invented certainty about another person’s mind? Did the work change my own independent thinking, or merely give me phrases I enjoyed recognizing? Have I been candid about substantial AI involvement? Am I willing to correct what I publish? Most importantly, do I take responsibility for the consequences of selection and publication?
AI may contribute enormously, sometimes more than I do to the surface form, while I remain responsible for what I choose, publish, believe, reject, pursue and allow to influence future action.
Responsibility is not proof of sole authorship. It is the non-transferable human burden created by the act of making the output public and consequential. An AI system cannot apologize to Prof.ssa Dohna if I misrepresent her. It cannot decide what kind of relationship I want this inquiry to sustain. It cannot live the changed practice. If authorship is distributed, responsibility is not thereby diluted into nothing.
The distinction becomes clearer when “publication” is divided into at least two acts. Publication can mean making something available, preserving a state of thought and inviting encounter. It can also function as epistemic certification: a signal that the named author has carefully checked and can defend every substantive claim according to the norms of a scholarly field. A personal website can legitimately operate partly as a public notebook or evolving archive. An academic journal article carries much stronger expectations of verification, stable argument and accountable mastery. Calling both acts “publication” does not make their epistemic promises identical.
I reached that distinction only after an attempted defence became too broad. I first thought: this is my personal website, so publication can simply preserve exploration. That was true but incomplete. The page was also public, searchable and capable of affecting a real person whose words I quoted. “Public notebook” could become an excuse if it meant that unfinished status cancelled every obligation. The stronger formulation preserved exploratory publication while refusing to treat it as private rehearsal.
In my exploratory practice, the strange possibility arises that the public text may exist before I have completely become its reader. That need not be forbidden in art. A score can exceed one performance; a process can disclose consequences after it begins; a published experiment can invite the encounters through which its meaning develops. But the permission depends on honesty about genre and status. It does not justify fabricated sources, unexamined accusations, confident claims about private motives or carelessness presented as scholarship. The more a text affects other people, public knowledge or academic debate, the less defensible it is to say that I planned to understand it later.
The personal website therefore carries two nearly opposite functions. It gives a fragile thought enough public durability to return. It also exposes an unfinished state to real readers who cannot be treated as rehearsal objects. Prof.ssa Dohna’s role makes that tension concrete. Publishing can open a space of encounter; it can also fix a careless representation of someone else. Relational authorship requires relational accountability.
When Earlier Thought Becomes Encounterable: The Author Is Not the Same Person Three Times
The authorship problem became stranger only after I thought I had made it more precise. I had begun with a fairly ordinary discomfort: if AI generated most of the visible sentences in some of my writing, what exactly justified putting my name above them? Counting words seemed honest at first because it acknowledged something that would otherwise be easy to hide. Yet the percentage quickly became inadequate. It could tell me something about the production of language while saying almost nothing about where the problem originated, who supplied the experience and evidence, who rejected an attractive but false formulation, who changed the objective, who decided what could responsibly be published, or who later had to answer for the result. That failure led me toward relational and then diachronic relational authorship. I thought that distinction had solved the main conceptual difficulty.
Then another problem appeared. Even in that improved model, I was still treating the human side of “human–AI collaboration” as strangely static. The AI could generate, respond and change. The artifact could accumulate revisions. Other people could enter the process and redirect it. Yet “the human” remained a single box in the diagram, as though the person who began the inquiry, the person who judged the generated possibilities and the person who later returned to the published work all occupied the same epistemic position. My own experience was increasingly difficult to fit into that assumption.
The first formulation that helped me see the problem was surprisingly simple: perhaps there was a you₁, a you₂ and a you₃. At first I was unsure whether this was merely a convenient notation or something more interesting. The distinction should not be exaggerated. These are not three persons, personalities or metaphysical selves. They are three temporally distinguishable epistemic and authorial positions occupied by one continuing person. I remain responsible across them. My biography does not restart every time I revise a paragraph. What changes is the horizon from which I can perceive the problem.
you₁ → AI-mediated articulation and encounter → you₂ → persistent artifact/archive → later encounter → you₃ → further creation
you₁ is the initiating position: the person who has the lived experience, artistic difficulty, intuition, uncertainty or question from which the process begins. This position often knows much less than a polished final article later makes it appear to have known. In my case, it may begin with something as weak as “there is something strange here,” “this explanation feels wrong,” or “perhaps these two things belong together.” The thought may be important while remaining linguistically poor. It can disappear if I do not capture it quickly.
AI changes what can happen to that fragile state. A fragment can be externalized and immediately returned in a more developed form. Connections appear. Distinctions are proposed. Several paragraphs may materialize around an intuition that, a minute earlier, existed only as a sentence or even a hesitation. This is the point where the process can easily be misunderstood. The elaborated output may contain possibilities I had not explicitly formulated, but its fluency does not establish that those possibilities are correct. The first impressive expansion therefore creates a new task rather than completing the old one.
That task belongs to you₂. This is the discerning and responsible position: the self who encounters what AI has produced and begins accepting, resisting, correcting, testing and redirecting it. In the development of this article, some of the most consequential moments looked exactly like that. A formulation would initially seem powerful; then I would notice what it implied about another person, my earlier understanding, authorship or a theoretical claim. Sometimes a single word introduced an emotional implication the evidence did not support. At other times an entire conceptual frame made an earlier relation sound too binary. Those corrections were not merely cosmetic: they changed what I understood the article to be describing.
This mattered because it showed me where human agency was actually appearing. It was not adequately described by saying that I had typed prompts or clicked “accept.” Agency appeared in noticing that a persuasive account exceeded the evidence, recognizing that a formulation might hurt someone unnecessarily, supplying a missing distinction, rejecting reassurance that protected my prestige, or deciding that the question itself had become too narrow. At several points I did not merely ask the AI to produce a better answer. I altered what counted as an answer.
I might have stopped there. you₁ originates; you₂ discerns. That already offered a richer account of human–AI authorship than sentence percentages. But then the later reader entered the problem.
The third position appeared only when the work came back
I had already noticed something odd in my AI-assisted writing: sometimes I returned to an article and found implications that I did not remember consciously holding when I published it. Writers have always had this experience, so AI cannot claim to have invented it. A novelist can discover something in an old novel. A scholar can reread an earlier paper and realize that one argument carried consequences that were not fully visible at the time. What interested me was the intensity and frequency with which AI-assisted writing seemed able to produce this distance between the person who initiated a thought and the later person who encountered its elaborated form.
That later position became you₃. you₃ is the returning self: the person who encounters the preserved artifact after enough temporal or epistemic distance has developed for it to become partially unfamiliar. The article still belongs to my history. I remember why I wrote it. I may remember many of the prompts and objections. Yet the artifact is no longer identical with my present state of understanding. It can therefore return as something capable of surprising me.
The number three should not be taken literally. There can obviously be you₄, you₅ and many later states. Years from now I may return again and reject half of what now seems convincing. Three is useful because it appears to be the analytical minimum needed to reveal the first complete recursive return. With you₁ and you₂, there is development. With you₃, something more happens:
maker → artifact → beholder → changed maker
That was the moment when the authorship problem unexpectedly returned me to the central problem of this entire essay.
The formulation that triggered the recognition did not originally come from me. During the AI-assisted discussion, the system proposed that an artifact could “let an earlier epistemic state remain present after the person has moved beyond that state.” The sentence immediately arrested me. My reaction was not simply that the wording was elegant. I suddenly realized that this was encounter again. I had approached the issue through authorship, temporal identity and AI-assisted writing, yet the structure that emerged was the same structure that had already appeared elsewhere in the artwork.
An artifact can allow an earlier epistemic state to remain encounterable after the person has already moved beyond that state.
The emotional force of that recognition mattered because it changed what I investigated next. I remember the physical excitement of it—the kind of sudden conceptual recognition that can produce goosebumps before one has had time to decide whether the idea is actually defensible. The feeling was not evidence that the claim was true. It was evidence that something in the structure demanded further attention. Instead of treating the three positions as a small authorship diagram, I began asking what it means for one state of a person to become encounterable by another.
The archive resists the way the present rewrites the past
Ordinary memory does not preserve earlier epistemic states very faithfully. Once I understand something differently, I tend to reconstruct the earlier path from the standpoint of the present. The hesitation disappears. A discarded interpretation begins to look obviously inadequate. A concept that once required several conversations starts to feel as though I had always understood it. The final theory colonizes its own genealogy.
A preserved artifact can resist that process. It cannot contain the earlier self in any literal sense, but it can preserve traces of what that self could see, what it could not yet see, what it feared, what it misunderstood, what seemed plausible and what had not yet become askable. A draft, dialogue history, article or archive therefore does something more interesting than reminding me of a conclusion. It can confront the present self with evidence of a previous horizon.
This is why the “yes, but…” moments in AI-assisted inquiry have become methodologically important to me. If I polish them all away, the final article may become smoother while becoming less truthful about how understanding actually developed. “At first I thought X; then Y seemed persuasive; then a fact or objection made Y insufficient; then a third distinction became necessary” can look untidy beside a finished argument. Yet those transitions show where judgment occurred. They allow a later reader—including the later version of myself—to see that the final position was not simply downloaded from a model or present from the beginning.
Research on human–AI writing already demonstrates why process traces matter. The CoAuthor dataset preserves detailed histories of requesting, accepting, editing and rejecting during human–AI writing sessions, revealing forms of collaboration that the finished text alone conceals (Lee, Liang & Yang, 2022). My present question extends that insight beyond the drafting session. Even a complete record of what happened while the text was being produced may not capture what happens when the text returns later to the person who helped produce it.
This produces a peculiar form of self-encounter. When I read another scholar, I encounter a genuinely different biography, intellectual history and set of experiences. That otherness is irreplaceable. Reading my own earlier artifact gives me something different. It is close enough to belong to my history and distant enough to resist my present understanding. Sometimes I read a sentence and think, “yes, of course.” Sometimes I think, “did I really believe this?” The archive can answer with an irritating lack of diplomacy: apparently, yes. That experience can reveal something about learning that ordinary introspection easily loses. The difference between the earlier artifact and my present judgment is itself evidence of change. I can ask why a formulation that satisfied me three days ago now feels inadequate, which distinction became available in the interval, or why an argument that once appeared complete now generates another question. The object is therefore not merely a memory aid. It gives the later state something against which its own difference can become perceptible.
I do not want to generalize too quickly from my own disposition. I find this kind of intellectual archaeology fascinating. Another person may not. Some learners may benefit more from discussion with others, practical experimentation or entirely different forms of reflection. Recursive engagement with one’s own archive can also become self-confirming if nothing external interrupts it. The methodological advantage, if there is one, depends partly on resistance. Other people, scholarship, evidence and events must remain capable of telling the recursive system that it is wrong.
Encounter returned from an unexpected direction
This was the point at which the structure of the writing process began to mirror the structure of the artwork itself. I had already described how a generated visual form could surprise its maker. Prof.ssa Dohna encountered the work; her response altered my interpretation; I returned to questions in her thought differently; those questions then changed how I understood the artwork. Now a similar topology appeared inside the act of writing:
earlier state → externalized artifact → later encounter → changed state → renewed creation
The repetition matters because I did not begin the authorship discussion by trying to prove that everything was an encounter. I began with a much narrower anxiety about AI-generated sentences. The concept returned because the narrower explanation became insufficient. Once the artifact could preserve enough of an earlier epistemic position to confront a later one, the relationship between maker and work had become reciprocal again.
The analogy nevertheless has an ethical boundary. Prof.ssa Dohna cannot be reduced to an element in my cognitive state machine. She is another person whose freedom includes the possibility of agreeing with my interpretation, deepening it, correcting it, disagreeing with it, declining to respond, or seeing relations in the episode that I have missed. An artifact has no comparable interiority. My earlier text cannot decide that it dislikes what I am doing with it. The resemblance is therefore structural rather than personal: both another person and a persistent artifact can confront my present interpretation with something that is not reducible to what I presently intend. This distinction, actually, strengthens the role of encounter instead of weakening it. It suggests that encounter is not merely another word for “receiving information.” Something becomes encounter-like when it introduces enough resistance or difference to make the current configuration of the self unstable. Prof.ssa Dohna can do this in the fullest interpersonal sense because she is genuinely other. An artifact can do it in a narrower epistemic sense because it preserves traces from a state I can no longer completely occupy.
At this point another AI-generated formulation became useful: you₁ is collaborating with you₂ through an artifact mediated by AI, and you₃ inherits both. I initially liked the sentence because it gave the three positions a relationship rather than treating them as labels. Then it created another question. What exactly does you₃ inherit?
Not simply text. you₃ inherits the originating problem of you₁, the expansions supplied by AI, the selections and refusals of you₂, the mistakes that survived, the evidence already gathered, the relationships implicated in the writing and the responsibilities created when the artifact became public. The inheritance therefore includes both intellectual gain and unfinished obligation. If you₃ discovers that you₂ misrepresented someone, later understanding does not erase the earlier publication. The later self inherits the duty to correct it.
The “three yous” therefore began to look less like three boxes and more like a trajectory of accountable transformation.
A state machine was useful until it became too rigid
My first computational analogy was a state machine. It made intuitive sense: S₁ becomes S₂, then S₃, through identifiable transitions. Yet the analogy quickly became too rigid. In an ordinary finite-state machine, the relevant states and transition rules are generally specified in advance. Here the encounter can change the rules by which later encounters will be interpreted. you₂ may not simply know more than you₁; you₂ may have changed what counts as a worthwhile question. you₃ may later reject even that criterion. I therefore moved toward the looser image of a state-space trajectory or a reflexive, history-dependent state-transition process. I use this language cautiously. I am not claiming to have defined a formal dynamical model of authorship. The analogy is useful because it emphasizes that the state of the person at time t₂ partly depends on the trajectory through which that state was reached, and because future transitions may depend on changes produced by earlier ones. In other words, the process has memory.
The most important transition operator in this provisional model may be encounter. An ordinary feedback loop can change a parameter while preserving the model that defines the parameter. An encounter can sometimes change the model itself: what I am looking for, what I consider evidence, what I think the artwork contains, or what I believe the original problem was. That is why “state machine” helped me see the temporal structure but could not fully explain the epistemic transformation.
AI alters more than the speed of writing
Earlier in this inquiry I had repeatedly described one effect of generative AI as collapsed latency. That still seems correct. The distance between intuition, externalization, criticism and reformulation can become extraordinarily short. Yet the three-position model showed me that speed was only part of the change. AI can alter the sequence in which articulation and understanding occur.
The simplified traditional model looks something like this:
thinking → understanding → writing
No serious account of writing has ever been quite that simple. Writers discover things while writing, scholars think through correspondence, artists learn from materials, and students change their minds in conversation with teachers. AI did not invent thinking through externalization. What it makes unusually ordinary is another sequence:
partial intuition → externalization → AI elaboration → encounter with elaboration → objection or adoption → changed understanding → renewed articulation
The difference became especially clear to me when a half-formed intuition could become several pages of articulated possibility while I was still mentally inside the originating question. I could object immediately: “yes, but this makes the x sound too y,” or “this implies that I understood z before,” or “that interpretation assigns a private motive to another person that the evidence does not support.” The generated material changed in response, and the changed material altered my next objection. The process could continue before the cognitive atmosphere that produced the original question had disappeared. A brilliant tutor or collaborator can do something much richer than this, of course. The historical novelty should therefore not be exaggerated. What seems significant is the combination of semantic responsiveness, immediate availability, substantial linguistic generation, interdisciplinary range, persistent context and repeatability. A kind of intense dialogical assistance that once depended upon unusually available human collaborators can now be present through an ordinary writing session.
That availability creates a danger symmetrical with its power. The AI can elaborate a thought faster than I can deserve it. It can turn a weak intuition into polished prose before the underlying judgment has matured. The artifact can become more sophisticated than the learner.
The essay may have advanced intellectually while the student has remained where they began.
When this sentence emerged, the problem stopped being only about my own authorship and became a question about higher education.
The educational problem was not identical with plagiarism
My first instinct was still to frame the issue through plagiarism. If AI had generated a large proportion of the prose, perhaps the central institutional question was attribution: whose contribution was being represented as whose? That remains a legitimate question. Traditional plagiarism and unauthorized assistance have not ceased to matter simply because generative AI complicates authorship. But the moment I asked how an examiner could actually verify AI authorship, another problem appeared. Conventional plagiarism can often be investigated by comparing the submitted work with an identifiable source. Generative AI frequently leaves no equivalent textual source. A teacher may become suspicious because of style, vocabulary or a sudden change in quality, but suspicion is not the same thing as evidence. If the institution cannot reliably establish the attributional fact, its greater power does not convert uncertainty into knowledge.
This is not a purely hypothetical procedural concern. Oxford’s AI Competency Centre stated in February 2026 that the University did not endorse digital AI detectors for academic decision-making, citing technical limitations and procedural fairness and emphasizing that such tools cannot reliably determine whether AI has been used (Webb-Davies, 2026). That does not imply that unauthorized AI use can never be established. A student may disclose it, process records may exist, or other evidence may be available. The narrower point is epistemic: an accusation should not acquire certainty merely because the institution needs a decision.
This made the phrase “learning does not excuse plagiarism” feel simultaneously true and insufficient. Of course genuine learning does not erase deliberate misattribution. Yet in practice the AI-age problem cannot be solved simply by declaring that distinction if the alleged attribution cannot itself be established reliably. Worse, excessive reliance on suspicion risks transforming the teacher from someone assessing knowledge into someone policing stylistic authenticity. The resulting damage is relational as well as procedural. A student who is wrongly suspected does not experience the accusation as an abstract discussion of policy. Trust between teacher and learner becomes part of the cost.
The problem began to look deeper than plagiarism. Perhaps the institution was asking the wrong first question.
Purpose before detection
This is why Taylor and LaCroix’s 2026 article Purpose before policy: academic integrity, generative AI, and rhetorical stance became unexpectedly important to the argument. Their claim is not that AI-related misconduct is unreal. They ask what must logically come first. Whether a particular use of GenAI undermines academic integrity depends on what the university believes its educational function to be, and incoherent policy emerges when institutions promote AI while failing to clarify the pedagogical purposes against which its use is being judged (Taylor & LaCroix, 2026).
One passage became especially relevant to my problem. They argue that generative systems can allow students to satisfy required outputs without undergoing the pedagogical processes those outputs were supposed to cultivate. If a machine-generated essay can satisfy an assessment that claims to measure learning, then the difficulty may reveal something about the assessment itself. A polished output is no longer reliable evidence that the learner possesses the capacities represented by it.
That is almost exactly the problem expressed by the sentence that had emerged from my own discussion:
The essay may have advanced intellectually while the student has remained where they began.
Taylor and LaCroix do not propose the you₁/you₂/you₃ structure, and I do not want to retroactively place my model into their argument. Their work establishes the prior institutional question. My present reflection suggests one possible extension: if the purpose of an assessment includes learning, judgment, critical thinking or Bildung, perhaps we need evidence not only of how the artifact was produced but of what happened to the learner through and after its production.
This was where you₃ suddenly became educationally important.
The return may be where formation becomes visible
you₁ can give evidence of origination. The student can explain what problem, experience, source or question initiated the work. you₂ can give evidence of discernment during development: why one AI suggestion was rejected, why another was modified, what evidence changed an interpretation, which sources were checked, what assumption had to be abandoned. These traces already tell us considerably more than an AI detector score. Yet neither stage completely answers the question of what survived. A student may participate actively in an AI-assisted session and still depend on the system to maintain the conceptual structure. What happens when the conversation closes? What happens tomorrow?
This is the point of you₃. The later learner encounters the artifact again. Can they explain the central argument in language that is not simply reproduced from the text? Can they identify a section they now consider weak? Can they say where the AI was wrong? Can they transfer a concept to an unfamiliar case? Can they defend the evidence? Can they revise a conclusion when given a counterexample? Most interestingly, can they generate a question that did not exist when the artifact was produced?
Research on evidence-centered approaches to human–AI writing already points toward examining process data and cognitive activity rather than relying exclusively on final outputs (Cheng et al., 2024). The three-position model adds a provisional temporal question: perhaps some evidence of learning appears only after production, when the artifact returns to the learner. I do not think this should become another bureaucratic test in which every student is required to perform a theatrical confession of personal transformation. Nor does you₃ provide magical proof that the text has become “truly theirs.” The claim is more modest. Later criticism, transfer, independent explanation and consequential revision are stronger evidence of epistemic integration than the polished artifact by itself. This also prevents an easy rhetorical rescue of human authorship. If AI generated eighty percent of the sentences, later understanding does not transform those sentences retroactively into unaided human prose. The production history remains distributed. you₃ cannot travel backward in time and make AI disappear. What the returning self may establish is a different relation to the artifact: the ability to understand it, resist it, extend it and become answerable for what remains.
That distinction may eventually require better terminology, but I do not want to solve it by inventing another impressive label too quickly. “Epistemic ownership,” “responsible integration” and “appropriation” each capture something and introduce other problems. For now, the question is more useful than the name: what must happen after AI-assisted production for the intellectual structure of the artifact to become demonstrably consequential in the learner?
Evidence should be designed before suspicion begins
The same reasoning also changed how I thought about misconduct detection. If universities cannot reliably infer AI use from the final textual surface, perhaps assessment should be designed so that evidence of learning and authorized collaboration is produced prospectively rather than reconstructed under suspicion afterward.
This would change the examiner’s role. Instead of receiving a polished essay and asking, “Can I prove that AI secretly wrote this?”, the assessment could make clear from the beginning what forms of AI assistance are allowed, what should be disclosed, which stages of reasoning matter and what later demonstrations may be required. Revision histories, declared AI interactions, short reflective accounts, source checks, oral defence or transfer tasks could then provide evidence aligned with the educational objective. The point would not be total surveillance of the learner. It would be to stop pretending that the final artifact contains evidence it no longer reliably contains.
The fairness principle that emerged for me was simple:
Evidence of learning should be designed into assessment prospectively; evidence of misconduct should not be manufactured retrospectively from suspicion.
This does not eliminate misconduct. It places an epistemic obligation on the institution alongside the obligations it places on the student. If academic integrity includes accuracy, justification and responsibility, then those virtues should govern accusations as well as submissions.
The shift also brings the teacher–student relationship back into the picture. A later oral conversation need not function primarily as a trap designed to expose AI use. It can become another encounter. The teacher can ask what changed, what remains unclear, what the student would now reject, or how the argument behaves when moved into another case. The educational question becomes less “can I catch you?” and more “can I encounter the learner who now stands behind this artifact?” That does not remove assessment or standards. It makes the relationship between evidence and educational purpose more explicit.
My earlier authorship model now looked too static
This also caused me to reinterpret my earlier work on generative-AI assessment. In my previous DAP/WAM work, a weighted authorship matrix could distinguish different contributions to an AI-assisted artifact: origination, generation, selection, verification, revision and other forms of participation. That still seems useful because it resists reducing authorship to a single percentage. Yet the present case revealed something that a matrix alone cannot capture. A matrix is principally structural: it asks how contributions are distributed. The three-position model is dynamical: it asks what happens to the participant over time:
contribution structure ≠ formation trajectory
A student might demonstrate substantial human contribution without learning much. Another might use extensive AI generation while undergoing significant critical development. Neither fact automatically settles questions of authorization or attribution, but the distinction matters if the institution claims to assess formation. The structure of contribution and the trajectory of learning are different variables.
I did not reach this conclusion by starting with a theory of dynamical authorship. The path was almost embarrassingly recursive. I began by asking how much of the prose was “mine.” Percentages failed. That led to relational authorship. Relational authorship then failed to account for temporal difference inside the human participant. The later reader became you₃. AI formulated the idea that an earlier epistemic state could remain encounterable. I recognized, with considerable excitement, that encounter had returned from a completely different direction. That recognition made the artifact look like a bridge rather than an endpoint. Once the artifact became a bridge between states of the learner, the higher-education problem appeared. Then Taylor and LaCroix’s question about the purpose of the university became newly relevant. Each answer changed the next question.
That genealogy is more than background to the chapter. It is part of the evidence for what the chapter is trying to describe. The earlier formulations survived. I encountered them again. Some became inadequate. The inadequacy changed the model. The changed model produced questions that the earlier state could not yet ask.
The argument about diachronic authorship was itself produced diachronically.
Sometimes learning occurs because the learner has changed
The most personally significant implication may lie slightly outside authorship. Earlier in this essay I had been trying to describe why ideas associated with encounter, Bildung and metanoia acquired a new immediacy for me through the artwork. I was careful not to describe that development as a movement from ignorance to understanding. I had encountered many of the ideas before. The experience did not erase an earlier understanding and replace it with the correct one. It added another dimension.
The three-position structure now gives me another way to describe that development. Sometimes learning happens because new information arrives. But sometimes the information was already there.
Sometimes learning does not happen because new information arrives. It happens because the learner has become newly capable of encountering information that was already there.
That is what makes the relation among you₁, you₂ and you₃ more interesting than a simple accumulation of knowledge. The later state may encounter an old text, an old conversation, an old artwork or an old concept differently because intervening experiences have altered what can become meaningful. The object may be unchanged. The conditions of encounter are not.
This is also why the role of Prof.ssa Dohna in the larger inquiry cannot be reduced to supplying concepts that I later applied to an artwork. Her response became part of the trajectory through which a later version of me returned to questions I had already encountered and found them newly concrete. The artwork made parts of her theoretical vocabulary newly legible to me; that vocabulary then made dimensions of the artwork newly legible; the resulting writing preserved the transformation; and the preserved writing can now return again to a still later version of me. The relation is recursive without becoming closed.
AI occupies an important but limited place inside that recursion. It accelerates articulation, preserves conversational context, proposes connections, responds to objection and helps build artifacts capable of surviving the originating mental state. It does not determine which encounter matters. It cannot guarantee that the later self has learned anything. It cannot make an interpretation true because it is beautifully expressed. The system generates possibilities; the trajectory still requires judgment, evidence, other people and reality.
I therefore do not want to end by declaring you₁, you₂ and you₃ a finished theory of AI-age authorship. At present they are a way of seeing a problem that the simpler diagram human + AI → text hides. The human participant has a history. The artifact has persistence. AI can amplify a partially articulated state into an object that survives beyond it. A later state of the same person can then encounter that object from a position the earlier state did not yet occupy. Three is not the maximum number of states. It is simply enough to make the first return visible. you₁ initiates. you₂ encounters possibilities and takes responsibility for what is allowed to survive. you₃ receives an artifact carrying traces of both and tests whether anything has actually returned as understanding, criticism, transfer or changed practice. After that, the trajectory can continue.
The question I began with was essentially quantitative: how much of this text did AI write? That question still matters, especially where disclosure and attribution matter. But it is no longer large enough to contain what happened. The stronger question now seems to be this: if AI-assisted writing can preserve an earlier epistemic state, amplify it into an artifact and return it to a later version of the same person, what kind of authorship and learning occurs when the maker becomes the beholder of a work that still belongs to their own intellectual history?
Higher education sharpens the question further. “Did the student write this?” cannot simply disappear, particularly where specific forms of assistance are prohibited. Yet if education claims to cultivate knowledge, judgment, critical capacity or Bildung, another question may now need to stand beside it:
What happened when the work returned to the student?
I do not yet know how far that question can be operationalized, how much temporal distance you₃ requires, whether every discipline would recognize the same evidence of transformation, or whether this form of recursive self-encounter is especially suited to people who already enjoy examining the history of their own thought. Those uncertainties matter. They prevent a personal experience from becoming a universal pedagogy by declaration. What this case has shown me is narrower and, for now, sufficient: a generated artifact need not be the end of a cognitive process. It can become the object through which an earlier state of thought remains available for encounter. When that encounter changes what the later person can see, reject, ask or create, the work has returned—and the author who receives it is the same person, but no longer quite the same epistemic state from which it began.
A faster loop is not yet a formative one
What, then, did AI distinctively contribute? Not runtime generativity by itself. The browser could have selected and arranged images through conventional programming. AI mattered more strongly in the loops around that system. Four mechanisms were especially important.
My earliest answer was simply “speed.” Work that might once have required long interruptions between an intuition, a search, a draft and an experiment could continue within one period of concentrated attention. That was observable, but “speed” made the contribution sound like faster clerical production. What mattered was not only that each step took less time. The result of one step remained psychologically and conceptually present when it became the input to the next. This was why I later preferred “collapsed latency.”
First was collapsed latency. The delay between intuition, articulation, implementation, output, criticism and revision became dramatically shorter. I could see whether a verbal idea survived contact with form while the question that produced it was still alive. Second, AI offered a cross-disciplinary translation surface. The inquiry could move among computer science, mathematics, generative art, aesthetics, Guardini, epistemology, pedagogy and theology without stopping completely each time the vocabulary changed. Translation did not confer expertise, but it kept the question mobile long enough for unexpected relations to become examinable.
The last qualification arose from another correction. At first, the ease of moving among disciplines felt like a new kind of command over them. AI could explain a term, compare traditions and supply a plausible bridge almost immediately. Then source checking and rereading exposed how quickly translation could simulate mastery. A cross-disciplinary surface can keep a question moving; it cannot replace the historical and methodological resistance of each field. The connection to theology became serious only when Prof.ssa Dohna’s actual work, not a generic summary of Guardini, changed the interpretation.
Third was responsiveness to a changing learner. A book can transform its reader, but its next paragraph does not rewrite itself because the reader has just raised a new objection. An AI interlocutor can receive a changed formulation and answer the new state of the inquiry. Fourth was an externalized thought surface. A vague intuition could become language immediately, where I could inspect, resist, preserve or reformulate it instead of relying on working memory.
Together these mechanisms produced what I began to call a high-frequency hermeneutic loop: a sharply reduced distance between intuition, externalization, interpretation, criticism and renewed formulation. The phrase sounds efficient, but efficiency is not its main interest. A shorter distance can preserve the heat of an emerging question. It can also accelerate hallucination, self-confirmation and intellectual theatre. A loop becomes faster before it becomes better. The phrase itself came after “fast feedback” proved insufficient. Feedback described successive correction, but not the reinterpretation of the question by a changed participant. “Hermeneutic” marked that the loop concerned meaning as well as performance. “High-frequency” marked the shortened interval. I accepted the term only provisionally because it carried its own danger: frequency can be measured, whereas depth cannot. A rapid interpretive loop may produce many revisions without producing one honest encounter.
This distinction matters pedagogically. The central question is not whether AI can provide answers. It plainly can provide many forms of answer, some reliable and some not. The harder question is: what kind of human participation converts an AI-rich environment from an answer machine into a recursive environment of formation? The history of this artwork suggests that the necessary participation includes questioning, resistance, comparison, implementation, surprise, rejection, reinterpretation, movement across disciplines and the willingness to change the original question.
Chi and Wylie’s ICAP framework distinguishes passive reception from active, constructive and interactive engagement, with deeper learning generally associated with forms of participation that generate and negotiate beyond the presented material (Chi and Wylie, 2014). Human–AI co-creativity research likewise treats interaction design—not only output quality—as central to what collaborators can do together (Rezwana and Maher, 2023). These frameworks help explain why “AI produced a sophisticated answer” is not evidence that the person learned. The pedagogically relevant evidence lies in what the person can subsequently notice, infer, contest and make.
One event in my own conversation became unexpectedly important. On more than one occasion, while reading a long AI analysis, I stopped and wrote down an implication before reaching the later paragraph in which the AI developed almost the same implication. In one instance, I had begun thinking in terms of metanoia before arriving at the section that named the experience as metanoia. The chronology matters: I did not merely read the term and then report agreement.
The convergence felt uncanny for a moment. One possible story was that the AI had somehow anticipated my private thought; another was that I had unconsciously absorbed a cue and mistaken recognition for invention. Neither story could be established from the feeling. I therefore treated the order of events as limited evidence rather than a mystery: I had written the implication before seeing the later paragraph, but after a long sequence in which the relevant conceptual relations had already been developed. I do not interpret this as mind-reading, mystical synchronization or proof that the AI and I had become one intelligence. A more plausible explanation is pedagogically more interesting. The conceptual structure had begun to become generative inside my own thinking. Earlier parts of the dialogue, together with my prior experience and Prof.ssa Dohna’s vocabulary, had supplied relations from which I could independently infer a next move. When the later AI passage converged with that move, the convergence offered limited evidence of internalization. I was no longer only recognizing an explanation after it appeared; I had begun using its structure to think forward.
The process was therefore not adequately represented as AI thinks → human agrees. Nor was it a pure human monologue decorated by machine prose. It was closer to AI contribution → human internal development → human inference → further AI development → convergence or disagreement. Sometimes the convergence was exciting. Sometimes disagreement was more valuable, because it forced a distinction the smooth answer had concealed.
This leads to formative recursion. In ordinary computational recursion, a procedure operates again on a new state. Here the changing state includes the participant. The person who enters a later iteration has been affected by the earlier one:
you₁ ≠ you₂ ≠ you₃
making → encounter → changed learner → different question
→ further making → further encounter
The claim is not that every conversation transforms the whole person. Many interactions are trivial, repetitive or numbing. The point is that an adequate model of some AI-assisted learning cannot track only how the document changes. It must ask whether the participant’s capacities of attention, inference, resistance and judgment change too. Can the person formulate a better question without assistance? Can an idea travel into another domain? Can it alter practice? Can it survive when the original conversation is closed?
Recent empirical work gives reason for caution. In a survey study of knowledge workers, higher confidence in generative AI was associated with less self-reported critical-thinking effort, while reported critical thinking often shifted toward verification, integration and stewardship of the task (Lee et al., 2025). Because the study concerns self-reports and associations, it does not establish a simple causal law that AI weakens thought. It does, however, sharpen the issue. Offloading some production can make room for judgment, or it can remove the very friction through which judgment develops. The outcome depends partly on how the human remains active. My independent anticipation of metanoia is not enough to prove deep formation. It is one piece of experiential evidence, alongside changed questions, rereading, implementation and a revised relation to Prof.ssa Dohna’s work. The educational claim must remain proportionate: the process sometimes enabled conceptual moves to become my own before I saw them completed for me. That is more than passive agreement, but it is not a diploma issued by a conversation.
Generated possibility made judgment scarce
The speed of the loop created a peculiar kind of intellectual pleasure. Connections arrived quickly and with unusual density. A browser background touched generative art; generative art opened questions of encounter; encounter returned me to Guardini and Prof.ssa Dohna; AI-assisted writing opened authorship and pedagogy; rereading opened memory, time and the archive. The experience possessed conceptual intensity. It felt as if the project had suddenly become a junction through which many disciplines could see one another.
That intensity supplied real energy. Wonder can sustain attention where obligation cannot. Intellectual eros can make a person reread a difficult text, test a connection, follow a reference and remain with an unsettled question. It would be a mistake to treat excitement as an enemy of serious thought. But it would be a greater mistake to treat excitement as evidence that the interpretation is true.
Conceptual intensity is epistemic energy, not epistemic warrant.
Intensity is a reason to investigate, not a reason to believe.
AI can produce conceptual intensity at industrial speed. It can place an experience beside cybernetics, hermeneutics, theological anthropology and distributed cognition within minutes. Sometimes the juxtaposition reveals a real structural similarity. Sometimes it merely produces the sensation of depth. Rhetorical beauty can make a speculative phrase feel established. The most dangerous interpretation may not be an obvious falsehood, but a gorgeous connection that I want to be true because it makes the entire afternoon appear momentous.
My first safeguard was conventional verification. Check the quotation, open the source, distinguish a peer-reviewed study from a preprint, and do not let AI invent what Prof.ssa Dohna privately meant. Those practices remained indispensable. They did not answer the whole problem. Every citation in a paragraph could be accurate while the relation among them remained superficial. A true sentence about cybernetics could still be a poor explanation of an artistic encounter. Factual checking could expose hallucination; it could not by itself decide whether a generated possibility deserved to reorganize my practice.
The required movement is therefore passion → sustained attention → discernment. Passion keeps the question alive. Sustained attention exposes it to more than one mood and more than one source. Discernment asks which generated possibilities deserve continued life. AI literacy is part of this: I need to know about hallucination, verification, model limits, source quality and automation bias. Discernment is larger. It asks what a possibility is doing to my relation with reality and with other persons.
This was the concrete point at which theological language entered rather than being added decoratively. The question had moved from “Is this output factually correct?” to “What kind of attention and action does this interpretation produce?” That is a question about consequences, orientation and relation. The vocabulary of discernment became useful because it asks not only whether an idea can be stated coherently but how it moves through a life and what fruits appear over time.
Does the connection survive resistance? Does it remain within what the exchange actually supports, or encourage me to infer more about another person than the evidence permits? Does it make me more attentive to her work, or merely more impressed with my own story? Does it lead to more responsible making? Can I state what would count against it? Am I pursuing the idea because it bears fruit, or because AI has made it rhetorically intoxicating? Is the system enlarging my agency, or quietly replacing the acts by which agency is formed?
Theological traditions of discernment are relevant here only if their greater seriousness is preserved. In his catechesis on discernment, Pope Francis stresses attention to one’s history, the significance of time and the need to examine the end and fruits of a movement rather than isolating an impressive moment (Francis, 2022). Antiqua et nova similarly argues that AI may assist human activity but cannot inherit human moral responsibility or substitute for the wisdom that relates parts, wholes, decisions and consequences (Dicastery for the Doctrine of the Faith and Dicastery for Culture and Education, 2025). These sources do not give a ready-made religious test for good prompts. They resist the reduction of judgment to output evaluation. Prof.ssa Dohna’s own work makes the connection less superficial. Verso nuovi occhi brings artistic form into relation with spiritual discernment, suggesting that seeing is not simply the capture of an object but a disciplined way of allowing form and meaning to disclose themselves (Dohna Schlobitten, 2023). Returning to that framework altered my question. I no longer asked only whether the AI-generated interpretation was clever. I asked whether it trained a truer gaze—one more capable of receiving the other without reducing the other to evidence for my theory.
This is particularly important because the artwork’s central turning point belongs to a relationship. If Prof.ssa Dohna’s response became artistically consequential for me, that should also make me more careful in how I represent the encounter. A fruitful interpretation should preserve humility about what I do not know, gratitude for what her question made possible, and responsibility for the way I describe the exchange. The conceptual development matters, but it should never obscure the independence of the person whose response helped set it in motion.
AI generates abundance; the human must discern what deserves continued life.
When that sentence emerged in the dialogue, I first liked its clarity. Then I noticed a possible overcorrection. Discernment is not an exclusively solitary human act performed after AI finishes generating. My judgment is itself formed through artworks, other people, traditions, evidence and sometimes the AI dialogue. The sentence survived only after “the human” ceased to mean an isolated sovereign and came to mean the responsible person who remains answerable within those relations.
I call this, provisionally, the discernment of generated possibility. It concerns sentences and images, but also connections, research programmes, self-descriptions and future actions. When possibilities become cheap, judgment becomes scarce. The artistic task is no longer exhausted by producing another variation. It includes deciding which variation should become an encounter, which encounter should alter the practice, which interpretation should be archived and which seductive branch should end.
The decision will not always be solitary. Other people, traditions, evidence and the resistance of the artwork can all correct the maker. Prof.ssa Dohna’s question did precisely that: it prevented my first interpretation from closing too quickly. Discernment, in this sense, is not the artist guarding sovereign control against AI. It is the practice of remaining answerable within a field of generated possibilities and real relations.
Metanoia changed from a word into a problem of knowing
The word metanoia had been present in Prof.ssa Dohna’s work before it became present in my experience. I could translate it, discuss it and recognize it among related ideas of encounter, figuration and Bildung. Conceptual language can prepare perception. Metanoia had already been familiar to me as a concept through reading, conversation and translation. The artwork added another mode of encounter with it: I began relating the term to a change occurring within my own practice of seeing, making and interpreting. Then the order changed.
There was still a danger of naming too quickly. Once AI described the sequence as metanoia, the word gathered the whole experience into a compelling pattern. I had independently begun moving toward the same term, which made the convergence more significant to me, but convergence did not settle whether the term was proportionate. I objected that metanoia carries philosophical and theological weight that should not be awarded to every stimulating afternoon. The claim therefore changed from “this was metanoia” to the more careful statement that the experience gave the concept an experiential dimension it had not previously possessed for me in quite that form.
metanoia as an object of knowledge → metanoia as a concept that recognizes an experienced change → metanoia as an experience that changes my account of knowing
I call this, cautiously, a metanoia of epistemology. The phrase does not mean that every revised opinion is a spiritual conversion, that a few hours at a screen equal the theological depth of metanoia, or that AI administered transformation. It names a more precise reversal. At first I thought I lacked information about the concept. The event showed that I also lacked the mode of participation through which its significance could become intelligible. The deepest change was not “Now I know the definition.” It was: Now I understand why the definition by itself was insufficient.
“Metanoia of epistemology” was not my first description. I initially described the experience too simply as understanding metanoia better. That still treated the transformation as a new item of knowledge. The next question—what had changed in the act of understanding?—forced the stronger distinction. The experience had not only supplied an example of the concept; it had challenged the model in which concepts are fully understood by possessing correct definitions. That is why the epistemology itself entered the phrase.
This is where Bildung became more than an elegant word for education. If learning only adds propositions to an unchanged subject, then the person who knows more remains essentially the same knower. Formative recursion suggests something else: the experience can alter the dispositions with which the person attends, asks and judges. The learner entering the next iteration is not identical to the learner who entered the last. What changes is not only the answer available to consciousness, but the kind of question consciousness can form.
Prof.ssa Dohna’s work had approached art as a way of seeing in which form, knowledge and relation belong together. I had encountered that claim before through reading, conversation and translation. The generative artwork supplied an experience in which form changed relation, relation changed interpretation and interpretation returned to form. Theory became intelligible from within practice; practice became intelligible through theory. The absurdity is not lost on me: I needed a WordPress background engine to rediscover Guardini?!! But perhaps the comedy protects an important fact. Formation does not always arrive through the door labelled “formation.” The experience also changed something in my habitual way of approaching theoretical ideas. I often understand a concept most readily when I can see what question it helps me ask, what distinction it clarifies or what situation it allows me to perceive differently. There is nothing wrong with that practical orientation, but this experience exposed one of its limits. What proved especially important in this case was that her framework did not simply answer a problem I had already defined. Instead, it helped make visible a dimension of the situation that I had not previously known how to formulate: that an encounter could alter the observer and, through that transformation, alter what the work itself could subsequently become.
The programming analogy remains helpful. Concurrency is not “useful” merely because it fixes one known defect. Once a programmer understands race conditions, events that previously looked random become perceptible as a class of problem. Aesthetic and moral concepts can work similarly. They do not alter the event retroactively; they alter what the observer can recognize in it. This is why a powerful theory can render new questions askable. It is also why failure to see a theory’s significance is not decisive evidence that significance is absent.
The qualification is essential. Delayed recognition does not vindicate every difficult text. Some theories remain confused after patient attention; some concepts illuminate one case and distort another. Epistemic humility must move in both directions. My strong perception that the present connection is meaningful does not prove its significance. My earlier failure to perceive significance did not prove its absence. The observer changes, but change alone does not guarantee that the later observer sees more truly.
This problem connects with an earlier essay I wrote on Éric Rohmer’s A Tale of Winter. There I distinguished subjective certainty from external truth: a narrative’s eventual vindication of a character does not retroactively supply epistemic justification for everything the character believed. The present case adds a complementary difficulty. Intense recognition is not proof. Yet absence of recognition is not disproof, because an encounter can alter the horizon within which something becomes intelligible. The lesson is neither “trust your conviction” nor “distrust every experience.” It is to examine how conviction arose, what resists it and what it allows us to see. My anticipation of the later metanoia paragraph mattered within this structure. It did not certify the concept. It showed that a relation first encountered through another person’s work and an AI dialogue had begun to produce inferences in me. That is evidence of learning as formation, not evidence that the interpretation is beyond criticism. The distinction lets intellectual excitement remain alive without appointing it judge.
After reproduction, the singularity of the encounter
Walter Benjamin’s essay on the work of art in the age of technological reproducibility remains unavoidable here, but it should not be used as a prestigious backdrop. Its central historical problem concerns what happens to art when technical media detach works from inherited situations of singular presence and make circulation, repetition and new modes of reception structurally important (Benjamin, 1936/2008). My browser artwork belongs to a later condition in which reproduction is no longer the only useful model.
My first comparison was too clean. Mechanical reproduction seemed to belong to an old regime of copying, while generativity seemed to belong to a new regime of difference. That opposition was attractive because it gave the project a clear historical position. It was also misleading. Benjamin’s reproductions do not enter identical situations of reception, and a generative system does not guarantee meaningful difference. A repeated output can occur; a technically different output can be aesthetically trivial. The contrast had to remain schematic rather than become a claim that generativity simply supersedes reproduction.
Mechanical reproduction can be schematized, roughly, as one work → many substantially similar reproductions. A generative system offers another structure: one system → potentially many non-identical manifestations. The difference is not absolute. Reproductions are encountered differently, and algorithmic outputs may repeat. But the system is designed to produce variation rather than merely to distribute copies of a stable visual object.
AI does not own this difference. A traditional programmer, an instruction-based artist or a composer working with chance can create runtime generativity without machine learning. Three levels need separation. Runtime generativity occurs when the rules produce varying manifestations. Developmental generativity occurs when encounters with outputs change the specification and future system. Epistemic or formative generativity occurs when the surrounding dialogue accelerates reflection, translation and learning so that the participants themselves enter later cycles differently. AI’s strongest contribution in this case belongs to the third level, with an important role in the second. This three-part distinction arose because my first account credited AI with too much. If varying manifestations were the decisive innovation, then AI was incidental: conventional code could already produce them. I then moved AI’s contribution to development, but reflective revision also predates AI. Only at the third level did the distinctive acceleration in this case become visible—the dense loop of articulation, cross-disciplinary translation, objection, preservation and response to a changing learner. Even there, “distinctive” does not mean exclusive or automatically beneficial.
This makes Benjamin’s question newly strange. What happens when technical reproducibility does not merely multiply a work but helps produce potentially unrepeatable encounter-events? A visitor can refresh the page and receive another composition. Yet visual difference alone does not create singularity. The more consequential uniqueness may lie in the convergence of one manifestation, one moment, a visitor’s prior memory, the text in the foreground and whatever response returns. The pixels may be reproducible while the full event is not.
It is tempting to say that aura migrates from object to event—to propose a “procedural aura” or “event-based aura.” I keep those phrases exploratory. Benjamin’s aura belongs to a specific account of distance, tradition, cult value, exhibition and technological modernity; it should not be repaired casually by attaching it to novelty. A personalized or statistically rare output is not automatically auratic. Platforms manufacture apparent uniqueness continuously.
The migration of aura was one of the most rhetorically satisfying AI-assisted suggestions, and for that reason it required particular resistance. The phrase seemed to solve the Benjamin problem in one movement: aura lost by the reproducible object would reappear in the singular encounter. But that was too restorative and too convenient. I retained the question while withdrawing the solution. What the case supports is a shift of attention toward the encounter-event, not proof that Benjaminian aura has returned under a procedural name.
Still, the case suggests a real shift in artistic attention. The singularity I care about is not scarcity in the marketplace. It is the irreducibility of an encounter in which the work and beholder acquire a history together. The words of an exchange can be quoted, but the encounter cannot be reproduced for a second first time. Its significance depended partly on the history that preceded it. A later visitor can encounter the resulting article, but cannot occupy precisely that history. Her following question changed my relation to the work because of years that preceded it. A later visitor can encounter the resulting article, but not occupy precisely that history. Technical generativity multiplies occasions; human temporality makes each consequential occasion more than an interchangeable output. The creator-beholder relation intensifies the point. I can reopen the same published article, but I cannot return as exactly the reader who published it. The archived object may remain stable while its originator changes. Conversely, the generative visual system may change while a returning visitor brings memory of earlier manifestations. The artwork’s temporality runs in both directions: variation in the object meets variation in the beholder.
Benjamin therefore helps me resist two simplifications. The first treats the generated image as a conventional unique masterpiece simply because a particular arrangement may never recur. The second treats digital reproducibility as the disappearance of every form of singular presence. The more interesting artistic question lies between them: whether a technically repeatable system can compose situations in which an encounter becomes singular because it changes what can happen next.
The work remains open because the maker has changed
It would be possible now to collect the phrases produced by this history—formative generative practice, high-frequency hermeneutic loop, recursive conservation of insight, diachronic relational authorship, discernment of generated possibility, metanoia of epistemology—and present them as a finished theory. That would repeat the retrospective error with which this essay began. Each phrase arose because an earlier description failed. None has yet earned independence from the case that made it necessary.
The browser composition was too small a unit, so I followed causal returns through manifestation, encounter and renewed making. “Memory aid” was too weak for an archived thought that later changed its originator, so I proposed recursive conservation. “AI-assisted writing” concealed the separation of lived experience, verbal production, selection and later understanding, so authorship became relational and diachronic. “Fast feedback” did not capture a changed interpreter, so the loop became hermeneutic and potentially formative. “AI literacy” did not contain the ethical and relational question of which possibilities should continue, so discernment entered. “Knowing the term” did not explain why experience changed what knowing meant, so metanoia reached the epistemology itself.
The human–AI division of contribution is also clearer when reconstructed through these changes rather than estimated from the final prose. AI gave me formulations that opened genuine paths: “Where exactly is sense?”, “The person can become implicated,” and the idea that the creator may return as a beholder. It helped connect the artwork with feedback, distributed cognition, authorship, metanoia and Benjamin. My contribution included recognizing those openings, but also refusing their first attractive forms. “Co-creator” gave the visitor too much control. “Everything around the artwork” erased the boundary I needed to explain. Feedback alone could not distinguish a new parameter from a new criterion. Human direction did not automatically settle authorship. Fact-checking did not settle discernment. Metanoia could not be declared merely because the analogy felt powerful. Aura could not simply migrate because the sentence was elegant.
Each objection returned new evidence or a new constraint to the dialogue. AI then reasoned within a changed problem, and I encountered the revised formulation from a changed position. This does not reveal one hidden point at which either the human or the machine authored the whole trajectory. It reveals a sequence in which generation and judgment repeatedly changed what the next contribution could be. The history of those corrections is therefore part of the authorship evidence.
These proposals resemble and depend upon existing traditions: generative and process art, relational aesthetics, reflective practice, second-order cybernetics, distributed cognition, hermeneutics, enactivist approaches, human–AI co-creativity, metacognition, formation and theological discernment. I do not claim to have invented their questions. The personal originality, if there is any, lies in the particular convergence and in the documentary history through which the concepts became newly necessary to me. A case can matter without founding a field.
I still do not know which of the provisional terms will survive outside this case. Further scholarship may reveal that an existing vocabulary is more precise. Later rereading may expose a distinction I have again made too quickly. Prof.ssa Dohna may reject the way I have connected her work to the artwork, or may see a relation I have missed. Those possibilities are not defects to be hidden before publication. They define what remains open, while the references and documented chronology define what can presently be defended.
The emerging constellation might be called a meta-epistemology of AI-assisted formation, but even that name should remain a question rather than a banner. What becomes of human formation when AI does not merely transmit information or generate outputs, but enters recursive loops through which people externalize thought, encounter their own productions, reinterpret other persons, preserve fragile insights, revise their standards of judgment and become different participants in the next iteration? Under what conditions can making, encounter, reflection and human relationship alter not only what a learner knows, but the kind of learner who continues knowing?
The conditions matter more than the label. There must be a real world against which generated language can fail; other persons who remain other rather than becoming material for self-confirmation; an archive that preserves revision rather than erasing it; time for intensity to become attention; practices of verification and correction; and human responsibility that cannot be outsourced. There must also be making. Without a work that resisted my first intentions, the whole reflection might have remained an elegant conversation detached from consequence.
I can now distinguish the evidential layers of the story. The running visual system, the published texts and the preserved exchange are documentary parts of the record. My surprise, discomfort and changed understanding are first-person reports. The claim that her question transformed the artistic practice is an interpretation supported by subsequent rereading and revised intention. The conceptual terms developed with AI are analytical proposals. The connections to Guardini, Benjamin, cybernetics, distributed cognition and discernment are scholarly comparisons that can be tested. The possibility of a broader theory of AI-assisted formation remains speculation.
Keeping these layers visible does not drain the artwork of mystery. It allows mystery without fabrication. I do not claim to know everything Prof.ssa Dohna intended by her question; what I can describe with confidence is how I encountered it and what changed in my own thinking afterward. I do not know whether every future technical change will embody the theory developed here. I do know that I can no longer design the work as if visitors, responses and later selves were external aftereffects. I do not know whether AI enlarged my authorship or revealed that authorship had always been more distributed than I admitted. I do know that the responsibility for deciding what bears my name remains mine.
The performative circle is now visible, though it has no final endpoint:
knowing → creating → encounter → transformed knowing → transformed creating → new encounter
Prof.ssa Dohna developed a project concerned with knowing, creating, encounter, figuration, Bildung and metanoia. I had been familiar with many of these questions for years, but this experience gave me a new point of entry into them through my own artistic practice. I created an AI-assisted computational artwork for another reason. The artwork produced unforeseen relations. She encountered it. Her response unsettled my existing interpretation and made a connection visible that I had not anticipated. Questions running through her work began to illuminate dimensions of the artwork differently, while the artwork gave those questions a new concreteness for me. AI helped me articulate what was changing, and gradually I found myself not only following the emerging conceptual movement but also questioning, resisting and extending it in directions I had not foreseen. This essay may now return to her, and her response—if she chooses to give one—may change the process again.
That possibility does not make her responsible for completing the artwork or confirming my interpretation. The openness of encounter includes the freedom of the other person not to play the role the artist has imagined. A future response may deepen the argument, correct it, refuse it or move the relation somewhere I cannot foresee. The work remains open because encounter is not a programmable output.
I began by wanting the website to look cooler. I still care whether it looks good. The art did not become serious by escaping its visual body, and the philosophy does not excuse a bad composition. What changed is the horizon within which I create. A refresh can still produce a mosaic. It can also place a visitor before an arrangement I did not individually compose, preserve that meeting within memory, provoke a response, return the response to the maker and alter what the next act of creation can mean.
The generator has not become a philosopher, and I have not become the sole master of everything it helped me say. Something more modest and more demanding has happened. Creation became capable of arranging the conditions for an encounter; encounter changed the person who returned to create; and Prof.ssa Dohna’s question helped make it impossible for me to continue treating the generated image as the whole work. The next manifestation will appear on a screen. The next artwork may begin in what that appearance changes between people.
References
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