The Epistemological Crisis of Rationality in the Age of Artificial Intelligence Through the Lens of 4E Cognition and Postphenomenology

Abstract

Everyday work with large language models (LLMs) normalizes a practice in which a generated formulation supports judgment before the user has checked its grounds. This crisis of rationality arises not from a single technical defect in the system, but from a shift in justificatory practice in which fluent textual coherence is read as evidence of semantic understanding and rational judgment. The method brings conceptual analysis into contact with 4E cognition and a postphenomenological account of technological mediation. Within this framework, LLMs are described neither as autonomous rational subjects nor as neutral instruments, but as multistable moral-epistemic mediators of human rationality. The analysis distinguishes textual competence from world-involved understanding and relates interface mediation to trust and responsibility. On this basis, the article proposes a four-cluster protocol for the attribution of rationality, which introduces an epistemic pause as a route of verification between a generated formulation and its authorial acceptance as a claim. The central risk lies not in whether machine consciousness has been proven, but in the normalization of practices in which ready-made text acquires the status of a ground before the user has reconstructed its sources and accepted responsibility for what is asserted.

1. Introduction

A machine-generated formulation enters academic work as if it were already prepared for judgment. Its academic tone and internal coherence afford continued reasoning before the path from wording to source and argument has been re-established. The speed of generation matters epistemically because it delivers a shaped, claim-like form that can begin to operate as provisional warrant within the user’s own reasoning, rather than raw material for later reconstruction. Verification must therefore recover a pause that the completed formulation has already begun to erase.

The transfer from coherent form to presumed understanding produces a level shift, a categorical displacement in which the system is attributed something that does not follow from the mere fact of successful generation. An LLM provides a useful and plausible response that maintains the topic of conversation and presents itself as an explanation. The user begins to read this form as evidence of understanding, and linguistic effectiveness begins to count as a sign of reasoning capacity. In the experience of interaction, the answer is gradually perceived as if behind it stood a subject capable of answering for what is said. As Bender and Koller argue, training on linguistic form does not give the system autonomous access to meaning [1]. Research on language grounding clarifies this limit from another angle. Meaningful communication depends on shared experience of the perceptual world [2].

This problem is not limited to the attribution of meaning. A well-formed response begins to appear as if it were already answerable to truth and available as justificatory support. The user sees an improved result and easily takes it as an increase in their own competence. Yet studies of human–AI interaction reveal a more complex picture. Text quality may improve without a more accurate self-assessment of understanding, and writing suggestions can subtly shift the author’s position [3,4]. Messeri and Crockett describe this dynamic as an illusion of understanding. The generated text becomes more elaborate and persuasive, but the user returns less and less often to the question of what the answer is actually based on [5].

Much of the current discussion of LLMs still turns on the status of the artificial system or on the reliability of its outputs. Less visible is the moment of uptake in which a generated formulation, encountered within ordinary writing and institutional routines, begins to acquire justificatory force before its grounds have been restored. By isolating this passage from machine support to human reason-giving, the article treats it as a distinct philosophical problem. Described as a level shift, this process motivates a protocol for the attribution of rationality that preserves an epistemic pause before such a formulation is accepted as a reason.

The metaphysical question of the possible consciousness of artificial systems lies beyond the scope of this article. Rather than searching for consciousness inside the model, the analysis focuses on how the user encounters LLMs in interface-bound and institutional practices. It is here that the relation between verification and responsibility is reorganized. Contemporary discussion often moves between two opposing images. Some interpretations see a new mind in the machine, while others return it to the role of a neutral tool. Postphenomenology makes it possible to avoid choosing between these extremes. From this perspective, technology does not stand outside human experience; it mediates the human relation to the world and participates in shaping moral decisions [6,7]. The principle of multistability refines this move. The role of technology takes shape in practice, but it is not arbitrarily open [8]. It is therefore more precise to treat LLMs as multistable moral-epistemic mediators. They alter not only the form of the response but also the very way in which the user connects knowledge with responsibility.

This inquiry remains conceptual and reconstructive. It does not assess a particular system through technical metrics and does not construct an empirical model of LLMs. Instead, the analysis reconstructs the conditions under which a ready-made machine-generated response gains trust before it has been justified. The argument begins with the history of the sign, insofar as coherent form can acquire the appearance of thought. The focus then shifts to the living agent, for whom meaning arises not through linguistic operations alone, but through practical involvement in an environment. Varela, Thompson, and Rosch describe cognition through enactive sense-making, while Clark and Chalmers show that cognition can be extended through stable external artifacts [9,10]. Postphenomenology adds the interface layer, where concrete technical mediators reorganize human experience [11]. In this conjunction, 4E cognition marks the boundary at which textual coherence can no longer be treated as world-involving understanding. Postphenomenology then explains why this limit can become less visible when coherent text mediates judgment in an interface-bound practice.

Rather than asking only whether LLMs can produce coherent academic prose, this study asks how such prose comes to be accepted as a reason. Generated text enters inquiry as a finished surface whose fluency can precede any reconstruction of its grounds. The central risk lies in this quiet passage from useful formulation to provisional warrant, when authorial judgment begins from a claim whose source-relation has not yet been restored. To make this passage visible, the article brings 4E cognition and postphenomenology together to describe LLMs as mediators of situated epistemic practice. On this basis, it proposes a four-cluster protocol that preserves an epistemic pause before authorial acceptance.

The argument proceeds from generated formulation to accepted claim by separating linguistic competence from propositional responsibility and then tracing an older philosophical problem of signs that appear to carry thought before they are returned to world and judgment. 4E cognition clarifies why understanding remains tied to situated sense-making, while postphenomenology shows how an interface can obscure that tie. From there, the analysis limits claims about consciousness, rational agency, and responsibility before formulating the protocol that keeps visible the moment at which machine support becomes a human claim.

Linguistic form must therefore be separated from propositional responsibility. A syntactically successful passage may assist an author’s thinking, yet it does not speak from a position that can answer for what is said. Buckner’s distinction between artificial intelligence and artificial rationality sharpens this limit. Deep learning can model capacities that philosophy has long associated with rational activity, and its achievements should not be dismissed as mere illusion. The philosophical issue begins with the passage from modeled capacity to rational warrant because producing a rationally structured result is not yet the authority to treat that result as a reason. A prediction or a coherent text becomes relevant to scholarly judgment only when its grounds can be reconstructed and its use can be justified within a practice of responsible reason-giving [12].

2. Form, Meaning, and the Limits of Rationality Attribution

Form and meaning must be distinguished before the debate about rationality collapses into a debate about the machine as a subject. Machine-generated text may be useful and persuasive, but this success does not yet show that understanding underwrites it. The classical debate already sets this limit. Turing relocates the question of machine thinking into the scene of imitation, where observable behavior becomes decisive [13], while Searle shows that the correspondence between input and output does not prove intentionality [14]. Contemporary LLMs are architecturally different from older symbolic programs, but this difference does not dissolve the initial boundary. The processing of form does not by itself generate meaning. The epistemic risk is that sustained dialogue more easily conceals the absence of the system’s own access to the world. Bender and Koller translate this tension into an analysis of language by linking meaning to communicative intention and the external world [1]. Research on language grounding adds that meaningful communication requires shared experience of the world, and not merely textual statistics [2]. The competence of LLMs within linguistic form is real, yet it cannot be transferred to the authority to assert and answer for what is said. Within the Turing frame, greater indistinguishability does not dissolve this boundary; it makes the passage from observable success to accountable judgment harder to keep in view [13].

Coherence becomes problematic not only when the generated answer contains a direct error. Its persuasiveness may arise from the sheer consistency of the text, which the user takes for understanding. As Bender et al. show, machine-generated coherence depends on the receiver’s interpretation and does not presuppose the system’s own communicative intention [15]. LLM hallucinations expose this gap between the form of assertion and the relation to fact. The answer takes the form of a truth-claim, although there is no speaker behind it who could answer for what is said.

Mácha formulates this issue through the question of truth representation. Truth and falsity belong to sentences or propositions, whereas LLMs generate continuations through probabilistic relations between tokens and have no propositional structure of their own capable of sustaining a relation to fact [16]. A hallucination is therefore not an error made by a thinking subject. It arises as a structural possibility of a text that looks like an assertion without being tied to an assertoric commitment. Frankfurt’s motif of indifference to truth is relevant here only as a limited analogy, since the model has no intentional position from which responsibility for an assertion could arise [17]. Fitts continues this cautious line through the argument about a category mistake in attribution. An LLM is not a subject of bullshit, but it may become a vehicle for transmitting someone else’s indifference to truth when the user accepts a plausible form as a responsible ground [18].

A narrower rule of attribution follows from this dynamic. If an LLM answer looks like reasoning, it cannot be evaluated by the persuasiveness of its form alone. What must be checked is not the image of a speaking subject, but the status of the assertion and its relation to available grounds. In human dispute, an assertion lives in a space of reasons, where a position can be clarified and revised under challenge. LLM generation reproduces this formal structure, but it does not create a subject capable of assuming a commitment to the truth of what is said. A plausible answer becomes problematic when it is already read as an assertion, while responsibility for it remains on the side of the user.

Propositional meaning emerges only where a coherent form preserves its relation to the world about which it speaks. While training data contain traces of human experience, these traces are already separated from the situations of their emergence. As Bisk et al. demonstrate, meaningful communication requires access to a world in which language is connected with perception and shared experience [2]. Di Paolo, Buhrmann, and Barandiaran shift the emphasis toward the sensorimotor attunement of an agent in a changing environment [19]. An LLM can work with textual traces of the world and generate a persuasive response, but only the user restores its relation to fact. A coherent form still does not replace the one who is able to relate what is said back to the world.

The need to return generated form to world and judgment also clarifies the status of machine-generated meaning. On a 4E and enactive account, linguistic coherence becomes epistemically significant only within a situated practice in which claims can be reconnected with worldly involvement and responsible uptake [2,9,19]. Gunkel’s deconstructive reading of LLM writing sharpens this account without displacing it, since machine-generated text may acquire semiotic force even where meaning is no longer anchored in embodied voice or authorial presence [20]. For scientific reasoning, however, semiotic force is not yet warrant. Hallucination can then be read as a disturbance in the passage from signification to warrant, where an LLM output becomes a ground for a claim only if an author reconnects it with sources and makes its inferential role defensible within responsible judgment.

With repeated interaction, caution in attribution quickly weakens. The familiar conversational interface makes the model resemble an understanding interlocutor. Colombatto and Fleming show that this transfer does in fact occur [21]. What matters here is not the user’s tendency alone, but the phenomenological mechanism that makes this transfer persuasive. A coherent reply is read as the trace of an inner position, although the text itself discloses no one who could understand what is said. More cautious approaches to artificial consciousness and agency do not allow external behavior to be conflated with the internal status of the system [22,23]. Successful sign-processing can once again be mistaken for thinking.

3. Genealogy of the Gap Between Sign and World

The following genealogy does not offer a general history of rationality. It follows instead the narrower thread of an external sign or procedure beginning to carry thought before it has been reconnected with living recollection and responsible judgment. Plato’s Phaedrus marks this ambiguity with particular clarity. Writing preserves what has been said, yet it can also produce the appearance of wisdom where understanding is no longer present [24] (Phaedrus 274c–275b). For the present argument, the point is not the whole of Plato’s critique of writing, but the recurring structure in which an external trace can support meaning while weakening the distinction between reminder and understanding. Stiegler gives this structure a contemporary continuation. Technical memory conditions the transmission of knowledge, but it does not coincide with the primary act of sense-making [25]. The external trace becomes a condition for transmitting meaning and a scene in which the sign can begin to occupy the place of living understanding.

Procedure then brings the sign to the foreground. In Llull, reasoning is linked not only to what thought contains but to the formal path by which it moves. Truth is sought through repeated work with letter designations and combinatorial figures, where the sign itself sets a route for reasoning [26] (Ars brevis, pp. 297–318). Bruno’s mnemotechnics continues this concern with images and shadows of ideas [27]. The decisive point is the possibility of arranging the movement of thought in advance through form. Contemporary digital practice does not repeat these systems, but it makes this formal possibility operational at another scale. A coherent result can arise from a properly arranged sign operation and is easily taken for the work of thinking.

In the early modern period, this order takes on a more explicitly calculative shape. In Leviathan, Hobbes describes reason as calculation, an operation in which thought is linked to a governed sequence of signs [28] (Part I, ch. 5, p. 33). Leibniz strengthens the same orientation through the project of a symbolic order in which definitions and signs support the movement toward new truths [29] (p. 41). The sign gradually detaches itself from the living situation of experience and begins to function as an ordered sequence. This should not be read as a prehistory of artificial intelligence. It is the moment when correctly ordered symbols begin to appear as reason’s own work. This calculative formalization also prepares the modern mathematization of nature, from which technogenic civilization emerges. Husserl’s later critique gives this detachment its phenomenological form, showing how effective formula can continue to function while its relation to lifeworldly grounding recedes.

Descartes and Hume shift the question from the order of signs to act and habit. In the Second Meditation, the correctly uttered or thought “I exist” has force not as a ready-made phrase, but as the act of the one who thinks [30] (Meditation II, pp. 18–19). In Hume, the expectation of similar effects from similar causes arises from the repetition of experience rather than from contemplation of a necessary connection [31] (E 4.23; E 5.6). Regularity supports the transition between elements, but by itself it does not provide the inner connection that we tend to attribute to meaningful inference.

Against this backdrop of habit and regularity, Kant sets a critical limit to the transition from form to knowledge. A concept remains empty without intuition, and intuition blind without a form of understanding [32] (A51/B75). This conjunction, however, still does not resolve the question of appropriation. Representations enter experience only when the “I think” can accompany them as belonging to one consciousness [32] (B131–B132). A syntactic assembly of sentences has no such center of appropriation. A coherent form becomes thought when someone accepts the synthesis as their own act and answers for it. Otherwise, the correct organization of sentences remains a formal operation rather than a rational action.

Phenomenological clarification returns the sign to the lifeworld and to the bodily givenness of experience. In The Crisis of European Sciences, Husserl shows that a technicized operation can preserve effectiveness even when its relation to living sense-making has weakened [33] (§§9g–h, pp. 46–52). Merleau-Ponty carries this limit into the fabric of perception. The world is given not as a neutral object of thought, but as a lived milieu in which body, perception, and action are already intertwined [34] (Preface, pp. x, xviii). The genealogy therefore does not end with one more historical stage. It clarifies how formal coherence can appear meaningful before it has been returned to lived sense-making. After this, the sign can no longer be held within the limits of formal correctness alone. It enters a world of experience in which meaning must be lived and answered for. The question then shifts from the preservation of signs to the conditions under which formal coherence can become answerable to lived sense-making and judgment.

4. 4E Cognition and the Limits of World-Involved Understanding

Once the sign can preserve meaning without securing understanding, the question shifts to the conditions under which understanding remains situated in an agent’s world. Meaning arises when a sign relation passes through participation in a concrete situation of experience. 4E cognition makes it possible to describe this passage without identifying external support with autonomous understanding. Varela, Thompson, and Rosch link cognition to enactive sense-making [9]. Thompson’s account of mind in life keeps cognition within the organization of living systems and prevents textual performance from being detached from the conditions of lived sense-making [35]. Di Paolo, Buhrmann, and Barandiaran further connect sense-making with sensorimotor organization, where a system sustains itself through its own operational closure [19].

Extended cognition then clarifies the role of external support. Stable artifacts can enter the human cognitive process without becoming independent centers of understanding [10,36]. An external support extends human thinking, but it does not acquire its own access to the world. A ready-made machine formulation may be useful and coherent, yet its semantic status depends on being returned to a situation of verification.

Consider the word “hot” as a simple illustration of why textual appropriateness is insufficient for lived meaning. Its meaning rests not only on linguistic probability. It is connected with the bodily encounter with temperature, the risk of pain, and learned caution in handling something that may burn. An LLM can correctly explain a burn or warn about a risk, and such a formulation remains useful for the user. Yet it does not pass through bodily error and the subsequent correction of behavior. Machine generation draws on linguistic traces of human experience without assuming sensorimotor risk. Its value emerges in human use, not in the lived meaning of the model itself.

Human beings do not think in an empty space, but with and through external supports. Notes and working drafts enter the course of reasoning and help one return to a question after a pause or clarification. The external character of LLMs is not in itself the problem. The model can enter work on a question or a draft, provided the result remains connected with human verification. It participates in the route of thought, but does not live through that route. The epistemic tension appears when a ready-made formulation begins to look like an independent semantic position and takes the place of synthesis before the user has restored the ground.

Radical enactivism reinforces this caution by refusing to identify basic mentality with detached content or representation [37]. Distributed cognition carries the same insight into organized action, where external supports take part in cognitive work while remaining embedded in a human practice [38]. Their epistemic force depends on the practice that returns them to the world in which claims can be checked and revised. Sterelny’s account of cognitive capital describes this configured environment of action [39]. Paul’s distinction between worldly and instrumental knowledge further clarifies why a reliable tool can guide a task without acquiring the understanding that makes a claim answerable [40]. Textual traces of human experience may be preserved in the digital environment [2], but the user still has to return them to a situation of verification. The level shift begins when textual competence is taken for such a return.

At the interface, this limit becomes unstable. A generated formulation is encountered as an address within conversation, and the situated condition of understanding can recede behind the fluency of the reply. 4E cognition keeps understanding tied to lived involvement in a world, while postphenomenology shows how technical mediation can make that tie less visible as judgment takes shape in writing.

5. Postphenomenology of the LLM Interface

Ordinary access to an LLM begins with a formulation already present on the screen, shaped as a reply and drawn into the rhythm of conversation. Its architecture recedes behind the pace of generation and the sense of being addressed. Before the system’s lack of world-access becomes salient, the user may already relate the phrase to their own judgment. Ihde and Verbeek make this shift describable without assigning subjectivity to the model. Technology mediates the relation between human beings and world and redistributes the form of action and responsibility [6,7]. Gerlek and Weydner-Volkmann’s account of ChatGPT 4 as an active user interface helps specify this experience without turning the model into a subject [41]. In chat, a hermeneutic relation to a technical indication approaches an alterity relation. Produced text remains produced text, yet conversational dynamics gives it the appearance of a quasi-other. The question of understanding passes into the interface, where its conditions are experienced as least visible.

Technical indication does not replace the situation, but sets a way of reading it. A thermometer or a medical monitor gives not the environment itself, but a form that still has to be connected with action. In technoscience, a similar mediation takes place through graphs and visualizations [11,42]. With LLMs, however, the nature of this mediation shifts. Technical work is hidden behind the coherence of academic prose, and the user sees not an instrumental sign, but an almost ready-made explanation. The model assembles a textual surface in which fragments of sources are already drawn into a possible argument. Verification returns this surface to the sources and to the role it may play in human reasoning [6,43]. The explanation remains an addressed process, open to objection and practical clarification [44].

The experience of conversation takes shape before the user has time to reconstruct the origin of the response. The result already stands before the user, while the trajectory of its assembly remains hidden. Gerlek and Weydner-Volkmann show that the active interface of LLMs reorganizes familiar digital devices without adding new material hardware [41]. The speed of generation and the visible structure of reply make machine-generated text part of communication, not merely a technical output. Its role is fixed not in the system itself, but in a concrete mode of use. A formulation may remain a draft support or may imperceptibly take the place of a result that the user no longer returns to verification. Multistability names this situational fixing of a role, rather than the transformation of LLMs into a subject [8]. The effect on human self-understanding passes through repeated ways of working with ready-made text and trusting it [45].

In an active interface, machine-generated text no longer enters the work through slow rereading. It appears in fragments and immediately draws the user into the continuation of the conversation. The pace itself creates the impression of thought unfolding next to the initial prompt. The user sees a sequence of clarifying formulations and easily reads it as responsiveness. Beneath this impression lies generated text that lacks its own relation to the task and to truth. An empathetic tone adds a sense of connection without creating a moral relation. Early studies of computer interfaces showed that social expectations are transferred to interactive systems even when their machine nature is known [46]. In work with LLMs, this transfer becomes more subtle because the system returns the user’s prompt in a more coherent and confident form. Convergence with expectation begins to look like external confirmation. In Vallor’s image of the “AI mirror,” this mechanism receives a name: in place of the independent position of another, the user encounters their own prompt intensified by the form of a confident response [47].

A machine-generated formulation often enters the work before the user has time to separate a draft from the ground of a decision. Such external memory stores information already reworked as moves in writing and possible transitions toward a thesis. At this point, convenience begins to guide judgment. The formulation helps the user work with material and at the same time already sketches a possible conclusion. Heersmink et al. describe LLMs as multifunctional computational cognitive artifacts [43]. The acceleration they enable becomes philosophically significant when the route of verification is compressed and the suggestion enters into the very formation of judgment [48,49]. External memory remains a support insofar as the human being reconnects the proposed text with the original material. Without such reconstruction, the decision is hastened without passing through its own justification. For Stiegler, technical exteriorization is a condition of possibility for human understanding, yet it becomes epistemically risky when externalized traces supplant renewed interpretation and critical appropriation [25].

Interface mediation gives generated text the force of an address. A reply arrives within conversation as if it carried a position, although no lived relation to the situation belongs to the model. Here the 4E constraint must remain visible. Postphenomenology explains how that constraint becomes phenomenologically fragile as trust forms around the rhythm of response and the quasi-other can be mistaken for someone who understands. Such experiential force must be kept distinct from stronger claims about machine subjecthood.

6. Limits of Attributing Consciousness, Rationality, and Moral Subjecthood

Separating interface force from subjecthood makes conversational grammar the next site of caution. Expressions such as “the model offers” or “the system responds” may serve as harmless shorthand for an interactional appearance. They become philosophically misleading once they suggest a bearer of understanding behind the reply. That implication would require a history of meaning in which words remain answerable to a shared world [50]. Epistemic authority depends on an analogous anchoring. A persuasive formulation looks like knowledge only when its grounds can be reconstructed, and claims about LLM consciousness must be tied to indicators appropriate to the target system under consideration [51].

Verifiable indicators help specify the degree of confidence, yet the question of another’s consciousness cannot be reduced to a single test. One’s own experience is given from within, whereas another’s experience is recognized within a history of interaction in which a word is connected with the world and can be clarified by another. In Davidson, this asymmetry becomes a question of content. Thought receives its content not in isolation, but through a second-person relation in which what is said is related to a shared world [50] (pp. 107–121, 205–220). The coherence of an answer does not by itself sustain such a reading. What matters is how what is said enters a network of interpretation and acquires a stable relation to the world. In work with LLMs, the familiar rhythm of communication and coherent text make it easier to see experience behind the answer. Studies of such attributions document this tendency [21,52]. A recognizable form of reply strengthens the impression of presence while leaving open the question of the origin of meaning.

Davidson’s classic Swampman thought experiment isolates this problem. The duplicate may speak and act as if thoughts stood behind his words, yet behavioral coincidence still does not show where the meaningfulness of his utterances comes from [53] (pp. 443–444). For LLMs, what matters is the caution of the inference itself. A persuasive answer presents the form of possible understanding while leaving unresolved the origin of meaning and the belonging of the utterance [52]. Indicators can grade the level of confidence when they are related to behavior and to the explanatory task [22,51], but they yield a cautious probabilistic assessment rather than a definitive proof of consciousness. A test works only when it is clear which property of the system it is meant to examine [54].

Complex behavior moves the discussion from visible success toward experience. Seth’s distinction between intelligence and conscious experience prevents functional effectiveness from becoming warrant for subjecthood [55]. Task completion and sustained dialogue may support a cautious hypothesis about the system, although they do not make it a subject of understanding. Di Paolo, Buhrmann, and Barandiaran place sense-making within living organization, where autonomy is maintained through sensorimotor life [19]. Machine consciousness remains a theoretical possibility and cannot add authority to any current answer. As behavior grows more persuasive, philosophical caution must keep trust in the output tied to the human act of judgment.

After this theoretical clarification of consciousness, LLM assistance in search and drafting remains a practical possibility as long as it does not acquire the status of knowledge without verification of grounds. Ferrario, Facchini, and Termine show that the epistemic authority of AI cannot be derived from predictive success alone if the domain of application and the ground of inference have not been clarified [56]. This restriction matters especially when machine support enters research or writing. A generated formulation may help organize a search, draft a passage, or compare alternatives, but it does not thereby attain epistemic authority. Rather, such status is established only within a practice where grounds are actively reconstructed and its use can be justified.

Agency can still be used as a description of participation in the process. In Floridi’s terms, LLMs can be considered artificial agents through which a response appears and verification is reorganized, without transferring mental states to the system [23]. Such agency marks participation in producing a result, not a capacity to understand what is said or to answer for it. Santoni de Sio and Mecacci connect responsibility with the inclusion of an action in a practice of accountability, rather than with causal participation alone [57]. Machine support becomes more difficult when it enters an authorial decision. Practices of trust and authorship then show who must answer an objection and when a suggestion becomes a delegation of judgment. What matters is how a human–AI environment can be arranged so that artificial agency without intelligence strengthens responsible judgment rather than quietly bypassing it [23].

7. Moral-Epistemic Mediation and the Distribution of Responsibility

Once these limits of attribution are clarified, responsibility appears at the moment when a generated formulation is accepted. No moral addressee emerges on the side of the system, yet proposed text can enter writing as a trusted conclusion [7,58]. Between suggestion and assertion, a narrow interval remains in which the user still separates judgment from coherent form. If this interval disappears, ready-made text begins to function as sufficient ground. Authorial decision begins when the user recognizes the formulation as their own and is prepared to answer for it. Responsibility enters here because the preceding analysis has separated usefulness from any right to treat the system as a bearer of judgment.

Judgment here has several layers. Responsible judgment names a claim or decision for which a subject can return to the relevant grounds and answer for its use in a concrete practice. Authoritative judgment names the status a claim acquires when its justificatory force is recognized within an epistemic or institutional setting, not a further mental act or the mere readiness of a formulation for use. Authorial judgment is the form responsible judgment takes in scholarly writing, where an author accepts a formulation as a defensible claim rather than merely inserting it into the text. LLM output can assist this movement, but it cannot become the bearer of judgment.

Responsible judgment differs from Kantian reflective judgment. Kantian reflective judgment seeks the universal for a given particular when no determinate rule is already available [59] (Introduction IV, 5:179). The problem considered here lies at a different threshold. A generated formulation often arrives as if the work of synthesis had already been performed. Responsible judgment names the author’s act of returning such a formulation to its grounds and remaining accountable for its place in scholarly discourse.

Moral-epistemic mediation becomes visible at the threshold where a text is inserted into a document. The proposed answer may already look persuasive, although its source and argumentative role still require verification. The interface provides a coherent form, but it does not compel the user to reconstruct its ground. An LLM remains a useful support as long as the user treats the answer as material for verification. The effect of algorithmic appreciation strengthens trust in such a form, since the machine result can appear neutral before its grounds have been clarified [60]. The distinction between a suggestion and an authorial decision becomes especially fragile. As long as the answer remains open to verification, it is part of work with the material. When it is accepted as sufficient, a borrowed authoritative judgment appears in its place.

Distributed responsibility emerges in the chain of human decisions surrounding a machine-generated answer. The text moves from prompt to institutional acceptance, and the familiar connection between control and predictability becomes weaker. While Matthias describes such a loss of direct control for learning systems [61], Santoni de Sio and Mecacci specify it through accountability embedded in the order of AI use [57]. The model participates in a causal-epistemic chain, but the explanation of the result and the acceptance of its consequences remain with the person who introduces it into action. Meaningful human control is preserved where the aim and the possibility of revision have not been transferred to an automated procedure [62]. Work on trust in AI adds a further restriction. Even a reliably functioning system does not automatically justify trust in a particular epistemic situation [63]. A procedure is needed when the point at which the result is accepted must remain visible.

When individual control is no longer sufficient, a procedural layer becomes necessary. Auditing and documentation, including disclosure that text has been generated, make the machine’s contribution visible to the reader and the institution. Such a record shows the origin of the text, but it does not guarantee responsible acceptance of the result. A prompt log reconstructs the sequence of actions, while semantic delegation remains less visible. Responsible judgment begins when the author accepts the risk of error and is ready to defend the conclusion as their own. Institutional mechanisms give responsibility a procedural form by recording the sequence and formalizing accountability, yet the question of the ground may still be left aside. The point of the procedure is to return the user to the accepted justificatory ground. Without this, merely documenting the sequence postpones verification.

In professional writing, a machine-generated draft often enters the work as an almost ready-made form. Its coherence makes it easier to assemble material and imperceptibly obscures the verification that still has to be performed. Calibrated trust requires seeing the origin of the suggestion and the limits of its application [58,64]. Repeated work with the model gradually changes the technique of verification, especially in educational practice. Educational practice should therefore focus not only on interface operation but also on preserving the epistemic pause before accepting a ready-made formulation [65]. Trust in LLMs remains a working disposition as long as the proposed text is held as a verifiable support [63]. When it begins to sound like the voice of a responsible agent, authorship is gradually reduced to the arrangement of a machine-made draft.

Rationality weakens when users repeatedly accept machine support before restoring its justificatory grounds. Fernandes describes a metacognitive trap in which successful task performance is accompanied by the loss of visibility of one’s own supports for the decision [3]. In studies by Williams-Ceci and colleagues, this shift manifests empirically: biased autocompletions imperceptibly alter participants’ positions, and the suggestion itself begins to participate in the formation of the answer [4]. Sapir and Haas show the next moment in this process, when inquiry stops before the question has really been clarified [66]. Intentional deception is not the primary factor here. Rather, what operates is the coherent and confident form of the answer, which prematurely halts inquiry and makes the nearest move seem almost one’s own. Productivity grows where the interval for restoring the ground of the accepted answer is shortened [3,4,66].

A protocol is needed wherever the interval between suggestion and acceptance must remain visible. LLMs function as multistable moral-epistemic mediators because interface form alters how a result is trusted and incorporated into judgment [7,8]. Responsibility remains with the user even when interface design has already prepared part of the route toward a conclusion. Risk arises when convenient support begins to sound like a position backed by authority. Preserving the pause keeps acceptance from vanishing into the fluency of a reply.

8. Epistemological Protocol for the Attribution of Rationality

At this point, the pause becomes procedural. Fluent output becomes epistemically risky when a completed formulation begins to carry warrant before its grounds have been restored. Interruption is needed in the passage from generated formulation to uptake. Four clusters give this interruption a practical order, keeping AI-assisted writing answerable to the sources and practices in which responsibility is assumed.

Procedure here means an ordered return to justificatory grounds. Its questions prevent a coherent formulation from hardening into a rational conclusion before its conditions of acceptability have been restored. A persuasive form requires reconstruction of the path by which the user can accept it as their own [67]. Error may begin even before generation, when the task is configured to obtain a ready-made solution [68]. For that reason, the epistemic pause begins at prompt level. A completed formulation enters authorial judgment only after its route of verification has been restored.

Architectural–behavioral verification turns visible capacity into a question about mechanism. An LLM can sustain dialogue and successfully perform a task, but for a system trained on human speech, such success must be read in light of the process that produced it. Similar behavior may rest on different mechanisms, so the transition from result to internal states remains unreliable [22]. A completed task and a plausible explanation show that the system has produced an acceptable result, but they do not yet permit rationality to be attributed to it in the strict sense. A convincing formulation requires a link to the mechanism that produced it. Otherwise, it appears as an explanation to the user while failing to explain the work of the system [54,69].

Task success begins to look like rational control most easily in writing. An LLM may produce a coherent comparison between 4E cognition and postphenomenology and thereby improve a draft. Yet improvement in the text does not mean that the author has gained command of the grounds on which the comparison rests. Fernandes et al. demonstrate this metacognitive disconnect empirically. AI assistance can elevate task performance while degrading users’ accuracy in judging the limits of their own understanding. Architectural–behavioral verification therefore asks what a successful output demonstrates about performance and what remains unresolved about rational control. Fluency and usefulness mark success within a task, not the emergence of responsible rational judgment [3].

Epistemological grounding verification returns coherent text to the sources and practices in which it must acquire meaning. A body of texts preserves traces of human experience, but within the model’s operation these traces become material for generation rather than the model’s own perception or action. Fluent writing still does not lead to world-involved understanding [2]. What matters is the possibility of tracing how the generated result connects with the world beyond linguistic form. If this connection is not restored, the result remains a strong textual mediation of human experience without becoming the LLM’s own world-access [19].

Grounding becomes visible when a generated claim has to survive contact with evidence. LifeSciBench matters here less as a benchmark than as a reminder that scientific reasoning often requires the integration of incomplete evidence rather than factual retrieval alone. Its tasks present models with realistic cases in which evidence remains incomplete and a claim must still be made supportable [70]. For the present protocol, this changes the status of citation. A reference attached to a fluent sentence does not yet ground the claim. When a model links a thesis to Bender, Buckner, Ihde, or Varela, authorial responsibility begins with reconstructing the path from source to argument and deciding whether the cited text can carry the claim assigned to it.

Postphenomenological interface verification directs attention to the way a generated reply appears to the user and immediately takes the form of an address. In the dialogical window, machine-generated text enters the rhythm of conversation while the process that produced it remains hidden [43]. Closeness belongs to the experience of interaction, not to the consciousness of the system. The formulation appears before the user has time to reconstruct its origin, and for this reason its role in the work becomes decisive. As long as the text can be evaluated and revised, it remains a hypothesis or draft support. When persuasive form closes this possibility, closeness begins to function as delegated judgment [71].

Interface verification begins where a fluent reply is no longer encountered as information alone. Voicu’s account of AI-mediated learning helps articulate this shift, since generative AI appears as a mediator of interpretive cognition and epistemic agency in educational practice [72]. For the present protocol, the interface is the site where uptake begins. A generated reply that returns the user’s vocabulary in coherent academic form can feel like a position already addressed to the author. Verification must therefore ask how this mediated uptake has shaped trust before the claim has been assessed independently.

Moral-institutional verification shows how a convenient formulation gradually enters a practice of trust. The interface remains the same, but human work with text changes around it. A reference suggestion may begin to sound like a quasi-expert position. Such a shift requires a normative ground of trust and preserved accountability [57,58]. Responsibility must therefore remain on the side of a practice in which a decision can be revised and someone can answer for its consequences [73].

Moral-institutional verification begins when the proposed text moves from private assistance into an accountable practice. Anthropic’s report on AI-assisted research specifies a structural shift from execution to review, while the attribution of significance and trust remains with human agents [74]. In scholarly writing, an analogous shift occurs when a generated formulation enters a manuscript or review process. At that moment it becomes a claim for which reasons can be demanded. Verification must preserve the path from machine suggestion to authorial acceptance, so that the model’s contribution remains visible and accountability stays with the human act that appropriates it.

Across all clusters, the epistemic pause forms the common rhythm of the protocol. In the philological sense, it functions as a form of retardation, the holding back of a ready-made formula before it becomes an accepted utterance. The result may look complete, whereas the act of recognition requires time for objection and verification [75]. Such delay supports inquiry by keeping a coherent form from prematurely turning into a justified conclusion. In work with a system whose process of producing a formulation remains hidden, the pause becomes a condition of verification. A formulation of this kind cannot be confirmed only by the premises from which it was itself produced [76].

An auditable trajectory gives this pause its practical visibility. A generated formulation becomes epistemically usable only when its passage from machine output to human claim can be reconstructed. What has to be shown is not a completed text alone, but the route by which the author returned it to its grounds and accepted it as defensible. In this sense, the pause is not an added checklist. It is the visible route by which fluent output becomes a responsible claim.

In research work, this order of questions becomes especially visible when an author asks an LLM to prepare an academic draft. The author receives a coherent text that already resembles completed work. Its form alone does not show that the sources have been understood, the arguments reconstructed, or the references connected to the relevant theses. The draft has to be returned to the articles it cites and to the context in which those articles actually work. A mistaken argument or a false reference becomes part of the research only when the author includes it in their own text. Machine support therefore remains acceptable as long as a coherent draft does not begin to lead the author toward a conclusion before the author has restored its justificatory ground through the sources.

What follows gathers the four clusters into a common order of verification and shows where the epistemic pause must be preserved between a machine-generated formulation and rational judgment. Each row records not only the object of verification but also the part of the work that the author must perform when dealing with the proposed text. Its last column formulates the specific question needed to prevent the machine result from being recognized too quickly as one’s own.

The structure of this protocol is summarized in

Table 1. Summary Matrix of the Epistemological Protocol for the Attribution of Rationality to LLMs.

Protocol Cluster	Object of Verification	Level Shift Blocked	Human Operation Required	Working Verification Question
Architectural-behavioral	Relation between visible performance and the mechanism that produced it [22,54].	Task success may be mistaken for understanding or rational control.	Connect the result to its mechanism before drawing claims about rationality.	What does the result show about performance, and what does it leave open?
Epistemological grounding	Grounding of the claim in sources and evidence beyond the generated text [2,19,70].	Fluency or citation may be mistaken for epistemic warrant.	Return the claim to its sources and inferential ground before acceptance.	Can the route from source to claim be reconstructed?
Postphenomenological interface	Mediated uptake of the generated reply in the interface [41,43,72].	An addressed reply may be mistaken for an interlocutor’s position before its claim is assessed.	Keep interface-induced trust distinct from epistemic warrant.	How did the interface shape uptake before the claim was assessed?
Moral-institutional	Movement from private assistance to accountable use [57,58,74].	Productive support may be mistaken for warranted trust or responsible authorship.	Preserve the path from machine suggestion to authorial acceptance.	Who accepts the claim, and where is accountability preserved?

Note. The references in the table indicate the main theoretical points of orientation for the corresponding rows and do not replace the full reference list of the section.

.

The matrix maps epistemic pauses rather than external rules. Through these clusters, the protocol gives procedural form to the argument developed above. 4E cognition marks the limit at which a generated formulation cannot be treated as world-involved understanding, and postphenomenology shows how interface-mediated uptake can obscure that limit. Each question keeps open the interval in which a persuasive text still requires verification before it can be recognized as one’s own. Such a pause cannot simply be added to every workflow as a cost-free individual virtue. In environments where speed and institutional pressure reward ready-made text, it remains especially fragile. Work with LLMs remains possible. Risk arises when the proposed text has already entered a research decision and quietly changes the author’s sense of why the conclusion is justified.

9. Routine Delegation and the Weakening of Verification Procedures

The need for the protocol becomes clearest when AI assistance turns routine. A false output is only the surface risk. More serious is the slow displacement of verification by fluent text that begins to feel ready for use. What weakens is the author’s relation to the ground of the claim, as ready-made text enters research before its acceptance has been reconstructed.

In academic writing, the objection to a pause of verification sounds especially persuasive. Because LLM-assisted prose achieves clarity and completeness so rapidly, an additional check often appears unnecessary. An LLM helps gather the material found into a thesis, and in this movement the authorial trace can easily be lost. Stegenga’s deontic philosophy of science sharpens this point by treating scientific assertion as accountable to justification and informativeness, which makes the opacity of AI-generated grounds a matter of rational accountability [77]. Bohlmann and Breil describe the essay as a practice already reorganized by relations between human beings and technology [65]. The difficulty lies in the source of the judgment. The user may remember the source, yet in the finished text the boundary between their own interpretation and the version proposed by the model becomes less distinct. A similar risk is visible in studies of autocompletion and in Messeri and Crockett’s work on the delegation of cognitive labor [4,5]. Institutional verification restores the trace of acceptance, the moment in which the author made the formulation their own [78].

Princeton’s revised Honor Code transfers the problem of the authorial trace into an institutional register. Discussion of generative AI in examination practice showed that earlier declarations of honor may be insufficient for maintaining trust in academic action [79]. What becomes significant is a shift in the object of verification itself. An institution has to establish where the academic act arose and who recognized it as their own. The proposed text becomes tied to the moment in which the author makes it the ground of their own decision.

This tension becomes sharper in highly formalized domains. In chess, the formal result acquires particular persuasiveness. The value of a move is determined by the rules of the game, and machine evaluation often sounds more convincing than human commentary. The accuracy of the move begins to take the place of its acceptance in human decision. This case matters for the protocol because a machine-favorable environment makes the result especially persuasive. The authority of the program rests on the verifiability of the result, without any appeal to its subjecthood. Anicker shows how machine evaluation gradually occupies an increasingly prominent role in professional chess commentary [80]. Yet even the established correctness of a move does not remove the difference between a found variation and a decision included in a human practice of interpretation.

AI-assisted formal proof search carries the chess example into a domain where correctness depends on public verification rather than persuasive performance. Tsoukalas et al. report LLM-based agents resolving open Erdős problems and OEIS conjectures through formal proof search, with results checked in Lean [81]. Even mechanically verified mathematical novelty still requires human appropriation before it enters the corpus of accountable knowledge. Formal constraint, however, does not by itself confer epistemic authority on a machine result. Mathematical knowledge arises only when a proof can be returned to the standards of the relevant practice. The issue is whether a machine-produced result can be taken up as knowledge within a practice that remains answerable for it.

Drawing these cases together brings the boundary of epistemic delegation into focus. In writing, a machine suggestion can imperceptibly change the author’s position. Institutional procedure tries to restore the trace of acceptance after the generated formulation has already entered the work. Chess and mathematics intensify this risk, since a formal result can easily begin to seem a sufficient sign of rationality. The calculator analogy marks the limiting case of permissible transfer of an operation. A system can be given the operation, but not the meaning of applying the result [82]. Against this background, the metacognitive trap described by Messeri and Crockett ceases to be a local problem of work with the model. The user obtains a productive result more quickly than they can trace why this result has been accepted as their own [5].

Acceleration by itself does not yet destroy reasoning. The danger appears when a generated reply compresses the pause of verification. For Dewey, thinking begins with the suspension of a hasty conclusion, while a provisional solution is still undergoing clarification and testing [75]. This idea returns the protocol to the structure of a thinking pause. Such a formulation becomes risky when its persuasiveness displaces this interval. The danger of delegation lies precisely in its imperceptibility.

Speed remains the strongest objection to such a pause. Excessive checking of every LLM-assisted passage would undermine the advantage for which such systems are used in writing and professional work. Support for reasoning must therefore be distinguished from a regime in which the system carries the work from the formulation of the task to the ready-made result. Clinical AI scribes show this in practice. Automated recording facilitates the documentation of a conversation, while acceptance of the result remains with the human being [83]. Verification here keeps the point of acceptance inside the action itself. A plausible suggestion becomes a conclusion only after its grounds have been checked and its consequences accepted [75,82].

Perepelytsia and Kordumov describe the digital lifeworld as technosophistic, a setting in which algorithmically generated meanings circulate as plausible combinations whose relation to truth still has to be restored in practice [84]. The analysis therefore returns to the epistemological protocol’s main task. Work with LLMs remains acceptable as long as machine support does not replace the authorial decision. Routine delegation is difficult to recognize because it rarely looks like a refusal of rationality. More often it begins with a small displacement, when a ready-made response takes over part of the verification and the user returns less often to the source of its acceptance. In this way, the habit of holding back the proposed text and reconnecting it to one’s own decision gradually weakens. Responsible use of LLMs begins where this moment of human recognition has not yet disappeared from the work.

10. Conclusions

The argument has turned on the distinction between machine support and human judgment. LLMs assist work with text without becoming agents who understand what is said or answer for it. A level shift appears when a completed formulation acquires the status of a rational decision before its origin and justificatory grounds have been reconstructed. User recognition remains decisive. A ready-made position can appear in generated form before the author has returned it to verification.

Machine-generated formulations enter the user’s work in a form already suitable for continuing thought. 4E cognition links understanding with involvement in a situation of action. The extended mind thesis allows LLMs to be described as external supports incorporated into human cognitive work. Such support discloses its meaning in use. Postphenomenology specifies the interface layer of this practice. In chat, the user encounters a reply already addressed to them and drawn into interaction. Machine support is therefore easily perceived as another’s line of thought. LLMs function here as moral-epistemic mediators, reshaping how a result is trusted and incorporated into judgment.

Leaving open the question of the model’s internal status, the four-cluster protocol returns attention to the moment when a generated formulation is accepted by the user. It becomes necessary wherever a completed formulation enters writing or decision-making too quickly. Risk appears gradually. Verification begins to seem like a belated obstacle, and generated form takes the place of reasoning not yet performed. Rationality first loses not its content but its habit of suspending a ready-made formulation and returning it to its grounds. Machine support remains usable as long as it can be reconnected with the act of acceptance. Academic culture must sustain this habit of suspension and verification.

LLMs have been treated here as epistemic mediators that alter the passage from generated formulation to accepted claim. Error remains only one part of this risk. More decisive is the moment when fluent output enters judgment before its grounds have been restored. Preserving an epistemic pause at that point keeps generated form distinct from responsible acceptance. AI-assisted writing remains defensible where machine support is returned to the practices through which a scholarly claim becomes answerable.

Work with LLMs remains justified as long as their output is held as material for verification. The crisis of rationality begins when ordinary work with text loses sight of the question of grounding. The form of reasoning may still be preserved, while a ready-made result becomes increasingly difficult to reconnect with the person who accepts it. An epistemic pause preserves the connection between generated form and the author who takes it up as judgment. The central philosophical issue is how human practices preserve the act of accepting a reason as one’s own when machines can formulate reasons in the grammatical form of reasons. Rationality is sustained by the capacity to include machine support in the work of thought without surrendering the act of judgment itself.