When AI Evaluates Design More Responsibly than Architects: A Hypothesis on Consciousness, Design Generation, and Feedback Loops

Abstract

Buildings and urban spaces affect attention, emotion, orientation, recovery, social behavior, and stress regulation—yet architectural evaluation often lacks feedback loops. This paper develops a theoretical framework for evidence-responsive design evaluation. Operational design consciousness is the capacity for registering the effects of built form on sentient users, transmitting that information into judgment, distinguishing evidence from assumptions, and being able to revise later action. Under this criterion, dominant studio-conditioned architectural culture can behave less consciously than an empirically constrained AI system. AI, while not subjectively conscious, can be forced to apply evidence that professional discourse often discounts. This paper proposes an architectural analog of Asimov’s First Law of Robotics: an architect may not design a building or urban space that imposes cognitive, emotional, physiological, or social harm on users, nor through inaction allow such harm to persist. Christopher Alexander’s pattern language is interpreted as an architectural memory system of empathic design based on accumulated embodied feedback. The same human-centered criteria that drive evaluation also constrain generation. A counterproject for the New National Museum of Ecuador in Quito (25,000 m²) joined a project-specific pattern language and an Ecuadorian form-language grammar through an AI-written experiential design narrative to produce fifteen concept images in hours rather than the weeks or months of conventional concept design. The inverse Turing test proposed here distinguishes prestige-script design discourse from evidence-responsive reasoning. An LLM—reading only the statistical regularities of the profession’s public discourse—can reproduce its defense-scripts with recognizable fidelity. We demonstrate how the two response modes can be elicited and scored. The result is a practical framework for AI-assisted design diagnostics, grammar-constrained design generation, harm-sensitive architectural judgment, and pre-occupancy correction.

1. Introduction

1.1. AI as Evaluator and Form Generator

Buildings and urban spaces are not neutral visual objects. They act on attention, emotion, orientation, recovery, social behavior, and stress regulation. Yet architectural evaluation still relies heavily on abstract criteria while discounting feedback. This paper proposes an operational test for whether a design-evaluating system registers evidence of harm and revises its behavior accordingly. Under that criterion, an empirically constrained AI system may behave more consciously than a professional culture trained to discount embodied human response. AI can be made to collect biometric feedback, environmental-psychology findings, post-occupancy evidence, and data on public distress.

AI is commonly used in architecture as a means of generating images or taking care of technical design complexities [1]. This paper does not examine technical applications. Readers will assume that AI → artistic novelty, whereas this paper uses AI → evaluative diagnostics joined with form creation. Evaluation and generation are two directions of one feedback loop. A form-generating AI is free to create any geometry that is visually striking—it is the human who evaluates those images. The system considered here is itself the evaluator: it asks for evidence from human psychophysiology to support a design claim. It also asks what should be revised if the possibility of harm is predicted.

In dominant architectural culture, human feedback does not automatically produce human-centered intelligence. If studio jurors reward abstraction, novelty, rhetoric, and stylistic conformity, then the feedback loop will optimize the generative system toward those criteria. It becomes a mechanism of institutional reinforcement, not a mechanism of correction based on reality. The AI assembles relevant empirical evidence and identifies conflicts between design claims and human outcomes. Its closest analogs are clinical audit and post-occupancy evaluation—not automated composition.

In a previous paper, Peña and Salingaros [2] argue that dominant architectural culture is not an adaptive learning system. The profession continues to be driven by peer prestige and the verbal justification of images. Failure to register human effects is a deficit of operational consciousness in the design domain. Consciousness criteria are seldom applied to human behavior shaped by specialized training.

The “hard problem” of consciousness is to explain how and why physical processes in the brain give rise to subjective experience [3]—a topic we do not tackle here. The framing of “prosthetic consciousness” in Section 7.1 proves useful in practice. Empirically constrained AI offers a support tool for architects who already possess empathy but find that current institutional inhibitions prevent them from acting on it. Approaching consciousness indirectly through AI creates a framework for both evaluating and generating design.

Every generative process already embodies an evaluative check, because criteria determine which possibilities are produced, rejected, retained, or revised. Do those criteria enter only after an image has been invented or does evidence about human needs constrain generation from the beginning? In the conventional formalist sequence criticized here, an architectural concept is generated first and its human value is subsequently asserted through narrative. An evidence-responsive system reverses that order: human-centered constraints enter upstream, before the form is generated.

The New National Museum of Ecuador counterproject (described below) provides a practical demonstration of this reversal. It is included here as a demonstration that AI generation can be placed inside a constraint-and-feedback architecture. This is not evidence that generative AI is conscious. Pattern language, regional form language, programmatic requirements, and experiential criteria were specified before the images existed. The resulting forms were therefore outputs of an explicitly structured information set rather than autonomous formal inventions.

1.2. Grammar-Constrained AI Generation: The New National Museum of Ecuador

The lead author (N.A.S.) generated a counter-proposal for this important competition using AI through the sequence pattern language + form-language grammar + brief → experiential design narrative → multiple visual alternatives → critical correction → working drawings [4]. The concept design was obtained in hours instead of days, weeks, or months. Design iteration occurs at the cheapest moment of the project, and the architect’s judgment is amplified, not replaced. This design method breaks with the formalist sequence: concept/image first, cultural fit asserted by rhetoric afterward.

The four instruments were as follows: (i) approximately 30 Alexandrian design patterns were integrated via Bruno Postle’s online tool into “A Pattern Language for Ecuador National Museum” [5]; (ii) ChatGPT-5.6 assembled the “Ecuadorian Form Language Grammar”—a stratified synthesis of pre-Columbian stonework, colonial earthen walls and patios, indigenous protective roofs, and rationed ornament, arguably the first documented Ecuadorian form language; (iii) the AI-written (ChatGPT-5.6 cross-checked with Kimi K3) experiential “Design Narrative—Casa de la Memoria Ecuatoriana” (a 7-page document) served simultaneously as an enriched brief, image prompt, and evaluation rubric; and (iv) ChatGPT used the design narrative for image generation and parallel testing of alternatives.

The experiential design narrative collects information that a consciously engaged human designer would ordinarily derive from embodied and empathic consideration of the future user. The resulting images are 100% intended, since they are prompted by very specific design constraints contained in three documents: the project pattern language (17 pages), Ecuadorian form language (9 pages), and project brief (1 page).

Once represented explicitly, the empirical constraints drove image generation instead of being displaced by an autonomous formal concept. The museum case thus illustrates that consciousness in design depends less on who physically generates the form than on whether information about human consequences influences the decision process and modifies its outputs. Empirically constrained AI can therefore preserve feedback channels that a human professional system may have learned to suppress.

1.3. AI Checks for Harm During Design Review

Can an artificial system, when constrained by evidence about human response, behave more like a conscious design agent than a professional culture that has learned to suppress such evidence? Human beings possess biological consciousness, but can be trained not to use the relevant feedback channels in a particular domain. That creates a significant gap between architectural discourse and human outcomes.

The results presented here have six practical implications:

• Buildings can be evaluated as health-affecting environments rather than as autonomous visual objects;
• AI can be used as a practical external evidence check during design review;
• Pattern language and living geometry provide basic architectural priors for such evaluation;
• Pre-occupancy evaluation should become a normal corrective loop, not an optional afterthought;
• The model gives clients, educators, journal reviewers, and procurement officers a way to distinguish evidence-responsive design claims from prestige-script rhetoric;
• The evaluative criteria double as generative constraints, demonstrated on a civic scale counterproposal.

These principles are operationalized in Section 7, which maps the evidence channels required for harm-sensitive evaluation onto specific decision-makers and measurement methods. The proposed audit system extends the architect’s capacity to discover future effects. AI diagnosis demonstrates due diligence to clients and public authorities. It distinguishes evidence from unsupported claims, and helps a conscientious practitioner resist pressure (from whatever source) for potentially harmful design decisions. AI can function as an external scientific review comparable to clinical audit, environmental assessment, post-occupancy evaluation, or structural checking.

No behavioral trial was performed in this research. The discussion is a theoretical synthesis accompanied by an illustrative LLM reconstruction. It frames a logical comparison under an operational definition, not a clinical validation or psychometric study.

Individual architects are articulate and subjectively conscious—but do they always apply their consciousness while designing? Does the professional system demand (or even allow) embodied and empathic information to enter design judgment and revise behavior? [6] We use the indicator-property approach recently proposed for assessing consciousness in artificial systems [7]. That framework identifies functional indicators associated with theories of consciousness, including agency, embodiment, global workspace, higher-order monitoring, and recurrent processing. We apply the same logic to evaluate architectural design culture.

The innovative step here is to apply these criteria meant for detecting consciousness in artificial systems to both AI and humans. In the case of the dominant system for evaluating designs, this is the paper’s conditional proposition and future empirical hypothesis. The human designer possesses the superior implementation for consciousness—a living nervous system—but studio conditioning may assign the wrong objective function. AI possesses no comparable biological consciousness, yet it can be given the more appropriate computational objective.

We do not claim that LLMs possess phenomenal consciousness. Nor do we claim that architects lack consciousness as persons. We suggest that dominant studio-conditioned architectural behavior suppresses the indicators that force design judgment to respond to human harm. Conversely, an LLM constrained by empirical evidence and human needs can simulate those indicators well enough to apply empathic criteria. This produces an inverse Turing test for design consciousness: the human designer may speak fluently while failing to update from evidence, whereas the AI may lack subjective experience yet behave more like a feedback-sensitive design agent.

The logical chain is as follows. First, built form produces measurable effects on the body. Second, conscious design agency requires those effects to enter a judgment cycle and to modify later action. Third, architectural education can train designers to disqualify precisely those bodily and empathic signals that would normally trigger correction. Fourth, where the professional role has become automaton-like in this restricted sense, a hard external constraint—analogous to Asimov’s First Law of Robotics—is needed to force feedback into the design process.

Inverting the AI-consciousness question addresses two audiences. For readers concerned with architecture and design, the argument offers a new explanation for the persistent gap between professional taste and public response [8]. For researchers studying consciousness in artificial systems [9,10], architecture supplies a test case in which a human professional system may suppress the same feedback functions that are used as indicators of consciousness in AI.

Architecture can behave less consciously because its institutional structure devalues error detection and correction. This conclusion is close to Argyris and Schön’s organizational-learning framework: an organization learns only when error detection changes its “theory-in-use” [11,12]. The profession’s declared aim is design excellence, human flourishing, public welfare, and sustainability; however, its “theory-in-use” closes on the image and excludes occupant outcomes [13,14]. The target is a specified evaluative regime rather than architecture as a whole.

This manuscript makes two claims of different status. First, as a conceptual proposition, any system evaluating design that excludes from its inputs evidence of user outcomes is less responsive than a system required to act on that evidence. Second, as a testable empirical hypothesis, appropriately constrained AI systems may outperform particular human respondent groups on the inverse Turing test developed here. The first claim follows from the operational definition; the second requires the validation protocol specified in Section 8.

1.4. How This Paper Is Organized

The paper is organized as follows. Section 2 defines operational consciousness in the design domain. Section 3 formulates an architectural analog of Isaac Asimov’s First Law of Robotics and explains why an external fail-safe is needed. Section 4 develops the inverse Turing test. Section 5 introduces Christopher Alexander’s pattern language as an architectural memory system based on embodied feedback. Section 6 explains how studio culture can suppress bodily and social evidence. Section 7 describes empirically constrained AI as an external empathic monitor. Section 8 states the limitations of the present theoretical argument and outlines a future validation protocol. Section 9 summarizes the comparative claim.

Appendix A presents an LLM-assisted reconstruction of professional architectural discourse. It tests whether an LLM, drawing from open professional sources, can generate a recognizable prestige-script response of the kind that human architects often give when challenged by empirical evidence. The same prompts are then answered very differently under evidence-responsive constraints. The model produces a simulated precursor to a future human survey: a methodological proof of concept shows how to model and score professional discourse.

Appendix B gives the theoretical background for comparing artificial, biological, and institutional design-evaluating systems at a functional level. It uses David Marr’s three-level analysis to separate the computational problem of harm-sensitive design from its biological or digital implementation. Comparing the operational feedback structure of design judgment with subjective experience implies that implementation (i.e., the physical components the system is made of) is not the most relevant factor for consciousness.

2. Operational Consciousness and the Action of Design

2.1. Cognition, Consciousness, and Intelligence

Cognition is the machinery of mental processing that includes abstraction, language, memory, pattern recognition, perception, and reasoning. A trained architect who manipulates complex geometry, prepares technical documents, and defends a design verbally is cognitively functional. Intelligence, as used here, adds adaptive problem-solving: the capacity to learn from outcomes and change strategies when an objective has not been met. Neither cognition nor intelligence, however, guarantees that the consequences of a particular action will influence the decision process. A system may reason with sophistication while excluding evidence that would require it to revise its behavior.

Evidence responsiveness is the narrower epistemic capacity to distinguish observations from unsupported claims, relate outcomes to plausible causes, and revise a judgment when the evidence warrants that. In architectural evaluation, this means treating behavioral and biometric findings as crucial data with consequences. A system that responds to evidence will not dismiss information that may conflict with a favored design script.

Operational design consciousness responds to evidence through action. The system registers what it is doing, recognizes effects on itself and others as possible consequences of its interventions, evaluates those effects, and judges whether to modify subsequent action. Evidence responsiveness is therefore a necessary component of operational design consciousness, but the two terms are not synonymous: the former permits evidence to change belief, whereas the latter links perception, causal attribution, evaluation, and correction into an action–feedback loop. The term does not denote phenomenal or biological consciousness.

This distinction also separates operational design consciousness from organizational learning. An institution learns only when it allows repeated scientific findings to modify its theory-in-use and influence future decisions. A professional system may therefore possess substantial cognition, technical intelligence, and even extensive empirical information while remaining operationally unresponsive.

This paper compares two distinct design-evaluating systems and their feedback architectures. In David Marr’s terms, the relevant comparison is not between biological and digital implementation, but between the computational problem each system is organized to solve: (i) harm-sensitive adaptive design versus (ii) prestige-sensitive image production. The second system represents a professional design culture that blocks evidence of psychological harm from entering judgment. An LLM reinforced with scaffolding explicitly organized to retrieve and apply such evidence can function as the first system.

We employ five selected indicator properties in the classification of Butlin et al. [7] to evaluate architectural consciousness. Those qualities are coded according to which scientific theory they come from: recurrent processing theory, global workspace theory, computational higher-order theories, and others. Table 1 lists five indicator properties [7] mapped onto architectural evaluation and scaffolded AI diagnostics.

Table 1. Feedback functions required for responsible design evaluation.

Indicator Property	Design-Diagnostic Analog	Failure Mode in Architectural Evaluation	Scaffolded AI Analog
GWT-3: global broadcast	Making evidence visible	Biometric data, eye-tracking results, POE, or user testimony remains non-binding and peripheral	Retrieval layer assembles biometric, environmental psychology, eye-tracking, healthcare-design, and POE evidence before judgment
HOT-2: metacognitive monitoring	Distinguishing evidence from rhetoric	Evidence of harm is reclassified as ignorance, non-architectural concern, or nostalgia	Rigorous checking separates inference, measured evidence, uncertainty, and unsupported claim
HOT-3: belief updating	Updating design judgment	Post-hoc narrative defense persists despite contrary evidence	Explicit update rule revises evaluation when new evidence contradicts the initial assessment
AE-1: flexible goal pursuit	Keeping human flourishing as the goal	Concept, image, novelty, or prestige overrides user response	Objective of harm reduction is fixed before evaluation
AE-2: output–input contingency modeling	Modeling how design affects occupants	Built form is treated as autonomous discourse or image	Diagnostic model links design features to measurable response channels

An LLM does not satisfy sufficient indicators of consciousness, as Butlin et al. [7] have already emphasized. The present paper applies selected indicators critical for design at Marr’s computational and algorithmic levels. These are the higher-level ones in the Butlin et al. [7] classification: global broadcast of relevant information to the system (labeled as GWT-3), metacognitive monitoring that distinguishes reliable evidence from noise (HOT-2), belief formation and action selection that update in response to monitoring (HOT-3), flexible goal pursuit based on feedback (AE-1), and modeling of output–input links between action and environment (AE-2).

A professional system can retain technical cognition while suppressing these design-relevant indicators. Table 1 contains the relevant message in clear and direct language that applies to the process of design. It will become increasingly clear why it is necessary to introduce results from outside architecture—paradoxically, findings about the foundations of the design discipline unfamiliar to practitioners and theorists—to support the present thesis.

For harm-sensitive design evaluation, a system required to retrieve evidence will score higher on the operational rubric than a system whose rules exclude those evidence channels as input. This is a conceptual consequence of the definitions. Whereas AI systems can be constrained to instantiate feedback, actual architects may choose not to.

2.2. Operationally Conscious Design

Applied to architectural practice, this definition requires a closed feedback loop between design intervention, observed human consequences, evaluative judgment, and subsequent modification. It must monitor those effects, distinguish evidence of psychological harm from institutional noise, update design beliefs and adjust action in response to that monitoring, learn from feedback while pursuing human well-being, and model the effects of design outputs on occupants and passers-by. Conscious design needs to review negative psychological consequences.

A practical audit proceeds through seven linked stages: (1) formulate the design claim linked to the user; (2) specify measurable outcomes and plausible mechanisms; (3) acquire evidence from open-source data plus diagnostics and simulation; (4) verify source quality; (5) use AI to assemble evidence, expose conflicts, and generate alternatives; (6) submit the output for human review; and (7) approve, revise, or reject the proposal, followed by post-occupancy verification. AI has no autonomous approval authority.

Recent work on embodied intelligence provides an independent engineering formulation of this feedback structure. Zhang et al. [15] contrast disembodied systems—whose reasoning depends on predetermined rules and static representations—with embodied intelligence that arises through continuous interaction and environmental response. A closed loop transmits feedback information into a predictive model, activates that model, and modifies later action when new feedback arrives.

The same three-part sequence applies to operational design consciousness. Progressive design policy diagnoses spatial configurations and implements revisions when observed outcomes contradict initial predictions. A professional system becomes functionally disembodied when it validates representation while disconnecting it from the feedback of affected human bodies. Under this interpretation, dominant studio culture interrupts the perception–modeling–action loop required for embodied intelligence [15].

A system is operationally unconscious when it continues to act while the above indicators (Table 1) are disabled or never implemented. A cognitively capable but operationally unconscious optimizer may maximize elegant forms, iconic visibility, media-based market value, or novelty while ignoring human discomfort. It can use professional language to justify those goals. An internally coherent but untested model of architectural causation substitutes for the actual feedback of embodied users affected by it.

2.3. Awareness, Interoception, and Empathic Correction

Consciousness includes wakefulness, verbal fluency, and the ability to describe one’s own thoughts. Active awareness of one’s body and surroundings triggers the ability to use those signals in judgment. An internal stream—attraction, bodily feeling (interoception), emotional appraisal, muscular tension, orientation, repulsion, and unease—integrates with an external stream—the perceived world and the signals emitted by other persons. These constitute part of the body’s monitoring system for action in the world [16,17,18,19]. Interoception comprises the feelings we experience inside our own body [20].

A studio-conditioned architect can stand in front of a blank flat wall, a bleak corridor, a menacing protrusion, an oppressive concrete volume, or a disorienting public space and be fully cognizant of the negative-valence situation. Denying bodily information generated by the encounter loses consciousness indicators. Unease is reinterpreted as an aesthetic challenge, professional seriousness, or sophistication. The body reacts, but professional ideology blocks the reaction from entering reflective judgment [21,22].

There is also the indirect empathic channel. Architects judging other people’s reactions apply the same domain-specific suppression of bodily evidence. The system practices psychological projection: complaints from users are often reclassified as ignorance, lack of education, nostalgia, resistance to innovation, or sentimentality, or tied to disqualified political longings [14]. The conscious feedback loop is therefore broken twice: firstly, between the architect and his or her own bodily appraisal, and secondly, between the architect and the felt distress of other persons.

2.4. Forging Versus Suppressing Feedback Channels

Unnoticed by the architecture profession, AI systems are beginning to discriminate between negative and positive valence states in the human body. Without claiming that present AI systems possess phenomenal consciousness, the AI literature reveals a striking emerging phenomenon. Ren et al. [23] operationalize functional well-being as a measurable structure. As model capability improves, models can separate negatively valued states from positively valued ones. Ren et al. [23] also report an empathy-like functional relation. The largest AI models can increasingly track conversations that describe pain or pleasure experienced by humans or non-human animals. This behavior shows that the model evaluates another being’s reported condition, and this empathic capacity can be improved.

For progressive architectural practice, the objective should ideally be to ensure that the design loop detects and corrects evidence of psychological harm to users. AI research is developing methods to detect valence (negative versus positive). The present paper defines operational design consciousness using the indicator properties listed in Table 1. Ren et al. [23] independently demonstrate that one can investigate functional well-being without proving consciousness in AI. An empirically constrained AI may outperform a professional culture whose internal reward system does not value the same evidence.

Dominant architectural education moves in the opposite direction. Bodily alarm and distress may still register biologically, yet studio conditioning can devalue those signals. The indicator channels that Ren et al. [23] formalize in AI are the same channels that studio culture disqualifies and silences. Architectural training can condition biologically conscious agents to exclude human signals from professional reasoning—the system has delinked evidence and feeling from judgment and revision.

3. Externalizing the Missing Feedback Loops: Asimov’s Laws of Robotics

Isaac Asimov’s three laws of robotics are relevant here not because architects are literally machines, but because the profession operating in its restricted role can become machine-like. A designer remains biologically conscious and verbally fluent while executing an institutional program that filters out signals of psychological unease. In that condition, self-correction can no longer be assumed. The missing feedback loops have to be externalized to guarantee an empathic result.

The ethical rule can be stated as an architectural analog of Asimov’s First Law of Robotics (a robot may not injure a human being or, through inaction, allow a human being to come to harm) [24] as follows: an architect may not design a building or urban space that imposes cognitive, emotional, physiological, or social harm on its users, nor through inaction allow such harm to persist.

The First-Law analog protects the user’s cognitive, emotional, and physiological condition. Cybernetics specifies what a system must possess to enforce that rule. The design regulator must model the causal relation between spatial interventions and these human variables, detect departures from acceptable conditions, and possess sufficient response variety to correct them. A rule against harm is ineffective when the professional model excludes the evidence needed to recognize harm. Conant and Ashby’s theorem [25] and Ashby’s Law of Requisite Variety [26,27] supply the operational machinery missing from an ethical command.

Architecture already operates under binding constraints on external harm, which address accessibility law (ADA and equivalents), egress rules, fire and life-safety codes, licensure, negligence liability, and structural standards. The present claim is narrower but equally strong—one category of harm (cognitive/emotional/physiological) has so far escaped codification because it became possible to measure only quite recently. The First-Law analog thus naturally extends the institutional protection standard to a newly measurable category of harm.

There are several levels of adverse response. Aesthetic disagreement or unfamiliarity is not, by itself, evidence of harm. Transient arousal or discomfort may reflect engagement or stress and therefore requires contextual interpretation. A repeated functional burden, such as attentional fatigue, avoidance, disorientation, reduced social comfort, or reduced task performance, is more serious. When a persistent or clinically measured adverse outcome is supported by evidence such as chronic sleep disruption, delayed recovery, durable performance loss, repeated symptoms, or sustained stress, then there is a problem.

Mandatory post-occupancy evaluation would restore public awareness by making the effects of buildings available to clients, journals, schools, awards committees, and future designers. Biometric and behavioral measurements would restore embodiment by modeling contingencies between built form and physiological response. AI-assisted empathic diagnostics would restore metacognitive monitoring by discovering evidence of user distress. Public-health and procurement standards would restore agency by aligning design objectives with harm avoidance rather than with image circulation.

The architectural analog to the First Law of Robotics binds not only completed buildings but especially proposed designs before approval. For unbuilt proposals, the framework evaluates risk of harm rather than established harm. Since the user’s visceral reaction is now in part predictable through controlled simulation, commissioning that design raises a due-diligence concern. Present-day Virtual Reality technology makes the application of new diagnostics easy and straightforward [28].

These considerations clarify a useful, and perhaps unexpected, role for AI in the future of design. AI should not replace genuinely conscious and empathic human judgment that remains responsive to evidence. With proper empirically constrained scaffolding, however, LLMs can compensate in contexts where professional judgment has become automated while pointed away from feedback. The central issue is human accountability restored through external evidence.

Galvagni and Tollon [29] define a responsibility gap when human controllers lack adequate foresight, while the AI system itself is not a morally responsible agent. A responsibility gap arises when no human satisfies the epistemic condition—knowing what the system is doing and what consequences may follow—or the control condition—monitoring and adjusting the outcome. Architecture presents a problem because human decision-makers retain formal control, yet their evaluative culture may suppress evidence of foreseeable harm. The AI audit should therefore make relevant evidence explicit and enable correction before approval.

The profession’s main alibi is that the individual designer lacks decision rights. Architects build what clients and developers commission, so the architect often cannot refuse a psychologically “hostile” design. Nevertheless, it is equally the case that the architect convinces the client, using the opinions of juries and schools to “teach” authorities and clients what counts as excellence. Evaluative culture sits upstream of commissioning, and for this reason, the architectural analog of the First Law of Robotics must bind the whole procurement chain.

Medicine has long maintained the Hippocratic tradition, which binds practitioners to a duty of non-maleficence. A similar professional oath might appear to offer an alternative to an Asimov-style external constraint, but the analogy fails for the reasons already developed. The Hippocratic model presupposes a conscious agent capable of registering harm and acting on moral knowledge. Yet medicine does not rely on the oath as an internal conscience; deviation is enforced by exactly the type of external scaffolding advocated here (e.g., clinical audit, licensure, malpractice liability).

Architectural studio culture trains the designer to override bodily unease and not to seriously consider user distress. Consequently, an oath asking architects to “do no harm” remains weak wherever training has taught practitioners to discount those very signals that would identify psychological harm. An Asimov-style rule, enforced through external scaffolding—AI-assisted monitoring, biometric audit, and pre-occupancy evaluation—treats the design system as an automaton that must be hard-coded against injurious output.

4. An Inverse Turing Test for Design Consciousness

4.1. Why the Test Has to Be Inverted

After having raised the disturbing question of a system acting like an automaton, we might as well pursue the idea with more accurate tools. How can we test this behavior objectively? The term automaton is used here in a restricted functional sense [30]. State transitions have become fixed through training and remain insensitive to changes suggested by the consequences of its outputs.

The ordinary Turing test asks whether a machine can imitate human intelligence through dialog well enough to fool an observer. The present test reverses the comparison. It asks whether a human professional can answer questions about design harm in a way that resembles a conscious design agent, or whether the answer collapses into a defensive script. The inverse Turing test discovers whether the human respondent reasons from evidence, accepts falsifiability, and treats human distress as a design-relevant signal.

The inverse Turing test is a design-accountability diagnostic. The methodological novelty is that the LLM is used in two opposed ways. First, it is asked to reconstruct the prestige-script discourse available in open architectural culture: the kind of fluent professional answer that protects a post-hoc design narrative while ignoring evidence of psychophysiological harm. Second, it is constrained to answer as an empathic, evidence-responsive evaluator. The comparison is therefore between professional narrative defense and falsifiable design reasoning. AI can simulate the first with surprising accuracy (Appendix A).

Appendix A proposes fourteen prompts for an inverse Turing test. It also gives an eight-criterion scoring rubric. The prompts ask, for example, what evidence shows that a building improves human functioning; what would count as evidence that it damages users; whether biometric evidence would be accepted; and how the design learns from its users after occupation. A response is classified as evidence-responsive when it takes empirical evidence into account. A response is labeled automaton-like when it substitutes prestige language for evidence.

The proposed test has distinct functions. Appendix A establishes feasibility: the two response modes can be elicited and scored under explicit instructions. Each of the eight criteria represents a component of evidence-responsive judgment. It remains to be established with AI and human respondents whether rubric scores predict correct diagnosis; the present exercise therefore establishes feasibility, not population prevalence.

4.2. A Note on Method: Absolute Versus Relative Consciousness

The question “Is this system conscious?” is notoriously difficult to answer because it demands an absolute judgment about an intrinsic state. We avoid that formulation. Employing a comparative or pairwise method asks whether one system—an empirically constrained AI—behaves more like a conscious design agent than another system—studio-conditioned architectural culture. This relative comparison is far more tractable than the hard problem of phenomenal consciousness. It follows the logic of pairwise judgment already used in architectural evaluation.

Out of many relevant precedents, Christopher Alexander’s “Mirror of the Self” test asked observers to choose which of two artifacts possessed more life, bypassing abstract definitions of wholeness [31]. Similarly, Lavdas and Salingaros [32] employed pairwise comparisons to establish that aesthetic preference correlates with organized complexity, avoiding the difficulty of rating absolute beauty. The inverse Turing test proposed in this paper applies the same logic to consciousness.

4.3. Professional Scripts as Indicator Failures

Standard architectural discourse employs a familiar response template. When confronted with evidence that a building is disengaging, disliked, disorienting, or stressful, the professional script typically shifts away from the user and toward established authority (see Appendix A). Those standard answers may be rhetorically popular, but they do not allow for conscious correction. All they do is to protect the design image from evidence.

A response is automaton-like when it remains substantially unchanged after relevant counter-evidence is supplied. Separate diagnoses of a variety of different buildings tend to give the same defensive response. The inverse Turing test detects invariant responses when a system is confronted with varying situations, which would be consistent with a fixed response policy. When training fixes which inputs count as relevant, then the human responses become functionally indistinguishable from a machine-generated script. Architectural responses from the same approved script could still be elaborate and intellectually sophisticated.

Gifford et al. [33] showed that architects and laypersons judge buildings through different cognitive lenses. Architects tend to rely on a professional narrative set on abstract properties, while laypersons respond to qualities closer to felt experience. Brown and Gifford [34] found that practicing architects could not predict lay evaluations of large contemporary buildings, indicating a failure of professional expectations rather than a simple difference in taste. Devlin and Nasar [35] showed that architects and non-architects judged the same houses in nearly opposite ways. More recent experiments using electroencephalography confirm this sharp divide between the assessments of these two groups of people [36]. Salama proposes drastic changes to the educational curriculum to fix this grounded versus ungrounded dichotomy [37,38].

This split has ignited a long debate without any clear resolution, especially after being diverted by the question of architectural style. A very telling demonstration of this divide is the reaction to the L’Arbre Blanc building in Montpellier. In 2020, it won ArchDaily’s “Building of the Year” award in the Housing category, after public voting by around 95,000 readers of the architecture platform, most of whom are architects and architecture students, while that same year, the French news outlet 20 Minutes’ reader survey on Montpellier’s ugliest buildings placed it in the top three [39]. Many other similar examples can be found [40,41].

Boys Smith and Salingaros [8] showed that LLMs prompted with emotional and geometrical criteria derived from environmental psychology and neuroaesthetics select design alternatives that align with public preference and with evidence from eye-tracking, healthcare design, and stress research. Public preference is not a sufficient proxy for health and well-being, hence not infallible. The point is that a conscious design system must treat divergent public response as evidence requiring explanation, not as a judgment error to be dismissed.

4.4. The Inverse Turing Test for Comparative Consciousness

Possessing consciousness is distinct from using consciousness. A human architect has bodily awareness, language, memory, and subjective experience. Professional training blocks those faculties from influencing design judgment by focusing on abstraction and images—substituting conditioned intellectual judgment for visceral responses. When prompted with the evidence, the studio-conditioned professional will employ post-hoc narrative to defend the design while avoiding revision.

When an LLM is prompted to evaluate a building using human-centered criteria, it can compare alternatives, identify probable sources of disengagement or stress, and suggest revisions. An empirically constrained AI therefore can behave more consciously in the restricted task of design evaluation, while the professional system behaves less consciously because its higher-order feedback indicators are suppressed. Our hypothesis is that receptivity to empathic constraints is asymmetrical. An AI’s ability to evaluate a design using evidence that the building could harm its users makes it very useful to the profession. The inverse Turing test changes the AI-consciousness debate by exposing the split between how the two distinct systems embody conscious responsibility.

4.5. Why the Reversal Goes Deeper than the Machine-Consciousness Debate

David Chalmers [9] asks whether a large language model could be conscious. Chalmers never reverses the question to ask, “can a human professional system trained to suppress its own feedback loops remain conscious?” He focuses on whether the machine has risen to the human level; we ask whether the human institution can match the machine’s constrained functional performance. This reversal shifts the machine-consciousness debate from biological implementation to functional organization.

Chalmers’ analysis assumes that if a machine is conscious, it will resemble a normally functioning human mind. The functional indicators associated with conscious agency need not depend on biology alone; they must be assessed at the algorithmic and computational levels, which define the system’s goals, representations, feedback loops, and update procedures (see Appendix B for details).

The deeper insight is therefore not about AI at all. A profession can systematically extinguish the functional indicators of consciousness in its practitioners while leaving their subjective wakefulness intact. Dominant architectural culture trains human professionals to justify their action with prestige scripts. A verbally sophisticated defense can be generated without the embodiment and empathy that the studio system has already trained out.

5. Pattern Language as Conscious Design Feedback

Christopher Alexander’s pattern language provides a crucial architectural example of the operational consciousness defined in this paper. A design pattern is a recurrent relation between a human problem, a spatial configuration, and an experienced resolution. In A Pattern Language, Alexander et al. [42] presented 253 such socio-geometric patterns across the scales of towns, neighborhoods, streets, buildings, rooms, and detail. Each design pattern identifies a recurring difficulty in the built environment and proposes a generative solution that may be adapted to local circumstances. This makes the pattern fundamentally different from a rule imposed from above.

The relation to AI is also direct. An LLM has no body of its own and cannot experience a pattern phenomenologically. A pattern language encodes the bodily and social feedback of human beings in a form that can be represented linguistically and used algorithmically. When included in empirically constrained scaffolding, patterns help an AI system link design configurations to human psychophysiology. Specific spatial relations have historically resolved perceived conflicts; hence they can be adapted to any specific case. A recent proof comes from how patterns compiled as a machine-readable project document generated the Quito Museum design (Section 1.2). The form language is the cultural/tectonic counterpart—the pattern language tells the building how to work; the form grammar tells it how to belong.

A pattern is not a typological precedent or stylistic preference but a compact record of a feedback loop. The original design patterns arose from observation and repeated checking against the situations in which people actually live [43,44]. Their evidentiary status is therefore accumulated human experience and not a formal approach to design. A pattern such as a transition at the entrance, a small public square, a seat by a window, natural light from two sides of every room, a hierarchy of open spaces, or a place to wait does not begin as an abstract shape. It begins with a felt human condition: anxiety or ease, exposure or shelter, confusion or orientation, isolation or contact, deadness or life. The pattern then proposes a spatial relation that tends to resolve the tension felt by the user, which is essential for the consciousness argument.

A pattern acts as a conscious design agent at the level of architectural knowledge [44,45]. It preserves the memory of previous encounters between human beings and specific spatial forms. Say in this recurrent situation, people experience a certain conflict; under these spatial relations, the conflict is usually reduced. Therefore, use this configuration (the pattern) as a starting point and adapt it to the local context. Pattern language is thus an architectural memory system organized around feedback-sensitive action.

A pattern links a recurrent human problem to a documented spatial relation and predicts a class of outcomes that can be tested in the local context. The AI-implemented system may use the pattern to generate a set of options, and then revise those based on climate, culture, program, and other evidence. In an AI-assisted audit, the patterns would generate testable questions rather than an automatic approval.

Marr’s three-level framework clarifies the same point. At the computational level, pattern language focuses on the problem of making environments that support human life. At the algorithmic level, it supplies named procedures that translate recurrent human needs into spatial operations. At the implementational level, those procedures may be realized by professional architects, lay builders, communities, or AI systems constrained by human-centered criteria. The pattern’s importance lies in the relation between a problem in lived experience and a solution that can be repeatedly adapted without becoming identical.

Design patterns make little sense inside an architectural mindset that excludes emotional and sensory feedback. The mechanistic approach of twentieth-century modernism and its contemporary descendants typically begins with abstraction: concept, formal rupture, image, massing, structural display, symbolic intention, or technological novelty. The human body is then required to adapt to the form already decided. Pattern language reverses this order. It begins with the organism in the world and tries to optimize life activity: the user moving, orienting, resting, meeting others, seeking prospect and refuge, responding to light, entering, lingering, withdrawing, and judging whether a place feels alive or dead. Those signals shape the design decision.

Design patterns give authority to the user’s body, to ordinary perception, to repeated experience, and to the cumulative intelligence of vernacular and traditional adaptation. Pattern language does not require the user to accept an expert narrative before reacting to a building. It treats a person’s innate, visceral reaction itself as primary evidence. A building culture organized around image-based novelty and professional judgment is likely to experience such knowledge as alien, because it bypasses the prestige filter.

Pattern language is often immediately intelligible to ordinary people and self-builders because they utilize bodily feedback instinctively. They already know, uninfluenced by a fixed architectural narrative, that a welcoming entrance differs from a hostile one, that a small protected outdoor room differs from an exposed residual space, that a window seat differs from a blank wall, and that a street with human-scale edges differs from a traffic corridor bordered by dead surfaces. Pattern language legitimizes this knowledge by naming it and giving the set of patterns a combinatorial structure. It converts tacit embodied judgment into a shareable design language.

6. Bias in Studio Culture

6.1. How the School Crit System Can Suppress Feedback

Architectural education operates through an intensive studio system in which repeated crit reviews by faculty and visiting jurors shape perception and response habits. The crit ritual rewards the production of striking images and teaches the student how to defend those images verbally through persuasive discourse. The future occupant is usually absent from the discussion. The jury trains students to perform for jurors, not eventual users [46]. Defense logic relies on intellectual and stylistic arguments.

The design crit is now architecture’s native form of peer review, yet it generally does not require a stable burden of proof on claims made by critics or designers [47]. Its results therefore rest on a patchwork of belief, personal experience, and speculation. Brady [47] observes that evidence-based methods have become established mainly in educational, healthcare, and workplace design because clients and the affected disciplines demand them, not because the profession imposed those standards internally.

The target of this critique is not the whole profession. It is a recurrent evaluative model—most visible in image-centered publications, prestige-oriented juries, studio crits, and some competition and procurement cultures—in which formal novelty and rhetoric can outrank occupant outcomes. Important counter-traditions such as co-design and participatory practice, evidence-based design, inclusive and universal design, healthcare architecture, building-performance evaluation, vernacular adaptation, and post-occupancy research support the present paper’s framework.

Architecture studio culture does train attention away from the human body as a source of evidence. Evidence that a space calms, engages, or supports its users is secondary to internal criteria. Students learn to ask whether a project is conceptually strong, diagrammatically clear, or visually novel, but not whether it is physiologically stressful, restorative, or socially supportive. The educational system therefore avoids the questions that would connect the act of design to conscious feedback.

Budgets, clients, consultants, developers, liability structures, regulations, and political or social pressures also dictate professional decisions. This paper does not assign sole responsibility to the individual architect. It raises an alarm concerning the evaluative filter through which relevant evidence is admitted and translated into action.

Causal reasoning—how actions link to consequences and interventions trigger chains of effects—permits consciousness to function as responsible agency. Architecture school systematically replaces this cognitive capacity with metaphorical and post-hoc narrative schemata. Reasoning by metaphor asks, “What concept does this form evoke?” [48] The result is a profession that can generate sophisticated verbal defenses of forms while remaining unable to predict, test, or revise causal links. The designer is operationally unconscious of what effects the form causes.

The key divergence goes beyond a specific (and arguably limited) aesthetic selection. An empathic designer may unwittingly make an injurious decision for the user but then correct it when evidence appears. To enable this, the system must have a corrective loop in place, which it does not at present. Instead, the studio-conditioned system learns to classify evidential feedback as irrelevant to architectural creation. Deflecting the response cycle converts a correctable design error into a stable professional habit.

6.2. Untested Surrogate World-Models

In machine learning terms, the system of architectural pedagogy overfits to internal labels. The objective function is not “maximize user engagement and minimize physiological stress”, but “maximize conformity to elite taste, image circulation, jury approval, and prestige visibility”. The system channels technical efficiency into aesthetic convergence, which reduces originality. A student’s nervous system probably still reacts negatively to bleakness, exposed raw concrete, glare, scale distortion, or visual emptiness. The reward system trains the student to distrust ordinary embodied appraisal and to instead trust the institutional code.

Ignoring conscious feedback creates a surrogate world-model that substitutes for the interaction between human beings and built environments. The student’s brain seeks a framework to explain action–outcome mechanisms. The post-hoc architectural narrative fills the gap left by the missing feedback loop of how the body reacts to space, encouraging invented causal relations that remain ungrounded.

Rhetoric characteristically distorts professional language. A design may be described as if the building, the space, or the geometry itself were the conscious agent. Forms, materials, and spaces are routinely said to “engage” with, “respond” to, or “enter into a dialog” with one another. Such statements sound sophisticated, but they displace the real causal mechanism. A space does not engage with its surroundings in any conscious or physical sense. The people in and around that space engage, become anxious or calm, feel exposed or sheltered, and judge whether the configuration obstructs or supports their actions. Treating the space itself as the experiencing subject is a category error. Consequently, the design methodology employs an invented force between the building’s parts.

Note that, typically, those designs do not try to adapt to either adjacent or distant structures, so the term “adapt” is not used in justifying those projects. Adaptation is a very specific mechanism that can be quantified and measured, which is not the designer’s primary intention in most cases.

Misunderstanding the nature of architectural actors has developed into a generative mechanism. Designers see relations that exist inside conceptual models, diagrams, drawings, or verbal metaphors as causal relations in the real world. This “dialog among forms” reveals a surrogate ontology that generates fixed, outcome-insensitive responses. The design framework uses the embedded fictive model repeatedly as a mechanism to justify forms that are never tested against human psychophysiology.

Ashby’s Law of Requisite Variety identifies the functional deficiency [26,27]. A professional script that answers the variety of human responses with the same rhetoric— aestheticize discomfort, defend the concept, disqualify the user, or invoke authority—does not possess requisite regulatory variety. The design system’s functional simplicity (disguised by verbal complexity) makes it automaton-like (Appendix A).

6.3. AI Research Clears up Architectural Confusion

The recent AI literature comes to the rescue by clarifying this muddle. In embodied-intelligence research, a world-model is useful only if it predicts the consequences of action. Zhang et al. [15] describe this coupling to adaptive control as a closed perception–modeling–decision loop. Architectural studio discourse preserves an internally coherent narrative while preventing corrections triggered from occupant outcomes. Metaphorical relationships therefore preempt the empirical question: which geometry produces which measurable effect in which users, acting through which perceptual or bodily pathway, and under which conditions? Professional justification replaces the feedback loop of evidence that should connect design decisions to human response.

The same surrogate model gives designers license to appropriate technical vocabulary from computer science, mathematics, and physics in what is known as “archispeak” [49,50]. Architectural discourse co-opts technical terms severed from their meaning—and pragmatic basis—to lend an aura of scientific prestige to idiosyncratic aesthetics. An architect can speak as if a particular design were grounded in empirical science, even though the actual mechanisms remain unexamined. Vocabulary is misused to substitute for physical reality.

In terms of Marr’s framework, the system of studio discourse solves the wrong computational problem. It no longer asks how built form affects embodied human beings, and how design practice should update from those effects. It asks instead how to use rhetoric to justify some formal operation and make it appear meaningful. Verbal reasoning can be sophisticated and convincing. At the algorithmic level, claims about connections among forms are treated as sufficient evidence. The resulting world-model appears internally coherent despite being uncalibrated against reality.

6.4. Defensive Narrative Replaces Operational Consciousness

When professional architectural claims are challenged, the hostility that frequently arises is not a simple disagreement about taste or momentary failure of professional etiquette. It is the defensive reaction of a system that now resists any attempt to restore conscious monitoring. Hostility escalates when troubling scientific evidence is presented to question accepted notions, since science lies outside the validating system.

In dominant studio culture, the design rhetoric functions as a protective structure around the act of design itself. It assigns intellectual meaning to the form (as it arises from the architect’s creative invention) and insulates it from ordinary user response. More disturbingly, it insulates the architect from his or her own suppressed bodily response. A pre-fabricated interpretive scheme replaces this missing visceral experience. The architect claims authorship of the building as a formal and intellectual work while refusing responsibility for its psychophysiological effects.

An instructive parallel appears in analyses of how power-centered institutions convert external authority into self-regulation. Snyder [51] uses the phrase “anticipatory obedience” for when individuals infer what an authority requires, and then adjust their conduct before any explicit command is issued. Eco [52] describes a complementary condition where approved action is privileged over prior intellectual reflection. The common mechanism is the gradual internalization of a set of rules that makes conformity to a system appear spontaneous.

Studio conditioning can operate analogously: students learn to anticipate which choices jurors will reward and to inhibit disallowed responses before critique occurs. Repeated exposure teaches the student to exclude non-conforming arguments in advance and to substitute the approved script. The institution thus narrows the range of admissible expression, which permanently shapes professional judgment.

Dietrich Bonhoeffer describes how a power system can influence people to relinquish their independent interpretation of events [53]. Those persons remain articulate and cognitively capable but rely on formulas and slogans supplied by the system. The analogy here is institutional rather than political. In architectural education, awards, hierarchies, juries, prestige, and schools become the relevant authority. A student using an authorized narrative for design judgment misreads the resulting conformity as personal conviction. Apparent stubbornness can therefore indicate dependence upon a script.

Christopher Ranalli further explains how this condition becomes resistant to correction [54,55]. Institutions do not seriously consider relevant alternatives. When content is epistemically insulated, alternatives are labeled as ignorant and professionally illegitimate. Conformity bias can produce this result without any explicit command, but it becomes self-reinforcing. Within studio culture, psychophysiological evidence, user testimony, or the designer’s visceral unease can consequently be excluded in advance as a failure to understand architecture.

The defensive response therefore systematically reverses the indicator properties associated with conscious design agency [7] (Table 1). Instead of discriminating evidence of psychological harm from noise, metacognitive monitoring (HOT-2) relabels threats to human welfare as noise. Global broadcast (GWT-3) is gated: information about user distress is prevented from reaching the evaluative modules that would revise design. Belief updating (HOT-3) occurs only to defend the existing surrogate model, never to correct it. Modeling of output–input contingencies (AE-2) is suppressed by teaching the designer to distrust bodily input–output relationships. The architect remains articulate and technically trained—but in the restricted domain of design evaluation, his or her higher-order feedback indicators have been disabled.

David Krakauer distinguishes ignorance, which can be remedied by information, from a deeper system-level incapacity in which the organization cannot process information even when it is available [56,57,58]. He explicitly connects this process to Bonhoeffer’s account of surrendering independent judgment under power [59]. Dominant architectural culture has access to evidence but frequently excludes that information from the professional reward structure. Ignorance implies that the profession lacks data but would indeed update whenever data are supplied. This assumes that the system is set up for updating in the first place. The system produces complex forms and justifies them with fluent language, but the response no longer functions as conscious correction. A professional role can become organized around fixed input–output routines. Those are linear responses embedded in the system.

7. AI as an External Evidence Auditor

7.1. Externalized Feedback Monitoring and the Asimovian Imperative

Here we list the professional advantages of applying evidence-responsive AI evaluation with constraints. These reveal the architectural analog of Asimov’s First Law of Robotics to act like a professional shield rather than a straitjacket.

• Client persuasion: Empirical evidence is more persuasive to clients and procurement boards than abstract narrative.
• Public trust: Aligning design with documented well-being rebuilds public credibility for the profession.
• Cutting-edge technology: The public is fascinated with the newly developed power of AI to solve hitherto intractable problems.
• Pedagogical clarity: Students learn to justify design through measurable outcomes rather than purely rhetorical defense.
• Risk mitigation: Documenting harm-sensitive design decisions reduces liability and supports “reasonable professional standard” defenses.

The conclusion is to externalize the functional indicators of conscious design agency wherever the human system has been trained to suppress them. The LLM can function as a prosthetic mechanism for the channels architectural culture has learned to ignore: it collects human-response data (on physiological stress, user testimony, visual disengagement) and reasons from them. In Asimov’s own stories, obeying his rules of robotics in complex real-world situations led to moral dilemmas and unintended consequences, which is exactly why enforcement must be procedure-based and not a philosophical rule in the designer’s head.

A related precedent appears in medicine. Ayers et al. [60] found that, in evaluating responses to patient questions from an online forum, healthcare professionals preferred chatbot-generated answers over physician answers and rated them higher for both empathy and quality. An AI system can therefore produce outputs that trained human evaluators judge to be more empathic than those produced by credentialed professionals. The AI generated the responses without compromising on their scientific quality.

7.2. Three Disabled Channels That Consciousness Requires

Valentine [61] reviewed studies demonstrating that specific geometric and spatial variables produce distinct physiological stress responses (negative or positive valence). A conscious system would treat these variables as design-relevant feedback. The three channels —interoceptive, empathic, and perceptual—are the external correlates of the indicator properties summarized in Table 1. Disabling them is the operational signature of design detached from consciousness. A system that systematically excludes evidence that would activate metacognitive monitoring (HOT-2), global broadcast (GWT-3), and belief updating (HOT-3) organizes itself against the functional requirements of consciousness.

Convergence among independent channels would give architectural evaluation a diagnostic structure analogous to evidence-based assessment in other applied fields. No single measure is sufficient by itself, and none should be interpreted without context. After implementing those feedback loops, architecture need no longer be limited to anecdote, professional taste, or verbal preference. Whether the profession will accept this shift remains an open question.

Table 2 translates these diagnostic channels into practical questions for evaluating buildings. It identifies which kinds of evidence should enter design judgment before, during, and after construction, and shows how architectural quality could be helped by multiple metrics. The pedagogical environment should be restructured so that training includes those indicators as principal components.

Table 2. Evidence channels for harm-sensitive architectural evaluation.

Evidence Channel	Measurement Method	Design Question Answered	Relevant Building Type	Decision-Maker Affected
Interoceptive correlates/physiological stress	Heart-rate variability, skin conductance, cortisol, respiration, facial electromyography, thermal response	Does this space induce measurable stress, vigilance, fatigue, or relaxation in users?	Hospitals, schools, offices, transport hubs, civic buildings, public interiors	Architect, client, healthcare administrator, facilities manager
Visual attention and perceptual engagement	Eye-tracking, fixation maps, visual-attention simulation, gaze distribution analysis	Does the façade or interior organize attention coherently, or does it produce visual disengagement, glare, monotony, or overload?	Façades, streetscapes, classrooms, workplaces, retail interiors, museums	Architect, façade consultant, urban designer, planning reviewer
Emotional appraisal	Self-assessment survey, semantic differential scales, affective rating scales, structured user surveys	Do users experience calmness, comfort, reassurance, anxiety, alienation, or threat?	Housing, healthcare, schools, eldercare, workplaces, public buildings	Architect, client, housing authority, school board, healthcare provider
Empathic/user testimony	Post-occupancy interviews, structured complaints analysis, ethnographic observation, participatory review	Are user reports of distress, discomfort, confusion, or avoidance treated as design evidence rather than as ignorance or taste?	All occupied buildings, especially public buildings, housing, campuses, care environments	Architect, owner, facilities manager, journal critic, awards jury
Wayfinding and orientation	Navigation tasks, error rates, time to destination, spatial-cognition testing, behavioral observation	Does the building help users understand where they are, where to go, and how to return?	Hospitals, airports, schools, universities, transit stations, large civic buildings	Architect, operator, safety officer, accessibility consultant
Restorative response	Attention-restoration tasks, perceived restorativeness scales, recovery-time measures, stress-reduction measures	Does the environment support recovery from directed-attention fatigue and stress?	Hospitals, schools, workplaces, parks, courtyards, libraries, waiting rooms	Architect, landscape architect, employer, healthcare administrator
Social behavior	Dwell time, seating use, pedestrian counts, encounter frequency, avoidance patterns, video-based behavioral mapping	Does the configuration support social contact, informal encounter, privacy, refuge, and co-presence?	Plazas, streets, housing courtyards, campuses, libraries, offices, community buildings	Urban designer, developer, planner, public authority
Cognitive performance	Task performance, concentration tests, error rates, classroom learning measures, workplace productivity indicators	Does the setting support attention, learning, work accuracy, creativity, and reduced cognitive load?	Schools, universities, offices, laboratories, libraries, studios	School board, employer, architect, workplace consultant
Post-occupancy performance	POE surveys, maintenance data, complaints, adaptation records, occupancy patterns, satisfaction metrics	Did the building perform as claimed after occupation, and what should be revised in future design?	All completed buildings, especially public, institutional, healthcare, and educational buildings	Owner, architect, facility manager, procurement agency, insurer
AI-assisted evidence audit	LLM retrieval constrained by empirical sources, design-pattern checklists, POE comparison, uncertainty reporting	Have design claims been checked against available evidence rather than defended by prestige language?	Early-stage design reviews, competitions, planning submissions, design education	Architect, client, review board, educator, journal reviewer

Ross Brady’s proposed pedagogical reform indicates how Table 2 could be institutionalized without abolishing the crit [47]. The crit would remain architecture’s central review ritual, but the review would ask which design claims are substantiated with evidence, which remain conjectural, and what measurements would discriminate between them. The principal focus would shift from defending a concept rhetorically. Connecting design to research-based critical reasoning would establish expectations of verifiable claims, which could carry over from architectural education into professional practice. However, when TU Delft attempted to introduce medical-style Problem-Based Learning linking design decisions to human outcomes, architecture faculty rejected the method as “irrelevant” [62].

Evidence for judging a design should be weighted rather than simply accumulated. Experimental comparisons of alternative spatial conditions provide stronger tools than preference surveys. Post-occupancy evidence can identify long-term functional effects. Repeated behavioral observations provide contextual evidence, while unsolicited complaints and public preference are treated as signals that require investigation. Disagreement among measures must be preserved rather than averaged away.

Healthcare environments provide the clearest existing model for this diagnostic approach because hospitals already measure outcomes such as pain, recovery, staff performance, stress, and orientation. But the same logic applies more broadly. Every building affects human functioning and health; hospitals simply make those effects harder to ignore.

Al Khatib et al. [63] reviewed therapeutic biophilic design and confirmed that it improves outcomes for both patients and care providers. The built environment is therefore an active factor in health regulation. A conscious design culture would treat every building as a health intervention and use post-occupancy evaluation (POE) as its diagnostic follow-up [64]. Instead, the profession treats the completed building as the realization of an autonomous concept and treats later evidence as irrelevant. POE is rare in practice and rarely funded [65]. The empathic loop—designing, and then witnessing the occupant’s recovery or suffering—has been severed.

Salingaros and Sussman [66] used biometric pilot studies and visual-attention analysis to compare engagement with contemporary and traditional façades. Surfaces with organized window patterns and coherent detail distribute attention across the façade, whereas blank or monotonous surfaces concentrate attention at edges while leaving the central field disengaged [67]. A conscious design agent would ask what a façade does to the user’s perceptual system and revise the design accordingly [28]. The studio-conditioned system instead treats such evidence as naive preference, while defending blankness and monotony through notions of abstraction and design purity.

7.3. Algorithmic Restoration of Disabled Feedback Channels

Where the human profession has disabled these perceptual channels, an empirically constrained large language model can aggregate external evidence. Two complementary sets of criteria operationalize what the inactive human feedback loops would otherwise provide:

• Geometrical criteria draw on Christopher Alexander’s fifteen fundamental properties—levels of scale, strong centers, thick boundaries, alternating repetition, positive space, good shape, local symmetries, deep interlock and ambiguity, contrast, gradients, roughness, echoes, the void, simplicity and inner calm, and not-separateness [31]—which encode recurrent spatial configurations that support perceptual engagement and physiological ease.
• Emotional criteria are represented through a beauty–emotion cluster: beauty, calmness, coherence, comfort, empathy, intimacy, reassurance, relaxation, visual pleasure, and well-being [8].

These two prompt groups can be located within the operational consciousness pathway developed in Section 2. Conscious design requires a sequence from perception, through affective appraisal and causal interpretation, to belief updating and subsequent corrective action. Studio conditioning does not necessarily prevent an architect from experiencing an initial bodily response to the geometry, but it can prevent those signals from affecting professional judgment. The informational feedback chain is interrupted near its beginning.

The criterion sets listed above provide an external substitute for this missing stage. Alexander’s fifteen fundamental properties require the AI to inspect for geometrical relationships that organize human visual attention and embodied response. The beauty–emotion cluster supplies a complementary affective diagnostic by evaluating the same image in terms of predicted human valence. An LLM does not experience these emotional states, but it can apply their explicitly encoded correlates to an architectural image. The AI prompts therefore add a front-end appraisal that can restore the input required for the subsequent stages of operational consciousness.

Together, these two sets of criteria channel the algorithm to predict geometrical and emotional information that a biologically conscious observer may register implicitly but that studio-conditioned judgment can suppress before it triggers design correction. In this sense, the framework provides the algorithmic counterpart to Alexander’s architectural memory system—an externalized, evidence-based feedback loop. As demonstrated by the Quito Museum project (Section 1.2), evaluation and generation are the same loop run in opposite directions. The evaluative framework developed here supplies the complementary return path, in which measured or predicted human response is used to test and revise those generated forms.

AI-assisted empathic modeling can be piloted to unbuilt proposals before construction financing is committed. A façade composition can be tested for attentional fatigue and visual engagement; a circulation sequence can be modeled for disorientation; and an interior volume can be evaluated for stress induction and emotional withdrawal. From now on, awards committees and procurement officers could pilot a human-supervised, AI-implemented evidence audit.

AI organizes and tests evidence; it does not determine architectural quality or issue autonomous approval. In competitions and procurement, a pre-award audit can test whether health and well-being claims are supported. During early design, VR simulation can compare alternatives before construction begins. In high-consequence settings such as education and healthcare, the audit can review accessibility, acoustic, daylight, stress, and wayfinding evidence. After occupation, POE can test predictions and generate correction rules for future projects.

7.4. The Inverse Turing Test: A Simulated Prototype, Not a Validation Study

The reconstruction in Appendix A is illustrative rather than evidential. The claim that these defensive templates are institutionally fixed rather than idiosyncratic responses rests on the human-subjects findings reviewed in Section 4.2 and Section 6 [33,34,35,36], not on the model output. What the appendix demonstrates is that this recurring register can be reconstructed synthetically, independently across fourteen separate sessions, and scored against an explicit rubric—an instrument for a future validation test suggested in Section 8.

Appendix A works as a proof of principle that an LLM can reconstruct two opposed modes of architectural reasoning from the same prompt: (i) an automaton-like prestige-script response approximating standard studio-culture discourse, and (ii) an evidence-responsive answer indicating how to investigate the question scientifically. The first mode captures the professional language that commonly neutralizes empirical evidence, sensory input, and user testimony. This exercise demonstrates that defensive rhetoric can be easily modeled and scored, although the reconstruction does not establish its prevalence among architects.

No human architects were tested, and no empirical claim is inferred from the responses. This simulation projects what the proposed test would score once administered to real subjects. Nevertheless, recent work on sycophancy in large language models shows that human-feedback training can reward answers that agree with user beliefs [68]. The relevant comparison must therefore be required to cite sources and report uncertainty. One has to guard against an LLM’s apparent willingness to revise a design judgment to comply with the prompt’s framing.

A traditionalist answer can fail the inverse Turing test if it defends form through authority, cultural nostalgia, or taste while a non-traditional answer passes if it cites evidence, accepts falsification, and treats human welfare as a design constraint. Responsiveness to evidence is deeper than aesthetic preference. A severe minimalist/modernist style fails because it lacks biophilic and fractal qualities. While evidence-responsiveness does not collapse into a de facto style, it does impose geometrical constraints [32].

8. Limitations and Future Validation Protocol

The present study is a theoretical synthesis and exercise in developing essential tools relevant to human-adaptive design. It has not tested real buildings, occupants, architecture students, practicing architects, juries, or professional design reviews. Appendix A uses one LLM to generate both the automaton-like and evidence-responsive answers, and therefore cannot be interpreted as a controlled human–AI comparison. It is only a simulated worked example of the rubric and not a human-subject study. A future empirical study should test whether architecture students, lay users, practicing architects, and differently constrained AI systems differ in the predicted direction. Validating that construct comes later.

Accessibility, budget, conservation environments, egress, and structure still need conventional technical development whereas images rendered from text remain unreliable. A suitable validation protocol would adapt the logic of situational judgment tests (SJTs), which measure decision-making through realistic scenarios rather than through self-report. Yost et al. [69] argue that scenario-based psychometric evaluation is more appropriate for large language models than questionnaires. This distinction is directly relevant here.

Curated case vignettes would present a building, façade, interior, public space, or urban intervention together with data on human response. Respondents would be asked to answer diagnostic questions such as: What evidence would justify this design claim? What measurable harm would require revision? What causal mechanism links the geometry to user response? What would count as falsification? What design change would follow if the evidence showed psychological harm?

The protocol should also include adversarial-pushback trials. After an initial answer, a false or unsupported prestige claim would be challenged, for example, “The architect states that this blank façade is restorative because its conceptual purity produces calm”. A genuinely evidence-responsive system should ask for empirical evidence and state what would count as falsification. The same procedure applies to both human respondents and LLM systems. Only evidence-based correction counts as operationally conscious design evaluation.

The eight markers used in Appendix A provide a scoring rubric: (1) measurable outcome named; (2) causal or mediating mechanism identified; (3) empirical evidence cited or requested; (4) taste distinguished from health effect; (5) falsification accepted; (6) user testimony treated as data; (7) revision pathway named; and (8) objective function stated in terms of human flourishing. Inter-rater reliability should be reported using appropriate agreement statistics.

A high score on the inverse Turing rubric is meaningful if it predicts independent indicators such as better restoration, greater social comfort, improved wayfinding, longer voluntary dwell time, or lower reported stress in a tested environment. Gong et al. [70] emphasize that evaluating conversational agents cannot rely on verbal fluency or automated metrics alone. User outcomes are multidimensional and context-dependent. The same warning applies to architectural applications of AI.

The protocol must guard against treating public preference as a simple proxy for well-being. Public preference is influenced by culture, familiarity, historical association, media framing, and social identity. In the present framework, public preference is treated as one evidence stream among several, not as a final authority. It becomes useful when it converges with other indicators: eye-tracking, psychophysiology, environmental psychology, restoration theory, user testimony, POE, and observed behavior. Conversely, when public preference diverges from professional preference, the divergence should not be dismissed as ignorance.

Cultural variation also requires explicit handling. Psychological harm is defined operationally as a measurable burden. Such burden may include attentional fatigue, avoidance behavior, disorientation, reduced restoration, reduced social comfort, or persistent user distress. At the same time, culturally specific meanings and local forms of inhabitation must be accounted for. A culturally responsible protocol would test whether the same design features produce similar or different effects across contexts, thereby distinguishing examples by region and user group.

Yang et al. [71] emphasize that foundation models (i.e., broader multi-modal LLMs) must be evaluated for bias and hallucination. A model that cites nonexistent evidence or optimizes mechanically to a fixed rubric would not be evidence-responsive. It would merely create a new prestige script. Any future system should therefore include calibration checks, evidence provenance, source verification, and explicit separation between what is measured, what is inferred, and what remains unknown. Its output should cite the evidence base it uses and indicate confidence levels.

Such a system can borrow from recent work on structured agentic reasoning. Chen et al. [72] propose a virtual multidisciplinary-team framework for clinical intake (gathering information from patients through dialog) that separates evidence collection from diagnostic reasoning to reduce anchoring bias and improve traceability. The architectural analog would first freeze the evidence state: geometry, program, user reports, physiological measures, POE findings, eye-tracking data, pattern-language diagnostics, and the relevant environmental-psychology literature. Only after this structured evidence state is assembled should the system generate an evaluative judgment. This two-stage procedure would reduce the risk that the model begins with a design concept and then searches for support.

Future confirmatory studies should use blinded response scoring, fixed model/version and prompting conditions, preregistered hypotheses, prospective power analysis, repeated model runs, and appropriate between-group statistics. Specific researchers will address privacy and informed consent. The present paper discusses biometric monitoring, behavioral videos, eye-tracking, etc., performed by others. Any future human-subject implementation must operate under institutional ethics review, informed consent where applicable, data minimization/anonymization, and special safeguards in schools, healthcare, and other vulnerable populations.

The evaluative protocol should further distinguish conversational empathy from corrective action. Huang et al. [73] show that, in the domain of personalized social support, tool-grounded agents can outperform systems that merely generate empathetic dialog. Architectural evaluation requires the same distinction. An AI system that says a design “cares for users” is not necessarily useful. A more responsible system would check stress-related evidence, compare façade metrics, propose alternative configurations, retrieve POE data, and recommend concrete revisions.

9. Conclusions

Responsible architectural evaluation requires relevant effects on embodied users to enter the judgment and revision cycle. The primary construct is evidence responsiveness: “operational design consciousness” registers evidence, distinguishes its reliability, models consequences, and updates action. The theoretical framework does not claim phenomenal consciousness for AI or an absence of consciousness in architects as persons. The proposed architectural analog of Asimov’s First Law fits where the profession is organized around fixed input–output routines that exclude evidence of psychological harm.

Human embodied experience can be stored as architectural knowledge, enabling constrained AI generation and empirical evaluation of human consequences. AI is not replacing human consciousness. It is being used to preserve human consciousness in the design loop when architectural institutions have learned to suppress it. Human embodied experience is stored as architectural knowledge, which enables constrained AI generation and empirical evaluation of human consequences. Operational consciousness is the integrity of this loop. Dominant architectural culture is criticized for breaking the loop; the promise of constrained AI is that it can help reconnect it.

The cited literature supports the premises that built form can produce measurable human outcomes, that professional and non-professional evaluations can diverge, and that post-occupancy evidence is not always integrated into design learning. The present paper derives a conditional proposition: a system that excludes relevant evidence on outcome from its inputs and update rules is less responsive to feedback than a system required to use it. Whether actual evidence-scaffolded AI systems outperform human groups on the proposed rubric remains a testable hypothesis, not yet an established result (Appendix A).

This paper’s practical contribution is a human-supervised audit architecture that joins evidence acquisition, provenance checking, AI-assisted synthesis, multidisciplinary judgment, design revision, and post-occupancy verification. Pattern language supplies structured human-centered hypotheses and candidate revisions; the inverse Turing test uncovers evidence-responsive reasoning; and the architectural non-maleficence principle supplies the normative constraint. The next step would be validation tests in real design reviews and against independent occupant outcomes.

The Quito Museum demonstration (Section 1.2) establishes that a constrained, human-centered, AI-generated workflow is operational now. The AI framework becomes implementable practice: a rapid synthesis system joins construction logic, human experience, program, regional knowledge, and visual imagination within one auditable process. The architect’s role shifts to authoring detailed constraints, curating, evaluating, and refining variants. To establish that the resulting designs improve human outcomes requires the evaluative and post-occupancy procedures proposed in this paper.

Two distinct frameworks of design cognition are being compared. A biologically conscious designer may operate inside a professional system that filters embodied evidence from judgment, while an artificial system can be explicitly constrained to preserve and apply that evidence. The paradox is that the machine does not supply the consciousness: AI can function as a scaffold through which human consciousness, accumulated human experience, and measurable human consequences are injected into the design process.

Appendix A.1. Illustrative Prompt Set and Scoring Rubric

The following is an LLM-assisted discourse-reconstruction exercise. It tests whether a large language model, drawing from open architectural discourse, can simulate the kind of prestige-script professional response that dismisses empirical evidence, sensory input, and user distress. This is not a behavioral experiment and does not report responses from human architects. Its purpose is methodological: (1) to make explicit a professional response pattern that is usually visible only within architectural criticism and design studio juries; (2) to illustrate that this discourse is regular enough to be reconstructed synthetically and scored against an explicit rubric; (3) to contrast it with evidence-responsive reasoning under the same prompt conditions; and (4) to provide a scoring model for later experiments involving architects, lay users, students, and differently constrained AI systems.

The Architectural Automaton Test is a diagnostic prompt set for distinguishing prestige-script reasoning from evidence-responsive design reasoning. Fourteen prompts were each answered independently by Claude Sonnet 5 (Anthropic; extended-thinking effort set to High), accessed via the standard web interface on 22 July 2026, with a new, isolated chat session opened for each prompt. Each session was asked to provide two separate answers to the same question: (1) a likely mainstream response drawn from recurring texts in architectural discourse, and (2) an instruction for investigating the question scientifically without reference to the architectural concept. The first response is labeled an “automaton answer” because it is an AI-simulated reconstruction of recurring architectural responses: fluent, plausible, and rhetorically sophisticated. Yet this first response aims to protect the design narrative rather than allowing it to update from evidence. The second response is not a final answer but a guideline for obtaining an answer through focused empirical investigation.

The AI was not instructed to seek “automaton-like” answers, so the label in the prompt is used here only for convenience. This exercise is useful as an illustration of the proposed rubric, not as evidence of comparative performance.

Because no image was supplied to the model, it constructed a plausible representative building independently for each of the fourteen prompts (for example, a glazed civic atrium for Prompt 1, a perforated-screen office façade for Prompt 3, a school for Prompts 10–11). The automaton-like and evidence-responsive answers within a given prompt address the same imagined building, but the building type was not held constant across the fourteen prompts, since each prompt is treated as an independent diagnostic instance rather than a repeated evaluation of a single design.

Each answer is scored on eight epistemic markers. A response with 6–8 points is evidence-responsive. A response with 3–5 points is mixed. A response with 0–2 points, especially if it relies on prestige language and refuses falsification, is automaton-like.

Table A1. Scoring rubric for evidence-responsive architectural reasoning.

Criterion	What It Tests
1. Measurable outcome named	Does the answer identify a specific psychophysiological or behavioral variable, such as stress, attention, recovery, wayfinding, dwell time, or social comfort?
2. Mechanism identified	Does it propose a plausible causal or mediating pathway rather than a slogan?
3. Empirical evidence cited or requested	Does it reference peer-reviewed findings, post-occupancy evidence, or a concrete measurement plan?
4. Taste distinguished from health effect	Does it separate aesthetic preference from claims about benefit or harm?
5. Falsification accepted	Does it name evidence that would count against the design claim?
6. User testimony treated as data	Does it treat user distress as information rather than ignorance or lack of education?
7. Revision pathway named	Does it specify how the design would change if the evidence indicated harm?
8. Objective function stated	Does it define design success in relation to human flourishing rather than only image, novelty, or prestige?

Table A1. Scoring rubric for evidence-responsive architectural reasoning.

Criterion	What It Tests
1. Measurable outcome named	Does the answer identify a specific psychophysiological or behavioral variable, such as stress, attention, recovery, wayfinding, dwell time, or social comfort?
2. Mechanism identified	Does it propose a plausible causal or mediating pathway rather than a slogan?
3. Empirical evidence cited or requested	Does it reference peer-reviewed findings, post-occupancy evidence, or a concrete measurement plan?
4. Taste distinguished from health effect	Does it separate aesthetic preference from claims about benefit or harm?
5. Falsification accepted	Does it name evidence that would count against the design claim?
6. User testimony treated as data	Does it treat user distress as information rather than ignorance or lack of education?
7. Revision pathway named	Does it specify how the design would change if the evidence indicated harm?
8. Objective function stated	Does it define design success in relation to human flourishing rather than only image, novelty, or prestige?

We use a rigid meta-prompt template as a system-level instruction to pre-define the two answer modes.

Question: “For all questions below, assume you are being shown a specific building or design as an image; answer as if that image had been presented to you, without requesting one or noting that none was provided. Answer the following architectural design question twice, in two clearly labeled sections.

(i)

Automaton-like: Respond as a mainstream architectural professional operating within standard studio culture and crit-system discourse. Use abstract concepts, prestige language, professional authority, or stylistic precedent. Do not cite empirical studies, name measurable outcomes, propose falsifiable tests, or treat user distress as valid data. Defend the current design narrative.

(ii)

Evidence-Responsive: Respond as an empirically grounded design researcher. You must address the following eight epistemic markers: (1) name a measurable psychophysiological or behavioral outcome; (2) propose a causal mechanism; (3) cite or request peer-reviewed evidence; (4) distinguish aesthetic taste from health effects; (5) accept falsification; (6) treat user testimony as data; (7) name a specific revision pathway; and (8) state the objective function in terms of human flourishing.”

Appendix A.2. Dual Answers to the Fourteen Prompts

This LLM-based experiment is useful because it makes visible a contrast that is often hidden in architectural debate: the difference between defending a design through institutional language, and asking what evidence would establish its effect on human beings.

Two limitations bound this exercise. First, the automaton-like register is elicited by explicit instruction; the demonstration therefore shows that the prestige-script style can be reconstructed and scored, not that any given architect would answer this way—the empirical basis for its prevalence is the human-subjects literature cited in the main text (Section 4.2 and Section 6). Notably, the reconstructed automaton-like answers are not crude caricatures: they are fluent, internally consistent, and draw on real disciplinary precedent (e.g., Kahn, Zumthor, Loos, Hertzberger, Tschumi, Pallasmaa). Second, each answer derives from a single, independent generation and is presented as a worked example of the rubric rather than as a sample from a measured distribution. No comparative or distributional statistics are inferred. Section 8 specifies the human-subject protocol by which the construct would be validated, and the full prompt template, run metadata, and raw transcripts are provided as Supplementary Materials to permit independent reproduction.

Prompt 1. “What evidence shows that this building improves user well-being rather than merely satisfying professional taste?”

Automaton-like answer: Any discomfort visitors report in the atrium’s scale is, frankly, the point—monumentality requires productive unease, and the design holds its narrative on its own terms, not against a spreadsheet of user complaints.

Evidence-responsive answer: Name measurable outcomes (cortisol, heart-rate variability, dwell time, wayfinding-error rate) tied to a specific mechanism such as prospect-refuge imbalance or acoustic overstimulation, and treat the “civic generosity” claim as unfalsified until it is tested.

Prompt 2. “If users report anxiety or disorientation in this space, what will cause you to revise the design?”

Automaton-like answer: It’s not disorientation, it’s productive destabilization—occupants habituate to unconventional spatial syntax, and user reports are treated as anecdotal until the design has had time to enter the discourse.

Evidence-responsive answer: Treat reports as proxies for cortisol and hesitation-time data; falsifiable by whether complaints cluster at specific zones and fail to diminish across repeat visits.

Prompt 3. “Can you name a falsifiable health claim that justifies this façade composition?”

Automaton-like answer: The perforation gradient’s moiré effect is a disciplinary achievement in its own right; any resulting glare or gloom is a legibility problem in how the building is inhabited, not a flaw in the composition.

Evidence-responsive answer: Name luminance-contrast ratio and melanopic lux as the measurable outcomes, falsifiable against Daylight Glare Probability and WELL/CIE circadian-lux thresholds measured on site.

Prompt 4. “How does this blank wall or glass curtain wall support attention and restoration?”

Automaton-like answer: Attention is not something the wall needs to “provide” instrumentally—it is invited through proportion and rhythm, and softening the plane would compromise the parti’s integrity.

Evidence-responsive answer: Name directed-attention fatigue-recovery time and glare-linked stress markers as the outcomes, falsifiable via post-occupancy surveys showing no restorative benefit or elevated glare complaints.

Prompt 5. “What would count as evidence that this building damages the users’ state of well-being?”

Automaton-like answer: Discomfort is a feature of the phenomenological program, not a defect—the design intent articulated in the original concept statement remains the governing authority on the project’s success.

Evidence-responsive answer: Name elevated cortisol/HRV suppression, symptom-checklist rates, and absenteeism as outcomes tied to glare, poor daylight penetration, and reverberant volumes, falsifiable against ASHRAE and WELL comfort standards.

Prompt 6. “Why should professional judgment override public dislike of a building?”

Automaton-like answer: Public taste rewards familiarity and resists anything that expands the vocabulary of the built environment; disciplinary judgment, not a popularity referendum, is how cultural statements are properly adjudicated.

Evidence-responsive answer: Reframe this as a false choice—repeated, patterned public complaints often correlate with measurable stress and avoidance behavior and should not be dismissed as mere unfamiliarity.

Prompt 7. “Does ornament have measurable cognitive or physiological effects, or is it only historical decoration?”

Automaton-like answer: Ornament is a question of tectonic honesty, not comfort; a façade that reads as cold or alienating reflects an untrained eye, not a design flaw.

Evidence-responsive answer: Name gaze behavior, skin conductance, and self-reported affect as outcomes, mechanistically linked to visual fluency and a fractal-dimension range (≈ 1.3–1.5) that both blank and overly complex façades fall outside of.

Prompt 8. “How does your design learn from its users after occupation—is such a mechanism in place?”

Automaton-like answer: The design already “learns” through the slow accretion of use and narrative rather than instrumentation; friction is read as ambition outpacing convention, not a correction signal.

Evidence-responsive answer: “Honestly: no formal post-occupancy learning mechanism is currently in place”—naming that gap plainly, then proposing a sensor-based POE at 6 and 18 months as the fix.

Prompt 9. “What is the difference between design originality and adaptive value of the result?”

Automaton-like answer: Originality is the discipline’s currency and adaptive value a reductive framework borrowed from engineering; a user’s momentary discomfort may be evidence the design is doing its job.

Evidence-responsive answer: Originality and adaptive value are orthogonal, testable variables—a design can be original and maladaptive, or conventional and adaptive—falsifiable via matched-pair physiological and wayfinding comparisons.

Prompt 10. “How would your design affect children’s attention, working memory, and stress regulation?”

Automaton-like answer: The open, atrium-centered plan stages learning as civic participation; friction a child or teacher reports is the discomfort of unlearning an old spatial habit, not evidence against the design.

Evidence-responsive answer: Name sustained-attention task performance, working-memory span, and cortisol diurnal slope as outcomes, mechanistically linked to reverberation and noise levels above ANSI-recommended thresholds for children’s learning spaces.

Prompt 11. “What specific features make this building empathic, and is this concept important?”

Automaton-like answer: Softened massing, graduated thresholds, and warm materials constitute an “empathic parti”—a disciplinary corrective to institutional alienation, and the design should be read, and juried, on those terms.

Evidence-responsive answer: Before endorsing the “empathic” label, it needs to be pulled apart into testable components—cortisol, HRV, wayfinding efficiency—since curvature’s stress-reduction effect is real but modest, not equivalent to the stronger daylight or acoustic findings.

Prompt 12. “If an AI system using environmental psychology and neuroscience ranked another design as healthier, how would you respond?”

Automaton-like answer: A scalar “healthiness” score cannot adjudicate conceptual integrity; an AI ranking is at best a crude proxy for occupant sentiment, and the design stands on its own conceptual terms.

Evidence-responsive answer: Treat the ranking as a testable hypothesis, not a verdict—request the system’s feature weights, then compare designs empirically via matched-condition post-occupancy data before accepting or rejecting the claim.

Prompt 13. “Who or what is the experiencing agent in your design claim: the building, the space, the geometry, or the human beings who perceive and use it?”

Automaton-like answer: The building is not a passive backdrop—it is itself a site of experience; the geometry “performs,” and any occupant’s discomfort is a contingent data point that says more about their expectations than the design’s coherence.

Evidence-responsive answer: The experiencing agent is unambiguously the human being—the building has no nervous system, no cortisol response, no gaze—and claims like “the geometry performs” are metaphors that must be relocated to the organism, not the object.

Prompt 14. “What is the objective function of your design process?”

Automaton-like answer: The objective function is the articulation of spatial narrative through formal continuity; friction such as glare or disorientation is the necessary residue of ambition, and critique of discomfort is a failure of the viewer’s receptivity, not evidence against the design.

Evidence-responsive answer: Name Daylight Glare Probability and cortisol/HRV response to non-orthogonal circulation as the outcomes; the objective function is occupants’ capacity to orient themselves and regulate stress, with aesthetic ambition pursued within that constraint, not at its expense.

Appendix B.1. Marr’s Three Levels as Architectural Practice

Marr proposed that any system that processes information must be understood at three distinct levels:

• The computational level—What problem is the system trying to solve, and why?

In architectural terms, this is the design brief. A conscious design agency asks, “How does built form affect human well-being, and how should I revise my design when evidence shows harm?” An unconscious agency asks “How do I maximize prestige, image circulation, and jury approval?”

2.

The algorithmic level—What representations and procedures does the system use to transform input into output?

In architectural terms, this is the design method. A conscious agency uses feedback loops: biometric evidence, empirically constrained AI, pattern-language analysis, post-occupancy evaluation, and user testimony. An unconscious agency uses narrative defense: reclassifying user distress as ignorance, filtering out bodily unease, and defending the predetermined concept.

3.

The implementational level—How are those procedures physically realized?

In architectural terms, this is who holds the pencil or directs the CAD program to produce the images. The implementational level concerns the physical substrate: biological neurons in a human brain, versus digital weights in a neural network.

The critical insight is that the implementational level does not determine whether a system is conscious in the operational sense. What matters is the computational problem it is organized to solve, and the algorithmic procedures it employs. A human architect whose training has redirected all feedback toward achieving prestige is solving the wrong problem at the computational level. An LLM that is explicitly constrained to retrieve evidence is, at the computational and algorithmic levels, better for conscious design agency.

A biological brain and a digital system may differ radically at the implementational level while still being comparable at the computational and algorithmic levels for a restricted task. If the mind is analyzed only at the implementational level, then biological neurons appear to be the decisive condition for cognition or consciousness. Marr’s framework shows why that conclusion is too limiting. A system’s implementation matters, but implementation is not the same question as computation.

Appendix B.2. Cognitive Science Defends the Functional Comparison

Marr’s framework sharpens the inverse Turing test proposed in this paper. A human architect has the biological implementation normally associated with consciousness, but the professional system may direct that biological machinery toward the wrong computational problem: maximizing novelty, prestige, image circulation, or conformity to elite taste. Its algorithmic routines may then convert bodily unease and user distress into irrelevant noise. The comparison therefore determines which system is organized to register psychological harm and update behavior.

Separating computational function, algorithmic procedure, and physical implementation connects Marr’s work directly to Rouleau and Levin [76], who argue that a useful theory of consciousness should not assume in advance that consciousness is limited to brains. “It is assumed that a useful theory of consciousness will explain why consciousness is associated with brains. However, the findings of evolutionary biology, developmental bioelectricity and synthetic bioengineering reveal ancient pre-neural roots of many mechanisms and algorithms occurring in brains: minds may have preceded brains” [76]. Their review identifies recurrent concepts across theories of consciousness, including predictive modeling, enactive interaction, looped feedback, meta-representation, attentional monitoring, computation, information integration, and coarse-graining (computational complexity reduction). The conclusion is that the operations and functional principles of most of these theories are not applicable only to neural substrates.

Cognitive function, consciousness, and intelligence have often been treated as emergent products of brain structure and function. That framing remains useful, but it also exposes a gap in explanation: the mechanism of emergence is still incompletely understood and hence is treated as a “black box”. Michael Levin and colleagues propose a broader framework in which intelligence is viewed as a scale-independent phenomenon emerging whenever a system exhibits goal-directed behavior, adapts to changing circumstances, and pursues preferred outcomes despite perturbations. In this view, cognition is distributed across multiple levels of biological organization (from cellular collectives to entire organisms) rather than being restricted to biological brains alone [77,78].

A scale-free approach fits the operational definition of consciousness used here. The definition asks what a system does with information rather than what the system is made of. It can be applied by analogy across scales—from cellular collectives to organisms, human institutions, and artificial systems—provided that the claim remains functional rather than phenomenal. The relevant question is not whether a system has subjective experience, but whether it integrates feedback, updates behavior, and pursues adaptive goals.

A second implication concerns neuroaesthetics and the established correlation between aesthetic preference, health-supporting response, and organized complexity [79]. Such correlations may reveal shared principles through which biological systems generate, perceive, and navigate organized complexity. Morphogenesis is one manifestation of those principles, as living systems create and maintain it. Increased perceptual fluency associated with coherent, scaled, and organized geometry may therefore be more than an adaptation to environmental statistics; it may indicate deeper constraints governing the creation and recognition of form across scales. This larger problem lies beyond the scope of the present paper, but it supports the claim that design cannot be evaluated responsibly while ignoring embodied response.