Beyond Detection: An Epistemic Limit of Purely Computational Misinformation Defense
FARHANA FERDOUSI LIZA, School of Computing Sciences
University of East Anglia, UK
The dominant paradigm for countering misinformation prioritizes computational detection, classification, and automated fact-checking. This paper argues that such approaches rest on an unexamined epistemic assumption that human critical capacity remains stable and sufficient to interpret, verify, and govern algorithmic outputs. We identify a structural blind spot in this paradigm, particularly salient in educational settings, where increasing reliance on generative artificial intelligence risks eroding the very epistemic capacities required for effective human–AI collaboration. To make this claim precise, we develop an epistemic modal logic framework that demonstrates the insufficiency of purely computational defenses to guarantee critical understanding in human agents. We conclude that technical defenses are necessarily contingent on prior educational investment in human epistemic virtues, reframing misinformation defense as a problem of socio-cognitive-technical resilience rather than algorithmic optimization alone.
CCS Concepts: • Human-centered computing → HCI theory, concepts and models; Collaborative and social computing theory, concepts and paradigms; • Computing methodologies → Cooperation and coordination; Philosophical/theoretical foundations of artificial intelligence; • Social and professional topics → Computing / technology policy; Computing literacy; • Theory of computation → Modal and temporal logics; • Security and privacy → Social aspects of security and privacy.
Additional Key Words and Phrases: Generative AI, Misinformation defense, Epistemology, Logic, HCI, AI literacy, Socio-cognitive- technical systems
ACM Reference Format:
Farhana Ferdousi Liza. 2026. Beyond Detection: An Epistemic Limit of Purely Computational Misinformation Defense. In Companion Proceedings of the ACM Web Conference 2026 (WWW Companion ’26), April 13–17, 2026, Dubai, United Arab Emirates. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/3774905.3796493
1		Introduction and Problem Statement
Prevailing research on misinformation defense adopts a techno-centric paradigm, focusing on computational, mainly, Artificial Intelligence (AI) based detection, classification, and automated rebuttal [2, 49, 67]. While such approaches improve benchmark performance, they suffer from well-documented limitations, such as AI hallucination [26, 62], brittleness across contexts [25], and the limited durability of automated corrections [14]. More critically, this paradigm treats the human as a passive endpoint rather than an evolving epistemic agent. Recent work shows that sustained AI use reshapes cognition itself, increasing epistemic dependency and cognitive offloading [24, 32, 46, 63]. Defense systems implicitly assume a statically competent human-in-the-loop, an assumption increasingly untenable in emerging educational environments.
Author’s Contact Information: Farhana Ferdousi Liza, F.Liza@uea.ac.uk, School of Computing Sciences University of East Anglia, UK.
This work is licensed under a Creative Commons Attribution 4.0 International License.
© 2026 Copyright held by the owner/author(s).
Manuscript submitted to ACM
Thus AI has created a dual-use paradox: the AI and large language models (LLMs) that accelerate the generation of misinformation, implicitly through hallucination and explicitly through targeted manipulation like fakenews, are increasingly proposed as computational tools for their automated detection and correction [1, 26, 33, 61]. This technical arms race obscures a deeper vulnerability that the human agent embedded within the defense system. In educational contexts where epistemic capacities are formed rather than assumed, reliance (dependency) on AI for reasoning, synthesis, and verification risks eroding the very critical understanding required to evaluate AI outputs [24, 32, 46]. This produces a self-defeating cycle: dependency degrades the human capacity on which defense effectiveness depends. This paper intervenes at a foundational level in current approaches to misinformation defense. We argue that we need a formalization [13, 23, 50] to interrogate the foundational assumptions upon which the current computational paradigm rests and investigate the critical gaps. The sections 2, 3 articulate the formalization, grounded in epistemic logic and virtue epistemology [8], is not only a philosophical supplement but a diagnostic necessity for analyzing the structural limitations of purely computational defenses. We examine the structural assumptions embedded in prevailing AI based system designs, identifying systemic gaps (Section 2) that cannot be resolved through technical measures alone. To make these gaps explicit and actionable for mitigation purposes, we formalize the epistemic limitations of purely computational defenses using a modal logic framework (Section 3). Our central contribution is to articulate the human preconditions, grounded in justified belief, evaluative competence, and cognitively mediated belief revision, setting as prerequisites for AI-assisted defenses to function effectively in educational settings.
2		The Computational Paradigm and Its Epistemic Blind Spot
Within the computational paradigm, the problem of misinformation detection and academic integrity violations is framed primarily as one of large-scale detection, classification, and automated correction, with success measured in terms of accuracy, recall, robustness, and scalability [2, 18, 49, 56, 67] . These metrics are not always well-suited [5, 7] to optimization or educational application, and they provide little insight into whether such systems support the epistemic aims of education. Institutional deployments typically converge on three technical strategies, such as automated detection of AI-generated content, attribution mechanisms such as watermarking and provenance tracking, and filtering or moderation pipelines integrated into learning platforms [17, 20, 28]. Collectively, these systems have achieved substantial technical sophistication. However, they rest on a largely unexamined assumption that human users possess a stable and sufficient capacity for critical evaluation, enabling them to appropriately interpret, contest, or override algorithmic outputs when necessary [12, 39, 64]. This assumption appears in several recurring design patterns. Human-in-the-loop [39, 64] architectures presume that users will reliably detect and correct algorithmic errors [6]. Explainability mechanisms, such as confidence scores or feature attributions are frequently presented as sufficient safeguards for human oversight [38]. Such assumptions align with what is characterized as a model of perfect intermediation, in which humans are expected to function as consistently competent mediators between technical systems and their real-world consequences. In educational settings, however, this expectation is particularly problematic, as learners are precisely those agents whose epistemic capacities are still under development.
Despite strong benchmark performance, computational approaches to misinformation and academic integrity exhibit persistent limitations in educational settings. Detection systems remain vulnerable to adversarial evasion and rapid concept drift, while empirical studies report uneven performance across demographic groups, raising fairness concerns [7, 45, 48]. Pedagogically, detection-centered interventions prioritize policing over learning and risk damaging trust within educational relationships, while favoring institutions with greater technical resources [51, 65]. At a deeper epistemic level, opaque or binary algorithmic judgments offer little support for developing critical understanding, and Manuscript submitted to ACM
sustained reliance on such systems may inhibit independent evaluative skills, reinforcing concerns about automation- induced deskilling [11, 12, 27].
The most consequential limitation of the computational paradigm is its treatment of human epistemic capacity as static rather than dynamic. This manifests along several dimensions. First, repeated delegation of evaluative tasks to automated systems can lead to cognitive deskilling, whereby precisely those skills assumed by system designers, such as source evaluation, justification, and critical reasoning, atrophy through disuse [9, 12, 59]. Second, prolonged reliance on algorithmic judgments will inevitably lead to epistemic dependency, in which users come to rely on systems not only for answers but for standards of evaluation themselves [15, 53, 54]. Third, even when humans are positioned as overseers, their judgments remain subject to motivated reasoning and automation bias, leading them to over-trust or selectively discount algorithmic outputs depending on prior beliefs and incentives [22, 35, 41, 55]. These issues are amplified in educational contexts, where learners’ epistemic capacities are still forming. Treating students as epistemic equivalents of experts ignores extensive evidence that critical understanding develops through structured practice, feedback, and scaffolding rather than passive exposure to correct answers [4, 34, 43]. We argue that the central blind spot is the failure to recognize epistemic capacity as a dynamic variable.
Taken together, these limitations indicate that purely computational approaches are insufficient for addressing AI-related challenges in education. This work argues for a fundamental transition from the pursuit of technical control to the practice of epistemic design. We argue that effective responses require a dual emphasis of detection techniques and the development of epistemic capacity, both classification and comprehension, and both enforcement and enablement of informed judgment. Recognizing human epistemic capacity as a dynamic variable, one that can be cultivated or eroded through technological mediation, is a prerequisite for designing defenses that are robust over time [16]. The following section formalizes this insight within an epistemic logic that treats critical understanding as a central system variable rather than a background assumption.
3		A Epistemic Logic of the Insufficiency of Purely Computational Defenses
We formalize our core argument using the framework of epistemic logic [19, 21]. This framework allows us to model knowledge, belief, and learning dynamics with precision, and to demonstrate why a purely computational agent cannot guarantee the epistemic states required for defending against misinformation in educational contexts.
3.1		Formal Preliminaries
We model epistemic states using Kripke structures of the form M = ⟨𝑊 , 𝑅𝐾 , 𝑅𝐵 ,𝑉 ⟩, where 𝑊 denotes a non-empty set of possible worlds, 𝑅𝐾 ⊆ 𝑊 × 𝑊 and 𝑅𝐵 ⊆ 𝑊 × 𝑊 are accessibility relations corresponding to knowledge and belief respectively, and 𝑉 is a valuation function assigning truth values to atomic propositions at each world. Knowledge is treated as factive, while belief is not required to be so. Within this framework, we employ standard epistemic modal operators. For an agent 𝑖, the formula 𝐾𝑖 𝜙 denotes that agent 𝑖 knows that 𝜙, meaning that 𝜙 holds in all worlds accessible via 𝑅𝐾 from the actual world. By contrast, 𝐵𝑖 𝜙 denotes that agent 𝑖 believes that 𝜙, which requires only that 𝜙 hold in all worlds accessible via 𝑅𝐵 , without the guarantee of truth in the actual world. We distinguish between two epistemic agents. The first, denoted 𝐻 , represents a human learner whose epistemic state evolves through educational interaction, reflection, and belief revision over time. The second, denoted 𝐴, represents a computational agent, such as an automated fact-checking system or a large language model, whose epistemic outputs are generated through algorithmic processes rather than interpretive understanding.
Critical Understanding. Rather than postulating an additional primitive modal operator, we define critical understanding as a compound epistemic condition expressible within the existing modal framework. For a human agent 𝐻 and proposition 𝜙, critical understanding is defined as: 𝐶𝐻 𝜙 ≡ 𝐾𝐻 𝜙 ∧ 𝐾𝐻 Justified(𝜙) ∧ 𝐾𝐻 SourceReliable (𝜙) . This definition characterizes 𝐶𝐻 𝜙 as more than the mere possession of true belief or propositional knowledge. In addition to knowing that 𝜙 is true, the agent must have reflective epistemic access to the justification for 𝜙 (Justified(𝜙)) and to the reliability of the sources from which 𝜙 is derived (SourceReliable(𝜙)). The resulting notion aligns with virtue-epistemic and educational accounts that emphasize understanding, reason-giving, and evaluative competence as central epistemic aims [30, 44, 52, 66]. Within this framework, the epistemic objective of education-oriented misinformation defense is not simply to induce correct beliefs, but to bring the human agent into a state of critical understanding. Accordingly, for any contested proposition 𝑝 in public discourse, the target outcome is the attainment of either 𝐶𝐻 𝑝 or 𝐶𝐻 𝑝, depending on which proposition is warranted by the available evidence.
3.2		Model-Theoretic Constraints on Admissible Interpretations
The formal system is based on a standard multi-modal epistemic logic with operators 𝐾𝐻 (propositional knowledge), 𝐵𝐻 (belief), and 𝐶𝐻 (critical or competent understanding). The axioms internal to the object logic include the familiar principles of epistemic and doxastic logic, such as factivity (𝐾𝐻 𝜑 → 𝜑), positive introspection, and standard knowledge– belief interaction axioms. Due to space limitations, and without loss of generality, these object-level axioms are not reproduced here. Rather than extending the object logic with additional axioms, we introduce a set of model-theoretic constraints on admissible Kripke interpretations. These constraints restrict the class of possible worlds and accessibility relations that qualify as educationally and epistemologically plausible models of the formal language, thereby encoding substantive assumptions about human and computational agents at the level of semantic interpretation rather than syntactic derivation.
Constraint P1 (Epistemic Goal of Education). Admissible models must evaluate educational systems relative to their capacity to promote critical understanding in the human agent. Formally, success conditions are defined in terms of 𝐶𝐻 𝜑, rather than mere propositional knowledge 𝐾𝐻 𝜑. Models in which knowledge transmission occurs without access to justification and source evaluation are therefore deemed epistemically insufficient for educational success [37, 40, 42, 60].
Constraint P2 (Epistemic Limits of Computational Agents). In all admissible models, computational agents lack accessibility relations corresponding to justificatory and normative evaluation [47, 57, 62]. Formally, for any proposition 𝜑 , M |= 𝐾𝐴 ^Justified(𝜑) ∧ 𝐾𝐴 ^SourceReliable (𝜑) . As a result, while a computational agent 𝐴 may output propositions that induce belief states, Output(𝐴, 𝜑) → 𝐵𝐴𝜑, these belief states are not required to be factive. In particular, admissible models permit the possibility (i.e., ^ 𝐵𝐴 𝜑 ∧ 𝜑 ) that the agent believes 𝜙 AND 𝜙 is actually false, capturing the possibility of hallucination and epistemic error characteristic of statistically grounded language models [26].
Constraint P3 (Dynamics of Human Belief Revision). In all admissible epistemic models, belief revision in human agents is non-monotonic and cognitively mediated. In particular, assertion by an external agent does not entail belief uptake: Assert(𝐴, 𝑝) ̸|= 𝐵𝐻 𝑝. Belief updating is therefore not modeled as a logical consequence, but as a context-sensitive mediation process: 𝐵𝐻new = M𝐻 𝐵o𝐻ld, Assert(𝐴, 𝜑), Context , where M𝐻 is a human-specific mediation function incorporating trust in the source, prior commitments, cognitive load, and motivational alignment. Moreover, even when Manuscript submitted to ACM
belief revision occurs, it does not suffice for critical understanding: 𝐵𝐻 𝑝 ̸|= 𝐶𝐻 𝑝. Accordingly, admissible models must permit belief perseverance, motivated reasoning, and partial or unstable belief change, reflecting well-established empirical findings in cognitive and social psychology [14, 29, 31, 36].
Constraint P4 (Risk of Epistemic Dependency). Admissible models must also permit diachronic degradation of human epistemic capacity under sustained delegation of justificatory labor. Formally, DelegatedTo (𝐻 , 𝐴, Verify (𝜑)) → Decrease Pr(𝐶𝐻 𝜑) . This constraint captures the inductive risk of cognitive deskilling and epistemic dependency arising from prolonged reliance on automated systems, as documented in studies of automation and AI-assisted learning [12, 63].
3.2.1		Theorem (Epistemic Insufficiency of Purely Computational Defense). For any admissible epistemic model M satisfying Constraints P1–P4, if a computational agent 𝐴 is the sole epistemic defense mechanism, then critical understanding by the human agent 𝐻 is not guaranteed:
M |= ^Computational(𝐴) ∧ SoleDefense (𝐴) → -□(𝐶𝐻 𝜙).
Proof. Assume Computational (𝐴) ∧ SoleDefense(𝐴) .
By Constraint P2, 𝐴 lacks epistemic accessibility relations corresponding to justification and source evaluation. Hence, for any 𝜙, M |= 𝐾𝐴 (Justified(𝜙)) ∧ 𝐾𝐴 (SourceReliable(𝜙)), and therefore 𝐴 cannot instantiate or transmit the justificatory components required for 𝐶𝐻 𝜙. By Constraint P3, epistemic updates in 𝐻 induced by assertion from 𝐴 do not guarantee belief revision, and belief revision alone does not entail critical understanding: Assert(𝐴, 𝜙) ̸|= 𝐶𝐻 𝜙.
Since 𝐴 is the sole defense mechanism, no alternative epistemic process is available to supply the missing justificatory and evaluative components. Thus, there exists at least one epistemically accessible world 𝑤′ ∈ 𝑊 such that M,𝑤′ |= 𝐶𝐻 𝜙.
It follows that 𝐶𝐻 𝜙 does not hold in all accessible worlds, and therefore: M |= □(𝐶𝐻 𝜙).	□
The formalization transforms informal critiques into precise, demonstrable claims about the structural epistemic limits of computational misinformation defense. The formal logic defines exactly what “critical understanding” means (i.e., 𝐶𝐻 𝜙 ≡ 𝐾𝐻 𝜙 ∧ 𝐾𝐻 (Justified(𝜙)) ∧ 𝐾𝐻 (SourceReliable(𝜙))), and makes explicit the assumptions about human and computational agents through model-theoretic constraints. This yields a theorem proving that purely computational defenses cannot guarantee critical understanding, not because of current technical limitations, but as a matter of logical necessity, given that computational agents lack justificatory capacities and human belief revision is cognitively mediated. Formalization thus entails a fundamental reorientation; misinformation defense approaches should equally emphasize technical correction and epistemic cultivation. A shift is needed from static human-in-the-loop assumptions to dynamic models of cognitive development, and from evaluating systems by accuracy alone to assessing their long-term effects on users’ justificatory practices. It provides a principled foundation for resisting techno-solutionism only and for designing systems that support rather than substitute for the educational cultivation of epistemic virtues.
4		Discussion: Interpreting the Formal Results
The formal framework developed in the section 3 yields a precise articulation of why purely computational misinformation defenses cannot, even in principle, guarantee the epistemic outcomes that educational contexts require. This section discusses the philosophical, practical, and design implications of these results, addressing potential objections and clarifying the scope of the claims advanced. The theorem 3.2.1 presented in section 3 establishes a structural insufficiency, more explicitly, for any admissible model satisfying Constraints P1–P4, a computational agent functioning
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as the sole defense mechanism cannot guarantee that a human agent attains critical understanding (𝐶𝐻 𝜙) of a contested proposition. Several features of this result merit careful interpretation.
First, the theorem is a possibility result, not an inevitability result. It demonstrates that there exists at least one epistemically accessible world in which critical understanding fails, given the specified constraints. It does not claim that critical understanding never occurs under computational defense, only that it cannot be guaranteed across all epistemically possible scenarios. This distinction is crucial as the argument is that purely computational systems lack the structural resources to ensure the justificatory and evaluative components of understanding, not that they invariably produce epistemic failure. Moreover, “understanding" is not a measurable verb, which makes it necessary to contextualize and make it actionable with measurable verbs [58].
Second, the theorem is conditional on the provided constraints (P1-P4). These constraints are not arbitrary; they encode empirically grounded features of human cognition (P3), computational limitations (P2), educational aims (P1), and dynamic risks (P4). One could reject the conclusion by rejecting one or more constraints, but doing so would require denying well-documented phenomena based on the constraints are grounded; such as LLMs hallucinate [26], that humans exhibit motivated reasoning [29, 36], that skills atrophy through disuse [10, 12], or that educational success requires more than true belief [30, 44, 52]. The constraints are thus defensible as minimal realistic assumptions about the domains the model purports to represent.
Third, the theorem does not entail that computational tools are useless or harmful. The antecedent specifies that 𝐴 is the sole defense mechanism. The result leaves open the possibility that computational systems, when integrated within broader educational ecologies that actively cultivate epistemic virtues, may contribute positively to critical understanding measured through the measurable verbs [58]. The target of the argument is not computational assistance but computational substitution—the replacement of justificatory labor.
The Significance of the Justification Gap: Constraint P2 formalizes a limitation that is often acknowledged informally but rarely accorded theoretical weight: computational agents lack the normative and justificatory capacities required for critical understanding. An LLM may output a proposition that is true, and may even accompany it with a plausible- seeming explanation, but it does not possess the justification in the sense required for 𝐾𝐴 ( Justified (𝜙)). Its “explanations” are statistical reconstructions of linguistic patterns associated with justification, not instances of the agent itself being warranted in its belief [47]. This gap has profound consequences for educational deployments. When a student receives a correction from an automated fact-checking system, they acquire a proposition. What they do not acquire and what the system cannot transmit is the normative standing to assert that proposition as justified. They cannot, based on the system’s output alone, answer the question “How do you know?” or “Why should I believe this source rather than that one?” These are the very competencies that education aims to develop [3, 52, 66]. A defense system that induces true beliefs without cultivating justificatory capacity may therefore achieve short-term correctness at the cost of long-term epistemic dependence.
Modeling the Dynamics of Epistemic Erosion. Constraint P4 introduces a diachronic dimension that distinguishes this framework from static evaluations of system performance. The claim that DelegatedTo (𝐻 , 𝐴, Verify(𝜙)) → Decrease(Pr(𝐶𝐻𝜙)) captures the intuition that epistemic skills are use-dependent: they develop through exercise and atrophy through disuse [11, 12, 59]. This constraint does not specify a rate of decrease or a threshold beyond which skills become irrecoverable; these are empirical questions that may vary across individuals, contexts, and task types. What the constraint provides is a formal warrant for treating epistemic capacity as an endogenous variable, something the system itself affects, rather than an exogenous constant. Models that omit this dynamic risk might be called the Manuscript submitted to ACM
calculator fallacy; the assumption that, because calculators do not erode arithmetic skills in experts who already possess them, they will not erode those skills in novices who are still acquiring them. The fallacy lies in treating all users as epistemically equivalent and all tool use as epistemically neutral. In educational contexts, where users are by definition novices with respect to some domain, this fallacy is particularly dangerous. The framework makes visible what purely technical evaluations obscure: that the very act of delegating verification and justification may alter the learner’s future capacity to verify or justify independently.
5		Conclusion
This paper argues that prevailing computational approaches to misinformation defense rest on a flawed epistemic model of the human agent. By treating human cognition as a static and reliable resource, current systems prioritize knowledge delivery and technical accuracy while neglecting the conditions under which critical understanding is formed and sustained. We have shown that effective defense requires a shift from correction to cultivation: from assuming epistemic competence to designing for its development. Education cannot be treated as an external constraint but must be integrated as a core system component.
Acknowledgments
The research work is supported by an EPSRC & AISI grant (ref-UKRI845: BRA(AI)N - Building Resilience and Accountability in Artificial Intelligence Navigation).
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