Epistemic Subordination: Generative AI and the Infrastructure of Knowledge

Intelligence beyond knowledge: control, architecture, and the structural law of artificial agency

Dynamic Derived Intentionality in Large Language Models: From Implicit Beliefs to Semantic Parasitism

Homogenization and Epistemic Dependency: How Generative AI Threatens Task-Based Language Assessment in ESP Contexts

Task-based language assessment assumes that the learner’s performance reflects the learner’s own developing communicative competence. Generative AI challenges this assumption by allowing students to delegate task completion to an external system, thereby threatening task authenticity, performance authenticity, and the interpretability of form. The study aims to examine how 40 university English for Specific Purposes teachers at three Ukrainian universities experience the impact of generative artificial intelligence on the task-based language teaching and assessment validity chain. This issue is significant since, in the absence of institutional AI policy, teachers are the primary regulators of AI use in assessed contexts, and their decisions are the primary mechanism through which validity is maintained or compromised, yet remain empirically under-examined. The study employed a qualitative approach, including analytical content analysis of open-ended survey responses and structured classroom observations of 12 teachers (a purposive subsample). The results were five-fold: teachers found artificial intelligence useful for task material generation and formative feedback; teachers had consistent validity concerns for summative assessment; formative-summative asymmetry emerged in different disciplinary clusters; teachers independently indicated that students’ output converged towards a single artificial intelligence-modified output; and students would not be able to critically evaluate the artificial intelligence-generated text. Our findings are interpreted using the Task-Based Language Teaching-Assessment-Artificial Intelligence Nexus, a preliminary heuristic model that identifies three interconnected nodes of validity threat. The study argues that this suggests the need for formal AI policy, and for teaching AI-critical genre literacy in English for Specific Purposes education.

The hand on the pen: generative AI and the Italian parliamentary experience

The integration of artificial intelligence into the legislative process is among the most significant—and least examined—constitutional developments of the decade. This article’s concern is not the composition of legislative text ex novo, but the function constitutional theory assigns to parliaments: the scrutiny of the texts and amendments tabled before them. The research question is accordingly how the deployment of AI in the amendment phase affects the capacity of parliaments to scrutinize legislation—and the accountability, transparency, and separation of powers it secures—and whether human oversight suffices to preserve it. The inquiry centres on two Italian systems at opposite ends of a governance spectrum. GEM (Gestore EMendamenti), the Senate’s amendment-management ecosystem, operational since 2016, ranks among the most sophisticated parliamentary AI tools in use; its functions concern the processing, clustering, ordering, and prediction of amendments, not autonomous drafting—a narrow but constitutionally consequential scope. GENAI4LEX-B, a hybrid architecture combining symbolic reasoning with generative models, has been selected for the Chamber of Deputies but not yet deployed; its value is not empirical but architectural, as a counter-model in which safeguards were designed ex ante rather than emerging reactively from practice. The article advances two arguments. First, it identifies four constitutional fault lines in AI-assisted amendment processing—consequential algorithmic error, deliberate disruption through AI-generated flooding, accountability opacity, and epistemic homogenization across branches—while recognizing the countervailing potential to strengthen scrutiny; it then tests human oversight against the empirical literature on automation bias, concluding that nominal oversight is necessary but not sufficient. Secondly, it contends that the European Union AI Act leaves a significant gap for the legislative process, and proposes a five-element governance framework to ensure that the distinction between algorithmic assistance and algorithmic authorship is maintained through enforceable standards rather than self-regulation alone.

Adversarial Social Epistemology for Assemblies of Humans and Large Language Models

We outline an adversarial social epistemology (ASE) for densely interactive communicative landscapes in which public assertions are scaffolded by chains of testimony, inference, institutional certification, and tacit trust. In such landscapes, agents have incentives and affordances to distort, color, omit, fabricate, or strategically under-specify information for private, reputational, rhetorical, or material gains. We argue that these phenomena are not adequately captured by familiar descriptions of epistemic bubbles, echo chambers, or misinformation diffusion. What requires explanation is how communicative agents exploit the commitments and entitlements that normally make scaffolded assertions trustworthy. We provide language that delivers the requisite analysis, outline mechanisms that subvert trust in scaffolded public communications, and outline machinery for auditing and redressing trust breaches arising from subverting the auditability of inferential chains, drawing on epistemic networks, enriched with an inferentialist semantics for interpreting assertions.

Regulating for AI Legitimacy

AI systems already govern. They rank speech and allocate attention, filter applicants and triage claims. The dominant frame for AI governance, alignment, asks whether such systems pursue the right objectives safely. It cannot answer a prior question: by what right are those objectives set and enforced? This Article argues that legitimacy is an autonomous regulatory objective, distinct from alignment and not secured by it. Legitimacy here is sociological: the belief among those subject to power that it is exercised rightfully. Performance does not produce that belief. We already have the proof of concept. Social media and search delivered enormous gains on every familiar metric and still triggered a legitimacy crisis, because publics questioned who authorized a handful of firms to set the rules of speech, visibility, and knowledge. It is possible to build a benevolent AI and still face a political crisis over its authority. The Article maps three sites where AI legitimacy falters: opacity, which blocks audiences from forming justified beliefs; private power, where firms exercise public-facing authority without recognizable authorization; and administrative automation, which strains reason-giving, participation, and review inside the state. It then asks what law can contribute. Thin legality (publicity, stability, consistent application) signals non-arbitrariness and buys real recognition, but invites legitimacy-washing when form drifts from practice. Thick legality supplies what form cannot: public authorship of the rules that bind. Three portable principles follow. Integration seats consequential AI rule-setting in venues a polity already treats as authoritative. Familiarity presents rules and reasons in locally credible forms. Contestation guarantees a credible second look with real remedies.