The Cognitive Sovereignty Threshold: An Interdisciplinary Humanities and Sciences Framework for Human Agency in Generative AI-Assisted Knowledge Work

Generative artificial intelligence can increase the speed and apparent quality of knowledge work, yet conventional evaluations rarely determine whether users remain capable of understanding, verifying, contesting, remembering and taking responsibility for AI-mediated outputs. This paper develops the Cognitive Sovereignty Threshold (CST), an interdisciplinary humanities and sciences framework for distinguishing sovereign augmentation from cognitively fragile efficiency. The study uses integrative conceptual synthesis and design-science modelling to connect extended cognition, cognitive offloading, automation reliance, metacognition, epistemic agency, narrative responsibility and institutional governance. Cognitive sovereignty is operationalised through five dimensions: epistemic authorship, verification capacity, metacognitive calibration, contestability and retention. A geometric aggregation model is combined with a delegation-oversight penalty to produce a Cognitive Sovereignty Index (CSI), while a Sovereignty-Adjusted Value measure links task performance to retained human agency. Seven transparent analytic scenarios illustrate how similar productivity levels can conceal sharply different sovereignty profiles. AI used as an adversarial critic or verified drafting partner produces the strongest joint performance and sovereignty outcomes, whereas opaque, mandatory or answer-first use produces fragile efficiency even when immediate task performance appears high. The paper introduces the principle of germane cognitive friction: human-AI systems should deliberately preserve the effort required for source inspection, counterargument, reason-giving, delayed recall and meaningful override. The framework contributes a testable construct, a formal threshold model and a practical audit architecture for education, organisations and public institutions. It concludes that responsible AI adoption should optimise not only output quality and risk controls, but also the continued human capacity to know, judge, explain and act without compulsory dependence on the system.