Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction

Kuangzhe Xu; Yu Shen; Longjie Yan; Yinghui Ren

cs.HCMar 23, 2026

Cognitive Agency Surrender: Defending Epistemic Sovereignty via Scaffolded AI Friction

Authors: Kuangzhe Xu, Yu Shen, Longjie Yan, Yinghui Ren

Organizations: Cyberspace Security University of China

Abstract

The proliferation of Generative Artificial Intelligence has transformed benign cognitive offloading into a systemic risk of cognitive agency surrender. Driven by the commercial dogma of "zero-friction" design, highly fluent AI interfaces actively exploit human cognitive miserliness, prematurely satisfying the need for cognitive closure and inducing severe automation bias. To empirically quantify this epistemic erosion, we deployed a zero-shot semantic classification pipeline ( $τ=0.7$ ) on 1,223 high-confidence AI-HCI papers from 2023 to early 2026. Our analysis reveals an escalating "agentic takeover": a brief 2025 surge in research defending human epistemic sovereignty (19.1%) was abruptly suppressed in early 2026 (13.1%) by an explosive shift toward optimizing autonomous machine agents (19.6%), while frictionless usability maintained a structural hegemony (67.3%). To dismantle this trap, we theorize "Scaffolded Cognitive Friction," repurposing Multi-Agent Systems (MAS) as explicit cognitive forcing functions (e.g., computational Devil's Advocates) to inject germane epistemic tension and disrupt heuristic execution. Furthermore, we outline a multimodal computational phenotyping agenda -- integrating gaze transition entropy, task-evoked pupillometry, fNIRS, and Hierarchical Drift Diffusion Modeling (HDDM) -- to mathematically decouple decision outcomes from cognitive effort. Ultimately, intentionally designed friction is not merely a psychological intervention, but a foundational technical prerequisite for enforcing global AI governance and preserving societal cognitive resilience.

Figures & tables

Figure 1: The Epistemic Skew and Agentic Takeover in High-Confidence HCI Literature (2023 – March 9, 2026). A deep-learning-based semantic stance classification of 1,223 highly confident, AI-related papers (filtered via a rigorous $\tau=0.7$ threshold from an 8,000-paper ontological baseline retrieved via the OpenAlex API). The analysis reveals the persistent, systemic prioritization of the frictionless paradigm (optimizing efficiency and reducing cognitive load). Crucially, a granular semantic breakdown exposes a recent ”agentic takeover”: a brief 2025 surge in research defending strict human cognitive sovereignty (19.1%) was abruptly suppressed in early 2026 (13.1%) by an explosive shift toward optimizing machine autonomy and autonomous AI agents (19.6%). This dynamic structural divergence empirically quantifies the escalating marginalization of human epistemic agency in contemporary human-AI interaction design.

Friction Paradigm & Source	Cognitive Load Profile	Emotional & Metacognitive State	Epistemic Outcome
Extraneous Usability Friction (Cluttered interfaces, system latency)	Extraneous Load: Depletes limited working memory without serving the core epistemic task.	Frustration & Annoyance: Triggers negative affect and degrades sustained attention.	Task Abandonment: Comprehension failure and breakdown of human-AI collaboration.
The Zero-Friction Trap (Highly fluent, single-conclusion AI outputs)	Minimal Load: Requires almost no mental effort to process information.	False Security & Overconfidence: Triggers illusion of explanatory depth, prematurely satisfying cognitive closure.	Cognitive Agency Surrender: System 1 heuristic takeover; severe automation bias.
Scaffolded Cognitive Friction (Machine-generated, structured logical divergence)	Germane Load: Forces working memory resources toward schema construction and critical analysis.	Epistemic Tension & Curiosity: Disrupts cognitive closure, stimulating epistemic vigilance and investigative motivation.	Synergistic Synthesis & Sovereignty Defense: Forced awakening of System 2 analytical deliberation.

Table 1: Extended Taxonomy of Friction in Human-Computer Interaction. This taxonomy conceptually decouples scaffolded cognitive friction from traditional usability barriers and the zero-friction trap, mapping each paradigm to its distinct cognitive load profile, metacognitive state, and ultimate epistemic outcome.

Figure 2: The Two-Dimensional Evaluation Space Model for Cognitive Agency. The X-axis represents the delegation of sensemaking control (AI Dependency), while the Y-axis represents the depth of epistemic tension and analytical load (Cognitive Friction). Long-term interaction within the Cognitive Agency Surrender quadrant drives systemic cognitive atrophy. In contrast, Synergistic Synthesis utilizes structured friction to break the zero-sum fallacy of automation, preserving epistemic sovereignty through Meaningful Human Control (MHC).

Figure 3: Causal Pathway of Human-AI Synergistic Synthesis. Unlike conventional consensus-driven workflows (upper section) that promote premature cognitive closure and heuristic acceptance, the scaffolded cognitive friction framework (lower section) utilizes heterogeneous agents and a Devil’s Advocate to expose structured contradictions. This injected epistemic tension elevates visual sampling entropy, forcibly awakening System 2 analytical reasoning to adjudicate logical trade-offs and preserving the human decision-making locus.

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Analytical Tier	Deep Learning Semantic Label (Stance Description)
1. The Frictionless Paradigm (Efficiency & Usability)	”optimizing system efficiency, seamless interaction, and reducing user effort”
2. Machine Autonomy (AI Agents & Reflection)	”improving machine autonomy, artificial intelligence agents, and algorithmic reflection”
3. Strict Human Agency (Epistemic Sovereignty)	”preserving human cognitive agency, critical thinking, and preventing automation bias”

Appendix

Table A1: Semantic Labels for Zero-Shot Stance Classification. The exact descriptive strings fed into the BART-large-MNLI model to compute the contextual ideological distribution within the AI-HCI corpus.

Figure A1: Normalized Confusion Matrix of Zero-Shot AI vs. Expert Goldline. Colors represent class-wise recall (accuracy per category), effectively neutralizing visual biases caused by extreme class imbalances. The pipeline demonstrates exceptional sensitivity (84.6%) for the marginalized Strict Human Agency paradigm. Overall pipeline accuracy is 87.0% evaluated on the 200-paper stratified sample.

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