RecalibrateGPT: AI Fatigue Resilient Conversational Interfaces

Abstract:Large language models are powerful, but their interfaces often devolve into a type $\rightarrow$ read $\rightarrow$ retype loop, creating conversational AI fatigue, cognitive load, and eventual task abandonment. To mitigate this, we present RecalibrateGPT, a system introducing five cross-turn operators (Anchor, Replay, Delta, Scope, and Steer) that each target a distinct fatigue type, recalibrating LLM responses through a structured panel by acting on the full conversation history with a single click. Users invoke these operators through the AssistiveButton in one of three operator palette layouts: Vertical, Arc, or Tablet. We conducted two pilot studies with the same 12 advanced LLM users. An initial formative qualitative study identifies a taxonomy of four fatigue types (retyping, scanning, decision paralysis, and context drift) and derives two design objectives for RecalibrateGPT. A follow-up quantitative evaluation finds it reduces perceived cognitive workload by half (NASA-TLX = 2.7) at high perceived usability (SUS = 86.5), suggesting AI fatigue is not just a model-quality issue but an interaction-flow cost that interfaces can remove.

5 pages, 3 figures. Accepted at UIST Adjunct 2026

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2609.00506 [cs.HC]

(or arXiv:2609.00506v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2609.00506 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3830397.3841834 Focus to learn more DOI(s) linking to related resources

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