PAUSE: A Privacy-Preserving Self-Reflection Tool for AI-Associated Cognitive Offloading

Abstract

Cognitive offloading is the use of external aids, such as notes, calculators, or search engines, to reduce mental effort. Large language models (LLMs) extend this to thinking itself, and by AI-associated cognitive offloading, I mean the pattern where a person routinely substitutes LLM output for their own reasoning, idea generation, learning, or communication. Recent empirical work reports associations between some patterns of LLM use and changes in critical thinking effort, neural engagement during assisted tasks, creative diversity, learning behaviour, and social dependence. Validated instruments for AI reliance, dependence, and literacy have begun to appear. I describe PAUSE (Patterns of AI Use: Self-Examination), a privacy-by-design web tool (link: https://anondo1969.github.io/pause) that occupies a different niche from these. It is a lightweight, non-diagnostic reflection aid for private individual use. PAUSE is organised around how a person's own LLM use may relate to cognitive offloading across four everyday domains ('reasoning & critical thinking', 'creativity & originality', 'research & learning', and 'social & communicative capacity'). It delivers a short, free, no-login self-check, scores it entirely in the browser, and returns descriptive, domain-aware reflections. The self-check pairs reverse-scored behavioural items with a claim-evaluation reasoning probe, an alternative-uses creativity probe, and a small retrospective before-and-after block. PAUSE does not assume that AI use is harmful. It only addresses where AI substitutes for effort a person may want to preserve. The application is privacy-preserving by design: scoring is deterministic and runs client-side, no personal data is required, nothing is transmitted or stored beyond the browser session, and no LLM is involved in production. PAUSE is a self-reflection tool. It is not a validated psychological instrument.