CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models

TL;DR

CatAlyst employs large generative models to contextually prompt workers to resume tasks, reducing cognitive load and enhancing digital well-being without domain-specific tuning.

Contribution

It introduces a domain-extensible intervention method using generative models to prevent task procrastination, applicable across various tasks without domain-specific adjustments.

Findings

Effective in writing and slide-editing tasks

Reduces time to resume tasks

Lowers cognitive load

Abstract

CatAlyst uses generative models to help workers' progress by influencing their task engagement instead of directly contributing to their task outputs. It prompts distracted workers to resume their tasks by generating a continuation of their work and presenting it as an intervention that is more context-aware than conventional (predetermined) feedback. The prompt can function by drawing their interest and lowering the hurdle for resumption even when the generated continuation is insufficient to substitute their work, while recent human-AI collaboration research aiming at work substitution depends on a stable high accuracy. This frees CatAlyst from domain-specific model-tuning and makes it applicable to various tasks. Our studies involving writing and slide-editing tasks demonstrated CatAlyst's effectiveness in helping workers swiftly resume tasks with a lowered cognitive load. The results suggest a new form of human-AI collaboration where large generative models publicly available but imperfect for each individual domain can contribute to workers' digital well-being.