# Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making

arXiv: https://arxiv.org/abs/2504.03207 · HTML: https://mlsanity.com/paper/2504.03207/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology](https://mlsanity.com/paper/2402.14252/) (2024)
- [An AI-Resilient Text Rendering Technique for Reading and Skimming Documents](https://mlsanity.com/paper/2401.10873/) (2024)
- [Timing Matters: How Using LLMs at Different Timings Influences Writers' Perceptions and Ideation Outcomes in AI-Assisted Ideation](https://mlsanity.com/paper/2502.06197/) (2025)

## Built on by (most cited first, 2 of 2)

- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [Reflecti-Mate: A Conversational Agent for Adaptive Decision-Making Support Through System 1 and System 2 Thinking](https://mlsanity.com/paper/2605.22509/) (2026)

## Abstract (arXiv)

How can we use generative AI to design tools that augment rather than replace human cognition? In this position paper, we review our own research on AI-assisted decision-making for lessons to learn. We observe that in both AI-assisted decision-making and generative AI, a popular approach is to suggest AI-generated end-to-end solutions to users, which users can then accept, reject, or edit. Alternatively, AI tools could offer more incremental support to help users solve tasks themselves, which we call process-oriented support. We describe findings on the challenges of end-to-end solutions, and how process-oriented support can address them. We also discuss the applicability of these findings to generative AI based on a recent study in which we compared both approaches to assist users in a complex decision-making task with LLMs.