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.
@article{arxiv.2504.03207,
title = {Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making},
author = {Zelun Tony Zhang and Leon Reicherts},
journal= {arXiv preprint arXiv:2504.03207},
year = {2025}
}