Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making
This work addresses the design of AI tools for enhancing human decision-making, but it is incremental as it builds on existing research in AI assistance.
The paper tackles the problem of designing generative AI tools to augment human cognition by comparing end-to-end solutions with process-oriented support, finding that process-oriented approaches better address challenges in AI-assisted decision-making.
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.