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

• URL: http://arxiv.org/abs/2504.03207v1
• Date: Fri, 04 Apr 2025 06:40:03 GMT
• Title: Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making
• Authors: Zelun Tony Zhang, Leon Reicherts,
• Abstract summary: In both AI-assisted decision-making and generative AI, a popular approach is to suggest AI-generated end-to-end solutions to users.<n>Alternatively, AI tools could offer more incremental support to help users solve tasks themselves.
• Score: 2.1680671785663654
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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