Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910903405903872 |
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| author | Zhang, Zelun Tony Reicherts, Leon |
| author_facet | Zhang, Zelun Tony Reicherts, Leon |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_03207 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making Zhang, Zelun Tony Reicherts, Leon Human-Computer Interaction Artificial Intelligence 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. |
| title | Augmenting Human Cognition With Generative AI: Lessons From AI-Assisted Decision-Making |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2504.03207 |