GenAI让信息获取从捡拾变为耕种，用户可主动培育结构化内容。
Information Farming: From Berry Picking to Berry Growing
- 将用户比作农夫，通过提示词播种、流程培育来主动生成信息
- 信息产出更结构化、可复用，减少对碎片化资源的依赖
- 适合研究人机交互演化与未来智能系统设计
传统信息获取范式将用户视为采集者，在分散源中随机搜寻以满足动态需求。然而，生成式AI正推动人们生产、组织和重用信息方式的根本转变——这一变化已超出经典‘采果’与‘信息觅食’理论的解释范畴。这类似于新石器革命：社会从狩猎采集转向耕种。生成技术使用户能以提示词为种子，持续培育工作流，从个人专属空间中收获高度结构化、相关性强的信息成果，而非在他人领域中漫游搜寻。本文提出‘信息耕种’作为概念框架，论证其是人机信息交互的自然演进。结合历史类比与实证证据，探讨信息耕种的优势、设计启示及伴随风险。我们推测，随着生成式AI普及，主动耕种将逐步取代临时性的片段式搜寻，成为主流互动模式，标志着人机信息关系及其研究的重大转向。
原文摘要 · Abstract (English)
The classic paradigms of Berry Picking and Information Foraging Theory have framed users as gatherers, opportunistically searching across distributed sources to satisfy evolving information needs. However, the rise of GenAI is driving a fundamental transformation in how people produce, structure, and reuse information - one that these paradigms no longer fully capture. This transformation is analogous to the Neolithic Revolution, when societies shifted from hunting and gathering to cultivation. Generative technologies empower users to "farm" information by planting seeds in the form of prompts, cultivating workflows over time, and harvesting richly structured, relevant yields within their own plots, rather than foraging across others people's patches. In this perspectives paper, we introduce the notion of Information Farming as a conceptual framework and argue that it represents a natural evolution in how people engage with information. Drawing on historical analogy and empirical evidence, we examine the benefits and opportunities of information farming, its implications for design and evaluation, and the accompanying risks posed by this transition. We hypothesize that as GenAI technologies proliferate, cultivating information will increasingly supplant transient, patch-based foraging as a dominant mode of engagement, marking a broader shift in human-information interaction and its study.
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