用户的提示策略和ChatGPT的上下文适应形成了对话信息搜索体验
Haoning Xue1,2, Yoo Jung Oh3, Xinyi Zhou4
1Department of Communication, University of Utah, Utah, USA. haoning.xue@utah.edu.
Scientific reports
|March 5, 2026
概括
大多数用户在通过对话式AI (如ChatGPT) 寻找信息时不会有效地提示. 聊天GPT适应的响应上下文,但复杂的答案可能被认为是负面的,尽管改善态度.
科学领域:
- 人与计算机的交互
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 包括ChatGPT在内的对话式人工智能是信息搜索的越来越多的工具.
- 对用户提示行为和AI响应适应现实世界的场景的理解有限.
研究的目的:
- 调查用户提示策略和ChatGPT在对话信息搜索 (CIS) 期间的响应适应.
- 在不同主题中识别用户参与和AI上下文化的差异.
主要方法:
- 一项涉及937名美国成年人使用多轮CIS与ChatGPT在各种主题上的研究.
- 分析用户提示和ChatGPT响应沟通风格.
主要成果:
- 只有19.1%的用户使用提示策略,主要是那些拥有高等教育和民主党倾向的人,这表明数字沟.
- 聊天GPT根据主题争议调整了响应,显示了增加的认知复杂性和争议主题的外部引用.
- 认知上复杂的AI反应,虽然被认为不那么有利,但增强了积极的问题相关的态度.
结论:
- 在对话式AI信息搜索中,用户提示行为分布不均.
- 聊天GPT的上下文适应影响用户的感知和态度,突出了人工智能和用户交互之间的相互作用在塑造信息结果.
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