对比传统搜索与大模型对话搜索，揭示AI如何改变学习中的信息获取方式。
Evolving Paradigms in Task-Based Search and Learning: A Comparative Analysis of Traditional Search Engine with LLM-Enhanced Conversational Search System
- 用大模型增强的对话式搜索支持多步推理和探索性学习。
- 用户在大模型系统中更倾向生成式提问，知识整合能力提升。
- 适合关注人机交互、AI辅助学习的研究者和教育技术开发者。
大型语言模型（LLMs）正在通过实现互动性、生成性和推理驱动的搜索，快速重塑信息检索。尽管传统关键词搜索仍是网络和学术信息获取的核心，但其在支持多步推理和探索性学习任务方面常显不足。以ChatGPT和Claude为代表的基于LLM的搜索界面引入了新能力，可能影响用户提问方式、信息导航路径及知识建构过程。然而，对这些影响的实证理解仍有限。本研究比较了标准搜索引擎与大模型增强型搜索系统中的搜索行为与学习成效，探讨：(1) 不同系统下搜索策略、查询构建与评估行为的差异；(2) 使用大模型如何影响搜索学习任务中的理解力、知识整合与批判性思维。研究结果揭示了生成式AI如何塑造信息寻求过程，为信息检索、人机交互与技术支持学习领域的持续讨论提供了依据。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are rapidly reshaping information retrieval by enabling interactive, generative, and inference-driven search. While traditional keyword-based search remains central to web and academic information access, it often struggles to support multi-step reasoning and exploratory learning tasks. LLM-powered search interfaces, such as ChatGPT and Claude, introduce new capabilities that may influence how users formulate queries, navigate information, and construct knowledge. However, empirical understanding of these effects is still limited. This study compares search behavior and learning outcomes in two environments: a standard search engine and an LLM-powered search system. We investigate (1) how search strategies, query formulation, and evaluation behaviors differ across systems, and (2) how LLM use affects comprehension, knowledge integration, and critical thinking during search-based learning tasks. Findings offer insight into how generative AI shapes information-seeking processes and contribute to ongoing discussions in information retrieval, human-AI interaction, and technology-supported learning.
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