对比三种学习方式，发现聊天机器人不降低长期记忆效果
Easy Come, Easy Go? Examining the Perceptions and Learning Effects of LLM-based Chatbot in the Context of Search-as-Learning
- 用书、搜索引擎和聊天机器人做对照实验，测试学习深度
- 聊天机器人虽快，但长期知识保留与传统方法无显著差异
- 适合教育科技设计者参考，平衡效率与深度学习
Search-as-Learning（SAL）的认知过程在促进信息主动编码时最有效。随着基于大语言模型（LLM）的聊天机器人兴起，其即时回答带来效率提升，但也引发学习浅层化的担忧。为探究这一问题，我们对教育工作者和学生进行了大规模调查，了解对聊天机器人的感知风险与收益。此外，采用编码-存储范式设计了被试内实验，92名参与者通过书籍、搜索引擎和聊天机器人三种方式完成SAL任务。结果表明：尽管聊天机器人和搜索引擎在即时概念理解上优于书籍，但并未出现长期记忆表现下降的预期。研究揭示了人机协同学习系统的设计方向——在保证学习效率的同时，提升认知参与度。
原文摘要 · Abstract (English)
The cognitive process of Search-as-Learning (SAL) is most effective when searching promotes active encoding of information. The rise of LLMs-based chatbots, which provide instant answers, introduces a trade-off between efficiency and depth of processing. Such answer-centric approaches accelerate information access, but they also raise concerns about shallower learning. To examine these issues in the context of SAL, we conducted a large-scale survey of educators and students to capture perceived risks and benefits of LLM-based chatbots. In addition, we adopted the encoding-storage paradigm to design a within-subjects experiment, where participants (N=92) engaged in SAL tasks using three different modalities: books, search engines, and chatbots. Our findings provide a counterintuitive insight into stakeholder concerns: while LLM-based chatbots and search engines validated perceived benefits on learning efficiency by outperforming book-based search in immediate conceptual understanding, they did not result in a long-term inferiority as feared. Our study provides insights for designing human-AI collaborative learning systems that promote cognitive engagement by balancing learning efficiency and long-term knowledge retention.
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