arXiv:2507.21490cs.HCcs.CY2025-07被引 4

研究聊天机器人如何影响跨学科学习者的信息搜索行为。

Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learning

  • 通过自传式观察法分析学习者与生成式AI的交互模式。
  • 有学习计划时聊天机器人助导航,无计划时反而干扰探索。
  • 传统信息线索在生成式回复中失效,依赖领域知识识别关键信息。

本研究探讨生成式AI(GenAI)对学习者体验的影响,重点关注学习者如何与生成的信息互动并加以利用。在在线学习环境中,学习者常需自主导航复杂信息空间,这一挑战在生物信息学等跨学科领域尤为突出,因学习者背景和先验知识差异大。本文研究了GenAI对生物信息学研究中信息搜索的影响:(1) 与GenAI聊天机器人的交互如何影响学习者的定向行为?(2) 学习者如何在聊天机器人回复中识别信息线索?采用自传式观察方法进行探究。结果表明,一旦建立学习计划,GenAI可辅助定向;但在缺乏计划前则产生反作用。此外,传统上价值较高的信息线索如项目符号和相关术语,在生成式回复中效果减弱。信息线索主要依赖学习者对领域的已有认知来识别。研究提示,应在跨学科学习环境中谨慎引入GenAI。

原文摘要 · Abstract (English)

This full research paper investigates the impact of generative AI (GenAI) on the learner experience, with a focus on how learners engage with and utilize the information it provides. In e-learning environments, learners often need to navigate a complex information space on their own. This challenge is further compounded in interdisciplinary fields like bioinformatics, due to the varied prior knowledge and backgrounds. In this paper, we studied how GenAI influences information search in bioinformatics research: (1) How do interactions with a GenAI chatbot influence learner orienteering behaviors?; and (2) How do learners identify information scent in GenAI chatbot responses? We adopted an autoethnographic approach to investigate these questions. GenAI was found to support orienteering once a learning plan was established, but it was counterproductive prior to that. Moreover, traditionally value-rich information sources such as bullet points and related terms proved less effective when applied to GenAI responses. Information scents were primarily recognized through the presence or absence of prior knowledge of the domain. These findings suggest that GenAI should be adopted into e-learning environments with caution, particularly in interdisciplinary learning contexts.

生成式AI跨学科学习信息搜索

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