Easy Come, Easy Go? Examining the Perceptions and Learning Effects of LLM-based Chatbot in the Context of Search-as-Learning

Abstract

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.