2024/10/02 by Yeonsun Yang, Yang, Yeonsun, Ahyeon Shin +7 · 1 citation
Business, Management and Accounting · Computer Science · #AI in Service Interactions #Artificial Intelligence (cs.AI) #Blockchain Technology Applications and Security #FOS: Computer and information sciences #FinTech, Crowdfunding, Digital Finance #Human-Computer Interaction (cs.HC) #Information Retrieval (cs.IR) #K.3.2
paper · pdf · doi:10.48550/arxiv.2410.01396
openalex publication_date 2024/10/02 · openalex created_date 2024/10/30 · openalex updated_date 2026/07/28
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.