Identification and Evolutionary Analysis of Learning States in the “Searching as Learning”

Zhao Yiming; Fang Zewei; Cai Mengliang

doi:10.13266/j.issn.0252-3116.2026.03.003

Library and Information Service >

2026 , Vol. 70 >Issue 3: 21 - 32

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.03.003

RESEARCH PAPERS

Identification and Evolutionary Analysis of Learning States in the “Searching as Learning”

Zhao Yiming ,
Fang Zewei ,
Cai Mengliang

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1 Center for Studies of Information Resources, Wuhan University, Wuhan 430072;
2 School of Information Management, Wuhan University, Wuhan 430072;
3 Big Data Institute, Wuhan University, Wuhan 430072;
4 Wuhan Institute of Data Intelligence, Wuhan 430072;
5 National Demonstration Center for Experimental Library and Information Science Education, Wuhan University, Wuhan 430072

Received date: 2024-12-13

Revised date: 2025-02-18

Online published: 2026-02-09

Supported by

This work is supported by the project of National Natural Science Foundation of China, titled “User Learning State Perception and Intelligent Response from the Perspective of ‘Search-as-Learning’” (Grant No. 72274146); and “Research on User’s Cognitive Processing and Learning Experience of Conversational Information Search in Human-AI Interaction Context” (Grant No. 72304177).

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Abstract

[Purpose/Significance] With the rapid advancement of AI-driven search engines, this study constructs a user-centered learning state model and explores its dynamic evolutionary mechanisms, contributing to the optimization of search recommendation strategies. [Method/Process] User experiments were conducted to collect subjective data and screen recordings, followed by data encoding and analysis. A dual-path framework for learning state recognition was developed from the perspectives of intrinsic cognition and explicit interaction. A combination of clustering and Markov models was employed to identify sequential patterns of learning states. [Result/Conclusion] Four types of learning state patterns are identified during the search process: exploratory, evaluative, disruptive, and explosive growth patterns, exhibiting long-term accumulation and rapid growth evolutionary mechanisms. Compared to traditional search engines, generative AI search engines demonstrate more pronounced disruptive and explosive growth states, suggesting that users achieve higher efficiency in knowledge acquisition.

Key words： learning state; state recognition; state evolution; searching as learning

Cite this article

Zhao Yiming , Fang Zewei , Cai Mengliang . Identification and Evolutionary Analysis of Learning States in the “Searching as Learning”[J]. Library and Information Service, 2026 , 70(3) : 21 -32 . DOI: 10.13266/j.issn.0252-3116.2026.03.003

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