The Impact of Generative Artificial Intelligence Technologies on Chinese Librarians' information Behavior and Ethical Discussion: An Empirical Study Based on a Small Sample

Authors

  • Yifei Chen Institute of Medical Information,Chinese Academy of Medical Sciences https://orcid.org/0009-0002-9393-6299
  • Yongjie Li Institute of Medical Information, Chinese Academy of Medical Sciences
  • Dechao Wang Institute of Medical Information, Chinese Academy of Medical Sciences
  • Yinan Sun Institute of Medical Information, Chinese Academy of Medical Sciences
  • Tingyu Lv Institute of Medical Information, Chinese Academy of Medical Sciences
  • Xiaoli Tang Institute of Medical Information, Chinese Academy of Medical Sciences

DOI:

https://doi.org/10.21900/j.alise.2024.1648

Keywords:

Generative Artificial Intelligence, Information Behavior, Information Ethics

Abstract

This study used a combination of questionnaires and interviews to survey 68 librarians in mainland China. The questionnaire was divided into three parts: (1) providing descriptive statistics of the interviewed librarians; (2) exploring the impact of generative technologies on librarians' information behaviour from work scenarios; (3) investigating librarians' concerns about ethics and strategies for coping with ethical challenges. The results show that generative AI technologies had a greater impact on information seeking, information encountering, and information using behaviours, and an insignificant impact on information sharing behaviours. In addition, the results of the study reflect that 67.65% of librarians showed a very high level of concern about privacy and security; 66.18% of them believed that the content generated by the tools needed further validation. The study also provided six recommendations from the perspective of libraries and librarians to address ethical challenges such as the spread of disinformation and bias.

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Published

2024-10-16

Issue

2024: ALISE Proceedings

Section

Juried Papers

License

Copyright (c) 2024 Yifei Chen, Yongjie Li, Dechao Wang, Yinan Sun, Tingyu Lv, Xiaoli Tang

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