农业图书情报学报 ›› 2024, Vol. 36 ›› Issue (11): 20-32.doi: 10.13998/j.cnki.issn1002-1248.24-0721

所属专题: 人工智能

• 研究论文 • 上一篇    下一篇

人工智能素养视域下高校学生用户使用AIGC信息行为分析

崔宇红, 赵锦涛()   

  1. 北京理工大学 教育学院,北京 100081
  • 收稿日期:2024-10-09 出版日期:2024-11-05 发布日期:2025-04-09
  • 通讯作者: 赵锦涛
  • 作者简介:

    崔宇红(1972- ),女,教授,博士生导师,北京理工大学教育学院,研究方向为科学计量与科技评价、大数据分析与情报研究、人工智能教育应用

  • 基金资助:
    2023年度教育部人文社会科学研究规划基金项目“开放科学场域中高校青年科研人员学术行为及治理机制研究”(23YJAZH021)

AIGC Using Behavior Analysis from the Perspective of Artificial Intelligence Literacy

Yuhong CUI, Jintao ZHAO()   

  1. Beijing Institute of Technology, Beijing 100081
  • Received:2024-10-09 Online:2024-11-05 Published:2025-04-09
  • Contact: Jintao ZHAO

摘要:

[目的/意义] AIGC技术为创造包容与广泛的学习环境提供机遇,针对滥用AIGC工具潜在风险,分析人工智能素养背景下影响学生用户使用AIGC工具的因素,探究学生用户使用影响因素模型框架与关联路径,为图书馆人工智能素养教育推进提供理论依据。 [方法/过程] 借鉴TAM构建概念关系模型,提出基本假设,通过结构方程模型和中介分析进行验证解释。 [结果/结论] 研究表明,努力期望直接影响学生用户对AIGC工具的实际使用,并且通过绩效期望和行为意图连续间接地增加他们对于AIGC工具的实际使用。此外,人工智能素养可以显著提高学生用户AIGC实际使用的转化率。研究弥补学生用户使用AIGC信息行为研究,揭示影响因素的内在关联路径,为图书馆人工智能素养培育提供科学的建议与启示。

关键词: 人工智能生成内容, 人工智能素养, 技术接受模型, ChatGPT

Abstract:

[Purpose/Significance] The development of artificial intelligence generated content (AIGC) technology has engendered novel prospects for the establishment of creating inclusive and expansive learning environments. In light of the potential risks associated with the misuse of AIGC tools, the present study analyzes the factors influencing students' use of AIGC tools within the context of artificial intelligence literacy. It constructs a conceptual model framework and explores the relational paths among influencing variables, aiming to provide a theoretical basis for the advancement of AI literacy education in libraries and other educational institutions. [Method/Process] This study adopts a mixed-method approach that primarily integrates Structural Equation Modeling (SEM) and mediation analysis to explore the relationships between the factors that influence AIGC tool usage. A conceptual relationship model was constructed based on the Technology Acceptance Model (TAM), which is widely utilized model for assessing users' acceptance of new technologies. The study builds on this model by adding AI literacy as a key variable to examine its moderating role in shaping the students' use of AIGC tools. The data were collected via a survey disseminated to university students who have used AIGC tools. The survey incorporated a series of inquiries designed to assess constructs such as effort expectancy, performance expectancy, behavioral intention, AI literacy, and actual usage of the tools. The SEM approach was employed to assess the proposed hypotheses and to validate the relationships between the identified factors. Mediation analysis was employed to assess indirect effects between variables. [Results/Conclusions] The findings indicate that effort expectancy exerts a direct impact on the actual use of AIGC tools by students, and indirectly promotes usage behavior through performance expectancy and behavioral intention. Furthermore, AI literacy plays a crucial role in improving the conversion rate from intention to actual usage. Specifically, AI literacy significantly enhances students' acceptance of AIGC tools, especially in terms of increasing their practical ability to use these tools effectively. The research also identifies key factors that influence students' use of AIGC tools, such as performance expectancy, effort expectancy, and behavioral intention, and highlights the significant moderating effect of AI literacy on the relationships among these factors. This study provides empirical evidence for the effective integration of AIGC technology into the education sector and offers theoretical guidance for libraries and educational organizations on how to design AI literacy education programs that help students adapt to a digitally driven society. Future research may encompass a more extensive examination of the utilization of AIGC tools across different academic disciplines, with a particular emphasis on their implementation in specialized domains. Additionally, the proposed model may be refined to better accommodate a wider range of educational contexts and learning scenarios.

Key words: AI-generated content, artificial intelligence literacy, technology acceptance models, Chat-GPT

中图分类号:  G40

引用本文

崔宇红, 赵锦涛. 人工智能素养视域下高校学生用户使用AIGC信息行为分析[J]. 农业图书情报学报, 2024, 36(11): 20-32.

Yuhong CUI, Jintao ZHAO. AIGC Using Behavior Analysis from the Perspective of Artificial Intelligence Literacy[J]. Journal of Library and Information Science in Agriculture, 2024, 36(11): 20-32.