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Journal of Library and Information Science in Agriculture ›› 2024, Vol. 36 ›› Issue (11): 20-32.doi: 10.13998/j.cnki.issn1002-1248.24-0721

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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

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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

CLC Number: 

  • G40

Fig.1

Research model"

Table 1

Measuring question items using AIGC influence variables"

变量 题项
努力期望EE 学习如何使用AIGC对我来说很容易
与AIGC的互动通俗易懂
AIGC易于使用来管理与发现知识
AIGC使用用户界面友好
AIGC易于访问
绩效期望PE AIGC在日常学习中提供所寻求的完整的相关信息
AIGC是搜索引擎的更好替代品
使用AIGC可以帮助我提高生产力更快完成任务
使用AIGC有助于理解与工作/学术相关的概念
使用AIGC有助于提高我的科研水平
行为意图BI 我会选择从AIGC获取知识与信息资源
值得向其他人推荐AIGC
我有兴趣在未来工作/科研更频繁地使用AIGC
人工智能素养AI 有足够的专业知识为自己使用AIGC提供技术支持
我能够利用AIGC找到需要的信息和内容
我知道如何验证AIGC生成内容是否可信
使用后能够评估当前AIGC产品的能力和局限性
实际使用AU 您实际使用AIGC工具的频率

Table 2

Results of reliability test"

潜变量 观测变量 标准化因子载荷
EE（感知易用性） Cronbach α=0.936
学习如何使用AIGC对我来说很容易 0.808
与AIGC互动通俗易懂 0.905
AIGC易于用来管理和发现知识 0.913
AIGC使用的用户界面友好 0.898
AIGC易于访问 0.809
PE（感知有用性） Cronbach α=0.947
AIGC能在日常学习中提供所寻求的完整信息 0.878
使用AIGC可以帮助我提高生产力更快完成任务 0.890
使用AIGC有助于理解与工作/科研中相关的概念 0.907
使用AIGC有助于提高我的科研水平 0.903
BI（行为意图） Cronbach α=0.953
我会选择继续从AIGC工具获取知识与信息资源 0.919
我愿意推荐其他人使用AIGC工具 0.899
我会保持或增加使用AIGC工具频率 0.912
我支持提供AIGC工具 0.928
AI（人工智能素养） Cronbach α=0.939
有足够的专业知识为自己使用AIGC提供技术支持 0.893
我能够利用AIGC找到需要的信息和内容 0.889
我知道如何验证AIGC生成内容是否可信 0.885
使用后能够评估当前AIGC产品的能力和局限性 0.899
AU（实际使用） Cronbach α=0.968
使用AIGC是搜索引擎的更好替代品？ 0.869
您使用生成式人工智能工具频率如何？ 0.614

Fig.2

Model normalized data results"

Table 3

Hypothetical model path analysis"

假设 假设路径 估计值 SE CR值 显著性 假设判断
H1：努力期望与绩效期望呈正相关 EE→PE 0.514 0.061 8.323 *** 支持
H2：努力期望与使用AIGC行为意图呈正相关 EE→BI -0.009 0.053 -0.159 .874 不支持
H3：努力期望与AIGC的实际使用呈正相关 EE→AU -0.023 0.069 -0.340 .734 不支持
H4：绩效期望与使用AIGC行为意图呈正相关 PE→BI 0.777 0.070 10.755 *** 支持
H5：绩效期望与AIGC的实际使用情况呈正相关 PE→AU 0.751 0.137 5.548 *** 支持
H8：行为意图与AIGC的实际使用呈正相关 BI→AU 0.058 0.126 0.479 .632 不支持
H10a：人工智能素养与绩效期望呈正相关 AI→EE 0.635 0.055 0.996 *** 支持
H10b：人工智能素养与绩效期望呈正相关 AI→PE 0.402 0.050 6.951 *** 支持
H10c：人工智能素养与行为意图呈正相关 AI→BI 0.187 0.043 3.610 *** 支持
H10d：人工智能素养与实际使用呈正相关 AI→AU 0.237 0.058 3.546 *** 支持

Table 4

Analysis of intermediation effects"

假设 中介路径 Effects BoostSE Bootstrap 95% CIs
LLCI ULCI
H6 EE→PE→BI 0.529 6 0.057 2 0.420 0 0.643 3
H7 EE→PE→BI→AU 0.194 8 0.028 7 0.130 7 0.245 4
H9 PE→BI→AU 0.153 5 0.039 9 0.078 0 0.236 1
H11 PE→AI→BI 0.147 0 0.041 5 0.072 5 0.232 7
H12 BI→AI→AU 0.119 3 0.026 8 0.069 9 0.174 9
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