人工智能素养视域下高校学生用户使用AIGC信息行为分析

doi:10.13998/j.cnki.issn1002-1248.24-0721

Abstract

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:

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.

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References 54

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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

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

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

假设	中介路径	Effects	BoostSE	Bootstrap 95% CIs
假设	中介路径	Effects	BoostSE	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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AIGC Using Behavior Analysis from the Perspective of Artificial Intelligence Literacy

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