人工智能素养对大学生在线自我调节学习的影响：AI支持与基本心理需求的链式中介作用
The Impact of Artificial Intelligence Literacy on College Students’ Online Self-Regulated Learning: The Chain Mediating Effect of AI Support and Basic Psychological Needs
摘要: 随着人工智能技术在教育领域的深度应用，大学生的人工智能素养对其在线学习效果的影响日益凸显，但人工智能素养如何影响在线自我调节学习，其内在心理机制尚不清晰。基于自我决定理论，本研究构建了一个链式中介模型，考察AI支持与基本心理需求在人工智能素养与在线自我调节学习之间的中介作用。采用方便抽样法对701名大学生进行问卷调查，运用结构方程模型和Bootstrap法分析数据。结果表明：(1) 人工智能素养对在线自我调节学习具有显著的正向预测作用；(2) AI支持与基本心理需求在人工智能素养与在线自我调节学习之间起链式中介作用。研究揭示了人工智能素养通过增强学生感知的AI支持，进而满足其基本心理需求，最终促进在线自我调节学习的心理机制。
Abstract: With the deep application of artificial intelligence (AI) technology in education, the impact of college students’ AI literacy on their online learning outcomes has become increasingly prominent. However, the underlying psychological mechanism of how AI literacy affects online self-regulated learning (OSRL) remains unclear. Based on self-determination theory, this study constructed a chain mediation model to examine the mediating roles of AI support and basic psychological needs in the relationship between AI literacy and OSRL. A questionnaire survey was conducted among 701 college students using convenience sampling, and data were analyzed using structural equation modeling and the Bootstrap method. The results showed that: (1) AI literacy significantly and positively predicted OSRL; (2) AI support and basic psychological needs played a chain mediating role between AI literacy and OSRL. This study reveals the psychological mechanism by which AI literacy enhances students’ perceived AI support, thereby satisfying their basic psychological needs, and ultimately promoting OSRL.
文章引用：邱雨晴 (2026). 人工智能素养对大学生在线自我调节学习的影响：AI支持与基本心理需求的链式中介作用. 心理学进展, 16(4), 457-464. https://doi.org/10.12677/ap.2026.164219

参考文献

[1] 陈凯泉, 何瑶, 仲国强(2018). 人工智能视域下的信息素养内涵转型及AI教育目标定位——兼论基础教育阶段AI课程与教学实施路径. 远程教育杂志, 36(1), 61-71.
[2] 贾利锋, 李海龙(2020). 临场感对在线学习者学习认知的影响——基于探究社区理论的条件过程分析. 电化教育研究, 41(2), 45-52.
[3] 梁迎丽, 刘陈(2018). 人工智能教育应用的现状分析、典型特征与发展趋势. 中国电化教育, (3), 24-30.
[4] 刘桂荣(2010). 自主支持和因果定向对创造力的影响. 硕士学位论文, 济南: 山东师范大学.
[5] 刘俊升, 张晓, 王磊(2013). 基本心理需求量表中文版修订研究. 心理科学进展, 21(5), 859-866.
[6] 罗云, 赵鸣, 王振宏(2014). 初中生感知教师自主支持对学业倦怠的影响: 基本心理需要、自主动机的中介作用. 心理发展与教育, 30(3), 312-321.
[7] 钱小龙, 宋子昀(2021). 终身学习者在线学习的自我调节: 理论指引、逻辑关系和实践方案. 中国电化教育, (11), 97-105, 114.
[8] 王思遥, 黄亚婷(2024). 促进或抑制: 生成式人工智能对大学生创造力的影响. 中国高教研究, (11), 29-36.
[9] 王佑镁, 王旦, 梁炜怡, 柳晨晨(2023). “阿拉丁神灯”还是“潘多拉魔盒”: ChatGPT教育应用的潜能与风险. 现代远程教育研究, 35(2), 48-56.
[10] 张俊, 高丙成(2019). 学校氛围和父母自主支持对小学生学业倦怠的影响: 基本心理需要的中介作用. 中国特殊教育, (1), 89-96.
[11] 周琼, 徐亚苹, 蔡迎春(2024). 高校学生人工智能素养能力现状及影响因素多维分析. 图书情报知识, 41(3), 38-48.
[12] Annamalai, N., Eltahir, M. E., Zyoud, S. H., Soundrarajan, D., Zakarneh, B., & Al Salhi, N. R. (2023). Exploring English Language Learning via Chabot: A Case Study from a Self Determination Theory Perspective. Computers and Education: Artificial Intelligence, 5, Article ID: 100148.[CrossRef] 
[13] Aulia, S. R., & Lin, W. (2024). Embracing the Digital Shift: Leveraging AI to Foster Employee Well-Being and Engagement in Remote Workplace Settings in the Asia Pacific Region. Asia Pacific Management Review, 30, Article ID: 100339.[CrossRef] 
[14] Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. (2009). Measuring Self-Regulation in Online and Blended Learning Environments. The Internet and Higher Education, 12, 1-6.[CrossRef] 
[15] Broadbent, J. (2017). Comparing Online and Blended Learner’s Self-Regulated Learning Strategies and Academic Performance. The Internet and Higher Education, 33, 24-32.[CrossRef] 
[16] Chang, D. H., Lin, M. P., Hajian, S., & Wang, Q. Q. (2023). Educational Design Principles of Using AI Chatbot That Supports Self-Regulated Learning in Education: Goal Setting, Feedback, and Personalization. Sustainability, 15, Article 12921.[CrossRef] 
[17] Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of Goal Pursuits: Human Needs and the Self-Determination of Behavior. Psychological Inquiry, 11, 227-268.[CrossRef] 
[18] Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Duckworth, D. (2020). Preparing for Life in a Digital World: IEA International Computer and Information Literacy Study 2018 International Report. Springer.[CrossRef] 
[19] Gagné, M. (2003). The Role of Autonomy Support and Autonomy Orientation in Prosocial Behavior Engagement. Motivation and Emotion, 27, 199-223.[CrossRef] 
[20] Hatlevik, O. E., Guðmundsdóttir, G. B., & Loi, M. (2015). Digital Diversity among Upper Secondary Students: A Multilevel Analysis of the Relationship between Cultural Capital, Self-Efficacy, Strategic Use of Information and Digital Competence. Computers & Education, 81, 345-353.[CrossRef] 
[21] Kohnke, L. (2022). A Qualitative Exploration of Student Perspectives of Chatbot Use during Emergency Remote Teaching. International Journal of Mobile Learning and Organisation, 16, 475-488.[CrossRef] 
[22] Lodge, J. M., Howard, S. K., Bearman, M., Dawson, P., & Associates. (2023). Assessment Reform for the Age of Artificial Intelligence. Tertiary Education Quality and Standards Agency.
https://www.teqsa.gov.au/sites/default/files/2023-09/assessment-reform-age-artificial-intelligence-discussion-paper.pdf
[23] Long, D., & Magerko, B. (2020). What Is AI Literacy? Competencies and Design Considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16). ACM.[CrossRef] 
[24] Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI Literacy: An Exploratory Review. Computers and Education: Artificial Intelligence, 2, Article ID: 100041.[CrossRef] 
[25] Peters, D., Calvo, R. A., & Ryan, R. M. (2018). Designing for Motivation, Engagement and Wellbeing in Digital Experience. Frontiers in Psychology, 9, Article 797.[CrossRef] [PubMed]
[26] Rix, J. (2022). From Tools to Teammates: Conceptualizing Humans’ Perception of Machines as Teammates with a Systematic Literature Review. In Proceedings of the Annual Hawaii International Conference on System Sciences (pp. 398-407). Hawaii International Conference on System Sciences.[CrossRef] 
[27] Segbenya, M., Bervell, B., Frimpong-Manso, E., Otoo, I. C., Andzie, T. A., & Achina, S. (2023). Artificial Intelligence in Higher Education: Modelling the Antecedents of Artificial Intelligence Usage and Effects on 21st Century Employability Skills among Postgraduate Students in Ghana. Computers and Education: Artificial Intelligence, 5, Article ID: 100188.[CrossRef] 
[28] Wang, C., & Lin, J. J. H. (2023). Utilizing Artificial Intelligence to Support Analyzing Self-Regulated Learning: A Preliminary Mixed-Methods Evaluation from a Human-Centered Perspective. Computers in Human Behavior, 144, Article ID: 107721.[CrossRef] 
[29] Williams, G. C., & Deci, E. L. (1996). Internalization of Biopsychosocial Values by Medical Students: A Test of Self-Determination Theory. Journal of Personality and Social Psychology, 70, 767-779.[CrossRef] 
[30] Yilmaz, R., & Yilmaz, F. G. K. (2023). The Effect of Generative Artificial Intelligence (AI)-Based Tool Use on Students’ Computational Thinking Skills, Programming Self-Efficacy and Motivation. Computers and Education: Artificial Intelligence, 4, Article ID: 100147.[CrossRef] 
[31] Zimmerman, B. J. (2002). Becoming a Self-Regulated Learner: An Overview. Theory Into Practice, 41, 64-70.[CrossRef]