A Bayesian meta-analysis of the association between AI literacy and self-efficacy

doi:10.3724/SP.J.1042.2026.1779

Abstract

Abstract:

To systematically synthesize evidence on the relationship between artificial intelligence (AI) literacy and self-efficacy among university students and identify sources of heterogeneity, this study included 36 independent studies (N = 25, 251) and conducted a meta-analysis using a Bayesian random-effects model, followed by moderator analyses. The results showed a robust positive association between AI literacy and university students’ self-efficacy (Bayesian r = 0.495, 95% CrI [0.420, 0.570]). Moderator analyses indicated that effect sizes varied significantly by self-efficacy type, AI literacy measurement approach, and academic discipline. Specifically, the association between AI literacy and both creative self-efficacy and entrepreneurial self-efficacy was stronger than its association with AI self-efficacy and general academic self-efficacy. Effect sizes were larger in studies using self-developed or integrated self-report measures than in those using direct AI literacy scales, proxy indicators, or objective assessments. In addition, the association was relatively stronger for students in education, media, and language disciplines than those in engineering, computer science, and business and management majors. These findings suggest that AI literacy is positively associated with university students’ self-efficacy, but the strength of this relationship depends on measurement approach, self-efficacy type, and disciplinary context. The findings further imply that AI literacy may function not only as a technical competence but also as a psychological resource linked to higher-order efficacy beliefs, which provide implications for differentiated AI literacy education in higher education.

Key words: AI literacy, self-efficacy, university students, Bayesian meta-analysis, international comparison, generative AI

CLC Number:

B844

WAN Qianyi, WEN Siqing, MA Zaoming, HU Bo. A Bayesian meta-analysis of the association between AI literacy and self-efficacy[J]. Advances in Psychological Science, 2026, 34(10): 1779-1794.

Figures/Tables 11

References 93

[1]	冯永刚, 吕鑫源. (2025). 生成式人工智能嵌入教育的伦理检视. 中国电化教育, (11), 18-25.
[2]	郭颢, 江楠, 江宏, 刘忠骅, 邓辉映. (2024). 人工智能驱动教育变革的伦理风险及其解蔽之路. 中国电化教育, (4), 25-31.
[3]	郭姿辰, 欧阳霞. (2025). 大学生数字素养对智慧课堂自我效能感的影响. 湘南学院学报*, 58(2), 101-106.
[4]	何珊云, 沈演. (2025). 学会提问: 大学生与生成式人工智能协同学习模式的研究. 华东师范大学学报(教育科学版), 43(2), 34-48.
[5]	林孔团, 王心杰. (2025). 人工智能素养与高校学生创新能力的关系--自我效能感的中介作用. 创新与创业教育*, 16(5), 24-32.
[6]	倪闽景. (2023). 从学习进化的视角看ChatGPT/生成式人工智能对学习的影响. 华东师范大学学报(教育科学版), 41(7), 151-161.
[7]	魏显勇, 刘珈宏. (2025). 生成式人工智能赋能教师跨学科教学素养提升: 价值、挑战及进路. 教育理论与实践, 45(26), 24-29.
[8]	吴河江, 吴砥. (2024). 生成式人工智能教育应用: 发展历史、国际态势与未来展望. 比较教育研究, 46(6), 13-23.
[9]	武松, 安富海, 张仁杰. (2025). 功能论视角下人工智能赋能教育的边界与协同机制研究. 教育理论与实践, 45(34), 25-30.
[10]	尹开国. (2024). 人工智能素养: 提出背景、概念界定与构成要素. 图书与情报, 44(3), 60-68.
[11]	钟柏昌, 刘晓凡, 杨明欢. (2024). 何谓人工智能素养: 本质、构成与评价体系. 华东师范大学学报(教育科学版), 42(1), 71-84.
[12]	中华人民共和国国务院. (2026). 政府工作报告. 新华社. https://www.gov.cn/yaowen/liebiao/202603/content_7062625.htm
[13]	宗树伟, 杨付, 龙立荣, 韩翼. (2025). 促进还是抑制?生成式人工智能建议采纳对创造力的双刃剑效应. 心理科学进展, 33(6), 905-915.
[14]	Abreh, M. K., Arthur, F., Akwetey, F. A., & Nortey, S. A. (2025). Modelling STEM students’ intention to learn artificial intelligence (AI) in ghana: A PLS-SEM and fsQCA approach. Discover Artificial Intelligence*, 5, 223.
[15]	Al-Abdullatif, A. M., & Alsubaie, M. A. (2025). ChatGPT in learning: Assessing students’ use intentions through the lens of perceived value and the influence of AI literacy. Behavioral Sciences*, 14(9), 845.
[16]	Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019-2023). Computers and Education Open, 6, 100173.
[17]	Asghar, M. Z., Duah, K. A., Iqbal, J., & Järvenoja, H. (2025). Evidence from West Africa on the interplay of affective behavioral cognitive and ethical dimensions of AI literacy in Ghanaian and Nigerian Universities. Discover Computing*, 28, 169.
[18]	Asio, J. M. R. (2024). AI literacy, self-efficacy, and self-competence among college students: Variances and interrelationships among variables. Malaysian Online Journal of Educational Sciences (MOJES)*, 12(3), 44-60.
[19]	Atchley, P., Pannell, H., Wofford, K., Hopkins, M., & Atchley, R. A. (2024). Human and AI collaboration in the higher education environment: Opportunities and concerns. Cognitive Research Principles and Implications, 9, 20.
[20]	Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
[21]	Bandura, A. (1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117-148.
[22]	Bandura, A., Freeman, W. H., & Lightsey, R. (1999). Self- Efficacy: The exercise of control. Journal of Cognitive Psychotherapy, 13(2), 158-166.
[23]	Bećirović, S., Polz, E., & Tinkel, I. (2025). Exploring students’ AI literacy and its effects on their AI output quality, self-efficacy, and academic performance. Smart Learning Environments*, 12, 29.
[24]	Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. (2025). AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students’ AI self-efficacy. Computers and Education: Artificial Intelligence*, 8, 100340.
[25]	Booth, A., Clarke, M., Dooley, G., Ghersi, D., Moher, D., Petticrew, M., & Stewart, L. (2012). The nuts and bolts of PROSPERO: An international prospective register of systematic reviews. Systematic Reviews, 1, 2.
[26]	Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2021). Introduction to meta-analysis. John Wiley & Sons, Ltd.
[27]	Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014.
[28]	Chaaban, Y., Qadhi, S., Chen, J., & Du, X. (2024). Understanding researchers’ AI readiness in a higher education context: Q methodology research. Education Sciences, 14(7), 709.
[29]	Chiu, T. K. F., & Sanusi, I. T. (2024). Define, foster, and assess student and teacher AI literacy and competency for all: Current status and future research direction. Computers and Education Open, 7, 100182.
[30]	Consoli, T., & Petko, D. (2025). Which educational approaches predict students’ generative AI confidence and responsibility? Computers and Education: Artificial Intelligence*, 9, 100431.
[31]	Cooper, H., Hedges, L. V., & Valentine, J. C. (2019). The handbook of research synthesis and meta-analysis. Russell Sage Foundation eBooks.
[32]	Cuijpers, P., Griffin, J. W., Furukawa, T. A., Cuijpers, P., Griffin, J. W., & Furukawa, T. A. (2021). The lack of statistical power of subgroup analyses in meta-analyses: A cautionary note. Epidemiology and Psychiatric Sciences, 30, e78.
[33]	Curaj, A., Hâj, C. M., & Pricopie, R. (2025). European higher education area 2030: Bridging realities for tomorrow’s higher education. Springer Nature.
[34]	Dekkers, R., Carey, L., & Langhorne, P. (2022). Making literature reviews work: A multidisciplinary guide to systematic approaches. Springer Nature.
[35]	Duong, D. C. (2025a). AI literacy and e-entrepreneurial intention: A serial mediation model of e-entrepreneurial self-efficacy and e-entrepreneurial identity aspiration. International Journal of Information Management Data Insights*, 5(2), 100349.
[36]	Duong, C. D. (2025b). AI literacy and higher education students’ digital entrepreneurial intention: A moderated mediation model of AI self-efficacy and digital entrepreneurial self-efficacy. Industry and Higher Education*, 40(2), 242-255.
[37]	Duong, C. D., Nguyen, T. H., Chu, T. V., Pham, T. V., & Do, N. D. (2024). Whether ChatGPT adoption inspires higher education students’ digital entrepreneurial intention? An integrated model of the SCCT and the TPB. International Journal of Innovation Science*, 17(5), 1210-1234.
[38]	Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629-634.
[39]	El-Sayed, B. K. M., El-Sayed, A. A. I., Alsenany, S. A., & Asal, M. G. R. (2025). The role of artificial intelligence literacy and innovation mindset in shaping nursing students’ career and talent self-efficacy. Nurse Education in Practice*, 82, 104208.
[40]	Feng, S., & Carolus, A. (2026). Artificial intelligence literacy at school: A systematic review with a focus on psychological foundations. Computers and Education: Artificial Intelligence, 10, 100551.
[41]	Frandsen, T. F., Nielsen, M. F. B., & Eriksen, M. B. (2022). Avoiding searching for outcomes called for additional search strategies: A study of Cochrane review searches. Journal of Clinical Epidemiology, 149, 137-144.
[42]	Gao, Z., Cheah, J.-H., Lim, X.-J., & Luo, X. (2024). Enhancing academic performance of business students using generative AI: An interactive-constructive-active-passive (ICAP) self-determination perspective. The International Journal of Management Education*, 22(2), 100958.
[43]	Getenet, S., Cantle, R., Redmond, P., & Albion, P. (2024). Students’ digital technology attitude, literacy and self- efficacy and their effect on online learning engagement. International Journal of Educational Technology in Higher Education, 21(1), 3.
[44]	Gupta, S., & Jaiswal, R. (2025). A deep learning-based hybrid PLS-SEM-ANN approach for predicting factors improving AI-driven decision-making proficiency for future leaders. Journal of International Education in Business*, 18(2), 234-268.
[45]	Hamamoto, R., Komatsu, M., Yamada, M., Kobayashi, K., Takahashi, M., Miyake, M., … Kaneko, S. (2024). Current status and future direction of cancer research using artificial intelligence for clinical application. Cancer Science, 116(2), 297-307.
[46]	He, T., Huang, J., Li, Y., Wang, L., Liu, J., Zhang, F., Fu, S., & Gong, S. (2025). The mediation effect of AI self-efficacy between AI literacy and learning engagement in college nursing students: A cross-sectional study. Nurse Education in Practice*, 87, 104499.
[47]	Heath, M. K., & Segal, P. (2021). What pre-service teacher technology integration conceals and reveals: “Colorblind” technology in schools. Computers & Education, 170, 104225.
[48]	Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. Academic Press.
[49]	Higgins, J. P. T., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ, 327(7414), 327-557.
[50]	Hornberger, M., Bewersdorff, A., & Nerdel, C. (2023). What do university students know about artificial intelligence? Development and validation of an AI literacy test. Computers and Education: Artificial Intelligence*, 5, 100165.
[51]	Huang, K., & Ball, C. (2024). The influence of AI literacy on user’s trust in AI in practical scenarios: A digital divide pilot study. Proceedings of the Association for Information Science and Technology, 61(1), 937-939.
[52]	Ji, Y., Zhong, M., Lyu, S., Li, T., Niu, S., & Zhan, Z. (2025). How does AI literacy affect individual innovative behavior: The mediating role of psychological need satisfaction, creative self-efficacy, and self-regulated learning. Education and Information Technologies*, 30, 16133-16162.
[53]	Jia, X.-H., & Tu, J.-C. (2024). Towards a new conceptual model of AI-enhanced learning for college students: The roles of artificial intelligence capabilities, general self-efficacy, learning motivation, and critical thinking awareness. Systems*, 12(3), 74.
[54]	Kallioinen, N., Paananen, T., Bürkner, P.-C., & Vehtari, A. (2024). Detecting and diagnosing prior and likelihood sensitivity with power-scaling. Statistics and Computing, 34, 57.
[55]	Kim, D., & Song, H. (2024). Designing an age-friendly conversational AI agent for mobile banking: The effects of voice modality and lip movement. International Journal of Human-Computer Studies, 187, 103262.
[56]	Koch, M. J., Carolus, A., Wienrich, C., & Latoschik, M. E. (2024). Meta AI literacy scale: Further validation and development of a short version. Heliyon, 10(21), e39686.
[57]	Kong, J., Liu, J., Chen, G., & Shang, W. (2025). Assessing AI literacy in college students: The mediating role of self-efficacy in motivational commitment pathways. Education and Information Technologies*, 30, 23957-23979.
[58]	Koo, T. K., & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-163.
[59]	Lan, Y., Liu, S., & Xia, L. (2025). How mindfulness shapes AI competence: A structural equation modeling analysis of mindfulness, AI literacy and behavioral intention in Chinese media students. Frontiers in Psychology*, 16, 1652934.
[60]	Lee, J., Hung, J.-T., Yilmaz Soylu, M., Popescu, D., Cui, C. Z., Grigoryan, G., Joyner, D. A., & Harmon, S. W. (2025). Socratic mind: Impact of a novel GenAI-powered assessment tool on student learning and higher-order thinking. arXiv preprint*.
[61]	Lee, K.-W., & Tseng, Y.-F. (2024). Driving the dual learning process of management knowledge: A social cognitive theory perspective. The International Journal of Management Education*, 22(1), 100940.
[62]	Li, H., Zhang, Y., Chen, M., Zhao, T., & Jou, M. (2025). Creative personal identity in the age of generative AI: A social-cognitive pathway of AI literacy, self-efficacy, and mindset. Computers in Human Behavior, 175*, 108838.
[63]	Li, R., Ouyang, J., & Lin, J. (2025). Mediating effect of AI attitudes and AI literacy on the relationship between career self-efficacy and job-seeking anxiety. BMC Psychology*, 13, 454.
[64]	Lilienthal, J., Sturtz, S., Schürmann, C., Maiworm, M., Röver, C., Friede, T., & Bender, R. (2024). Bayesian random‐effects meta‐analysis with empirical heterogeneity priors for application in health technology assessment with very few studies. Research Synthesis Methods, 15(2), 275-287.
[65]	Lin, C.-S. (2025). Co-creating with AI: How communication students learn, think, and rely on intelligent systems. Journal of Educational Computing Research*, 64(1), 155-179.
[66]	Lintner, S. (2024). A systematic review of AI literacy scales. NPJ Science of Learning, 9, Article 50.
[67]	Long, D., & Magerko, B. (2020). What is AI Literacy? Competencies and design considerations. CHI’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16), Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727
[68]	Mansoor, H., Ala, Bawazir, Alsabri, M. A., Alharbi, A., & Okela, A. H. (2024). Artificial intelligence literacy among university students-A comparative transnational survey. Frontiers in Communication*, 9, 1478476.
[69]	Maulana, A., Fenitra, R. M., & Sutrisno, S. (2025). Artificial intelligence, job seeker, and career trajectory: How AI-based learning experiences affect commitment of fresh graduates to be an accountant? Computers and Education: Artificial Intelligence*, 8, 100413.
[70]	Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO.
[71]	Midgley, C., Maehr, M. L., Hruda, L. Z., Anderman, E. M., Anderman, L. H., Freeman, K. E., ... Urdan, T. (2000). Manual for the Patterns of Adaptive Learning Scales (PALS). University of Michigan.
[72]	Montgomery, P., & Morton, M. (2010). Meta-analysis: Oxford Bibliographies Online Research Guide. Oxford University Press.
[73]	Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). AI literacy: Definition, teaching, evaluation, and ethical issues. Proceedings of the Association for Information Science and Technology, 58(1), 504-509.
[74]	Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal of Surgery, 88, 105906.
[75]	Pan, X. (2020). Technology acceptance, technological self-efficacy, and attitude toward technology-based self-directed learning: Learning motivation as a mediator. Frontiers in Psychology, 11, 564294.
[76]	Pan, Z., & Zhang, Y. (2025). Exploring L2 writing motivation in AI-mediated EFL contexts: The role of teacher affective support, AI literacy, and self-efficacy through the lens of self-determination theory. Learning and Motivation*, 92, 102214.
[77]	Park, J. (2025). A systematic literature review of generative artificial intelligence (GenAI) literacy in schools. Computers and Education: Artificial Intelligence, 9, 100487.
[78]	Schwarzer, R., & Jerusalem, M. (1995). Generalized self-efficacy scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 35-37). NFER- NELSON.
[79]	Sperling, K., Stenberg, C.-J., McGrath, C., Åkerfeldt, A., Heintz, F., & Stenliden, L. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6, 100169.
[80]	Sterne, J. A., & Egger, M. (2001). Funnel plots for detecting bias in meta-analysis: Guidelines on choice of axis. Journal of Clinical Epidemiology, 54(10), 1046-1055.
[81]	Testa, M., Apuzzo, A., & Pittaway, L. (2026). AI literacy alone is not enough: Student AI readiness and career adaptability in business and management education. The International Journal of Management Education, 24(2), 101394.
[82]	Urban, M., Děchtěrenko, F., Lukavský, J., Hrabalová, V., Svacha, F., Brom, C., & Urban, K. (2024). ChatGPT improves creative problem-solving performance in university students: An experimental study. Computers & Education, 215*, 105031.
[83]	Viechtbauer, W., & Cheung, M. W.-L. (2010). Outlier and influence diagnostics for meta-analysis. Research Synthesis Methods, 1(2), 112-125.
[84]	Wang, B., Rau, P. P., & Yuan, T. (2022). Measuring user competence in using artificial intelligence: Validity and reliability of artificial intelligence literacy scale. Behaviour and Information Technology, 42(9), 1324-1337.
[85]	Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence capability on students’ self-efficacy, creativity and learning performance. Education and Information Technologies*, 28(5), 4919-4939.
[86]	Wang, X., & Li, P. (2024). Assessment of the relationship between music students’ self‐efficacy, academic performance and their artificial intelligence readiness. European Journal of Education*, 59(4), 482-497.
[87]	Wang, Y.-Y., & Chuang, Y.-W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29, 4785-4808.
[88]	Wei, H., Dong, C., Zhu, Y., & Ma, H. (2020). Analysis of two minimally invasive procedures for osteoporotic vertebral compression fractures with intravertebral cleft: A systematic review and meta-analysis. Journal of Orthopaedic Surgery and Research, 15(1), 401.
[89]	Yilmaz Soylu, M., Lee, J., Hung, J.-T., Cui, C. Z., & Joyner, D. (2025). AI literacy as a key driver of user experience in AI-powered assessment: Insights from Socratic mind. Interactive Learning Environments*, 1-17. https://doi.org/10.1080/10494820.2025.2564739
[90]	Zhang, C., Rice, R. E., & Wang, L. H. (2024). College students’ literacy, ChatGPT activities, educational outcomes, and trust from a digital divide perspective. New Media & Society*, 28(2), 673-695.
[91]	Zhang, Q., Nie, H., Fan, J., & Liu, H. (2025). Exploring the dynamics of artificial intelligence literacy on English as a foreign language learners’ willingness to communicate: The critical mediating roles of artificial intelligence learning self-efficacy and classroom anxiety. Behavioral Sciences*, 15(4), 523-536.
[92]	Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64-70.
[93]	Zhu, C., Ning, Y., Chen, J., & Wijaya, T. T. (2025). Exploring the interplay among artificial intelligence literacy, creativity, self-efficacy, and academic achievement in college students: Findings from PLS-SEM and FsQCA. Education and Information Technologies*, 30, 21283-21316.

测量类型	代表量表	开发者	时间	核心定义
一般自我效能	一般自我效能量表	Bandura et al.	1999	个体广泛生活情境完成任务的胜任信念, 不特定指向某一学科
学习自我效能	Academic Efficacy	Midgley et al.	2000	学业任务完成能力、课堂表现与学习成功信念
技术自我效能	计算机自我效能	Schwarzer & Jerusalem	1995	使用计算机完成任务, 对自身操作能力的技术判断
AI自我效能	人工智能自我效能	Wang & Chuang	2024	测量个体对自身使用、互动AI技术能力的信念
AI学习自我效能	AI学习自我效能 (常见后续研究)	改编自Wang和Li的量表	2024	更具体地指向学习者在AI支持学习任务时的能力信念

测量类型	代表量表	开发者	时间	核心定义
一般自我效能	一般自我效能量表	Bandura et al.	1999	个体广泛生活情境完成任务的胜任信念, 不特定指向某一学科
学习自我效能	Academic Efficacy	Midgley et al.	2000	学业任务完成能力、课堂表现与学习成功信念
技术自我效能	计算机自我效能	Schwarzer & Jerusalem	1995	使用计算机完成任务, 对自身操作能力的技术判断
AI自我效能	人工智能自我效能	Wang & Chuang	2024	测量个体对自身使用、互动AI技术能力的信念
AI学习自我效能	AI学习自我效能 (常见后续研究)	改编自Wang和Li的量表	2024	更具体地指向学习者在AI支持学习任务时的能力信念

测量类型	代表量表	开发者	时间	核心定义
直接AI素养	人工智能素养量表(AILS)	Wang et al.	2022	自陈式量表, 包括认知, 使用, 评估和伦理四个维度, 12题
直接AI素养	AI素养评估量表(MAILS)	Carolus et al.	2023	自陈式量表, 覆盖使用与应用、理解、等维度, 是当前常用的AI素养量表。
直接AI素养	AI才能量表	Koch et al.	2024	以AI能力构建, 强调技术能力、批判思维、伦理意识与动机等。
直接AI素养	生成式人工智能素养量表	Chaaban et al.	2024	面向生成式AI情境的直接测量工具, 覆盖风险验证意识、使用能力等维度。
代理指标	感知人工智能	Testa et al.	2026	研究者自行采用替代指标, 反映个体对接触、采纳的状态, 不属于直接AI素养

测量类型	代表量表	开发者	时间	核心定义
直接AI素养	人工智能素养量表(AILS)	Wang et al.	2022	自陈式量表, 包括认知, 使用, 评估和伦理四个维度, 12题
直接AI素养	AI素养评估量表(MAILS)	Carolus et al.	2023	自陈式量表, 覆盖使用与应用、理解、等维度, 是当前常用的AI素养量表。
直接AI素养	AI才能量表	Koch et al.	2024	以AI能力构建, 强调技术能力、批判思维、伦理意识与动机等。
直接AI素养	生成式人工智能素养量表	Chaaban et al.	2024	面向生成式AI情境的直接测量工具, 覆盖风险验证意识、使用能力等维度。
代理指标	感知人工智能	Testa et al.	2026	研究者自行采用替代指标, 反映个体对接触、采纳的状态, 不属于直接AI素养

研究(年)	样本量	样本人群	性别 (male%)	AI素养量表	自我效能量表	研究设计	研究质量评价	国家 (多国)	数据提取
Maulana et al. (2025)	698	综合	50.6	AI素养量表	学业自我效能量表	横断研究	中等	印度尼西亚	零阶相关
Consoli & Petko (2025)	1157	综合	40.8	GenAI素养	AI自我效能量表	横断研究	很好	意大利	零阶相关
Duong (2025a)	504	综合	42.7	GenAI素养	AI自我效能量表	横断研究	很好	越南	零阶相关
Gao et al. (2024)	376	商管	50.8	GenAI素养	学业自我效能量表	横断研究	中等	中国	零阶相关
Zhang et al. (2024)	947	综合	73.2	AI素养量表	创业自我效能	横断研究	中等	中国	零阶相关
Bewersdorff et al. (2025)	1465	综合	45.5	AI素养量表	学业自我效能量表	纵向研究	很好	美/英/德	零阶相关
Urban et al. (2024)	145	综合	46.2	AI准备度	创业自我效能量表	横断研究	很好	捷克	零阶相关
Ji et al. (2025)	460	综合	44.2	人工智能能量表	AI自我效能量表	横断研究	中等	中国	零阶相关
Duong et al. (2024)	604	商管	50.2	AI素养量表	学业自我效能量表	横断研究	中等	越南	标准路径
Jia & Tu (2024)	637	理工	47.2	AI素养量表	AI自我效能量表	实验研究	很好	中国	零阶相关
Zhu et al. (2025)	385	综合	85.1	AI素养量表	AI自我效能量表	纵向研究	很好	中国	零阶相关
Zhang et al. (2025)	517	教传	53.1	AI素养量表	创业自我效能	横断研究	中等	中国	零阶相关
El-Sayed et al. (2025)	596	医护	47.8	AI素养量表	AI自我效能量表	纵向研究	中等	埃及	零阶相关
He et al. (2025)	1650	医护	45.2	AI素养量表	AI自我效能量表	横断研究	很好	中国	零阶相关
Li, Ouyang, & Lin (2025)	455	综合	—	AI素养量表	创造性自我效能	横断研究	很好	中国	零阶相关
林孔团, 王心杰 (2025)	327	教传	—	AI素养量表	AI自我效能量表	横断研究	中等	中国	零阶相关
Bećirović et al. (2025)	286	医护	48.8	AI素养量表	学业自我效能量表	横断研究	中等	奥地利	零阶相关
Kong et al. (2025)	1034	综合	48.5	AI素养量表	AI自我效能量表	横断研究	很好	中国	零阶相关
Abreh et al. (2025)	233	商管	48.7	AI素养量表	AI自我效能量表	横断研究	中等	加纳	零阶相关
Lee & Tseng (2024)	262	综合	48.8	AI素养量表	学业自我效能量表	横断研究	很好	中国	零阶相关
Duong (2025b)	1061	综合	48.6	AI素养量表	AI自我效能量表	横断研究	很好	越南	零阶相关
Li, Zhang et al (2025)	986	综合	58.6	AI素养量表	创造性自我效能	横断研究	中等	中国	零阶相关
Lan et al. (2025)	589	商管	—	AI智能准备度量表	AI自我效能量表	纵向研究	中等	中国	零阶相关
Gupta & Jaiswal (2025)	526	综合	—	AI素养量表	AI学习自我效能	横断研究	很好	印度	零阶相关
Asghar et al. (2025)	427	综合	45.5	感知人工智能量表	AI自我效能量表	横断研究	很好	印尼/ 加纳	零阶相关
Yilmaz Soylu et al. (2025)	309	教传	66.6	AI素养量表	AI自我效能量表	横断研究	中等	美国	标准路径
Wang & Li (2024)	1082	综合	62.3	AI素养量表	学业自我效能量表	横断研究	中等	中国	零阶相关
Wang et al. (2023)	561	理工	59.4	AI素养量表	AI自我效能量表	横断研究	很好	中国	零阶相关
Hornberger et al. (2023)	1286	综合	55.4	AI素养量表	AI自我效能量表	横断研究	很好	德国	零阶相关
Al-Abdullatif & Alsubaie (2025)	676	综合	44.0	AI素养量表	创造性自我效能	横断研究	中等	奥地利	零阶相关
Mansoor et al. (2024)	1800	综合	57.7	AI素养量表	学业自我效能量表	横断研究	中等	印度马来沙特埃及	零阶相关
Asio (2024)	1000	综合	46.3	AI素养量表	AI自我效能量表	横断研究	很好	菲律宾	零阶相关
郭姿辰, 欧阳霞 (2025)	874	综合	58.4	AI素养量表	AI自我效能量表	横断研究	很好	中国	零阶相关
Lee et al (2025)	309	商管	66.8	感知人工智能量表	AI自我效能量表	横断研究	中等	美国	零阶相关
Pan & Zhang (2025)	627	综合	31.42	AI素养量表	学业自我效能	横断研究	中等	中国	零阶相关
Lin (2025)	400	综合	48.8	AI素养量表	创业自我效能	横断研究	很好	中国	零阶相关