人工智能素养评估工具的研究进展及护理启示

doi:10.3761/j.issn.1672-9234.2026.02.005

Abstract

Abstract:

With the widespread application of artificial intelligence（AI） technology in healthcare and education，AI literacy has become a key factor influencing its effectiveness. This study systematically reviewed the development of AI literacy assessment tools，summarized the core concepts and dimensional framework of AI literacy，and categorized 11 representative tools into four groups：general-purpose tools，student-focused tools，teacher-focused tools，and technology-specific tools. Simultaneously，these assessment tools were evaluated based on the Consensus-based Standards for the Selection of Health Measurement Instruments（COSMIN）. Existing tools exhibited differences in theoretical foundations，dimensional frameworks，measurement methods，methodological quality，and measurement properties，each with their own strengths and limitations. Based on these findings，this study offers implications for clinical practice，nursing education，and research to guide the accurate assessment of AI literacy in relevant populations.

Key words: Artificial Intelligence, Health Literacy, Assessment Tool, Quality Assessment, Review

ZHANG Jinman, WANG Qi, LIU Qian, ZOU Huijing, YANG Bingxiang. Research progress of artificial intelligence literacy assessment tools and implications for nursing[J].Chinese Journal of Nursing Education, 2026, 23(2): 158-164.

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References 37

[1]	Topol EJ. High-performance medicine:the convergence of human and artificial intelligence[J]. Nat Med, 2019, 25(1):44-56. doi: 10.1038/s41591-018-0300-7 pmid: 30617339
[2]	Ng DTK, Leung JKL, Chu SKW, et al. Conceptualizing AI literacy:an exploratory review[J]. Comput Educ Artif Intell, 2021,2:100041.
[3]	Lintner T. A systematic review of AI literacy scales[J]. NPJ Sci Learn, 2024, 9(1):50. doi: 10.1038/s41539-024-00264-4 pmid: 39107327
[4]	Cho Kwan RY, Yan Tang AC, Ha Wong JY, et al. Navigating the integration of artificial intelligence in nursing: opportunities,challenges,and strategic actions[J]. Int J Nurs Sci, 2025, 12(3):241-245.
[5]	Gilster P. Digital literacy[M]. New York: Wiley Computer Publishing,1997.
[6]	Roll I, Wylie R. Evolution and revolution in artificial intelligence in education[J]. Int J Artif Intell Educ, 2016, 26(2):582-599. doi: 10.1007/s40593-016-0110-3
[7]	Long DR, Magerko B. What is AI literacy? Competencies and design considerations[C]// Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Honolulu HI USA. ACM,2020:1-16.
[8]	Ng DTK, Leung JKL, Chu KWS, et al. AI literacy:definition,teaching,evaluation and ethical issues[J]. Proc Assoc Inf Sci Technol, 2021, 58(1):504-509. doi: 10.1002/pra2.v58.1
[9]	Bubeck S, Chandrasekaran V, Eldan R, et al. Sparks of artificial general intelligence:early experiments with GPT-4(2023)[J/OL]. arXiv. (2023-04-13). https://arxiv.org/abs/2303.12712.
[10]	Carolus A, Koch MJ, Straka S, et al. 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[J]. Comput Hum Behav Artif Hum, 2023, 1(2):100014.
[11]	曹梅. 培养师范生智能教育素养的逆向教学设计模型研究[D]. 桂林: 广西师范大学, 2025.
	Cao M. A research on the reverse instructional design model for cultivating normal university students’ AI education literacy[D]. Guilin: Guangxi Normal University, 2025.
[12]	江玉凤. 小学教师智能教育素养的现状与影响因素研究[D]. 无锡: 江南大学, 2024.
	Jiang YF. A study on the current status and influencing factors of primary school teachers’ AI education literacy[D]. Wuxi: Jiangnan University, 2024.
[13]	陈依纯. 基于科研工作流的科研人员生成式人工智能应用需求分析[D]. 曲阜: 曲阜师范大学, 2025.
	Chen YC. An analysis of the application demands of generative AI among researchers based on the research workflow[D]. Qufu: Qufu Normal University, 2025.
[14]	Koch MJ, Carolus A, Wienrich C, et al. Meta AI literacy scale:further validation and development of a short version[J]. Heliyon, 2024, 10(21):e39686. doi: 10.1016/j.heliyon.2024.e39686
[15]	Wang BC, Rau PP, Yuan TY. Measuring user competence in using artificial intelligence:validity and reliability of artificial intelligence literacy scale[J]. Behav Inf Technol, 2023, 42(9):1324-1337. doi: 10.1080/0144929X.2022.2072768
[16]	Kahraman H, Akutay S, Yüceler Kaçmaz H, et al. Artificial intelligence literacy levels of perioperative nurses:the case of türkiye[J]. Nurs Health Sci, 2025, 27(1):e70059. doi: 10.1111/nhs.v27.1
[17]	Hobeika E, Hallit R, Malaeb D, et al. Multinational validation of the Arabic version of the Artificial Intelligence Literacy Scale(AILS) in university students[J]. Cogent Psychol, 2024, 11(1):2395637. doi: 10.1080/23311908.2024.2395637
[18]	Karaca O, Çalışkan SA, Demir K. Medical artificial intelligence readiness scale for medical students(MAIRS-MS) development,validity and reliability study[J]. BMC Med Educ, 2021, 21(1):112. doi: 10.1186/s12909-021-02546-6 pmid: 33602196
[19]	Chen XC, Chen YY, Xie YH, et al. Translation and psychometric validation of the Medical Artificial Intelligence Readiness Scale(MAIRS-MS) for Chinese medical students[J]. BMC Nurs, 2025, 24(1):1210. doi: 10.1186/s12912-025-03852-w
[20]	Lugito NPH, Cucunawangsih C, Suryadinata N, et al. Readiness,knowledge,and perception towards artificial intelligence of medical students at faculty of medicine,Pelita Harapan University,Indonesia:a cross sectional study[J]. BMC Med Educ, 2024, 24(1):1044. doi: 10.1186/s12909-024-06058-x
[21]	Tung AYZ, Dong LW. Malaysian medical students’ attitudes and readiness toward AI(artificial intelligence):a cross-sectional study[J]. J Med Educ Curric Dev, 2023,10:23821205231201164.
[22]	Hornberger M, Bewersdorff A, Nerdel C. What do university students know about artificial intelligence? Development and validation of an AI literacy test[J]. Comput Educ Artif Intell, 2023,5:100165.
[23]	Hornberger M, Bewersdorff A, Schiff DS, et al. Development and validation of a short AI literacy test(AILIT-S) for university students[J]. Comput Hum Behav Artif Hum, 2025,5:100176.
[24]	Choi WC, Chang CI, Ng SI, et al. Developing a Chinese version of the Self-reported AI Literacy Questionnaire(CAILQ)[J/OL]. TechRxiv.(2025-09-19). https://www.techrxiv.org/users/964510/articles/1335413-developing-a-chinese-version-of-the-self-reported-ai-literacy-questionnaire-cailq.
[25]	Sun ZL, Chan YY. AI literacy development in middle school:profiles and demographic predictors from an ABCE perspective[J]. Comput Educ Artif Intell, 2025,9:100480.
[26]	Kim SW, Lee Y. The Artificial Intelligence Literacy Scale for middle school students[J]. J Korea Soci Comput and Inform, 2022, 27(3):225-38.
[27]	方梅. 上海市小学教师智能素养现状研究:以F区和C区为例[D]. 上海: 上海师范大学, 2024.
	Fang M. A study on the current status of primary school teachers’ AI literacy in shanghai:taking F district and C district as examples[D]. Shanghai: Shanghai Normal University, 2024.
[28]	Zhang H, Perry A, Lee I. Developing and validating the artificial intelligence literacy concept inventory:an instrument to assess artificial intelligence literacy among middle school students[J]. Int J Artif Intell Educ, 2025, 35(1):398-438. doi: 10.1007/s40593-024-00398-x
[29]	Mannila L, Henry J, Bahr T, et al. Developing an AI concept inventory for non-experts[C]// Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 2. Nijmegen Netherlands. ACM,2025:705-706.
[30]	Ning YM, Zhang WJ, Yao DM, et al. Development and validation of the artificial intelligence literacy scale for teachers(AILST)[J]. Educ Inf Technol, 2025, 30(12):17769-17803. doi: 10.1007/s10639-025-13347-5
[31]	Celik I. Towards Intelligent-TPACK:an empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence(AI)-based tools into education[J]. Comput Hum Behav, 2023,138:107468.
[32]	魏慧敏, 赵媛, 陆丽云. 地理教师使用生成式人工智能辅助命题的行为意愿研究:基于结构方程模型与模糊集定性比较分析[J]. 地理教学, 2025(15):9-16.
	Wei HM, Zhao Y, Lu LY. Research on the behavioral intention of geography teachers to use generative artificial intelligence for test question generation:based on structural equation modeling and fuzzy set qualitative comparative analysis[J]. Geogr Teach, 2025(15):9-16.
[33]	Lee S, Park G. Development and validation of ChatGPT literacy scale[J]. Curr Psychol, 2024, 43(21):18992-19004. doi: 10.1007/s12144-024-05723-0
[34]	Huang CW, Coleman M, Gachago D, et al. Using ChatGPT to encourage critical AI literacy skills and for assessment in higher education[M]// ICT Education. Cham: Springer Nature Switzerland,2023:105-118.
[35]	Jin YQ, Martinez-Maldonado R, Gašević D, et al. GLAT:the generative AI literacy assessment test[J]. Comput Educ Artif Intell, 2025,9:100436.
[36]	Zhao J, Chapman E, Sabet PGP, et al. Generative AI and educational assessments:a systematic review[J]. Educ Res Perspect,2024:124-155.
[37]	Guyatt GH, Oxman AD, Vist GE, et al. GRADE:an emerging consensus on rating quality of evidence and strength of recommendations[J]. BMJ, 2008, 336(7650):924-926. doi: 10.1136/bmj.39489.470347.AD

Research progress of artificial intelligence literacy assessment tools and implications for nursing

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