Suppr超能文献

人工智能素养量表的系统评价。

A systematic review of AI literacy scales.

作者信息

Lintner Tomáš

机构信息

Department of Educational Sciences, Faculty of Arts, Masaryk University, Brno, Czech Republic.

Institute SYRI, Brno, Czech Republic.

出版信息

NPJ Sci Learn. 2024 Aug 6;9(1):50. doi: 10.1038/s41539-024-00264-4.

Abstract

With the opportunities and challenges stemming from the artificial intelligence developments and its integration into society, AI literacy becomes a key concern. Utilizing quality AI literacy instruments is crucial for understanding and promoting AI literacy development. This systematic review assessed the quality of AI literacy scales using the COSMIN tool aiming to aid researchers in choosing instruments for AI literacy assessment. This review identified 22 studies validating 16 scales targeting various populations including general population, higher education students, secondary education students, and teachers. Overall, the scales demonstrated good structural validity and internal consistency. On the other hand, only a few have been tested for content validity, reliability, construct validity, and responsiveness. None of the scales have been tested for cross-cultural validity and measurement error. Most studies did not report any interpretability indicators and almost none had raw data available. There are 3 performance-based scale available, compared to 13 self-report scales.

摘要

随着人工智能发展及其融入社会所带来的机遇和挑战,人工智能素养成为一个关键问题。使用高质量的人工智能素养工具对于理解和促进人工智能素养发展至关重要。本系统评价使用COSMIN工具评估了人工智能素养量表的质量,旨在帮助研究人员选择人工智能素养评估工具。本评价确定了22项验证16个量表的研究,这些量表针对不同人群,包括普通人群、高等教育学生、中等教育学生和教师。总体而言,这些量表显示出良好的结构效度和内部一致性。另一方面,只有少数量表进行了内容效度、信度、结构效度和反应度测试。没有一个量表进行过跨文化效度和测量误差测试。大多数研究没有报告任何可解释性指标,几乎没有研究提供原始数据。有3个基于表现的量表,而自我报告量表有13个。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/46ac/11303566/9564541bdd0c/41539_2024_264_Fig1_HTML.jpg

相似文献

1
A systematic review of AI literacy scales.
NPJ Sci Learn. 2024 Aug 6;9(1):50. doi: 10.1038/s41539-024-00264-4.
2
A systematic review of tools designed for teacher proxy-report of children's physical literacy or constituting elements.
Int J Behav Nutr Phys Act. 2021 Oct 8;18(1):131. doi: 10.1186/s12966-021-01162-3.
3
Instruments assessing medication literacy in adult recipients of care: A systematic review of measurement properties.
Int J Nurs Stud. 2021 Jan;113:103785. doi: 10.1016/j.ijnurstu.2020.103785. Epub 2020 Oct 2.
5
The measurement of collaboration within healthcare settings: a systematic review of measurement properties of instruments.
JBI Database System Rev Implement Rep. 2016 Apr;14(4):138-97. doi: 10.11124/JBISRIR-2016-2159.
6
Measurement properties of self-reported clinical decision-making instruments in nursing: A COSMIN systematic review.
Int J Nurs Stud Adv. 2023 Mar 16;5:100122. doi: 10.1016/j.ijnsa.2023.100122. eCollection 2023 Dec.
7
Instruments to assess self-efficacy among people with cardiovascular disease: A COSMIN systematic review.
Int J Clin Pract. 2020 Nov;74(11):e13606. doi: 10.1111/ijcp.13606. Epub 2020 Aug 27.
8
A systematic review of the measurement properties of self-care scales in nurses.
BMC Nurs. 2023 Aug 28;22(1):288. doi: 10.1186/s12912-023-01450-2.
9
Measuring General Health Literacy in Chinese adults: validation of the HLS-Q12 instrument.
BMC Public Health. 2024 Apr 15;24(1):1036. doi: 10.1186/s12889-024-17977-1.

引用本文的文献

2
Generative Artificial Intelligence Literacy: Scale Development and Its Effect on Job Performance.
Behav Sci (Basel). 2025 Jun 13;15(6):811. doi: 10.3390/bs15060811.
6
Artificial Intelligence and Radiologist Burnout.
JAMA Netw Open. 2024 Nov 4;7(11):e2448714. doi: 10.1001/jamanetworkopen.2024.48714.
8
Meta AI literacy scale: Further validation and development of a short version.
Heliyon. 2024 Oct 22;10(21):e39686. doi: 10.1016/j.heliyon.2024.e39686. eCollection 2024 Nov 15.

本文引用的文献

3
Scientific discovery in the age of artificial intelligence.
Nature. 2023 Aug;620(7972):47-60. doi: 10.1038/s41586-023-06221-2. Epub 2023 Aug 2.
4
Accelerating science with human-aware artificial intelligence.
Nat Hum Behav. 2023 Oct;7(10):1682-1696. doi: 10.1038/s41562-023-01648-z. Epub 2023 Jul 13.
5
Artificial Intelligence and Machine Learning in Clinical Medicine, 2023.
N Engl J Med. 2023 Mar 30;388(13):1201-1208. doi: 10.1056/NEJMra2302038.
6
Audio deepfakes: A survey.
Front Big Data. 2023 Jan 9;5:1001063. doi: 10.3389/fdata.2022.1001063. eCollection 2022.
7
Artificial intelligence in spine surgery.
Int Orthop. 2023 Feb;47(2):457-465. doi: 10.1007/s00264-022-05517-8. Epub 2022 Jul 29.
8
Understanding Medical Students' Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study.
Int J Environ Res Public Health. 2022 Jul 18;19(14):8733. doi: 10.3390/ijerph19148733.
9
Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda.
J Ambient Intell Humaniz Comput. 2023;14(7):8459-8486. doi: 10.1007/s12652-021-03612-z. Epub 2022 Jan 13.
10
Artificial intelligence: A powerful paradigm for scientific research.
Innovation (Camb). 2021 Oct 28;2(4):100179. doi: 10.1016/j.xinn.2021.100179. eCollection 2021 Nov 28.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验