从犹豫不决的初学者到自信的专家:广西幼儿教师的人工智能素养概况和预测因素

IF 4.9 3区 教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xi Bei Xiong;Simin Cao;Tianhang Gao;Xiya Feng;Hui Li
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引用次数: 0

摘要

本研究调查了中国广西幼儿教师的人工智能素养。利用来自1522名幼儿园教师的数据,我们通过因子分析开发并验证了一个文化适应的人工智能素养量表,确认了一个三构式结构:安全性、态度和能力。潜在特征分析发现三种不同的教师特征:“犹豫初学者”(9.6%)、“热情实践者”(64.2%)和“自信专家”(26.2%),显示出显著的异质性。教师普遍对人工智能持积极态度,但安全意识和能力水平较低。回归分析表明,教育水平、工作经验(负相关)、幼儿园类型和地理位置(城市/农村)显著影响人工智能素养水平和概况成员。这些发现强调了需要针对具体情况的评估工具和量身定制的教师教育计划,以提高他们的数字素养,并促进人工智能在中国幼儿教育中的公平整合。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From Hesitant Beginners to Confident Experts: Profiles and Predictors of AI Literacy Among Preschool Teachers in Guangxi, China
This study examined early childhood teachers’ artificial intelligence (AI) literacy in Guangxi, China. Utilizing data from 1522 kindergarten teachers, we developed and validated a culturally adapted AI literacy scale through factor analyses, confirming a three-construct structure: Safety, Attitude, and Capability. Latent profile analysis identified three distinct teacher profiles: “Hesitant Beginners” (9.6%), “Enthusiastic Practitioners” (64.2%), and “Confident Experts” (26.2%), revealing significant heterogeneity. Teachers generally exhibited positive attitudes toward AI but lower safety awareness and capability levels. Regression analyses indicated that education level, working experience (negatively associated), kindergarten type, and geographic location (urban/rural) significantly influence AI literacy levels and profile membership. These findings underscore the need for context-specific assessment tools and tailored teacher education programs to enhance their digital literacy and promote equitable AI integration in Chinese early childhood education.
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来源期刊
IEEE Transactions on Learning Technologies
IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
7.50
自引率
5.40%
发文量
82
审稿时长
>12 weeks
期刊介绍: The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.
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