Article(id=1288943044728828357, tenantId=1146029695717560320, journalId=1149652312894775300, issueId=1288943039720829345, articleNumber=null, orderNo=null, doi=10.19293/j.cnki.1673-8357.2025.06.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1756483200000, receivedDateStr=2025-08-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1785237569267, onlineDateStr=2026-07-28, pubDate=1766160000000, pubDateStr=2025-12-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1785237569267, onlineIssueDateStr=2026-07-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1785237569267, creator=13701087609, updateTime=1785237569267, updator=13701087609, issue=Issue{id=1288943039720829345, tenantId=1146029695717560320, journalId=1149652312894775300, year='2025', volume='20', issue='6', pageStart='5', pageEnd='112', issueExtLink='null', onlineDate='null', pubDate='1766160000000', pubDateStr='2025-12-20', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1785237568073, creator='13701087609', updateTime=1785285666266, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1289144778239615643, tenantId=1146029695717560320, journalId=1149652312894775300, issueId=1288943039720829345, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1289144778239615644, tenantId=1146029695717560320, journalId=1149652312894775300, issueId=1288943039720829345, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=66, endPage=75, ext={EN=ArticleExt(id=1288943044925960646, articleId=1288943044728828357, tenantId=1146029695717560320, journalId=1149652312894775300, language=EN, title=Artificial Intelligence Empowering Early Childhood Science Education: Theoretical Foundations, Application Practices, and Risk Responses, columnId=1288943042174497187, journalTitle=Studies on Science Popularization, columnName=Special Issue, runingTitle=null, highlight=null, articleAbstract=

As artificial intelligence increasingly permeates the field of education, early childhood science education is encountering both new opportunities and emerging challenges. Grounded in multimodal learning theory, this paper reviews the applications of artificial intelligence in scientific context creation, inquiry process support, and learning process evaluation, and analyzes the potential risks it may pose to child development, teacher professionalism, and educational ethics. The analysis indicates that artificial intelligence can enrich children’s learning methods and enhance the quality of science education through multimodal presentation, instant feedback, and continuous evaluation. However, its deep involvement may also weaken children’s experiential inquiry, diminish teachers’ professional judgment, and, in certain contexts, lead to the encroachment of technical logic upon educational values. In response, this paper proposes strategies across three dimensions: educational philosophies, teacher professionalism, and institutional safeguards. These strategies include upholding inquiry-based learning, strengthening teachers’ professional judgment, and improving ethical norms and data security mechanisms, aiming to promote the appropriate and effective empowerment of early childhood science education by artificial intelligence, ensuring that technological innovation genuinely serves children’s scientific learning and development.

, authors=Yujin Zhao¹, Maoming Pan^{1, 2}, Xiaohui Xu³, authorsList=Yujin Zhao, Maoming Pan, Xiaohui Xu, authorCompany=null, correspAuthors=Xiaohui Xu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1288943046746288583, articleId=1288943044728828357, tenantId=1146029695717560320, journalId=1149652312894775300, language=CN, title=人工智能赋能幼儿科学教育：理论基础、应用实践与风险应对, columnId=1288943042333880741, journalTitle=科普研究, columnName=专题, runingTitle=null, highlight=null, articleAbstract=

随着人工智能在教育领域的加速渗透，幼儿科学教育正面临新的机遇与挑战。本文以多模态学习理论为基础，梳理人工智能在科学情境创设、探究过程支持与科学学习评价中的应用，并分析其在儿童发展、教师专业角色与教育伦理层面可能带来的风险。本文指出，人工智能通过多模态呈现、即时反馈与过程性评价等方式，有助于拓展幼儿的学习方式并提升科学教育质量；但其深度介入也可能削弱幼儿的经验性探究、弱化教师的专业判断，并在某些情境下导致技术逻辑侵蚀教育价值。基于此，本文从理念、教师与制度3个层面提出应对策略，包括坚持探究为本、强化教师专业判断以及完善伦理规范与数据安全机制等，以期促进人工智能对幼儿科学教育的适切赋能，确保技术创新真正服务幼儿的科学学习与发展。

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赵毓瑾，首都师范大学教育学院博士研究生，研究方向：教师教育，E-mail：2230401034@cnu.edu.cn。

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赵毓瑾，首都师范大学教育学院博士研究生，研究方向：教师教育，E-mail：2230401034@cnu.edu.cn。

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