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科学教育是拔尖创新人才培养的关键路径,也是科技强国建设的基础性工程。GAI技术的快速发展正在重塑探究教学生态,对中小学科学教师的探究教学胜任力提出了新要求。然而,现有评价指标多基于通用教师能力框架或传统探究教学情境构建,难以充分反映GAI时代探究教学的新特征。基于此,该文以胜任力冰山模型为理论框架,聚焦中小学科学探究教学实践场域,综合运用扎根理论、德尔菲法与层次分析法,系统构建了GAI时代中小学科学教师探究教学胜任力评价指标体系。该体系包含GAI创新应用的信念与意识、探究教学设计力、探究教学实施力、探究教学评价力、探究教学研创力、探究教学育人生态建造力、 GAI应用伦理遵循与责任践行力等7个一级指标和23个二级指标。在此基础上,研究利用测评工具对中小学科学教师探究教学胜任力进行了实证调查,以检测评价指标体系的科学性。
Abstract:Science education is a critical path for cultivating top-tier innovative talents and a fundamental project for national technological advancement. The rapid development of GAI technology is reshaping the ecology of inquiry teaching, putting forward new requirements for the inquiry teaching competency of primary and secondary science teachers. Nevertheless, the majority of existing evaluation indicators are built upon general teacher capacity frameworks or traditional inquiry teaching scenarios, making it difficult to fully capture the new characteristics of inquiry teaching in the generative AI era.Based on this premise, taking the Competency Iceberg Model as the theoretical framework and focusing on the practical field of primary and secondary science inquiry teaching, this paper comprehensively utilizes Grounded Theory, the Delphi method, and the Analytic Hierarchy Process(AHP) to systematically construct an assessment indicator system for science teachers' inquiry teaching competency in the GAI context. This system contains 7 primary indicators and 23 secondary indicators. The primary indicators include: beliefs and consciousness of innovative GAI application, inquiry teaching design capability, implementation capability, evaluation capability, research and innovation capability, educational ecosystem construction capability, as well as ethical adherence and responsibility practice in GAI application. On this basis, the study conducted an empirical investigation using assessment tools to measure the inquiry teaching competency of primary and secondary science teachers, in order to test the scientific rigor of the evaluation indicator system.
[1]郭利明,汪灶文等.生成式人工智能赋能科学探究学习:价值内涵、实践框架与实践方略[J].电化教育研究,2026,47(3):78-84+120.
[2]教基[2025]7号,教育部等七部门关于加强中小学科技教育的意见[Z].
[3]高琳琳,解月光等.智能技术支持的智慧型探究教学模式构建研究[J].电化教育研究,2021,42(11):92-99.
[4]黄志南,李根等.多模态大模型赋能科学教育高质量发展:潜能、挑战与应用探索[J].中国电化教育,2025,(6):60-69.
[5]周才英.虚拟实验在初中科学教育场景中应用的实践探索[J].教育与装备研究,2025,41(8):37-42.
[6]陈海深,刘剑锋等.职前科学教师综合探究能力提升路径研究——理论学习与实践体验的比较分析[J].教育学术月刊,2022,(4):58-65.
[7]裴新宁,看召草等.我国场馆科学教师的专业能力如何——分析框架构建与实证调查[J].远程教育杂志,2024,42(6):43-52+72.
[8]荆鹏,吕立杰.中小学科学教师数字素养的内涵释义、现状省察与行动框架[J].中国教育学刊,2025,(11):95-101.
[9]赵云建.指向教师数字素养提升的课堂技术应用观测框架、现状诊断及改善路径[J].中国电化教育,2024,(11):102-109.
[10]Mc Clelland,D.C.Testing for competence rather than for“intelligence”[J].American Psychologist,1973,28(1):1-14.
[11]Spencer,L. M.,&Spencer, S. M. Competence at Work:Models for Superior Performance[M].New York:John Wiley&Sons,1993.
[12]苏虹,李春密.科学实践的内涵、价值及实施策略[J].中国教育学刊,2025,(6):67-73.
[13]马志强,崔鑫.交互主体性视角下的人机协同教学模式:理论框架与实践形态[J].电化教育研究,2026,47(3):14-21.
[14]钟绍春,杨澜等.数据驱动的个性化学习:实然问题、应然逻辑与实现路径[J].电化教育研究,2025,46(1):13-19+33.
[15]曾亚琼,贾伟.生成式AI助手“幻觉”现象的语言识别与教育应用探析[J].教育与装备研究,2025,41(12):3-8.
[16]徐蔼婷.德尔菲法的应用及其难点[J].中国统计,2006,(9):57-59.
[17]林书兵,张学波等.理解教师与教师理解:教师数据使用的现实困境和微观审视[J].中国电化教育,2024,(4):7-15.
[18]郭宏,幸泰杞.中小学教师人工智能教学的意愿测量与行为分析:基于全国范围的多维度实证研究[J].现代教育技术,2026,36(3):72-81.
基本信息:
中图分类号:G633.98;G434
引用信息:
[1]张明宇,刘晓,赵溢恩,等.GAI时代中小学科学教师的探究教学胜任力:评价指标及实证研究[J].中国电化教育,2026,No.476(09):66-73.
基金信息:
2023年国家社会科学基金教育学一般项目“混合式学习场景下学习者知识状态评测与干预机制研究”(项目编号:BCA230270)研究成果
2026-09-07
2026-09-07