This report summarizes the outcomes of a two-day international scoping workshop on the role of artificial intelligence (AI) in science education research. As AI rapidly reshapes scientific practice, classroom learning, and research methods, the field faces both new opportunities and significant challenges. The report clarifies key AI concepts to reduce ambiguity and reviews evidence of how AI influences scientific work, teaching practices, and disciplinary learning. It identifies how AI intersects with major areas of science education research, including curriculum development, assessment, epistemic cognition, inclusion, and teacher professional development, highlighting cases where AI can support human reasoning and cases where it may introduce risks to equity or validity. The report also examines how AI is transforming methodological approaches across quantitative, qualitative, ethnographic, and design-based traditions, giving rise to hybrid forms of analysis that combine human and computational strengths. To guide responsible integration, a systems-thinking heuristic is introduced that helps researchers consider stakeholder needs, potential risks, and ethical constraints. The report concludes with actionable recommendations for training, infrastructure, and standards, along with guidance for funders, policymakers, professional organizations, and academic departments. The goal is to support principled and methodologically sound use of AI in science education research.
翻译:本报告总结了一场为期两天的国际范围界定研讨会的成果,该研讨会聚焦人工智能(AI)在科学教育研究中的作用。随着AI迅速重塑科学实践、课堂学习和研究方法,该领域既面临新的机遇,也面临重大挑战。报告通过厘清关键AI概念以减少歧义,并综述了AI如何影响科研工作、教学实践和学科学习的证据。报告识别了AI与科学教育研究主要领域的交叉点,包括课程开发、评估、认知认知、包容性以及教师专业发展,重点指出了AI能够支持人类推理的案例,以及可能对公平性或效度带来风险的案例。报告还探讨了AI如何改变定量、定性、人种志和基于设计等传统研究方法论,催生出结合人类与计算优势的混合分析形式。为引导负责任地整合AI,报告引入了一种系统思维启发式方法,帮助研究者考量利益相关者需求、潜在风险及伦理约束。报告最后提出了针对培训、基础设施和标准的可操作建议,并为资助机构、政策制定者、专业组织和学术部门提供了指导。其目标是支持在科学教育研究中原则性地、方法论健全地使用AI。