人工智能集成的科学探究：以实践为中心的科学教育愿景
AI-Integrated Scientific Inquiry: A Practice-Centered Vision for Science Education
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中文总结 AI 辅助
探讨人工智能融入科学教育问题，提出将其视为科学仪器，学生在科学实践中使用，以实现真实探究与培养相关素养。聚焦探究核心介绍相关仪器示例，强调仪器应有反思点，并阐述智能代理人工智能的表示及学生学习基础。
中文摘要 AI 辅助
人工智能已成为科学探究的一部分，科学家用其观察、测量现象，识别数据模式并建模。随着人工智能进入科学探究，其与科学教育相关，学生应学习人工智能如何改变科学实践。本文提出将人工智能视为一套科学仪器，学生在《下一代科学标准》描述的科学实践中使用。该方法旨在让学生参与真实科学探究并理解人工智能在科学中的应用及误导之处。文章聚焦探究核心，介绍了观察、分析和建模的人工智能仪器示例，认为科学教育中的人工智能仪器应有反思点，还描述了跨整个探究的智能代理人工智能的表示方式，指出学生应先建立科学探究和人工智能仪器的基础理解。
英文摘要
Artificial intelligence (AI) has become part of scientific inquiry. Scientists use AI to observe and measure phenomena, to identify patterns in data, and to build models. As AI moves into scientific inquiry, it gains relevance for science education: students should learn how AI is changing scientific practices, ideally by engaging in AI-integrated scientific inquiry themselves. How to design such instruction, grounded in authentic scientific practice rather than taught as a standalone topic, remains an open question. In our vision, which we describe in this article, AI is treated as a set of scientific instruments that students use within the scientific practices described by the Next Generation Science Standards. Each instrument is a genuine scientific tool, pedagogically bounded: its controls are simplified while its core scientific function is preserved. The approach has two aims: engaging students in authentic scientific inquiry, and building an understanding of how AI is used in science and where it can mislead (discipline-based AI literacy, DAIL). In the article, we focus on the investigative core of inquiry, namely observing, analyzing, and modeling, and describe one exemplary AI instrument for each: computer vision for observing, clustering for analyzing, and generative modeling for modeling. We argue that every AI instrument in science education should carry a distinct reflection point that prompts critical evaluation of the AI instrument itself. Finally, we describe how agentic AI, operating across the whole inquiry rather than a single practice, could be represented, arguing that students should first build a foundational understanding of scientific inquiry and AI instruments before relying on agentic AI.
发表机构
- AI4STEM Education Center, University of Georgia（AI4STEM教育中心，佐治亚大学）
机构由 AI 辅助整理，请以论文原文为准。
