AI-Integrated Scientific Inquiry: A Practice-Centered Vision for Science Education

• URL: http://arxiv.org/abs/2607.21777v1
• Date: Thu, 23 Jul 2026 19:47:57 GMT
• Title: AI-Integrated Scientific Inquiry: A Practice-Centered Vision for Science Education
• Abstract summary: As AI moves into scientific inquiry, it gains relevance for science education.<n>Students should learn how AI is changing scientific practices, ideally by engaging in AI-integrated scientific inquiry themselves.<n>In our vision, AI is treated as a set of scientific instruments that students use within the scientific practices described by the Next Generation Science Standards.
• Score: 0.9785041136819492
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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