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

2026-07-23

AI Summary

This study addresses the challenge of effectively integrating artificial intelligence into science education such that AI becomes an organic component of authentic scientific practice rather than an isolated instructional topic. To this end, it proposes a design principle of “pedagogically constrained yet functionally faithful” AI instruments, embedding techniques from computer vision, clustering, and generative modeling into core scientific inquiry processes—namely observation, analysis, and modeling—while incorporating reflective prompts to guide students in critically evaluating AI’s applications and limitations. Grounded in the Next Generation Science Standards (NGSS), the project establishes an implementable instructional framework that introduces cross-process AI agents only after students have developed foundational inquiry competencies, thereby cultivating discipline-specific AI literacy within authentic research contexts.

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.

Problem

• AI-integrated scientific inquiry
• science education
• authentic scientific practice
• discipline-based AI literacy
• AI instruments

Innovation

• AI-integrated scientific inquiry
• discipline-based AI literacy
• pedagogically bounded AI instruments
• generative modeling
• agentic AI