Report on the Scoping Workshop on AI in Science Education Research 2025

TL;DR

This scoping report addresses how AI is reshaping science education research by clarifying terminology, surveying evidence, and mapping new inquiry avenues. It proposes a systems-thinking heuristic to navigate the complex ethical, methodological, and infrastructural implications of AI in education, and outlines concrete recommendations for training, infrastructure, and standards. The work highlights human–AI complementarity, the need for transparency and equity, and the shift toward AI-enabled research practices across curricula, assessment, and professional development. Collectively, it provides actionable guidance for funders, policymakers, professional organizations, and departments to foster principled and impactful AI integration in science education research.

Abstract

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.