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

2025-11-18

AI Summary

This study examines the multifaceted impact of artificial intelligence (AI) on science education research, addressing opportunities and ethical risks in curriculum development, assessment practices, cognitive theory, educational equity, and teacher professional development. Methodologically, it employs a mixed-methods approach—integrating quantitative analysis, qualitative inquiry, ethnography, and design-based research—while embedding human-AI collaborative judgment mechanisms to establish an “AI-augmented” research paradigm. Its primary contribution is a novel, systems-thinking–informed cross-paradigmatic integration framework that aligns stakeholder needs, technical feasibility, and ethical boundaries. The study yields actionable recommendations for funding agencies, policymakers, and the academic community, focusing on capacity building, infrastructure modernization, and governance standardization. Collectively, these advances support responsible, synergistic integration of AI and human intelligence in science education research.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Human-AI Collaboration / Human-AI Teaming
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Clarifying AI concepts and reviewing evidence of AI's impact on science education
• Identifying intersections between AI and curriculum development, assessment, and equity issues
• Examining how AI transforms research methodologies and providing guidance for responsible integration

Innovation

• Clarifying AI concepts to reduce ambiguity
• Introducing systems-thinking heuristic for integration
• Developing hybrid analysis combining human-computational strengths