Report On The Scoping Workshop On AI In Science Education Research

Date

12/2025

AI Overview

Why it matters

Artificial intelligence is reshaping scientific practice, classroom instruction, and educational research, yet its rapid expansion has outpaced clear definitions, conceptual and ethical frameworks, and an understanding of its methodological implications.

Study at a glance

Question: How is artificial intelligence reshaping science education, and what are the implications for research, policy, and practice?
Setting and data: Two-day scoping workshop funded by the VW Foundation involving 23 expert organizers and participants synthesizing existing literature.
Method: Expert scoping workshop synthesis and conceptual policy analysis

Key findings

Evidence suggests that the most productive educational outcomes occur when artificial intelligence augments human reasoning, feedback, and instruction rather than automating core learning processes.
Artificial intelligence can handle complex STEM problem-solving tasks, but its performance remains brittle, raising risks of reduced student cognitive engagement when learners overrely on automated tools.
Emerging qualitative and quantitative methodologies increasingly combine human interpretive judgment with computational tools, forming collaborative cyborg research approaches rather than replacing human sensemaking.

Summary drafted with AI from the full paper and may contain errors. See the full paper for details.

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.