ESERA SIGs

SIG 10: AI in Science Education

Artificial Intelligence (AI) is rapidly emerging as a dominant force in our society, with generative AI (GenAI) tools leading the charge by enabling the creation of content. As AI continues to develop and enter various sectors, it is already a cornerstone of scientific research and is making significant inroads into science education. This has the potential to help students, for example, through personalized learning, but it also raises many concerns regarding ethics, responsible use, and the spread of scientific misinformation. Given this landscape, the science education research community cannot afford to remain passive observers. We must engage critically, ethically, and inclusively to investigate how GenAI affects the nature, teaching, and learning of science.

To this end, the formation of a Special Interest Group (SIG) within ESERA, named “AI in Science Education,” is proposed to provide a dedicated space for researchers to explore how AI intersects with the epistemic foundations of science, its modalities of learning, and the ethical and moral implications arising in science education contexts. The SIG aims to discuss how AI influences the nature of science (NOS), scientific reasoning, and the development of technological pedagogical content knowledge (TPACK). Particular attention will be paid to how GenAI may support or, in some cases, hinder conceptual understanding, scientific literacy, and the development of scientific competencies.

While ethics in AI is a broad domain encompassing issues such as privacy, fairness, and bias, science education introduces specific ethical challenges, including gender bias and equity concerns. Moreover, the epistemological distinctiveness of science presents additional challenges in the context of AI. A critical concern that GenAI introduces to science education is its potential impact on the NOS itself, and the implications this transformation holds for the education of young children. Science, grounded in the study of materiality and our interactions with the physical world, stands in stark contrast to GenAI’s reliance on statistical probabilities and semantic patterns derived from a large language model that lack a connection to material reality. In order to handle this challenge, research suggests that effective AI literacy in education must emphasize both technological scientific knowledge (e.g., understanding AI systems) and socio-ethical technical awareness (e.g., AI ethics and the role of human agency in AI systems). For example, teacher content knowledge is essential in recognizing and correcting misinformation generated by AI tools. Additionally, even when GenAI provides accurate information, it may lead to metacognitive complacency among students by encouraging them to bypass essential learning processes. Therefore, the SIG will focus on the critical skills that must be preserved in students, such as technical competence, scientific reasoning, and socio-ethical understanding, all of which are crucial for facilitating the responsible and effective use of AI in science education.

The SIG AI in Science Education will bring together researchers passionate about science education and AI, fostering a critical examination of AI integration and creating a forum for dynamic discussions and groundbreaking research.

The SIG members’ concrete goals should include developing actionable frameworks and recommendations to guide the ESERA community and science educators across Europe and beyond. One of the first initiatives could be to review and potentially refine the European Reference Framework for Digital Competences (DigiCompEdu) in the context of AI and its associated competencies. Additionally, we will discuss the AI-ACT (https://artificialintelligenceact.eu/) in the context of science education. Furthermore, strategies need to be developed to manage the growing challenges of handling large datasets, safeguarding personal rights, and mitigating the risks of AI bias in data interpretation.

The SIG can make significant contributions to educational practices within ESERA. Expert meetings and seminars will play a crucial role in disseminating cutting-edge research. At the same time, workshops organized by the SIG will offer opportunities for ESERA members less familiar with AI to gain valuable insights and skills. All researchers interested in AI can join the SIG as science education experts.

We will address questions like:

• How does the use of AI align or conflict with the epistemic aspects of science?
• How can science education researchers responsibly use AI in their research?
• In what ways does GenAI reshape conceptual learning in teaching sciences?
• What ethical questions arise from the use of GenAI in science classrooms and research (e.g., algorithmic bias, data misuse, ownership of knowledge)?
• Can current learning theories explain AI-mediated science learning, or do we need new frameworks?
• What competences/ AI literacy do science educators and learners need to critically engage with GenAI in school and university settings?
• To what extent can AI support personalized learning in science education across schools and universities?

Coordinators
Lukas Mientus (lukas.mientus@ovgu.de), Otto-von-Guericke University, Magdeburg, Germany 
Yael Feldman-Maggor (yaelfeld@bgu.ac.il), Ben-Gurion University of the Negev, Be’er Scheva, Israel

Members

The list of members can be found here.