Collection

Artificial Intelligence in Science Education

Submission status
Open
Open for submission from
29 February 2024
Submission deadline
31 December 2025

Collections represent a chance for Editors to gather related papers on a topic of contemporary interest to the RISE readership and the wider science education research community. The current collection of “artificial intelligence (AI) in science education” from RISE explores the various ways in which AI tools have been and are being used in science education during what we call the pre-generative or predictive AI period and the post-generative AI period. Both pre- and post-generative AI make use of machine learning algorithms, yet they differ in their goals and functions. Predictive AI, as the name suggests, makes predictions, recommendations and decisions through a variety of machine learning and modelling techniques using structured data. Generative AI comprises models of deep learning capable of generating high-quality texts, images, codes and related content derived from large unstructured data upon which they were trained on. AI has a rich history of anticipation and promises, but the turning point marked by the public release of content-generating tools like ChatGPT in late 2022 presents potential opportunities and challenges to revolutionise science education. As seen from the range of archived papers published from 1992 to 2023, RISE has a long-standing history of publications on the application of pre-generative or predictive AI in science education. With the increasing prevalence of ChatGPT, Gemini, Bing AI Co-pilot and other AI tools, we anticipate more future papers on generative AI to add to the ongoing conversation illustrated in this collection.

This collection will remain open for the addition of further articles on both predictive and generative AI. We encourage interested readers to follow the cited references and more recent research to explore the area and help to develop this field further.

Editors

  • Tang Kok-Sing Tang Kok-Sing  &

    Tang Kok-Sing

    Kok-Sing Tang is Director of Graduate Research and Associate Professor in the School of Education at Curtin University. He holds a BA and MSc in Physics from the University of Cambridge and a MA and PhD in Education from the University of Michigan. His research examines the role of language, discourse, and multimodality in supporting scientific literacy, and more recently in generative AI. Kok-Sing is a founding leader of the ESERA Special Interest Group Languages & Literacies in Science Education.

  • Kim Nichols Kim Nichols

    Kim Nichols

Articles (8 in this collection)