International Journal of Science Education

Furthermore, GAI's potential to automate routine educational tasks—such as grading, assessment, and record management—portents a paradigm shift in educational processes. This automation can significantly enhance the efficiency and preciseness of these tasks, thereby alleviating the administrative burden on educators (Zhai et al., 2022; Zhai & Nehm, 2023) as well as reducing the time commitment and work load of educators. As a result, educators are afforded greater capacity to engage in complex, high-impact activities such as mentorship, discussion facilitation, and personalized feedback provision, all of which are pivotal for effective and equitable science education.

The integration of GAI applications into educational settings demands a reevaluation and redesign of educational materials to make them more equitable, interactive, engaging, and tailored to cater to the diverse learning needs and preferences of students. This approach has the potential to elevate the quality of educational content, making it more accessible, adaptable, and engaging thereby enriching the overall science learning experience. Commensurately, large language models offer new tools for the analysis of textual data which has the potential to provide educators with rich insights into both learners’ performance and understanding of science content and processes (Tai et al., 2024). Large longitudinal educational survey studies have contributed to the understanding of educational equity, but these studies do not offer solutions (Tai, 2022). The influence of GAI on science teaching is particularly significant, as it allows educators to adopt novel teaching strategies assisted by GAI that are precisely aligned with the specific needs and preferences of learners. GAI applications can provide educators with actionable, data-driven insights into students' learning patterns and preferences via natural language processing (Latif & Zhai, 2023) or computer vision (Lee & Zhai, 2023), enabling the adoption of more effective, engaging, and relevant multimodal methods (Lee et al., 2023). Moreover, GAI applications can enhance educational outcomes by improving both the quality and equity of learning, thereby augmenting learners’ ability to fulfill their science educational objectives and increasing their likelihood to succeed in subsequent STEM-related career pursuits.

In conclusion, the integration of GAI applications in science education holds the potential to fundamentally transform our approach to learning goals, processes, materials, pedagogy, and outcomes. This transformation advocates for a more personalized and equitable learning model, tailored to meet the unique requirements and preferences of individual learners. To realize this ambition, interdisciplinary collaborations between researchers in science education and AI are indispensable to propel research and practical implementation. Additionally, a thorough exploration of the challenges (e.g., bias, gender disparities) (Wilson et al., 2023) and opportunities associated with the deployment of GAI applications in science education is crucial to anticipate and ensure equitable educational experiences.

We thus call for papers focusing on this timely topic, specifically those that document what has been done related to the impact of GAI on science education and its effects on students and teachers, as well as the work that is needed in the future. The papers in this special issue will provide a timely and significant contribution to the field by addressing the following questions, but not limited to:

• What are the current developments in GAI applications that are relevant to science education?
• What are the benefits and limitations of GAI applications in science education?
• What validity evidence do we have for GAI applications and how reliable of GAI in science teaching and learning?
• What ethical and social issues need to be considered when using GAI applications in science education?
• What are the best practices for integrating GAI applications in science educational settings?
• What are the implications of GAI applications for the future of science education?

References
Herdiska, A., & Zhai, X. (in press). Artificial Intelligence-Based Scientific Inquiry. In X. Zhai & J. Krajcik (Eds.), Uses of Artificial Intelligence in STEM Education (pp. xxx-xxx). Oxford University Press.
Latif, E., & Zhai, X. (2023). Fine-tuning ChatGPT for Automatic Scoring. arXiv:2310.10072v1, 1-24.
Lee, G.-G., Shi, L., Latif, E., Gao, Y., Bewersdorf, A., Nyaaba, M., Guo, S., Wu, Z., Liu, Z., Wang, H., Mai, G., Liu, T., & Zhai, X. (2023). Multimodality of AI for Education: Towards Artificial General Intelligence. arXiv preprint arXiv:2312.06037.
Lee, G.-G., & Zhai, X. (2023). NERIF: GPT-4V for Automatic Scoring of Drawn Models. arXiv preprint arXiv:2311.12990.
Wilson, C., Haudek, K., Osborne, J., Stuhlsatz, M., Cheuk, T., Donovan, B., Bracey, Z., Mercado, M., & Zhai, X. (2023). Using automated analysis to assess middle school students’ competence with scientific argumentation. Journal of Research in Science Teaching, 1-32. https://doi.org/10.1002/tea.21864
Tai, R. H. (2021). A look at longitudinal research in science education through a multicultural lens. In International Handbook of Research on Multicultural Science Education(pp. 1-16). Cham: Springer International Publishing.
Tai, R. H., Bentley, L. R., Xia, X., Sitt, J. M., Fankhauser, S. C., Chicas-Mosier, A. M., & Monteith, B. G. (2024). An Examination of the Use of Large Language Models to Aid Analysis of Textual Data. International Journal of Qualitative Methods, 23. https://doi.org/10.1177/16094069241231168
Zhai, X., He, P., & Krajcik, J. (2022). Applying machine learning to automatically assess scientific models. Journal of Research in Science Teaching, 59(10), 1765-1794.
Zhai, X., & Nehm, R. (2023). AI and formative assessment: The train has left the station. Journal of Research in Science Teaching, 60(6), 1390-1398.