The Journal of Disciplinary and Interdisciplinary Science Education Research (DISER) announces a special issue on the opportunities and challenges of using AI in science education. With the rapidly increasing advances and adoption of digital technologies, AI applications have grown in many areas in education, including science education.
The goal of science education worldwide has shifted to learners making use of knowledge rather than just the recall of knowledge (Pellegrino & Hilton, 2012). In the past decade, science education researchers have leveraged machine learning approaches (e.g., natural language processing, convolutional neural network), to develop algorithms for automatically scoring students’ assessment artifacts (e.g., Zhai, He, & Krajcik, 2022) and further develop automated feedback systems for teacher timely instructional decisions and student learning (e.g., He et al., 2024). More recently, the emergence of generative AI technologies, especially large language models (e.g., ChatGPT and Gemini), has drawn the attention of science education researchers and practitioners using AI to 1) advance science teaching and learning in formal and informal settings and 2) support science teacher professional learning in pre-service teacher preparation programs and in-service teacher development programs. Although the field has taken advantage of using AI in science education, we acknowledge the challenges and concerns of introducing AI into science learning environments, especially for teachers and students from diverse cultural and educational backgrounds. Given the ubiquitous use of AI and its impact on everyone’s lives, science researchers need to learn more about the opportunities and challenges of using AI in K-12 and post-secondary science education. They need to explore:1) What aspects and to what extent can teachers use AI in science education to promote knowledge-in-use? 2) What are the advantages and challenges of using AI in science education? 3) What are the best uses of AI in science education? and 4) What are teachers’ and students’ attitudes, knowledge, and ability about using AI in science teaching and learning?
DISER invites scholars to submit a wide range of manuscripts on AI in science education, including empirical, theoretical and policy studies to understand how the use of AI to support all learners to develop deeper and more useable knowledge in formal and informal science settings. The criteria for selection will depend on new contributions to the literature on AI in science education and how the research moves the field forward to promote and sustain deep and useable student learning.
The Journal of Disciplinary and Interdisciplinary Science Education Research (DISER) promotes scholarship in education within and across science disciplines. DISER publishes original empirical, conceptual and policy studies reflecting the latest developments in science education from disciplinary and interdisciplinary perspectives. DISER bridges the divide and facilitates dialogue between formal and informal, disciplinary and interdisciplinary, K-12 and post-secondary, as well as English-speaking and non-English speaking country science education.
Selection Process
Scholars interested in the special issue should submit a five-page proposal (single-spaced, including references, author affiliation, and contact information) by October 31, 2024. The guest editors, Professors Joe Krajcik from Michigan State University, the CREATE for STEM Institute, and Peng He from Washington State University, the Department of Teaching and Learning, will review proposals. The editorial team for the special issue will select up to 10 proposals to develop into full papers.
Authors will be notified whether their proposals are accepted by November 30, 2024. The publication timeline is presented below.
References
He, P. Shin, N. Kaldaras L., & Krajcik, J. (2024). Integrating artificial intelligence into learning progression-based learning systems to support student knowledge-in-use: Opportunities and challenges. In Jin, H., Yan, D., & Krajcik, J. Handbook of Research in Science Learning Progressions. 461-487.
Pellegrino, J. W., & Hilton, M. L. (2012). Committee on defining deeper learning and 21st century skills. Washington DC: National Academies Press.
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.
