Special Issue Title
AI and STEM: Exploring the Impact of Artificial Intelligence on Science, Technology, Engineering, and Mathematics Education
Guest Editors
Ali Bicer, Associate Professor of Mathematics Education, College of Education, Texas A&M University. Email: abicer@tamu.edu
Stamatios Papadakis, Assistant Professor of Educational Technology, Department of Preschool Education, University of Crete, Greece. E-mail: stpapadakis@uoc.gr or stpapadakis@gmail.com
Tugce Aldemir, Assistant Professor of Learning, Technology, and Design, College of Education, Texas A&M University. Email: taldemir@tamu.edu
Ariffin Abdul Mutalib, Professor, School of Multimedia Technology and Communication, College of Art and Sciences, University Utara Malaysia, Sintok, Kedah, Malaysia. E-mail: am.ariffin@uum.edu.my
Aims & Scope
The rapid advancement of Artificial Intelligence (AI) is transforming various aspects of our lives, including education, with profound implications for the future of teaching and learning (Kohnke & Zaugg, 2025). The integration of AI in STEM education has garnered significant attention in recent years, driven by its potential to revolutionize the way students learn, and teachers instruct. Current research focuses on harnessing AI's power to enhance student outcomes, promote deeper learning, and develop essential skills for the digital age. For instance, Yerramsetty et al. (2025) are investigating the efficacy of AI-powered adaptive learning systems in personalizing instruction and improving student outcomes. Furthermore, Tian et al. (2024) posit that AI has the potential to fundamentally reshape the educational landscape, enabling more effective and efficient teaching methods. AI-powered tools and systems can facilitate personalized learning experiences, automate administrative tasks, and provide real-time feedback, thereby enhancing student outcomes and improving teacher effectiveness (Xu & Ouyang, 2022; Yelamarthi et al., 2024). By leveraging AI, educators can create more efficient, effective, and engaging learning environments that cater to the diverse needs of students, ultimately fostering a more inclusive and supportive learning experience (Kohnke & Zaugg, 2025). Additionally, Joseph and Uzondu (2024) suggest that AI can create adaptive learning environments tailored to individual student needs, which is particularly beneficial in rural mixed-age classrooms where resources may be limited. Notably, studies by Kim et al. (2021) and Zawacki-Richter et al. (2019) demonstrate that AI supports STEM education through intelligent tutoring systems, virtual labs, and simulation-based learning environments, enhancing student engagement, motivation, and achievement in STEM subjects. As AI continues to evolve, researchers are exploring new applications and implications for STEM education, including the use of machine learning, natural language processing, and robotics to support teaching and learning. This special issue aims to contribute to the growing body of research on AI in STEM education, providing insights into effective implementation strategies, innovative pedagogies, and future directions for research and practice.
This special issue aims to contribute to the growing body of research on AI in STEM education, providing insights into effective implementation strategies, innovative pedagogies, and future directions for research and practice.
Call for Papers
We invite researchers, educators, and practitioners to submit original papers exploring the intersection of AI and STEM education.
Submission Guidelines
This special issue will follow a two-stage submission process. In the first stage, submissions will be based on abstracts. After the abstracts are accepted by the guest editors, authors will be invited to submit full manuscripts in the second stage, which will undergo rigorous peer review to meet the quality standard of the European Journal of STEM Education. Abstracts should be submitted through the following emails: alibicer@tamu.edu, taldemir@tamu.edu, stpapadakis@uoc.gr and am.ariffin@uum.edu.my with a clear indication that they are intended for the special issue on “AI and STEM Education”.
Timeline
Abstracts Due: September 10, 2025
Decisions on Abstracts: September 20, 2025
Full Manuscript Due: December 15, 2025
Final Submission Due: April15, 2026
Special Issue Published: June 2026
References
Joseph, O. B. and Uzondu, N. C. (2024). Integrating AI and machine learning in STEM education: Challenges and opportunities. Computer Science & IT Research Journal, 5(8), 1732-1750. https://doi.org/10.51594/csitrj.v5i8.1379
Kim, J., Lee, Y. and Kim, B. (2021). The effect of AI-based learning on critical thinking and problem-solving skills in STEM education: A systematic review. Journal of Educational Computing Research, 53(4), 419-441. https://doi.org/10.1016/j.eswa.2024.124167
Tian, Z., Sun, M., Liu, A., Sarkar, S. and Liu, J. (2024). The Future of AI in Education: Human-Centered Learning and Technological Foundations. https://digitaleducation.stanford.edu/book-series/2025/future-of-learning
Xu, W. and Ouyang, F. (2022).The application of AI technologies in STEM education: A systematic review from 2011 to 2021. International Journal STEM Education, 9(59), 1-20. https://doi.org/10.1186/s40594-022-00377-5
Yelamarthi, K., Dandu, R., Rao, M., Yanambaka, V. P. and Mahajan, S. (2024). Exploring the potential of generative AI in shaping engineering education: Opportunities and challenges. Journal of Engineering Education Transformations, 37(2), 439–445. https://doi.org/10.16920/jeet/2024/v37is2/24072
Yerramsetty, A., Lee, J. and Smith, M. (2025). Enhancing student outcomes in STEM education with AI: A systematic review. Journal of STEM Education, 26(1), 1-15.
Zawacki-Richter, O., L_Recttin, F. and Smyrnova, V. (2019). AI in education: A review of the literature. Educational Technology & Society, 22(1), 133-145.