Abstract
Artificial Intelligence (AI) is fundamentally altering the landscape of Science, Technology, Engineering, and Mathematics (STEM) education. By shifting from simple procedural automation toward deep semantic reasoning, adaptive personalization, and real-time diagnostic support, AI technologies offer promising opportunities while simultaneously raising critical questions regarding pedagogical validity, cognitive load, data privacy, and systemic equity. This editorial note synthesizes the core contributions of six peer-reviewed papers published in this special issue. Together, these articles explore AI integration across diverse domains, including distance programming environments, PRISMA-based literature reviews, augmented reality in didactics, and inclusive, culturally contextualized STEM instruction. By situating these findings within the broader scholarly discourse indexed in major scientific databases, this note highlights key emergent themes—specifically, practitioner-led co-design, the alignment of pedagogical intent with AI capabilities, and the imperative for ethical, equitable deployment.
- Aldemir, T., Bicer, A., Kilinc, S., Moon, J., & Kwok, M. (2025a). Exploring emergent AI-TPACK competencies in a two-week AI literacy module for preservice teachers. Teaching and Teacher Education, 168, 105231. https://doi.org.10.1016/j.tate.2025.105231
- Aldemir, T., Kilinc, S., Bicer, A., Grant, P., Davis, T., & Sweany, N. W. (2025). Intelligent‑TPACK in practice: design and evidence from a three‑week teacher preparation module. Computers and Education Open, 100306.https://doi.org/10.1016/j.caeo.2025.100306
- Aldemir, T., Bicer, A., Kilinc, S., Moon, J., & Kwok, M. (2026). Challenges, safeguards, and professional learning needs for AI integration: insights from a two-week AI literacy module with preservice teachers. Cogent Education, 13(1), 2721038.https://doi.org/10.1080/2331186X.2026.2721038
- Awidi, I. T., & Paynter, M. (2024). An evaluation of the impact of digital technology innovations on students' learning: Participatory research using a student-centred approach. Technology, Knowledge and Learning, 29, 65–89. https://doi.org/10.1007/s10758-022-09619-5
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- Kinder, A., Briese, F. J., Jacobs, M., Dern, N., Glodny, N., Jacobs, S., & Leßmann, S. (2025). Effects of adaptive feedback generated by a large language model: A case study in teacher education. Computers and Education: Artificial Intelligence, 8, Article 100349. https://doi.org/10.1016/j.caeai.2024.100349
- Koutcheme, C., Dainese, N., Sarsa, S., Hellas, A., Leinonen, J., & Denny, P. (2024). Open-source language models can provide feedback: Evaluating LLMs' ability to help students using GPT-4-as-a-judge. In Proceedings of the 2024 Conference on Innovation and Technology in Computer Science Education (Vol. 1, pp. 52–58). Association for Computing Machinery. https://doi.org/10.1145/3649217.3653612
- Pankiewicz, M., & Baker, R. S. (2024). Navigating compiler errors with AI assistance: A study of GPT hints in an introductory programming course. In Proceedings of the 2024 Conference on Innovation and Technology in Computer Science Education (Vol. 1, pp. 94–100). Association for Computing Machinery. https://doi.org/10.1145/3649217.3653608
- Raza, F. A., Singh, A. D., Kovilpillai, J. J. S., Hamdan, A., & Rajaratnam, V. (2025). Safeguarding integrity in AI-enhanced education: Stakeholder perspectives on accuracy, validity, and ethics in ASEAN. European Journal of STEM Education, 10(1), Article 22. https://doi.org/10.20897/ejsteme/17307
- Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5
- Zacharis, G., & Papadakis, S. (2025). Can AI grade like a human? Validity, reliability, and fairness in university coursework assessment. Educational Process: International Journal, 19, Article e2025591. https://doi.org/10.22521/edupij.2025.19.591
APA 7th edition
In-text citation: (Bicer et al., 2026)
Reference: Bicer, A., Papadakis, S., Aldemir, T., & Mutalib, A. A. (2026). Artificial intelligence in STEM education — Innovations, pedagogy, ethics, and equity.
European Journal of STEM Education, 11(1), Article 38.
https://doi.org/10.20897/ejsteme/18917
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Bicer A, Papadakis S, Aldemir T, Mutalib AA. Artificial intelligence in STEM education — Innovations, pedagogy, ethics, and equity.
European Journal of STEM Education. 2026;11(1), 38.
https://doi.org/10.20897/ejsteme/18917
Chicago
In-text citation: (Bicer et al., 2026)
Reference: Bicer, Ali, Stamatios Papadakis, Tugce Aldemir, and Ariffin Abdul Mutalib. "Artificial intelligence in STEM education — Innovations, pedagogy, ethics, and equity".
European Journal of STEM Education 2026 11 no. 1 (2026): 38.
https://doi.org/10.20897/ejsteme/18917
Harvard
In-text citation: (Bicer et al., 2026)
Reference: Bicer, A., Papadakis, S., Aldemir, T., and Mutalib, A. A. (2026). Artificial intelligence in STEM education — Innovations, pedagogy, ethics, and equity.
European Journal of STEM Education, 11(1), 38.
https://doi.org/10.20897/ejsteme/18917
MLA
In-text citation: (Bicer et al., 2026)
Reference: Bicer, Ali et al. "Artificial intelligence in STEM education — Innovations, pedagogy, ethics, and equity".
European Journal of STEM Education, vol. 11, no. 1, 2026, 38.
https://doi.org/10.20897/ejsteme/18917
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Bicer A, Papadakis S, Aldemir T, Mutalib AA. Artificial intelligence in STEM education — Innovations, pedagogy, ethics, and equity. European Journal of STEM Education. 2026;11(1):38.
https://doi.org/10.20897/ejsteme/18917