AI in STEM/STEAM/STREAM education: Investigating the effect of Artificial Intelligence on teaching and learning approaches in STEM/STREAM/STEAM education

Guest Editors

Galih Albarra Shidiq1, Winarto2, Mohamad Termizi Borhan3

1 Faculty of Educational Science, Universitas Bhayangkara Jakarta Raya. Email: galih.as@dsn.ubharajaya.ac.id
2 Faculty of Mathematics and Natural Sciences, Universitas Negeri Yogyakarta. Email: winarto88@uny.ac.id
3 Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris Malaysia. Email: termizi@fsmt.upsi.edu.my

Aims and Scope

The rapid technological development of Artificial Intelligence (AI) has driven considerable changes in educational methodologies, particularly in the fields of Science, Technology, Engineering, and Mathematics (STEM) education and its interdisciplinary extensions, such as STEAM (Science, Technology, Engineering, Arts, and Mathematics) and STREAM (Science, Technology, Reading/Writing, Engineering, Arts, and Mathematics). AI technologies have grown beyond their conventional purpose as supporting instruments and are progressively impacting instructional design, learning environments, assessment methodologies, and educational decision-making processes (Chen et al., 2020; Hwang et al., 2020). Innovations in instructional technologies such as generative AI, intelligent tutoring systems, adaptive learning platforms, and learning analytics highlight possibilities for enhancing personalised learning, improve feedback mechanisms, and enable data-driven teaching practices (Zawacki-Richter et al., 2019; Kasneci et al., 2023).

In STEM/STEAM/STREAM education, AI integration has the potential to strengthen inquiry-based learning, computational thinking, creativity, scientific reasoning, and problem-solving skills by providing students with interactive and adaptive learning experiences (Hwang et al., 2020; Ng et al., 2023). Kasneci et al. (2023) have further expanded the potential for knowledge construction through the use of generative AI applications, such as large language models, which allow students to access immediate explanations, generate ideas, receive feedback, and participate in collaborative problem-solving activity. Furthermore, educational advantages of AI are determined based on the underlying pedagogical implementation, teacher competencies, and students' capacity to critically evaluate AI-generated information (Laupichler et al., 2022).

Despite increasing adoption of AI in educational contexts, empirical evidence regarding its effects on teaching and learning approaches in STEM/STEAM/STREAM education remains developing. Existing research highlights several challenges, including limited teacher preparedness, insufficient AI literacy, concerns regarding algorithmic bias, ethical issues, academic integrity, data privacy, and uncertainties regarding the influence of AI on higher-order cognitive skills (Zawacki-Richter et al., 2019; Laupichler et al., 2022; Kasneci et al., 2023). Therefore, further investigation is required to understand how AI technologies influence classroom practices, instructional strategies, student engagement, and learning outcomes across diverse educational contexts.

In this Special Issue aims to provide a comprehensive examination of the role and impact of AI integration in STEM/STEAM/STREAM education through empirical studies, theoretical contributions, systematic reviews, and innovative educational practices. The issue seeks to explore how AI influences pedagogical approaches, curriculum development, assessment strategies, classroom interactions, and student learning outcomes from primary education to higher education. Particular attention will be given to research investigating AI-supported teaching models, generative AI applications, intelligent learning systems, AI literacy development, teacher professional preparation, and ethical considerations in AI-enhanced education. Additionally, the Special Issue welcomes contributions that provide evidence-based insights into both the opportunities and challenges of AI implementation in STEM/STEAM/STREAM education. By examining the practical realities of AI-supported teaching and learning, this collection aims to support educators, researchers, and policymakers in developing responsible, effective, and pedagogically meaningful approaches for integrating AI technologies into future educational environments.

Potential Diversity of Authors and Countries Represented

To capture a holistic view of this issue. Our purpose is cultivating a diverse cohort of contributors across geographic, institutional, and demographic boundaries.

  1. Geographic Representation (Global Impact). The deployment and impact of different authors vary depending on regional infrastructure and funding. We actively seek contributions from:
    1. The Global South (Latin America, Africa, and Asia): Focusing on resourceoptimized AI tools, mobile-first learning implementations, and how AI bridges gaps in underfunded educational systems.
    2. North America and Europe: Showcasing longitudinal studies on institutional AI frameworks, large-scale classroom deployments, and evolving academic integrity policies.
    3. The Asia-Pacific Region: Highlighting public-private partnerships in AI education, early-childhood AI literacy initiatives, and high-tech robotics integration.
  2. Disciplinary & Institutional Diversity. AI in STEM/STEAM/STREAM education is inherently interdisciplinary. We encourage submissions from:
    1. Cross-Disciplinary Collaborations: Co-authored papers combining the expertise of computer scientists (who understand the algorithms) and educational psychologists or classroom teachers (who understand the human impact).
    2. K-12 and Higher Education Partnerships: Ensuring a balance between the distinct pedagogical realities of primary/secondary schools and universities.
  3. Diversity of Voice & Career Stages. We are dedicated to ensuring equity throughout the publication pipeline. The editorial board will prioritize gender balance among contributors and reviewers. In addition, we will establish a dedicated track or mentorship support for early-career researchers and classroom practitioners, ensuring that grassroots educational insights are elevated alongside established academic scholarship.

References

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of artificial intelligence in education. Computers and Education: Artificial Intelligence, 1, Article 100001. https://doi.org/10.1016/j.caeai.2020.100001

Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274

Laupichler, M. C., Aster, A., Schirch, J., & Raupach, T. (2022). Artificial intelligence literacy in higher and adult education: A scoping literature review. Computers and Education: Artificial Intelligence, 3, Article 100101. https://doi.org/10.1016/j.caeai.2022.100101

Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2023). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28, 8445–8501. https://doi.org/10.1007/s10639-022-11491-w

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0

Submission Guidelines

All submissions undergo a thorough double-blind peer-review.

Submission Process

  1. Visit https://www.editorialpark.com/ejmste and log in (or create an EditorialPark account if you do not have one).
  2. Click the “Submit New Manuscript” button.
  3. Under the “Issue Type” section, select “Special Issue”.
  4. Select “AI in STEM/STEAM/STREAM education: Investigating the effect of Artificial Intelligence on teaching and learning approaches in STEM/STREAM/STEAM education” from the special issue list and submit your manuscript.

Key Dates

Submission Deadline: June 30, 2027

Expected Publication: December 2027

Contact

Should you have any questions, please do not hesitate to contact us at ejmste@ejmste.com.