AI in STEM Education: An In-depth Review

Published Date : 24/06/2025 

Artificial intelligence (AI) has revolutionized STEM education by enhancing instructional design, learner agency, and ethical frameworks. This systematic review examines the integration of AI in STEM educational ecosystems, focusing on peer-reviewed studies from 2020 to 2025. 

Artificial intelligence (AI) has reshaped STEM education by influencing instructional design, learner agency, and ethical frameworks. This study conducted a systematic review of 41 peer-reviewed publications to examine how AI has been integrated into STEM educational ecosystems. The review focused on peer-reviewed studies published between 2020 and 2025 that addressed AI applications in STEM education, transdisciplinary approaches to AI integration, and the ethical challenges inherent in AI-driven learning environments.

Grounded in a Transdisciplinary Communication (TDC) framework, the review synthesized findings across three emergent themes: (1) the evolving role of student agency in AI-enhanced learning, (2) shifts in assessment paradigms toward adaptive, AI-mediated models, and (3) ethical tensions surrounding algorithmic transparency, equity, and automation in pedagogical design.

The analysis revealed considerable disciplinary divergence, ranging from efficiency-driven applications of AI to reflexive, equity-oriented implementations rooted in inclusive access. Drawing on the Universal Design for Learning (UDL) framework and trustworthy AI principles, the review offers a critical lens on inclusivity and design ethics in AI-mediated learning environments.

The study employed PRISMA protocols for transparency and utilized NVivo and VOSviewer to support thematic coding and bibliometric mapping. The results offer a conceptual foundation and a set of actionable strategies for institutions, educators, and policymakers seeking to implement AI technologies in ways that are ethically sound, inclusive, and informed by epistemic plurality.

AI has the potential to transform STEM education by personalizing learning experiences, providing real-time feedback, and facilitating collaborative learning. However, the integration of AI in educational settings also raises significant ethical concerns, such as bias in algorithms, data privacy, and the potential for increased inequality. Addressing these challenges requires a multi-faceted approach that involves stakeholders from various disciplines, including educators, technologists, ethicists, and policymakers.

In conclusion, the systematic review highlights the importance of a transdisciplinary approach to AI integration in STEM education. By focusing on student agency, adaptive assessment, and ethical considerations, educators can harness the power of AI to create more inclusive and effective learning environments. The findings provide valuable insights and practical recommendations for those involved in the design and implementation of AI-driven educational initiatives. 

Frequently Asked Questions (FAQS): 

Q: What is the primary focus of the systematic review?

A: The primary focus of the systematic review is to examine how artificial intelligence (AI) has been integrated into STEM educational ecosystems, particularly through peer-reviewed studies from 2020 to 2025.

Q: What are the three emergent themes identified in the review?

A: The three emergent themes identified in the review are: (1) the evolving role of student agency in AI-enhanced learning, (2) shifts in assessment paradigms toward adaptive, AI-mediated models, and (3) ethical tensions surrounding algorithmic transparency, equity, and automation in pedagogical design.

Q: How does the review address ethical concerns in AI-driven learning environments?

A: The review addresses ethical concerns by drawing on the Universal Design for Learning (UDL) framework and trustworthy AI principles, offering a critical lens on inclusivity and design ethics in AI-mediated learning environments.

Q: What tools were used to support the thematic coding and bibliometric mapping in the study?

A: The study employed PRISMA protocols for transparency and utilized NVivo and VOSviewer to support thematic coding and bibliometric mapping.

Q: What are the key recommendations for educators and policymakers from this review?

A: The key recommendations from this review include implementing AI technologies in ways that are ethically sound, inclusive, and informed by epistemic plurality, focusing on student agency, adaptive assessment, and ethical considerations. 

More Related Topics :