Artificial intelligence in STEM education: a transdisciplinary framework for engagement and innovation

Introduction Artificial intelligence (AI) has reshaped STEM education by influencing instructional design, learner agency, and ethical frameworks. However, the integration of AI into educational ecosystems raises critical questions regarding pedagogical coherence, assessment reform, and algorithmic ethics. Methods 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. A transdisciplinary communication (TDC) framework guided the synthesis of findings. The review followed PRISMA protocols for transparency and utilized Nvivo, Excel and VOSviewer to support thematic coding and bibliometric mapping. Results The analysis identified 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. Divergent disciplinary perspectives were noted, with some emphasizing efficiency and other prioritizing inclusive access and epistemic reflexivity. Discussion Drawing on the Universal Design for Learning (UDL) framework and trustworthy AI principles, this review offers a critical lens on inclusivity and design ethics in AI-mediated learning environments. 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 in STEM education.