AI in STEM Education: A Transdisciplinary Framework for Engagement

Artificial intelligence (AI) has revolutionized STEM education, transforming instructional design, learner agency, and ethical frameworks. A systematic review of 41 peer-reviewed publications from 2020 to 2025 reveals how AI is being integrated into STEM educational ecosystems. The study, grounded in the Transdisciplinary Communication (TDC) framework, identifies three key themes: the evolving role of student agency, shifts in assessment paradigms, and ethical tensions in AI-driven learning environments.

The Evolving Role of Student Agency

AI-enhanced learning environments are empowering students to take more control of their educational journey. Adaptive learning systems provide personalized learning paths, enabling students to progress at their own pace and focus on areas where they need the most support. This shift towards learner-centered education is supported by the Universal Design for Learning (UDL) framework, which promotes inclusive and accessible learning experiences.

Shifts in Assessment Paradigms

Traditional assessment methods are being replaced by adaptive, AI-mediated models. These new paradigms use data-driven insights to provide real-time feedback, helping educators identify areas where students may be struggling and adjust their teaching strategies accordingly. AI-driven assessments can also detect patterns and trends, offering a more comprehensive understanding of student performance.

Ethical Tensions in AI-Driven Learning

The integration of AI in education raises significant ethical concerns. Algorithmic transparency, equity, and automation in pedagogical design are critical issues that must be addressed. Ensuring that AI systems are transparent and unbiased is essential for maintaining trust and fairness. Additionally, the potential for AI to exacerbate existing inequalities must be carefully managed, with a focus on inclusive access and design.

Disciplinary Divergence

The study reveals considerable disciplinary divergence in AI applications. Some implementations focus on efficiency, while others prioritize equity and inclusivity. This diversity of approaches highlights the need for a balanced and thoughtful integration of AI in educational settings. By drawing on trustworthy AI principles, institutions can create learning environments that are both innovative and ethically sound.

Conceptual Foundation and Actionable Strategies

The findings of this review offer a conceptual foundation and a set of actionable strategies for implementing AI technologies in education. Educators, policymakers, and institutions can use these insights to develop AI-driven learning environments that are inclusive, ethical, and informed by epistemic plurality. The PRISMA protocols and tools like NVivo and VOSviewer were employed to ensure transparency and robust analysis.

In conclusion, AI has the potential to significantly enhance STEM education. By addressing the ethical challenges and leveraging the strengths of AI, educators can create more engaging, personalized, and effective learning experiences for all students. [Related: Ethical Considerations in AI Education]