This study explores the transformative role of artificial intelligence (AI) in Science, Technology, Engineering, and Mathematics (STEM) education by synthesizing contemporary research, empirical findings, and policy perspectives. Through a systematic review of international literature, the analysis identifies key applications of AI—including intelligent tutoring systems, adaptive assessment tools, virtual laboratories, and learning analytics—that enable personalized instruction, enhance experimental learning, and foster inquiry-based practices. The findings reveal that AI can significantly improve student engagement, conceptual understanding, and accessibility, offering opportunities for more inclusive and data-driven educational environments. However, challenges persist, including educator readiness, infrastructural limitations, algorithmic bias, data privacy concerns, and the risk of widening equity gaps. To address these issues, the study proposes a conceptual framework that integrates pedagogy, technology, and policy to guide the ethical and sustainable adoption of AI across diverse educational contexts. By balancing innovation with equity and human-centered practices, this framework supports the development of future-ready STEM learning environments. The paper contributes to the emerging discourse on AI in education by highlighting pathways for responsible implementation, offering strategic insights for educators, researchers, and policymakers committed to fostering adaptive, ethical, and inclusive STEM education in the era of artificial intelligence.
Konstantinos Τ. Kotsis (2025) studied this question.