Generative Artificial Intelligence in <scp>STEM</scp> Education: A Review of Applications, Benefits and Challenges

Abstract

ABSTRACT Generative artificial intelligence (GAI) has emerged as a transformative force in STEM education, offering new possibilities for personalised instruction, content creation and interactive learning. This review examines the current landscape of GAI applications in science, technology, engineering and mathematics, highlighting key tools such as GPT‐4, DALLE, AlphaCode and CodeGen. The paper synthesises recent research and practices to identify the pedagogical benefits of GAI, including enhanced self‐paced learning, improved access to resources and support for interdisciplinary instruction. It also addresses critical challenges, such as the reliability of generated content, ethical concerns, data privacy and teacher preparedness. These challenges were identified through a synthesis of recent empirical studies, policy reports and expert commentaries in the field of GAI in education, which consistently highlight these issues as major barriers to effective implementation. Based on these findings, the review describes implications for curriculum integration, professional development and institutional policy. Furthermore, the study situates GAI within a broader historical and theoretical context, tracking its evolution from traditional machine learning and deep learning approaches and aligning its educational applications with constructivist, cognitive load and personalised learning theories. By categorising specific use cases across STEM disciplines, such as automated scientific explanations, AI‐generated visualisations, intelligent tutoring systems (ITS), virtual engineering labs and adaptive math assessments, the review illustrates the diverse and practical utility of GAI in classroom and remote learning environments. These cases were selected to represent a cross‐section of the core instructional needs in STEM education: explanation, visualisation, guidance, experimentation and assessment, where GAI offers distinct functional advantages. They were categorised according to the primary instructional role they fulfil, allowing a pedagogically meaningful organisation of GAI capabilities aligned with common learning processes in STEM. The analysis also emphasises the importance of teacher agency, student participation and equitable access in shaping effective GAI adoption. Finally, this review identifies key future research directions, including the need for longitudinal studies on learning outcomes, efforts to improve the transparency and explainability of GAI models in educational contexts, the development of domain‐specific generative tools tailored to STEM subfields, and the exploration of collaborative human–AI learning environments.

Ähnliche Arbeiten