Personalized stem education empowered by artificial intelligence: a comprehensive review and content analysis
Daner Sun, Gary Cheng, Philip L. H. Yu, Jiyou Jia, Zhizi Zheng, Angxuan Chen
Abstract
The integration of Artificial Intelligence (AI) into STEM education has emerged as a critical area of research, particularly in facilitating personalized learning experiences. This study presents a systematic review of 33 studies published between 2012 and 2023, examining how AI supports personalized STEM education in K-12 settings. By categorizing studies based on publication year, geographical distribution, educational level, sample size, and research methodology, this review identifies evolving research trends and thematic shifts over time. It explores pedagogical designs and instructional strategies, highlighting diverse student-centered approaches that leverage AI to enhance learning outcomes through both quantitative and qualitative methods. Additionally, the review evaluates learning outcomes across three key domains: subject knowledge, generic skills, and affective-related outcomes. AI tools are classified according to their functionalities and underlying AI-driven mechanisms to provide a structured understanding of their educational applications. The findings reveal significant trends, methodological diversity, and global perspectives on AI-enhanced personalized STEM education. This study contributes to the growing body of knowledge at the intersection of AI and personalized learning, offering valuable insights for educators, policymakers, and technology developers to optimize STEM education through AI-driven adaptive learning strategies.