MAPPING THE LANDSCAPE: A COMPREHENSIVE ANALYSIS OF ARTIFICIAL INTELLIGENCE INTEGRATION IN STEM EDUCATION THROUGH MIXED-METHODS APPROACH

Abstract

This study utilizes a mixed-methods approach, combining thematic analysis and bibliometric analysis, to explore the complex landscape of artificial intelligence (AI) integration in STEM education. Through a meticulous search of the journal articles in the Web of Science and Scopus databases, utilizing keywords “STEM” and “artificial intelligence,” we identified 490 publications from various research domains, including Education, Social Sciences, Philosophy, History, Psychology, Arts and Humanities, Communication, and Behavioral Sciences. After a stringent screening and the removal of duplicates, 66 articles published between January 2009 and February 2024 were extracted. Our findings indicate a notable upward trajectory in the number of publications, particularly since 2021, with 2023 emerging as the pinnacle year, housing 50% of the total publications. The thematic analysis of the articles reveals five overarching themes: 1) Integration of Modern Technology in Education; 2) AI in Educational Support Systems; 3) AI in Curriculum Development; 4) Impact of AI on Student Learning Outcomes; and 5) Inclusivity, Diversity, Equity, and Policy Considerations. Furthermore, our study illuminates emerging trends, influential works, and collaborative networks within this field, while also exploring the extent of Open Access availability, thereby fostering opportunities for knowledge dissemination and collaboration. In sum, this research enriches our comprehension of the current landscape and future trajectories of AI integration in STEM education, offering valuable insights for researchers, educators, and policymakers.