AI-Supported Virtual STEM Learning for Enhancing Twenty-First Century Skills in Physics: A Literature Review
Abstract
The integration of Artificial Intelligence (AI) into educational contexts has significantly reshaped the landscape of teaching and learning, particularly within Science, Technology, Engineering, and Mathematics (STEM) education. Physics, as a discipline characterized by abstract concepts and complex problem-solving processes, often presents challenges for learners when approached through conventional instructional methods. In response, AI-supported virtual STEM learning environments have emerged as a promising alternative, offering interactive, adaptive, and personalized learning experiences. This study aims to explore how AI-supported virtual STEM learning contributes to the development of twenty-first century skills, including critical thinking, creativity, collaboration, and digital literacy. Employing a literature review approach guided by the PRISMA framework, this study systematically identifies, screens, and analyzes relevant scholarly articles published between 2018 and 2024. The analysis focuses on identifying patterns in instructional design, types of AI technologies used, and their pedagogical impact on physics learning. The findings indicate that AI-driven tools—such as intelligent tutoring systems, adaptive simulations, and virtual laboratories—can enhance student engagement and foster higher-order thinking skills. These technologies enable learners to actively construct knowledge, experiment in safe virtual environments, and receive immediate feedback tailored to their learning needs. However, the review also reveals that the number of empirical studies in this area remains limited, indicating that the field is still in a developmental stage. This study contributes to the growing body of knowledge by synthesizing current research and highlighting both the opportunities and limitations of AI integration in physics education. It also underscores the need for more comprehensive and methodologically rigorous studies to strengthen the evidence base.