APPLICATION OF ARTIFICIAL INTELLIGENCE IN LEARNING PHYSICS

The application of Artificial Intelligence (AI) in education has grown rapidly over the past decade, revolutionizing traditional teaching and learning methodologies. In physics education, where abstract concepts, mathematical rigor, and practical experimentation intersect, AI offers a promising avenue to bridge the gaps in traditional teaching methods [1]. Modern classrooms, despite technological advancements, still face persistent challenges, including a lack of personalization, limited access to laboratory resources, and difficulties in visualizing complex phenomena [2]. Physics, as a cornerstone of STEM education, requires students to engage in both conceptual thinking and experimental verification. However, studies show that many students struggle to connect theoretical knowledge with real-world applications, leading to a significant gap in learning outcomes [3]. Traditional teaching methods often adopt a one-size-fits-all approach, which fails to accommodate the diverse learning needs of students. Moreover, laboratory access is frequently constrained by financial and logistical factors, leaving students unable to explore hands-on experimentation. AI addresses these challenges through its ability to personalize learning, create immersive experiences, and automate repetitive tasks. For instance, adaptive learning systems tailor educational content to the unique needs of each student, ensuring that they progress at their own pace [4]. Virtual laboratories simulate real-world experiments, enabling students to manipulate variables and observe outcomes in a risk-free environment [5]. AI-powered tutors provide instant feedback and detailed guidance, mimicking the benefits of one-on-one instruction [6]. Furthermore, the integration of AI in physics education can extend beyond classrooms, enabling students to collaborate globally and access cutting-edge resources. For example, AI-driven platforms can connect students with virtual experiments in high-energy physics or astrophysics, which would otherwise be inaccessible due to resource constraints [7]. This article explores the multifaceted applications of AI in physics education, its impact on learning outcomes and engagement, and the challenges and future prospects of implementing AI at scale.