Special Issues: The Role of Generative Artificial Intelligence in Transforming Physics Teaching and Learning

2024-07-08

In recent years, the field of artificial intelligence (AI) has undergone unprecedented advancements, with generative AI emerging as one of the most transformative technologies. The integration of AI in education, particularly in physics teaching and learning, has opened up new avenues for enhancing pedagogical strategies, fostering deeper understanding, and personalizing learning experiences. We are pleased to announce a special issue dedicated to exploring "The Role of Generative Artificial Intelligence in Transforming Physics Teaching and Learning." This special issue aims to bring together researchers, educators, and practitioners to examine the impact of generative AI on physics education, share innovative approaches, and discuss the challenges and opportunities that lie ahead.

Generative AI, with its capability to create new content, simulate complex phenomena, and provide adaptive feedback, is revolutionizing the way physics is taught and learned. This special issue will cover a broad spectrum of topics, including but not limited to, the development of AI-driven educational tools, the use of virtual and augmented reality for immersive physics learning experiences, and the application of machine learning algorithms to personalize learning pathways. We invite contributions that present empirical research, case studies, theoretical frameworks, and reviews that highlight how generative AI is being utilized to enhance student engagement, improve conceptual understanding, and support educators in delivering effective instruction. By showcasing cutting-edge research and practical applications, this special issue seeks to provide a comprehensive overview of the current state of the field and inspire future innovations in physics education.

Important Dates:

  • Submission abstract Deadline: [August 1 2024]
  • Notification of Acceptance: [December 12, 2024]
  • Final Manuscript Submission: [January 30, 2025]
  • Expected Publication: [March 1, 2025]

The abstract-which consists of 1000 words- can be sent to e-mail of editor in chief [ayrusgumilar@gmail.com]