ARTIFICIAL INTELLIGENCE IN PHYSICS TEACHING: TOOLS FOR EDUCATIONAL INNOVATION

Abstract

This research aims to analyze how Artificial Intelligence (AI) and other educational technologies, such as recommendation systems, active methodologies and virtual simulators, can be used in a complementary way to modernize the teaching of Physics, promoting a more dynamic, personalized and meaningful learning. Theoretically based on authors such as Nogueira (2023), Moreira (2018) and Bardin (2011), qualitative research is based on case analysis to identify advantages, challenges and perspectives of these innovations. Research reveals that AI can personalize learning and optimize teaching, overcoming challenges such as teacher training and technological infrastructure. Active methodologies, such as Problem-Based Learning (PBL) and the Flipped Classroom, have proven effective in engaging students and developing critical skills, while tools such as Easy YouTube (EYT) and virtual simulators, such as Physics Education Technology (PhET), diversify content and facilitate the understanding of complex concepts. The text is structured in four main parts: the first presents the concepts of AI and active methodologies; the second discusses the role of recommendation systems and the effectiveness of virtual simulators; the third analyzes the possible transformations in the teaching of Physics based on the technologies explored; and, finally, the fourth part discusses the necessary conditions for the effective implementation of these tools, with emphasis on teacher training and school infrastructure. It is concluded that the integration between technological innovation and pedagogical practices configures a promising path to transform the teaching of Physics in the twenty-first century.