Mapping research on integrating artificial intelligence into physics learning

The problem and the aim of the study. The incorporation of AI in physics education promises to create a more personalized, streamlined, and efficient learning environment that caters to the varied needs of both students and educators. This study aims to provide insights into the evolving research landscape, identify key contributors and emerging trends, and help close gaps in current knowledge. This research is a descriptive analysis using bibliometrics with primary source database used is Scopus. Research methods. This research is a descriptive analysis using bibliometrics with primary source database used is Scopus. The primary source database used is Scopus. There are four main stages of bibliometrics research: developing a study strategy, acquiring evidence, analyzing the data, and summarizing and displaying data, including articles from the field's creation to the end of 2022. The first query produced 1,883 suitable publications for study, including articles from the field's creation to the end of 2022. Results. The result reveals that AI in physics education is a globally connected field with remarkable growth, diverse contributions, and international collaborations. The United States of America dominates in the corresponding authors' countries category. It is known that more than 200 documents are produced. Instead, 18 other countries obtained a total production of 10 to 100 documents. The research paper on AI in physics education distribution from 2013 to 2022 is tends to increase from 2013 to 2018 and fall into a start at 2022. Furthermore, these articles become crucial for future study, resulting in high citations and an influence on the growth of AI in physics education. The most relevant papers are presented in the order of publication journal Quartile 1 (Q1) with CiteScore 1.00-7.20 as of December 2023. Based on the reviewed paper, these studies make significant contributions to education, particularly in physics and the integration of AI. Prior studies demonstrate AI's beneficial effects on physics education, providing helpful information to help educators and policymakers overcome implementation obstacles. In conclusion, the field has grown significantly, which is indicative of growing interest as well as academic activity. The continual rise in research papers, particularly the notable increase from 2019 to 2022, indicates growing attention and relevance. Further research is urged to examine innovative teaching strategies, focus on student-centered approaches, examine actual-life classroom applications, identify new areas of interest, promote interdisciplinary collaboration, and monitor shifting trends. By ensuring that AI is in step with recent advancements and addressing fresh problems in this rapidly evolving field, these recommendations are meant to assist academics in furthering the development and enhancement of AI in physics education.