Khan, H.A.; Jamil, S.; Piran, M.J.; Kwon, O.-J.; Lee, J.-W. A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education. Technologies2024, 12, 72.
Khan, H.A.; Jamil, S.; Piran, M.J.; Kwon, O.-J.; Lee, J.-W. A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education. Technologies 2024, 12, 72.
Khan, H.A.; Jamil, S.; Piran, M.J.; Kwon, O.-J.; Lee, J.-W. A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education. Technologies2024, 12, 72.
Khan, H.A.; Jamil, S.; Piran, M.J.; Kwon, O.-J.; Lee, J.-W. A Comprehensive Survey on the Investigation of Machine-Learning-Powered Augmented Reality Applications in Education. Technologies 2024, 12, 72.
Abstract
Machine learning (ML) is enabling augmented reality (AR) to gain popularity in various fields, including gaming, entertainment, healthcare, and education. ML enhances AR applications in education by providing accurate visualizations of objects. For AR systems, ML algorithms facilitate the recognition of objects and gestures from kindergarten through university. The purpose of this survey is to provide an overview of various ways in which ML techniques can be applied within the field of AR within education. The first step is to describe the background of AR. In the next step, we will discuss the ML models that are used in AR education applications. Additionally, we discuss how ML is used in AR. Each subgroup’s challenges and solutions can be identified by analyzing these frameworks. In addition, we outline several research gaps and future research directions in ML-based AR frameworks for education.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.