Research on Augmented Reality with Machine Learning in Education:
ML advances augmented reality (AR) in various educational fields, enhancing object visualization and interaction capabilities. This study outlines the integration of ML in AR and discusses its applications from kindergarten to university. It discusses ML models such as support vector machines, CNNs, and ANNs in AR education. This study focuses on challenges, solutions, and future research directions, highlighting the need for AR to address traditional educational problems and improve collaboration. By comprehensively analyzing ML-based AR frameworks, this study aims to guide future research and development in educational technologies.
Analysis of Machine Learning-Based Augmented Reality in Education:
Medical education is a prominent application area of ââML-based AR, enhancing surgical training and patient data analysis. The impact of AR on student learning has been investigated, but often without a focus on ML models. Various studies have discussed ML models such as CNN, ANN, and SVM in AR in healthcare, agriculture, and e-learning, highlighting both advances and limitations. Challenges of integrating ML and AR, especially in technical aspects, have been identified. This survey considers the benefits, limitations, and evolving trends in this interdisciplinary domain, highlighting the need for an in-depth investigation of ML models in AR across the education sector.
Machine learning technology overview:
ML, a subset of AI, automates the creation of analytical models using training data. This process is essential in a variety of applications, including image and speech recognition, intelligent assistants, and autonomous vehicles. ML can be categorized into four types: supervised learning (SL), which uses labeled data for regression and classification tasks; unsupervised learning (UL), which identifies patterns without labeled data; semi-supervised learning (SSL), which combines labeled and unlabeled data; and reinforcement learning (RL), where an agent learns optimal behaviors through trial and error with the environment. Each type employs different algorithms for various real-world applications.
Introduction to Augmented Reality:
AR blends digital information with the physical world, enhancing the user experience without isolating the user from their surroundings. Accessible through devices such as smartphones and tablets, AR applications provide immersive 3D experiences with minimal equipment. AR is used in a variety of educational settings from primary to higher education, benefiting different learner groups, including those with special needs. There are three main types of AR systems: marker-based AR, which uses QR codes or barcodes; markerless AR, which relies on the environment for positioning; and location-based AR, which delivers content based on the user's physical location. Integrating machine learning models with AR further enriches the educational experience.
ML Techniques for AR in Education:
In AR education applications, various ML techniques enhance the learning experience. Support Vector Machines (SVMs) classify data by separating classes using hyperplanes, improving student understanding. K-Nearest Neighbors (KNNs) classify new examples based on stored data and are useful in multiple fields. ANNs solve complex nonlinear problems and are utilized in AR for object tracking and visualization. CNNs identify features autonomously and are essential for speech and face recognition tasks. Integrating ML such as SVMs and CNNs into AR applications has shown promising results in enhancing educational experiences, motor skill assessment, and interactive learning.
AR SL and USL models:
In 2019, researchers investigated gesture recognition in AR for children's education, using SVM for static gestures and hidden Markov models for dynamic gestures, enhancing the interaction between physical gestures and virtual learning. In 2022, the ARChem mobile app emerged, combining AR, AI, and ML to assist chemistry students with tasks such as equation correction and text summarization. Another innovation in 2022 was an interactive multimeter tutorial using AR and DL, integrating TensorFlow with Unity 3D for real-time component recognition and guided learning, showcasing the potential of ML and AR in technology education.
Conclusion:
This study provides an overview of current applications of ML-powered AR in education, but there are many research and development opportunities that remain to be explored. Future research should focus on investigating subject-specific applications such as mathematics and language acquisition and integrating real-time feedback mechanisms to improve learning outcomes. As ML-powered AR is further integrated into educational settings, it will be important to address ethical considerations such as privacy and algorithmic bias. Evaluating the impact of ML-powered AR on student engagement and learning outcomes in real-world settings is essential for its effective implementation. Interdisciplinary collaboration between ML experts, educators, and psychologists is essential to gain a comprehensive understanding of AR applications in education and optimize their effectiveness.
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Sana Hassan, a Consulting Intern at Marktechpost and a dual degree student at Indian Institute of Technology Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, she brings a fresh perspective to the intersection of AI and real-world solutions.
