Machine Learning (ML) significantly contributes to the augmentation of Augmented Reality (AR) across a variety of educational fields, promoting superior object visualizations and interactive capabilities. This analysis reviews the intersection of ML and AR, detailing the widespread applications from kindergarten education to university learning. It investigates ML frameworks including support vector machines, Deep Learning Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs) in AR education. Key challenges, possible solutions, and potential future research directions are underscored, emphasizing the need for AR to tackle conventional educational problems while enhancing collaboration.
In the field of medical education, ML-powered AR is exceedingly beneficial, boosting surgical training and analysis of patient data. AR’s significant effects on student learning have been widely explored, albeit often without concentration on specific ML models. Numerous studies have discussed ML frameworks such as CNN, ANN, and Support Vector Machine (SVM) in AR for diverse contexts like healthcare, agriculture, and e-learning. These studies often highlight both advancements and limitations. Numerous challenges exist in integrating ML and AR, particularly regarding technical aspects.
ML, as a subset of AI, automates the construction of analytical models utilizing training data. This procedure is pivotal in multiple applications, including image and speech recognition, intelligent aides, and self-driving vehicles. ML can be categorized into four types: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning, each employing different algorithms for wide-ranging real-world applications.
AR, on the other hand, combines digital information with the physical world, enhancing the user experience without disconnecting them from their surroundings. Available through devices such as smartphones and tablets, AR offers immersive 3D experiences with minimal equipment. AR has found wide application in various educational contexts, from elementary to higher education, and suits diverse learner groups, inclusive of those with special needs. When integrated with ML models, AR significantly enriches educational experiences.
In AR applications for education, various ML techniques significantly improve the learning experience. SVM, for instance, classifies data by separating groups with hyperplanes, aiding student comprehension. While ANN tackles complex, non-linear problems in AR for object tracking and visualization, CNN independently identifies features and is fundamental for tasks like speech and face recognition. The integration of ML and AR has shown promising results in enhancing learning experiences, assessing motor skills, and promoting interactive learning.
A few innovative applications of ML and AR have been developed in recent years. In 2019, gesture recognition in AR was explored for children’s education, using SVM for static gestures, and Hidden Markov Models for dynamic ones. In 2022, ARChem, a mobile app using AR, AI, and ML emerged, assisting Chemistry students with tasks such as equation correction and text summarization. An interactive multi-meter tutorial integrating AR and deep learning was also developed, showcasing the potential of ML and AR in technical education.
In conclusion, while this analysis offers a comprehensive overview of the present applications of ML-powered AR in education, there are numerous research and development opportunities yet to be exploited. Future studies should concentrate on investigating field-specific applications, such as mathematics and language acquisition, incorporating real-time feedback mechanisms to better learning outcomes. As ML-powered AR becomes more integrated into educational settings, addressing ethical considerations, such as privacy and algorithm bias, is crucial. Evaluating the impact of ML-powered AR on student engagement and learning outcomes in real-world environments is vital for its successful implementation. As such, interdisciplinary collaboration among ML specialists, educators, and psychologists will be decisive for gaining a thorough understanding and maximizing the potency of AR applications in the educational sphere.
