An Augmented Reality Framework for Education: Deep Learning Integration and Impact Evaluation

9Citations
Citations of this article
53Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

We propose a novel augmented reality (AR) framework that transforms education with immersive and interactive experiences that enhance student engagement and comprehension. However, existing AR-based learning solutions are built upon generic object detectors that struggle with recognizing domain-specific educational materials, thus limiting their effectiveness. To address this challenge, we introduce a novel fully automated image dataset generation pipeline that synthesizes high-fidelity images from 3D models by varying lighting, camera angles, and background occlusion. This variation enhances the diversity of the dataset, enabling robust training of domain-specific object detectors. The proposed pipeline stands out from existing methods because it provides scalability and adaptability features that allow researchers to build customized educational datasets. The study involved generating a dataset and evaluating it using four state-of-the-art object detection models: Faster R-CNN, SSD, YOLOv5n, and YOLOv7. The YOLOv7 detection model reached an accuracy of 97.2% with 99.5% mAP@0.5 and performed at a real-time speed of 45 frames per second (FPS) making it the best choice for AR applications. To assess the educational impact of our system, we conducted a pilot study involving 210 elementary students. The results showed notable improvements in learning outcomes: fourth graders’ scores increased from 68.78 ± 10.85 to 90.96 ± 11.70 , while fifth graders improved from 62.43 ± 11.53 to 75.43 ± 12.53 . Comprehensive statistical analyses, including ANOVA, regression, and paired t-tests, confirmed that our approach significantly enhances both academic performance and student engagement when compared to traditional learning methods. These findings demonstrate that our domain-focused data pipeline and optimized object detection framework effectively bridge the gap between deep learning research and AR real-world classroom implementation, offering a highly scalable and transformative solution for AR-based education.

Cite

CITATION STYLE

APA

Kaviyaraj, R., & Mohan, U. (2025). An Augmented Reality Framework for Education: Deep Learning Integration and Impact Evaluation. IEEE Access, 13, 56067–56084. https://doi.org/10.1109/ACCESS.2025.3551656

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free