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The application of extended reality (XR) technology in education has been growing for the last two decades. XR offers immersive and interactive visualization experiences that can enhance learning by making it engaging. Recent technological advances have led to the availability of high-quality and affordable XR headsets. These advancements have spurred a wave of research focused on designing, implementing, and validating XR educational interventions. Limited literature focuses on the recent trends of XR within science, technology, engineering, and mathematics (STEM) education. Thus, this paper presents an umbrella review that explores the exploding field of XR and its transformative potential in STEM education. Using six online databases, the review zoomed in on 17 out of 1972 papers on XR for STEM education, published between 2020 and 2023, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The results highlighted the types of XR technology applied (i.e., virtual reality and augmented reality), the specific STEM disciplines involved, the focus of each study reviewed, and the major findings from recent reviews. Overall, the educational benefits of using XR technology in STEM education are apparent: XR boosts student motivation, facilitates learning engagement, and improves skills, for example. However, using XR in education still has challenges that must be addressed, such as the physical discomfort of the learner wearing the XR headset and technical glitches. Besides revealing trends of using XR in STEM education, this umbrella review encourages reflection on current practices and suggests ways to apply XR to STEM education effectively.
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An XR Environment for AI Education: Design and First Implementation
This work in progress paper presents and motivates the design of a novel extended reality (XR) environment for artificial intelligence (AI) education, and presents its first implementation. The learner is seated at a table and wears an XR headset that allows them to see both the real world and a visualization of a neural network. The visualization is adjustable. The learner can inspect each layer, each neuron, and each connection. The learner can also choose a different input image, or create their own image to feed to the network. The inference is computed on the headset, in real time. The neural network configuration and its weights are loaded from an onnx file, which supports a variety of architectures as well as changing the weights to illustrate the training process.
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- Award ID(s):
- 2309564
- PAR ID:
- 10496728
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE VR KELVAR Workshop: K-12+ Embodied Learning through Virtual and Augmented Reality
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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