Haven't you ever imagined what it would be like to enter a wholly virtual space or to see digital content overlaid in the real world around you?

With AR and VR technologies continuing to evolve how we interact with educational material and learn, they are beginning to obfuscate the boundaries between the actual and virtual worlds, raising issues regarding the nature of reality and the influence of technology on how we perceive the world.

When we discuss each of these cutting-edge technologies separately, we uncover that VR uses cutting-edge hardware to simulate a brand-new environment to produce an immersive experience. While augmented reality (AR) enhances the physical experience by superimposing digital data on it; this technology is accessible by smartphones, tablets, AR glasses, or headsets. One way to think about the difference between AR and VR is that AR adds to the real world, while VR creates a new world altogether.

Since these technologies continue to evolve, we can presume to encounter further advanced and sophisticated applications, such as mixed reality (MR) that combine AR and VR to create an even more immersive, accessible, and affordable experience. Depending on the intended use of new technologies like 5G, Artificial Intelligence (AI), and the Internet of Things (IoT), the potential for AR and VR is enormous that will keep molding the future of human-computer interaction.

Transforming the way we work, learn, and interact with the world around us, solving a range of real-life problems in a variety of industries. Technicians use AR to see and access hidden or hard-to-reach components, reducing downtime and costs. VR can also be used in training and simulation, where workers can practice complex or dangerous procedures in a safe and controlled environment, reducing the risk of accidents and injuries can also be used to create immersive and engaging shopping experiences, such as virtual showrooms or product demos, that can help customers make informed decisions. Future advancements can be seen in machine learning projects Data Visualization, Training Data Generation, Human-Machine Interaction, and Autonomous Systems.