Abstract

Augmented reality (AR) and virtual reality (VR) are increasingly utilized in education to provide interactive and engaging learning experiences. However, most applications do not fully exploit the potential of AR/VR technologies for adaptive and personalized learning. This paper analyzes five recent conceptual models that integrate adaptive techniques into AR/VR educational systems to identify their core components and capabilities. All reviewed models incorporate a user profile, content repository, interaction data, environment representation, and device components. Detailed user information is collected, including demographics, knowledge levels, cognitive characteristics, sensory-motor abilities, and emotional-motivational factors. This enables adapting AR/VR content to individual learners' needs, styles, and states. Two key adaptation-influencing components were identified across the models - the environment and the user adaptation mechanism based on the user model. Additional components depend on the service level and specifics of the device. For mobile applications, cloud computing enables optimal processing of objects, location, and human data. The analysis determined these models provide a strong conceptual basis for adaptive AR/VR learning systems. However, further research is needed to develop a universal framework considering domain specifics. An ontological approach should be employed to allow customization for particular educational contexts. This could significantly enhance the state of adaptive AR/VR learning systems. Existing conceptual models incorporate promising techniques but lack holistic frameworks tailored to educational domains. Developing such frameworks is essential to advance research and practice in adaptive AR/VR learning. The analysis and findings presented provide a foundation to guide future efforts in designing and evaluating adaptive AR/VR educational systems.

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