The integration of Artificial Intelligence (AI) with Augmented Reality (AR) has transformed human-computer interaction, offering new opportunities for immersive learning and cognitive assessment. However, the relationship between user engagement in AR environments and memory retention remains underexplored. This study proposes an AI-driven framework for predicting memory retention using behavioural interaction data captured through Microsoft HoloLens 2 sensors. The model estimates the likelihood of object recall in AR-based learning environments by analyzing key interaction metrics such as gaze duration, interaction frequency, revisit counts, and head movement stability.
To validate the AI predictions, we compared model-generated retention scores with user-reported recall, demonstrating a strong alignment between predicted and actual memory performance. Our findings align with established cognitive theories, indicating that increased interaction and attentional engagement enhance memory retention. Furthermore, comparisons with prior research on perceptual judgments and spatial memory reinforce the model’s effectiveness in capturing real-world cognitive processes. This study introduces a scalable, non-invasive approach to cognitive modeling, bridging AI-driven analytics with AR-based learning. The results have broad implications for education, medical training, AR-based flight simulation training, and workforce development, where optimizing learning efficiency is crucial. By leveraging AI for real-time memory prediction, this research paves the way for more adaptive and personalized AR learning experiences.
The integration of Artificial Intelligence (AI) with Augmented Reality (AR) has transformed human-computer interaction, offering new opportunities for immersive learning and cognitive assessment. However, the relationship between user engagement in AR environments and memory retention remains underexplored. This study proposes an AI-driven framework for predicting memory retention using behavioural interaction data captured through Microsoft HoloLens 2 sensors. The model estimates the likelihood of object recall in AR-based learning environments by analyzing key interaction metrics such as gaze duration, interaction frequency, revisit counts, and head movement stability.
To validate the AI predictions, we compared model-generated retention scores with user-reported recall, demonstrating a strong alignment between predicted and actual memory performance. Our findings align with established cognitive theories, indicating that increased interaction and attentional engagement enhance memory retention. Furthermore, comparisons with prior research on perceptual judgments and spatial memory reinforce the model’s effectiveness in capturing real-world cognitive processes. This study introduces a scalable, non-invasive approach to cognitive modeling, bridging AI-driven analytics with AR-based learning. The results have broad implications for education, medical training, AR-based flight simulation training, and workforce development, where optimizing learning efficiency is crucial. By leveraging AI for real-time memory prediction, this research paves the way for more adaptive and personalized AR learning experiences.