Multisensory Learning: XR, AI, and Cognitive Performance

Extended reality (XR), including augmented reality (AR), mixed reality (MR), and virtual reality (VR), is moving learning research beyond screen-based presentation toward situated environments in which learners see, hear, smell, touch, move, collaborate, and receive adaptive feedback. These environments create new opportunities to examine learning as a multisensory, embodied, and socially mediated process across formal and informal contexts. At the same time, artificial intelligence and multimodal learning analytics are making it possible to model interaction traces that were previously difficult to interpret, including gaze, gesture, speech, motion, physiological signals, and haptic or olfactory engagement. However, much XR learning research still treats immersion as a broad intervention rather than examining how specific sensory cues and interaction patterns shape cognition, affect, attention, memory, spatial reasoning, motivation, and performance. A more precise psychological account is needed to explain how multisensory XR supports meaningful learning.This Research Topic aims to clarify the psychological mechanisms and learning outcomes associated with multisensory XR environments. Although XR can create rich visual, auditory, haptic, olfactory, embodied, and social experiences, it remains unclear when these sensory affordances enhance learning and when they increase distraction, cognitive load, or fragmented attention. By examining learners’ cognitive, motivational, behavioral, physiological, and neural responses to multisensory XR, this collection seeks to understand how immersive interaction shapes learning processes and performance across formal and informal contexts. Key questions include how multisensory cues support knowledge construction, procedural skill development, collaboration, metacognition, transfer, and long-term retention; how individual differences moderate these effects; and how AI-enabled multimodal learning analytics can transform gaze, gesture, speech, motion, physiological, neural, and interaction data into interpretable evidence. Our goal is to build a theoretically and empirically grounded understanding of how multisensory XR learning can be designed, measured, and adapted to improve cognitive performance and meaningful learning outcomes.This Research Topic aims to collect high-impact research across a variety of article types, including original research, reviews, theory, method, and perspective papers that advance knowledge on multisensory XR learning. Topics of interest include but not limited to:• Multisensory Interaction in XR Learning – How do visual, auditory, haptic, olfactory, embodied, social, and physiological interactions shape learning experience and performance in VR, AR, and MR environments? • Cognitive, Affective, and Neural Mechanisms – How do multisensory XR experiences influence attention, cognitive load, memory, spatial cognition, emotion, engagement, and metacognition, and what behavioral, physiological, or neural evidence can reveal these processes? • Learning Processes and Outcomes – How can XR support knowledge construction, procedural skill development, collaboration patterns, transfer, retention, and long-term learning impact across formal and informal contexts? • AI-Augmented Immersive Learning – How can AI agents, pedagogical avatars, generative AI, and adaptive feedback personalize or enhance multisensory learning experiences? • Multimodal Learning Analytics – How can gaze, gesture, speech, motion, neural, physiological, collaboration, haptic, olfactory, and log data be transformed into interpretable evidence about learning mechanisms, and how can methodological contributions advance the validation, synchronization, and analysis of sensory data collected within XR environments? • Applied XR Learning Contexts – What design principles and evidence emerge from XR-based learning in settings such as STEM, language learning, simulation/game-based learning, health professions education, safety training, spatial learning, and other formal or informal contexts?We encourage submissions from a wide range of disciplines and methodological approaches. Topic editors: Shufan Yu, Heng Luo, Mina C. Johnson-Glenberg, Huixiao Le, Jiayan Zhao, Zhichun Liu Source: https://www.frontiersin.org/research-topics/82489/multisensory-learning-xr-ai-and-cognitive-performance