Integrating VR, Cloud Computing, and Neuroadaptive Systems for Intelligent Immersive Learning and Autonomous Robotics
Gao Ping
Journal of Networking Technology, Vol. 17, No. 1, 2026.
DOI: https://doi.org/10.6025/jnt/2026/17/1/1-17
Link: https://www.dline.info/jnt/fulltext/v17n1/jntv17n1_1.pdf
Abstract:
This paper explores the integration of virtual reality (VR), cloud computing, EEG based neurofeedback,
visual attention modeling, and autonomous learning paradigms to advance next generation educational
and robotic systems. It compares three immersive learning approaches: traditional VR (high immersion,
limited adaptivity), cloud gaming based VR (scalable but bandwidth intensive), and EEG driven VR (highly
personalized but technically complex). A central theme is the need for systems that adapt not only to user
interactions but also to real-time cognitive and behavioral signals. The paper reviews visual attention
models highlighting their limitations in interactive VR due to dataset bias and examines autonomous learning
methods like Learning from Visual Observation (LfVO) and multi agent strategic learning, which show promise
but struggle with scalability and open ended tasks. Key research gaps include the lack of cognitive feedback
integration in VR education, insufficient attention aware streaming in cloud gaming, poor generalization of
attention models to VR, and the absence of real time human intent inference in multi agent systems. The work
concludes that future platforms must achieve â€œintelligence aware immersionâ€ combining cloud infrastructure,
biosignal driven personalization, and socially aware AI to deliver scalable, responsive, and human-centered
experiences in education and robotics. Success hinges on interdisciplinary advances in neurotechnology,
networking, AI, and human AI interaction, ensuring equitable access and meaningful enhancement of human
learning and collaboration.