Journal of Networking Technology Gao Ping 17 1 2026 https://doi.org/10.6025/jnt/2026/17/1/1-17 https://www.dline.info/jnt/fulltext/v17n1/jntv17n1_1.pdf 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 “intelligence aware immersion” 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.