Shared autonomy bridges human expertise with machine intelligence, yet existing approaches often overlook the impact of teleoperation delays. To address this gap, we propose a novel shared autonomy approach that enables robots to gradually learn from teleoperated demonstrations while adapting to network delays. Our method improves intent prediction by accounting for delayed feedback to the human operator and adjusts the arbitration function to balance reduced human confidence due to delay with confidence in learned autonomy. To ensure system stability, which might be compromised by delay and arbitration of human and autonomy control forces, we introduce a three-port extension of the Time-Domain Passivity Approach with Energy Reflection (TDPA-ER). Experimental validation with 12 participants demonstrated improvements in intent prediction accuracy, task performance, and the quality of final learned autonomy, highlighting the potential of our approach to enhance teleoperation and learning quality in remote environments.