Researchers at MIT's Fluid Interfaces group introduce InExChange and EmbER, mixed-reality systems leveraging haptic feedback and biometric sensors to exchange interoceptive signals like breath and heart rate. These platforms facilitate neural synchronisation and embodied empathy, improving collaborative problem-solving performance by over 20%.

Key points

• InExChange uses mixed-reality haptic actuators to share breathing signals, boosting reasoning performance by 24%.
• EmbER transmits heart rate variability and galvanic skin response via wearable actuators, enhancing empathy measures by 18%.
• Real-time biometric and EEG monitoring detect neural synchronisation, enabling AI to reinforce optimal group learning conditions.

Why it matters: By shifting from isolated content delivery to embodied social interaction, these AI systems redefine collaborative learning, offering scalable pathways to enhance empathy, problem-solving, and collective intelligence beyond conventional e-learning tools.