Integrating Artificial Intelligence with Virtual Reality for Cognitive Therapy

This literature review explores how Artificial Intelligence (AI), Machine Learning (ML), and immersive Virtual Reality (VR) come together to transform cognitive therapies in mental health, particularly in managing anxiety disorders and enhancing emotional regulation. It draws on from a wide range of sources, including research papers, journal articles, and systematic reviews to show the evolution of digital therapies and their current state. The reviewed studies consistently show that Virtual Reality Exposure Therapy (VRET) delivers outcomes comparable to conventional Cognitive Behavioural Therapy (CBT) for various anxiety-related conditions, such as phobias, generalized anxiety, and post-traumatic stress disorder (PTSD). A growing area of interest involves integrating biofeedback mechanisms that capture real-time physiological signals, such as Heart Rate Variability (HRV) and Electroencephalogram (EEG) data, to quantify emotional and bodily responses of a patient during therapy sessions. Although AI and ML are increasingly applied to build adaptive, datadriven therapeutic systems, current models have limitations. Traditional VRET often lacks personalization, while advanced deep learning frameworks demand large datasets and still struggle with interpretability. To overcome these challenges, this paper introduces a closedloop, multimodal, adaptive framework that seamlessly connects physiological sensing with dynamic virtual environments, creating a more personalized and responsive therapeutic experience.