Mental Health AI Must Move Beyond Diagnostic Prediction and Chat-Based Support: Toward Perspective-Aware, Multisensory Co-Experience

This position paper argues that AI for mental health must move beyond two dominant paradigms. The first is diagnostic prediction, which frames mental healthcare as symptom detection, diagnosis, and decision-making. The second is chat-based support, which reduces therapeutic interaction to language-mediated information delivery. Both approaches neglect what therapeutic research shows to be central to healing: experiential, affective, and embodied processes. In many therapeutic traditions, insight emerges not through information-delivery but through emotionally resonant, multisensory experiences that allow individuals to feel, reflect, and reframe their inner world. We propose an alternative framework: co-experiential AI systems that identify emotionally salient moments across modalities, modulate sensory experience to facilitate reflection, and adapt over time to a user's evolving responses and interpretations. Using dreamwork therapy as a stress test for mental health AI, we define three key challenges for AI systems: identifying affective anchors in indeterminate multimodal interactions; modeling user perspective as a situated context; and enabling continual adaptation to individuals without repeated retraining. We posit that addressing these challenges requires rethinking AI's role not as an expert, but as a perspective-aware participant in human meaning-making.