An Empathetic AI Coach for Self-Attachment Therapy
This work addresses the need for more engaging and empathetic digital therapy tools for individuals practicing self-attachment therapy, though it is incremental as it builds on existing rule-based methods with deep learning enhancements.
The authors tackled the problem of creating an empathetic AI coach for self-attachment therapy by developing a framework that combines rule-based conversation with deep learning for emotion classification and response generation, resulting in higher ratings for empathy, engagement, and usefulness compared to a simple rule-based system in a trial with 16 participants.
In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user's text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with. Our goal is to achieve a high level of engagement during virtual therapy sessions. We evaluate the effectiveness of our framework in a non-clinical trial with N=16 participants, all of whom have had at least four interactions with the agent over the course of five days. We find that our platform is consistently rated higher for empathy, user engagement and usefulness than the simple rule-based framework. Finally, we provide guidelines to further improve the design and performance of the application, in accordance with the feedback received.