An Empathetic AI Coach for Self-Attachment Therapy

TL;DR

This paper introduces an empathetic AI digital coach for self-attachment therapy, combining deep learning and rule-based methods to enhance engagement, empathy, and usefulness in virtual therapy sessions.

Contribution

It presents a novel framework integrating emotion detection, empathetic response generation, and customizable personas for digital therapy coaching.

Findings

Higher empathy ratings compared to rule-based systems

Increased user engagement and perceived usefulness

Effective emotion recognition in user responses

Abstract

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.