An empathetic AI coach for self-attachment therapy
File(s)
Accepted version
Author(s)
Alazraki, Lisa
Ghachem, Ali
Polydorou, Neophytos
Khosmood, Foaad
Edalat, Abbas
Type
Conference Paper
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.
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.
Date Issued
2022-04-13
Date Acceptance
2021-11-16
Citation
Proceedings of the Third IEEE International Conference on Cognitive Machine Intelligence, 2022, pp.78-87
Publisher
IEEE
Start Page
78
End Page
87
Journal / Book Title
Proceedings of the Third IEEE International Conference on Cognitive Machine Intelligence
Copyright Statement
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/9750315
Source
2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI)
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science, Theory & Methods
Computer Science
digital psychotherapy
chatbots
self-attachment
MENTAL-HEALTH
LOW-INCOME
Publication Status
Published
Start Date
2021-12-13
Finish Date
2021-12-15
Coverage Spatial
Atlanta, GA, USA
Date Publish Online
2022-04-13