Title
Fostering relatedness between children and virtual agents through reciprocal self-disclosure
Author
Burger, F.
Broekens, J.
Neerincx, M.A.
Contributor
Bredeweg, B. (editor)
Bosse, T. (editor)
Publication year
2017
Abstract
A key challenge in developing companion agents for children is keeping them interested after novelty effects wear off. Self Determination Theory posits that motivation is sustained if the human feels related to another human. According to Social Penetration Theory, relatedness can be established through the reciprocal disclosure of information about the self. Inspired by these social psychology theories, we developed a disclosure dialog module to study the self-disclosing behavior of children in response to that of a virtual agent. The module was integrated into a mobile application with avatar presence for diabetic children and subsequently used by 11 children in an exploratory field study over the course of approximately two weeks at home. The number of disclosures that children made to the avatar during the study indicated the relatedness they felt towards the agent at the end of the study. While all children showed a decline in their usage over time, more related children used the application more, and more consistently than less related children. Avatar disclosures of lower intimacy were reciprocated more than avatar disclosures of higher intimacy. Girls reciprocated disclosures more frequently. No relationship was found between the intimacy level of agent disclosures and child disclosures. Particularly the last finding contradicts prior child-peer interaction research and should therefore be further examined in confirmatory research.
Subject
Human & Operational Modelling
PCS - Perceptual and Cognitive Systems
ELSS - Earth, Life and Social Sciences
Computer science
Computers
Field studies
Mobile applications
Novelty effects
Peer interactions
Penetration theory
Self-determination theories
Self-disclosure
Social psychology
Artificial intelligence
To reference this document use:
http://resolver.tudelft.nl/uuid:180a0f4f-1839-47ee-ad7a-5000d5102aac
DOI
https://doi.org/10.1007/978-3-319-67468-1_10
TNO identifier
781355
Publisher
Springer Verlag
ISBN
9783319674674
ISSN
1865-0929
Source
28th Benelux Conference on Artificial Intelligence, BNAIC 2016. 10 November 2016 through 11 November 2016, 765, 137-154
Series
Communications in Computer and Information Science
Document type
conference paper