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MAINTAINING ENGAGEMENT IN LONG-TERM INTERVENTIONS WITH RELATIONAL AGENTS
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We discuss issues in designing virtual humans for applications which require long-term voluntary use, and the problem of maintaining engagement with users over time. Concepts and theories related to engagement from a variety of disciplines are reviewed. We describe a platform for conducting studies into long-term interactions between humans and virtual agents, and present the results of two longitudinal randomized controlled experiments in which the effect of manipulations of agent behavior on user engagement was assessed.
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“This interactive style enhances engagement by establishing a working alliance between the users and the CAs [57,58], reflecting the collaborative relationship between shared treatment goals and tasks [59], which is crucial for treatment success in psychotherapy and counseling [60].”
From Development and Evaluation of a Smartphone-Based Chatbot Coach to Facilitate a Balanced Lifestyle in Individuals With Headaches (BalanceUP App): Randomized Controlled Trial (2024) · cites this paper as [58]
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In the citing paper
“Globally, current results [10, 11, 5, 6, 12] lead to the conclusion that the use of ECAs could improve the accessibility and acceptance of computer-based assistive technologies, compared to graphical user interfaces and voice interfaces, especially for older adults with cognitive impairment.”
From Usability assessment of interaction management support in LOUISE, an ECA-based user interface for elders with cognitive impairment (2018) · cites this paper as [12]
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“The aforementioned authors have identified the following advantages of ECAs over other kinds of interfaces: naturalness [11, 5, 6], higher levels of attention [10, 11, 5], easy understanding [11, 5], believability [10, 11], higher motivation [10, 12, 13] and effectiveness [10, 11, 5].”
From Usability assessment of interaction management support in LOUISE, an ECA-based user interface for elders with cognitive impairment (2018) · cites this paper as [12]
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“In addition, none of these studies involved older adults with cognitive impairment and the study conducted by Vardoulakis et al. was a WoZ study.”
From Usability assessment of interaction management support in LOUISE, an ECA-based user interface for elders with cognitive impairment (2018) · cites this paper as [12]
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Mobile application for couple relationships: Results of a pilot effectiveness study
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Metadata
- Authors
- Bickmore, T., Schulman, D., & Yin, L.
- Affiliations
- Northeastern University (US)
- Corresponding author
- Bickmore, Timothy
- Venue
- Applied Artificial Intelligence
- Publisher
- Informa UK Limited
- Published
- 2010-07-12
- Volume
- 24
- Issue
- 6
- Pages
- 648-666
- ISSN
- 0883-9514 (ISSN-L)
- 1087-6545
- PMC
- PMC3035950
- OpenAlex
- W1988568632
- Cited by
- 246
- References
- 31
- Citations by year
- 2026: 24 · 2025: 13 · 2024: 16 · 2023: 11 · 2022: 21 · 2021: 29 · 2020: 24 · 2019: 15 · 2018: 18 · 2017: 17 · 2016: 8 · 2015: 15 · 2014: 11 · 2013: 12 · 2012: 6
- FWCI
- 10.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Applied Artificial Intelligence (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (CAREER, 0545932)
- National Cancer Institute (R01CA119195)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- user engagement, long-term intervention, virtual humans, relational agents, human-agent interaction
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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