MAINTAINING ENGAGEMENT IN LONG-TERM INTERVENTIONS WITH RELATIONAL AGENTS
Open accessAbstract
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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The interactive style of conversational agents enhances engagement by establishing a working alliance between users and the agents, which is crucial for treatment success in psychotherapy and counseling.
“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]
Section 2 (Related Work on Promoting Long-term Engagement; page unavailable) reports that users of a relational agent had higher Working Alliance scores than users of a nonrelational version. But the same section says the groups did not differ significantly in how often users talked to the agent or in retention. Thus, it supports the working-alliance part of the attribution, not the claim that this alliance enhanced engagement. The separate statement about treatment success is cited to [60], not this source. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe 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.
“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]
The Conclusion reports that making a relational agent more lifelike increased voluntary use, and the study methods describe participants aged 55 or older. However, the paper does not establish improved accessibility or acceptance relative to graphical or voice interfaces, or examine older adults with cognitive impairment. The touch-screen comparison in Related Work concerns a robotic coach tested with overweight adults, not those claimed comparisons. (LLM, conf=0.86)
Experimental — accuracy not yet measuredECAs have advantages over other kinds of interfaces: naturalness, higher levels of attention, easy understanding, believability, higher motivation, and effectiveness.
“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]
The Conclusion (page unavailable) reports that making relational agents more lifelike increased voluntary use and users’ desire to continue interacting, supporting an engagement or motivation advantage. It does not establish the full list of advantages over other interfaces, including easier understanding and greater effectiveness; the authors report that increased engagement did not improve exercise behavior. (LLM, conf=0.85)
Experimental — accuracy not yet measuredNone of the long-term studies involved older adults with cognitive impairment.
“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]
Section 3.1 (Common Study Methods; page unavailable) says participants were at least 55 and describes their computer access, but gives no cognitive-impairment criterion or assessment. Searches for cognitive impairment, dementia, and cognitive screening found no passage establishing that participants with cognitive impairment were absent. This paper also cannot establish that none of the other studies referred to in the citing sentence included such participants. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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- 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
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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.
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