Towards an Artificially Empathic Conversational Agent for Mental Health Applications: System Design and User Perceptions
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Abstract
BACKGROUND: Conversational agents cannot yet express empathy in nuanced ways that account for the unique circumstances of the user. Agents that possess this faculty could be used to enhance digital mental health interventions. OBJECTIVE: We sought to design a conversational agent that could express empathic support in ways that might approach, or even match, human capabilities. Another aim was to assess how users might appraise such a system. METHODS: Our system used a corpus-based approach to simulate expressed empathy. Responses from an existing pool of online peer support data were repurposed by the agent and presented to the user. Information retrieval techniques and word embeddings were used to select historical responses that best matched a user's concerns. We collected ratings from 37,169 users to evaluate the system. Additionally, we conducted a controlled experiment (N=1284) to test whether the alleged source of a response (human or machine) might change user perceptions. RESULTS: The majority of responses created by the agent (2986/3770, 79.20%) were deemed acceptable by users. However, users significantly preferred the efforts of their peers (P<.001). This effect was maintained in a controlled study (P=.02), even when the only difference in responses was whether they were framed as coming from a human or a machine. CONCLUSIONS: Our system illustrates a novel way for machines to construct nuanced and personalized empathic utterances. However, the design had significant limitations and further research is needed to make this approach viable. Our controlled study suggests that even in ideal conditions, nonhuman agents may struggle to express empathy as well as humans. The ethical implications of empathic agents, as well as their potential iatrogenic effects, are also discussed.
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CAs with advanced empathetic skills have improved user affinities and experiences.
“CAs with advanced empathetic skills have improved user affinities and experiences [77,78].”
From Conversational Agent Interventions for Mental Health Problems: Systematic Review and Meta-analysis of Randomized Controlled Trials (2023) · cites this paper as [78]
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- Authors
- Morris, R. R., Kouddous, K., Kshirsagar, R., & Schueller, S. M.
- Affiliations
- Columbia University (US)
- Northwestern University (US)
- Corresponding author
- Morris, Robert R
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2018-06-26
- Volume
- 20
- Issue
- 6
- Pages
- e10148
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC6039770
- OpenAlex
- W2801150160
- Cited by
- 273
- References
- 31
- Citations by year
- 2026: 34 · 2025: 42 · 2024: 34 · 2023: 46 · 2022: 36 · 2021: 35 · 2020: 30 · 2019: 14 · 2018: 2
- FWCI
- 25.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs (5R25MH08091607)
- George Washington University
- Washington University in St. Louis (5R25MH08091607)
- Quality Enhancement Research Initiative (5R25MH08091607)
- National Institute of Mental Health (R25MH080916)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- digital mental health, peer support, information retrieval, word embeddings, iatrogenic disease
- MeSH terms
- Communication, Empathy, Humans, Mental Health, Perception, Information Storage and Retrieval
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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