Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recovery
Open accessAbstract
Eating disorders (ED) are complex mental health conditions that require long-term management and support. Recent advancements in large language model (LLM)-based chatbots offer the potential to assist individuals in receiving immediate support. Yet, concerns remain about their reliability and safety in sensitive contexts such as ED. We explore the opportunities and potential harms of using LLM-based chatbots for ED recovery. We observe the interactions between 26 participants with ED and an LLM-based chatbot, WellnessBot, designed to support ED recovery, over 10 days. We discovered that our participants have felt empowered in recovery by discussing ED-related stories with the chatbot, which served as a personal yet social avenue. However, we also identified harmful chatbot responses, especially concerning individuals with ED, that went unnoticed partly due to participants' unquestioning trust in the chatbot's reliability. Based on these findings, we provide design implications for safe and effective LLM-based interventions in ED management.
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Choi et al. analyzed patient-chatbot conversations and found the risk of harmful responses on eating disorders.
“Denecke et al. analyzed various cases of harmful LLM-CB use, including a case of an LLM-CB promoting harmful eating disorder behavior among teenagers83, and Choi et al. analyzed patient-chatbot conversations and found the risk of harmful responses on eating disorders84.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [84]
The search string was validated using three pre-selected target articles.
“The search string was validated using three pre-selected target articles47,84,110.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [84]
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Enhancing Trust in LLM-Based AI Automation Agents: New Considerations and Future ChallengesPreprint
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FoodCensor: Promoting Mindful Digital Food Content Consumption for People with Eating Disorders
Choi, R., Park, S., Han, S., & Lee, S.-J. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641984 · Cited by 11 · see citing works
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LLM Critics Help Catch LLM Bugs
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My mobile story: therapeutic storytelling for children
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MART: Improving LLM Safety with Multi-round Automatic Red-TeamingPreprint
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A systematic review of ethical considerations of large language models in healthcare and medicine
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- Authors
- Choi, R., Kim, T., Park, S., Kim, J. G., & Lee, S.-J.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Georgia Institute of Technology (US)
- Venue
- Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-19
- OpenAlex
- W4409749025
- Semantic Scholar
- 34f8edeb2c8245ac5172a92b12b541fe67015c9f
- Cited by
- 19
- References
- 98
- Citations by year
- 2026: 15 · 2025: 4
- FWCI
- 225.87
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Research Foundation of Korea (RS-2024-00464269, RS-2024-00337007)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Eating Disorders and Behaviors
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Keywords
- large language models, chatbots, eating disorders, eating disorder recovery, digital mental health support, social support
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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