Evidence map›Paper›PMID 39321450›Full record

ArticleJMIR mental health2024

Empathy Toward Artificial Intelligence Versus Human Experiences and the Role of Transparency in Mental Health and Social Support Chatbot Design: Comparative Study.

Jocelyn Shen, Daniella DiPaola, Safinah Ali, Maarten Sap, Hae Won Park, Cynthia Breazeal

Abstract readComparative Study
In one paragraph

Article in JMIR mental health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The benefits and future potential of generative artificial intelligence (GAI) on mental health: a Delphi study.International journal of qualitative studies on health and well-being · 2026
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  5. Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jocelyn ShenMIT Media Lab, Cambridge, MA, United States.ORCID 0000-0001-7809-5474
Daniella DiPaolaMIT Media Lab, Cambridge, MA, United States.ORCID 0000-0003-4762-2838
Safinah AliMIT Media Lab, Cambridge, MA, United States.ORCID 0000-0003-1543-6301
Maarten SapLanguage Technologies Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, United States.ORCID 0000-0002-0701-4654
Hae Won ParkMIT Media Lab, Cambridge, MA, United States.ORCID 0000-0001-9638-1722
Cynthia BreazealMIT Media Lab, Cambridge, MA, United States.ORCID 0000-0002-0587-2065

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEmpathy is a driving force in our connection to others, our mental well-being, and resilience to challenges. With the rise of generative artificial intelligence (AI) systems, mental health chatbots, and AI social support companions, it is important to understand how empathy unfolds toward stories from human versus AI narrators and how transparency plays a role in user emotions.

objectiveWe aim to understand how empathy shifts across human-written versus AI-written stories, and how these findings inform ethical implications and human-centered design of using mental health chatbots as objects of empathy.

methodsWe conducted crowd-sourced studies with 985 participants who each wrote a personal story and then rated empathy toward 2 retrieved stories, where one was written by a language model, and another was written by a human. Our studies varied disclosing whether a story was written by a human or an AI system to see how transparent author information affects empathy toward the narrator. We conducted mixed methods analyses: through statistical tests, we compared user's self-reported state empathy toward the stories across different conditions. In addition, we qualitatively coded open-ended feedback about reactions to the stories to understand how and why transparency affects empathy toward human versus AI storytellers.

resultsWe found that participants significantly empathized with human-written over AI-written stories in almost all conditions, regardless of whether they are aware (t

conclusionsOur work sheds light on how empathy toward AI or human narrators is tied to the way the text is presented, thus informing ethical considerations of empathetic artificial social support or mental health chatbots.

Indexed as

Artificial IntelligenceEmpathySocial SupportAdultFemaleHumansMaleMental HealthMiddle AgedNarrationYoung Adultcrowdsourcingempathyethicshuman-computer interactionlarge language modelstransparency

Identifiers

PMID39321450
PMCPMC11464935

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.