Book contents
- Empathy and Artificial Intelligence
- Studies in Emotion and Social Interaction
- Empathy and Artificial Intelligence
- Copyright page
- Contents
- Contributors
- Acknowledgments
- AI and Empathy at a Crossroads
- Part I Understanding Empathy
- Part II Perceiving AI Empathy
- Chapter 7 Get Real
- Chapter 8 Humans and AIs May Fulfill Different Empathic Needs
- Chapter 9 Empathic AI Will Undermine Human Kindness
- Chapter 10 What LLMpathy Can Tell Us about Received Empathy
- Chapter 11 Machines That Care
- Chapter 12 What’s So Special about Human Empathy?
- Part III Beyond Text: Embodiment and Robots
- Part IV Moral Implications
- Index
- Studies In Emotion and Social Interaction
- References
Chapter 10 - What LLMpathy Can Tell Us about Received Empathy
from Part II - Perceiving AI Empathy
Published online by Cambridge University Press: 05 September 2026
Book contents
- Empathy and Artificial Intelligence
- Studies in Emotion and Social Interaction
- Empathy and Artificial Intelligence
- Copyright page
- Contents
- Contributors
- Acknowledgments
- AI and Empathy at a Crossroads
- Part I Understanding Empathy
- Part II Perceiving AI Empathy
- Chapter 7 Get Real
- Chapter 8 Humans and AIs May Fulfill Different Empathic Needs
- Chapter 9 Empathic AI Will Undermine Human Kindness
- Chapter 10 What LLMpathy Can Tell Us about Received Empathy
- Chapter 11 Machines That Care
- Chapter 12 What’s So Special about Human Empathy?
- Part III Beyond Text: Embodiment and Robots
- Part IV Moral Implications
- Index
- Studies In Emotion and Social Interaction
- References
Summary
Psychological theories of empathy have traditionally focused on the empathizer’s ability to perspective take and share another’s emotions. But people today live in a day and age where AI models, like Large Language Models (LLMs) devoid of emotions and other empathic capabilities, can still produce text that makes people feel heard and comforted. Surely what people perceive in these artificially-generated responses still amounts to some form of “empathy”? To better understand this, we motivate a focus on received empathy. How empathy is received is impacted by many factors, such as recipients’ concern about maintaining self-esteem, that are nevertheless not considered by traditional psychological theories of empathy. Most importantly, the existence of "empathy" expressed by these artificial agents – what we call "LLMpathy" – allows us to shift the discussion away from givers of empathy toward empathy as perceived by the recipient. We consider the implications of receiving LLMpathy from these agents.
Information
- Type
- Chapter
- Information
- Empathy and Artificial IntelligenceChallenges, Advances and Ethical Considerations, pp. 103 - 112Publisher: Cambridge University PressPrint publication year: 2026
References
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., & Smith, D. M. (2023). Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Internal Medicine, 183(6), 589–596. https://doi.org/10.1001/jamainternmed.2023.1838CrossRefGoogle ScholarPubMed
Batson, C. D., Duncan, B. D., Ackerman, P., Buckley, T., & Birch, K. (1981). Is empathic emotion a source of altruistic motivation? Journal of Personality and Social Psychology, 40(2), 290–302.CrossRefGoogle Scholar
Batson, C. D., Early, S., & Salvarani, G. (1997). Perspective taking: Imagining how another feels versus imagining how you would feel. Personality and Social Psychology Bulletin, 23(7), 751–758.CrossRefGoogle Scholar
Beattie, A. J., & High, A. C. (2022). I get by with a little help from my bots: Implications of machine agents in the context of social support. Human-Machine Communication, 4, 151–168.CrossRefGoogle Scholar
Bodie, G. D., Cannava, K. E., Vickery, A. J., & Jones, S. M. (2016). Patterns of nonverbal adaptation in supportive interactions. Communication Studies, 67(1), 3–19.CrossRefGoogle Scholar
Bodie, G. D., Vickery, A. J., & Gearhart, C. C. (2013). The nature of supportive listening, I: Exploring the relation between supportive listeners and supportive people. International Journal of Listening, 27(1), 39–49.CrossRefGoogle Scholar
Bohns, V. K., & Flynn, F. J. (2010). “Why didn’t you just ask?” Underestimating the discomfort of help-seeking. Journal of Experimental Social Psychology, 46(2), 402–409.CrossRefGoogle Scholar
Broadbent, E., Loveys, K., Ilan, G., Chen, G., Chilukuri, M. M., Boardman, S. G., Doraiswamy, P. M., & Skuler, D. (2024). ElliQ, an AI-driven social robot to alleviate loneliness: Progress and lessons learned. Journal of Aging Research & Lifestyle, 13, 22–28.CrossRefGoogle ScholarPubMed
Cameron, C. D., Hutcherson, C. A., Ferguson, A. M., Scheffer, J. A., Hadjiandreou, E., & Inzlicht, M. (2019). Empathy is hard work: People choose to avoid empathy because of its cognitive costs. Journal of Experimental Psychology: General, 148(6), 962–976.CrossRefGoogle ScholarPubMed
Choudhury, M., Elyoseph, Z., Fast, N. J., Ong, D. C., Nsoesie, E. O., & Pavlick, E. (2025). The promise and pitfalls of generative AI for psychology and society. Nature Reviews Psychology, 4(2), 75–80.CrossRefGoogle Scholar
Cohen, S., Janicki-Deverts, D., Turner, R. B., & Doyle, W. J. (2015). Does hugging provide stress-buffering social support? A study of susceptibility to upper respiratory infection and illness. Psychological Science, 26(2), 135–147.CrossRefGoogle ScholarPubMed
Cohen, S., & Wills, T. A. (1985). Stress, social support, and the buffering hypothesis. Psychological Bulletin, 98(2), 310–357.CrossRefGoogle ScholarPubMed
Cuadra, A., Wang, M., Stein, L. A., Jung, M. F., Dell, N., Estrin, D., & Landay, J. A. (2024). The illusion of empathy? Notes on displays of emotion in human-computer interaction. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Honolulu, HI, May 11–16 (pp. 1–18). Association for Computing Machinery, New York.CrossRefGoogle Scholar
Dakof, G. A., & Taylor, S. E. (1990). Victims’ perceptions of social support: What is helpful from whom? Journal of Personality and Social Psychology, 58(1), 80–89.CrossRefGoogle ScholarPubMed
Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. The Scientific World Journal, 6(1), 1146–1163.CrossRefGoogle ScholarPubMed
Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., Jones, M., Krettek-Cobb, D., Lai, L., JonesMitchell, N., Ong, D. C., Dweck, C. S., Gross, J., & Pennebaker, J. W. (2023). Using large language models in psychology. Nature Reviews Psychology, 2(11), 688–701.Google Scholar
DePaulo, B. M., & Fisher, J. D. (1980). The costs of asking for help. Basic and Applied Social Psychology, 1(1), 23–35.CrossRefGoogle Scholar
Farber, B. A., Suzuki, J. Y., & Lynch, D. A. (2018). Positive regard and psychotherapy outcome: A meta-analytic review. Psychotherapy, 55(4), 411–423.CrossRefGoogle ScholarPubMed
Floyd, K. (2014). Empathic listening as an expression of interpersonal affection. International Journal of Listening, 28(1), 1–12.CrossRefGoogle Scholar
Floyd, K., & Ray, C. D. (2017). Thanks, but no thanks: Negotiating face threats when rejecting offers of unwanted social support. Journal of Social and Personal Relationships, 34(8), 1260–1276.CrossRefGoogle Scholar
Gambino, A., Fox, J., & Ratan, R. A. (2020). Building a stronger CASA: Extending the computers are social actors paradigm. Human–Machine Communication, 1, 71–85.CrossRefGoogle Scholar
Hadjiandreou, E., & Cameron, C. D. (2022). Adversity-based identities drive social change. Trends in Cognitive Sciences, 26(9), 725–727.CrossRefGoogle ScholarPubMed
Halabi, S., Chernyak‐Hai, L., & Nadler, A. (2024). The meaning of seeking help: The effects of ascribed and achieved status of the help‐seeker on observer attributions, expectations, and willingness to offer help. Journal of Applied Social Psychology, 54(9), 509–522.CrossRefGoogle Scholar
Harlow, R. E., & Cantor, N. (1995). To whom do people turn when things go poorly? Task orientation and functional social contacts. Journal of Personality and Social Psychology, 69(2), 329–340.CrossRefGoogle ScholarPubMed
Hecht, C. A., Ong, D. C., Clapper, M., Jones, M., Demszky, D., Yang, D., Eichstaedt, J., Bryan, C. J., & Yeager, D. S. (2025). Using large language models in behavioral science interventions: Promise and risk. Behavioral Science & Policy, 11(1), 1–9.CrossRefGoogle Scholar
High, A. C., & Dillard, J. P. (2012). A review and meta-analysis of person-centered messages and social support outcomes. Communication Studies, 63, 99–118. doi: 10.7080/10510974.2011.598208.CrossRefGoogle Scholar
Jiang, L., Drolet, A., & Kim, H. S. (2018). Age and social support seeking: Understanding the role of perceived social costs to others. Personality and Social Psychology Bulletin, 44(7), 1104–1116.CrossRefGoogle ScholarPubMed
Jones, S. M. (2011). Supportive listening. International Journal of Listening, 25, 85–103.CrossRefGoogle Scholar
Joseph, A., Afifi, T. D., & Denes, A. (2016). (Unmet) standards for emotional support and their short- and medium-term consequences. Communication Monographs, 83, 163–193.CrossRefGoogle Scholar
Kim, S., Liu, P. J., & Min, K. E. (2021). Reminder avoidance: Why people hesitate to disclose their insecurities to friends. Journal of Personality and Social Psychology, 121(1), 59–75.CrossRefGoogle ScholarPubMed
Lee, Y. K., Suh, J., Zhan, H., Li, J. J., & Ong, D. C. (2024). Large language models produce responses perceived to be empathic. In Proceedings of the 12th IEEE International Conference on Affective Computing and Intelligent Interaction (ACII) (pp. 63–71).CrossRefGoogle Scholar
Lim, V. K., Teo, T. S., & Zhao, X. (2013). Psychological costs of support seeking and choice of communication channel. Behaviour & Information Technology, 32(2), 132–146.CrossRefGoogle Scholar
Lucas, G. M., Rizzo, A., Gratch, J., Scherer, S., Stratou, G., Boberg, J., & Morency, L. P. (2017). Reporting mental health symptoms: Breaking down barriers to care with virtual human interviewers. Frontiers in Robotics and AI, 4, 51.CrossRefGoogle Scholar
Lyons, R. F., Mickelson, K. D., Sullivan, M. J., & Coyne, J. C. (1998). Coping as a communal process. Journal of Social and Personal Relationships, 15(5), 579–605.CrossRefGoogle Scholar
MacGeorge, E. L., Feng, B., & Burleson, B. R. (2011). Supportive communication. Handbook of Interpersonal Communication, 4, 317–354.Google Scholar
Maples, B., Cerit, M., Vishwanath, A., & Pea, R. (2024). Loneliness and suicide mitigation for students using GPT3-enabled chatbots. NPJ Mental Health Research, 3(1), 4.CrossRefGoogle ScholarPubMed
McCain, M., Linthicum, R., Lubinski, C., Tamkin, A., Huang, S., Stern, M., Handa, K., Durmus, E., Neylon, T., Ritchie, S., Jagadish, K., Maheshwary, P., Heck, S., Sanderford, A., & Ganguli, D. (2025). How people use Claude for support, advice, and companionship. Anthropic. www.anthropic.com/news/how-people-use-claude-for-support-advice-and-companionshipGoogle Scholar
McLaren, R. M., & High, A. C. (2019). The effect of under-and over-benefited support gaps on hurt feelings, esteem, and relationships. Communication Research, 46(6), 785–810.CrossRefGoogle Scholar
Moore, J., Grabb, D., Agnew, W., Klyman, K., Chancellor, S., Ong, D. C., & Haber, N. (2025). Expressing stigma and inappropriate responses prevent LLMs from safely replacing mental health providers. In 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT), June 23–26. Association for Computing Machinery, Athens, Greece.CrossRefGoogle Scholar
Myers, S. (2000). Empathic listening: Reports on the experience of being heard. Journal of Humanistic Psychology, 40(2), 148–173.CrossRefGoogle Scholar
Nadler, A. (1991). Help-seeking behavior: Psychological costs and instrumental benefits. In Clark, M. S. (ed.), Prosocial behavior (pp. 290–311). Sage Publications, Inc.Google Scholar
Nadler, A. (2012). Relationship, esteem, and achievement perspectives on autonomous and dependent help seeking. In Strategic help seeking implications for learning and teaching (pp. 61–93). Routledge.Google Scholar
Nadler, A. (2015). The other side of helping: Seeking and receiving help. In Schroeder, D. A. & Graziano, W. G. (eds.), The Oxford handbook of prosocial behavior (pp. 307–328). Oxford University Press.Google Scholar
Ong, D. C. (2021). An ethical framework for guiding the development of affectively-aware artificial intelligence. In Proceedings of the 9th International Conference on Affective Computing and Intelligent Interaction (ACII 2021) (pp. 374–381), Nara, Japan. Institute of Electrical and Electronics Engineers, Inc.CrossRefGoogle Scholar
Ovsyannikova, D., de Mello, V. O., & Inzlicht, M. (2025). Third-party evaluators perceive AI as more compassionate than expert humans. Communications Psychology, 3(1), 4.CrossRefGoogle Scholar
Perry, A. (2023). AI will never convey the essence of human empathy. Nature Human Behaviour, 7(11), 1808–1809.CrossRefGoogle ScholarPubMed
Priem, J. S., & Solomon, D. H. (2015). Emotional support and physiological stress recovery: The role of support matching, adequacy, and invisibility. Communication Monographs, 82(1), 88–112.CrossRefGoogle Scholar
Priem, J. S., & Solomon, D. H. (2018). What is supportive about supportive conversation? Qualities of interaction that predict emotional and physiological outcomes. Communication Research, 45(3), 443–473.CrossRefGoogle Scholar
Rains, S. A., Tsetsi, E., Akers, C., Pavlich, C. A., & Appelbaum, M. (2019). Factors influencing the quality of social support messages produced online: The role of responsibility for distress and others’ support attempts. Communication Research, 46(6), 866–886.CrossRefGoogle Scholar
Reeves, B., & Nass, C. I. (1996). The media equation: How people treat computers, television, and new media like real people and places. Center for the Study of Language and Information; Cambridge University Press.Google Scholar
Reinhardt, J. P., Boerner, K., & Horowitz, A. (2006). Good to have but not to use: Differential impact of perceived and received support on well-being. Journal of Social and Personal Relationships, 23(1), 117–129.CrossRefGoogle Scholar
Robinson, J. D., Turner, J. W., Tian, Y., Neustadtl, A., Mun, S. K., & Levine, B. (2019). The relationship between emotional and esteem social support messages and health. Health Communication, 34(2), 220–226.CrossRefGoogle ScholarPubMed
Rogers, C. R. (1957). The necessary and sufficient conditions of therapeutic personality change. Journal of Consulting Psychology, 21(2), 95–103.CrossRefGoogle ScholarPubMed
Rubin, M., Li, J. Z., Zimmerman, F., Ong, D. C., Goldenberg, A., & Perry, A. (2025). Comparing the value of perceived human versus AI-generated empathy. Nature Human Behaviour, 9, 2345–2359.CrossRefGoogle ScholarPubMed
Sarason, B. R., Shearin, E. N., Pierce, G. R., & Sarason, I. G. (1987). Interrelations of social support measures: Theoretical and practical implications. Journal of Personality and Social Psychology, 52(4), 813–832.CrossRefGoogle Scholar
Schomakers, E. M., Lidynia, C., & Ziefle, M. (2022). The role of privacy in the acceptance of smart technologies: Applying the privacy calculus to technology acceptance. International Journal of Human–Computer Interaction, 38(13), 1276–1289.CrossRefGoogle Scholar
Sharma, A., Lin, I. W., Miner, A. S., Atkins, D. C., & Althoff, T. (2023). Human–AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nature Machine Intelligence, 5(1), 46–57.CrossRefGoogle Scholar
Siddals, S., Torous, J., & Coxon, A. (2024). “It happened to be the perfect thing”: Experiences of generative AI chatbots for mental health. NPJ Mental Health Research, 3(1), 48.CrossRefGoogle ScholarPubMed
Silvestrini, M., & Chen, J. A. (2023). “It’s a sign of weakness”: Masculinity and help-seeking behaviors among male veterans accessing posttraumatic stress disorder care. Psychological Trauma: Theory, Research, Practice, and Policy, 15(4), 665.CrossRefGoogle ScholarPubMed
Taylor, S. E. (2011). Social support: A review. In Friedman, H. S (ed.), The Oxford handbook of health psychology (pp. 189–214). Oxford University Press.Google Scholar
Williams, S. L., & Mickelson, K. D. (2008). A paradox of support seeking and rejection among the stigmatized. Personal Relationships, 15(4), 493–509.CrossRefGoogle Scholar
Yin, Y., Jia, N., & Wakslak, C. J. (2024). AI can help people feel heard, but an AI label diminishes this impact. Proceedings of the National Academy of Sciences, 121(14), e2319112121.CrossRefGoogle Scholar
Zaki, J., & Williams, W. C. (2013). Interpersonal emotion regulation. Emotion, 13(5), 803–810.CrossRefGoogle ScholarPubMed
Accessibility standard: WCAG 2.2 AAA
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The HTML of this chapter
complies with version 2.2 of the
Web Content Accessibility Guidelines
(WCAG), offering more comprehensive accessibility measures for a broad range
of users and attains the highest
(AAA)
level of WCAG compliance, optimising the user experience by meeting the most extensive
accessibility guidelines.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visualised data also available as non-graphical data
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
Visual Accessibility
Use of colour is not sole means of conveying information
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.