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Chapter 5 - Synthetic Support

Empathy and Human-Machine Communication

from Part I - Understanding Empathy

Published online by Cambridge University Press:  05 September 2026

By
Austin Beattie  and
Andrew C. High
Edited by
Anat Perry  and
C. Daryl Cameron
Anat Perry
Affiliation:
The Hebrew University of Jerusalem
C. Daryl Cameron
Affiliation:
Pennsylvania State University
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Summary

Who – or what – people engage for supportive conversations is rapidly evolving as technology becomes more sophisticated and users adapt their expectations and perceptions of communication. Empathy is central to understanding and responding to emotions along with communicating sensitive supportive messages. Questions surrounding the role of empathy when communicating with artificial intelligence, however, have yielded mixed and interesting conclusions. This chapter discusses perspectives on empathy, supportive communication, and the influence of machine agents in the process of communicating sensitive, empathic messages. We consider whether machine agents can simulate empathy and convey sensitive support or merely produce reactive content that might simply appear empathic. This chapter offers insights into the relationship between empathy and supportive interactions with artificial intelligence.

Keywords

Supportive CommunicationArtificial IntelligenceEmotional SupportVerbal Person-CenterednessBots

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Empathy and Artificial Intelligence
Challenges, Advances and Ethical Considerations
 , pp. 50 - 64
Publisher: Cambridge University Press
Print publication year: 2026

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References

Abendschein, B., Edwards, C., & Edwards, A. (2021). The influence of agent and message type on perceptions of social support in human-machine communication. Communication Research Reports, 38(5), 304–314. https://doi.org/10.1080/08824096.2021.1966405CrossRefGoogle Scholar
Beattie, A., Edwards, A. P., & Edwards, C. (2020). A bot and a smile: Interpersonal impressions of chatbots and humans using emoji in computer-mediated communication. In Nah, S., McNealy, J. E., Kim, J. H., & Joo, J. (eds.), Communicating artificial intelligence (AI) (pp. 41–59). Routledge.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, 151–172. https://doi.org/10.30658/hmc.4.2CrossRefGoogle Scholar
Bodie, G. D., Jones, S. M., Brinberg, M., Joyer, A. M., Solomon, D. H., & Ram, N. (2021). Discovering the fabric of supportive conversations: A typology of speaking turns and their contingencies. Journal of Language and Social Psychology, 40(2), 214–237. https://doi.org/10.1177/0261927X20953604CrossRefGoogle Scholar
Bodie, G. D., Vickery, A. J., Cannava, K., & Jones, S. M. (2015). The role of “active listening” in informal helping conversations: Impact on perceptions of listener helpfulness, sensitivity, and supportiveness and discloser emotional improvement. Western Journal of Communication, 79(2), 151–173. https://doi.org/10.1080/10570314.2014.943429CrossRefGoogle Scholar
Burleson, B. R. (1982). The development of comforting communication skills in childhood and adolescence. Child Development, 53, 1578–1588.CrossRefGoogle Scholar
Burleson, B. R. (1983). Social cognition, empathic motivation, and adults’ comforting strategies. Human Communication Research, 10, 295–304. doi:10.1111/j.14682958.1983.tb00019.xCrossRefGoogle Scholar
Burleson, B. R. (1987). Cognitive complexity. In McCroskey, J. C & Daly, J. A (eds.), Personality and interpersonal communication (pp. 305–349). Sage.Google Scholar
Burleson, B. R. (2003). The experience and effects of emotional support: What the study of cultural and gender differences can tell us about close relationships, emotion, and interpersonal communication. Personal Relationships, 10(1), 1–23. https://doi.org/10.1111/1475-6811.00033CrossRefGoogle Scholar
Burleson, B. R. (2008). What counts as effective emotional support? Explorations of individual and situational differences. In Motley, M. T. (ed.), Studies in applied interpersonal communication (pp. 207–227). Sage.Google Scholar
Burleson, B. R. (2009). Understanding the outcomes of supportive communication: A dual-process approach. Journal of Social and Personal Relationships, 26(1), 21–38. https://doi.org/10.1177/0265407509105519CrossRefGoogle Scholar
Burleson, B. R., & Caplan, S. E. (1998). Cognitive complexity and communication skill. In Trent, J. (ed.), Communication: Views from the helm for the 21st century (pp. 233–256). Allyn and Bacon.Google Scholar
Burleson, B. R., & Goldsmith, D. J. (1998). How the comforting process works: Alleviating emotional distress through conversationally induced reappraisals. In Andersen, P. A & Guerrero, L. K. (eds.), Handbook of communication and emotion: Research, theory, applications, and contexts (pp. 245–280). Academic Press.Google Scholar
Burleson, B. R., & MacGeorge, E. L. (2002). Supportive communication. In Knapp, M. L & Daly, J. A. (eds.), Handbook of interpersonal communication (3rd ed.) (pp. 34–424). Sage Publications.Google Scholar
Chutia, T., & Baruah, N. (2024). A review on emotion detection by using deep learning techniques. Artificial Intelligence Review, 57(8). https://doi.org/10.1007/s10462-024-10831-1CrossRefGoogle Scholar
Concannon, S., Roberts, I., & Tomalin, M. (2023). An interactional account of empathy in human-machine communication. Human-Machine Communication, 6, 87–116. https://doi.org/10.30658/hmc.6.6CrossRefGoogle Scholar
Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T. (2022). Blame the bot: Anthropomorphism and anger in customer–chatbot interactions. Journal of Marketing, 86(1), 132–148. https://doi.org/10.1177/00222429211045687CrossRefGoogle Scholar
Davis, M. H., & Oathout, H. A. (1987). Maintenance of satisfaction in romantic relationships: Empathy and relational competence. Journal of Personality and Social Psychology, 53(2), 397–410. https://doi.org/10.1037/0022-3514.53.2.397CrossRefGoogle Scholar
Eisenberg, N., & Miller, P. A. (1987). The relation of empathy to prosocial and related behaviors. Psychological Bulletin, 101(1), 91–119.CrossRefGoogle ScholarPubMed
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017). Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Mental Health, 4(2), e19. https://doi.org/10.2196/mental.7785CrossRefGoogle Scholar
Fortunati, L., & Edwards, A. (2021). Moving ahead with human-machine communication. Human-Machine Communication, 2, 7–21. https://doi.org/10.30658/hmc.2.1CrossRefGoogle Scholar
Gambino, A., Fox, J., & Ratan, R. A. (2020). Building a stronger CASA: Extending the computers are social actors paradigm. Human-Machine Communication, 1, 71–92. https://doi.org/10.30658/hmc.1.5CrossRefGoogle Scholar
Han, E., Yin, D., & Zhang, H. (2023). Bots with feelings: Should AI agents express positive emotion in customer service? Information Systems Research, 34(3), 1296–1311.CrossRefGoogle Scholar
Hancock, J. T., Naaman, M., & Levy, K. (2020). AI-mediated communication: Definition, research agenda, and ethical considerations. Journal of Computer-Mediated Communication, 25(1), 89–100. https://doi.org/10.1093/jcmc/zmz022CrossRefGoogle Scholar
High, A. C., & Dillard, J. P. (2012). A review and meta-analysis of person-centered messages and social support outcomes. Communication Studies, 63(1), 99–118. https://doi.org/10.1080/10510974.2011.598208CrossRefGoogle Scholar
Ho, A., Hancock, J., & Miner, A. S. (2018). Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. Journal of Communication, 68(4), 712–733. https://doi.org/10.1093/joc/jqy026CrossRefGoogle ScholarPubMed
Hohenstein, J., & Jung, M. (2020). AI as a moral crumple zone: The effects of AI-mediated communication on attribution and trust. Computers in Human Behavior, 106, 1–13. https://doi.org/10.1016/j.chb.2019.106190CrossRefGoogle Scholar
Howland, M. (2016). Reading minds and being invisible: The role of empathic accuracy in invisible support provision. Social Psychological and Personality Science, 7(2), 149–156. https://doi.org/10.1177/1948550615606195CrossRefGoogle Scholar
Jang, J. Y., Shin, S., & Gweon, G. (2024, November). Minimal yet big impact: How AI agent back-channeling enhances conversational engagement through conversation persistence and context richness. In Findings of the Association for Computational Linguistics: EMNLP 2024 (pp. 14509–14521). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.849CrossRefGoogle Scholar
Jones, S. M., Bodie, G. D., & Hughes, S. D. (2016). The impact of mindfulness on empathy, active listening, and perceived provisions of emotional support. Communication Research, 46(6), 838–865. doi: https://doi.org/10.1177/0093650215626983.CrossRefGoogle Scholar
Koss, J. (2006). On the limits of empathy. The Art Bulletin, 1, 139–157. https://doi.org/10.1080/00043079.2006.10786282CrossRefGoogle Scholar
Lee, K. M. (2004). Presence, explicated. Communication Theory, 14(1), 27–50. https://doi.org/10.1111/j.1468-2885.2004.tb00302.xCrossRefGoogle Scholar
Lim, V. K. G., Teo, T. S. H., & Shao, X. (2013). Psychological costs of support seeking and choice of communication channel. Behaviour & Information Technology, 32, 132–146. https://doi.org/10.1080/0144929X.2010.518248CrossRefGoogle Scholar
Liu, B., & Sundar, S. S. (2018). Should machines express sympathy and empathy? Experiments with a health advice chatbot. Cyberpsychology, Behavior, and Social Networking, 21(10), 625–636.CrossRefGoogle ScholarPubMed
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013CrossRefGoogle Scholar
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947. https://doi.org/10.1287/mksc.2019.1192ccGoogle Scholar
MacGeorge, E. L., Feng, B., & Burleson, B. R. (2011). Supportive communication. In Knapp, M. L & Daly, J. A. (eds.), Handbook of interpersonal communication (4th ed., pp. 317–354). Sage.Google Scholar
Marroquin, B. (2011). Interpersonal emotion regulation as a mechanism of social support in depression. Clinical Psychology Review, 31, 1276–1290. doi: 10.1016/j.cpr.2011.09005CrossRefGoogle ScholarPubMed
Meng, J., Rheu, M., Zhang, Y., Dai, Y., & Peng, W. (2023). Mediated social support for distress reduction: AI chatbots vs. human. Proceedings of the ACM on Human-Computer Interaction, 7(CSCW1), 1–25.CrossRefGoogle Scholar
Meyer, D. J., Boster, F. J., & Hecht, M. L. (1988). A model of empathic communication. Communication Research Reports, 5(1), 19–27. https://doi.org/10.1080/08824098809359796CrossRefGoogle Scholar
Miner, A. S., Milstein, A., & Hancock, J. T. (2017). Talking to machines about personal mental health problems. JAMA, 318(13), 1217–1218. https://doi.org/10.1001/jama.2017.14151CrossRefGoogle ScholarPubMed
Morris, R. R., Kouddous, K., Kshirsagar, R., & Schueller, S. M. (2018). Towards an artificially empathic conversational agent for mental health applications: System design and user perceptions. Journal of Medical Internet Research, 20(6). https://doi.org10.2196/10148CrossRefGoogle ScholarPubMed
Ovsyannikova, D., de Mello, V. O., & Inzlicht, M. (2025). Third-party evaluators perceive AI as more compassionate than expert humans. Communications Psychology, 3(1), 1–11. https://doi.org/10.1038/s44271-024-00182-6CrossRefGoogle Scholar
Ozsevim, I. (2023, June). The future? Woman “marries” virtual, AI-powered partner. AI Magazine. https://aimagazine.com/articles/the-future-woman-marries-virtual-ai-powered-partnerGoogle Scholar
Perry, A. (2023). AI will never convey the essence of human empathy. Nature Human Behaviour, 7(11), 1808–1809. https://doi.org/10.1038/s41562-023-01675-wCrossRefGoogle ScholarPubMed
Pew Research Center. (2025, April 3). How the U.S. public and AI experts view artificial intelligence. www.pewresearch.org/internet/2025/04/03/how-the-us-public-and-ai-experts-view-artificial-intelligence/Google Scholar
Rains, S. A., & Meng, J. (2022). Social enhancement and compensation in online social support among cancer patients: The role of social network properties. Health Communication, 37, 490–497. https://doi.org/10.1080/10410236.2020.1853327CrossRefGoogle Scholar
Reeves, B., & Nass, C. (1996). The media equation: How people treat computers, television, and new media like real people and places. Cambridge University Press.Google Scholar
Reniers, R. L., Corcoran, R., Drake, R., Shryane, N. M., & Völlm, B. A. (2011). The QCAE: A questionnaire of cognitive and affective empathy. Journal of Personality Assessment, 93(1), 84–95. https://doi.org/10.1080/00223891.2010.528484CrossRefGoogle ScholarPubMed
Samter, W., & MacGeorge, E. L. (2016). Coding comforting behavior for verbal person centeredness. In VanLear, C. A. & Canary, D. J. (eds.), Researching communication interaction behavior: A sourcebook of methods and measures (pp. 107–128). Sage.Google Scholar
Shank, D. B., Graves, C., Gott, A., Gamez, P., & Rodriguez, S. (2019). Feeling our way to machine minds: People’s emotions when perceiving mind in artificial intelligence. Computers in Human Behavior, 98, 256–266.CrossRefGoogle Scholar
Spence, P. R., Westerman, D., Edwards, C., & Edwards, A. (2014). Welcoming our robot overlords: Initial expectations about interaction with a robot. Communication Research Reports, 31(3), 272–280.CrossRefGoogle Scholar
Stiff, J. B., Dillard, J. P., Somera, L., Kim, H., & Sleight, C. (1988). Empathy, communication, and prosocial behavior. Communications Monographs, 55(2), 198–213. https://doi.org/10.1080/03637758809376166CrossRefGoogle Scholar
Sullins, J. P. (2005). Ethics and artificial life: From modeling to moral agents. Ethics and Information Technology, 7(3), 139–148.CrossRefGoogle Scholar
Trobst, K. K., Collins, R. L., & Embree, J. M. (1994). The role of emotion in social support provision: Gender, empathy, and expressions of distress. Journal of Social and Personal Relationships, 11, 45–62. https://doi.org/10.1177/0265407594111003CrossRefGoogle Scholar
Urakami, J., Moore, B. A., Sutthithatip, S., & Park, S. (2019, September). Users’ perception of empathic expressions by an advanced intelligent system. In Proceedings of the 7th international conference on human-agent interaction (pp. 11–18). Association for Computing Machinery, New York.CrossRefGoogle Scholar
Xiao, Y., & Yu, S. (2025). Can ChatGPT replace humans in crisis communication? The effects of AI-mediated crisis communication on stakeholder satisfaction and responsibility attribution. International Journal of Information Management, 80, 1–23. https://doi.org/10.1016/j.ijinfomgt.2024.102835CrossRefGoogle Scholar
Young, J., Jawara, L. M., Nguyen, D. N., Daly, B., Huh-Yoo, J., & Razi, A. (2024, May). The role of AI in peer support for young people: A study of preferences for human- and AI-generated responses. In Proceedings of the CHI Conference on Human Factors in Computing Systems (pp. 1–18). Association for Computing Machinery, New York.CrossRefGoogle Scholar
Yu, S., Xiong, J., & Shen, H. (2024). The rise of chatbots: The effect of using chatbot agents on consumers’ responses to request rejection. Journal of Consumer Psychology, 34(1), 35–48. https://doi.org/10.1002/jcpy.1330CrossRefGoogle Scholar

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