The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Artificial intelligence and communication: A Human–Machine Communication research agenda
Abstract
Artificial intelligence (AI) and people’s interactions with it—through virtual agents, socialbots, and language-generation software—do not fit neatly into paradigms of communication theory that have long focused on human–human communication. To address this disconnect between communication theory and emerging technology, this article provides a starting point for articulating the differences between communicative AI and previous technologies and introduces a theoretical basis for navigating these conditions in the form of scholarship within human–machine communication (HMC). Drawing on an HMC framework, we outline a research agenda built around three key aspects of communicative AI technologies: (1) the functional dimensions through which people make sense of these devices and applications as communicators, (2) the relational dynamics through which people associate with these technologies and, in turn, relate to themselves and others, and (3) the metaphysical implications called up by blurring ontological boundaries surrounding what constitutes human, machine, and communication.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 1×
“As Nass and Moon (2000) explain, people exhibit social behaviors toward technology because they are drawing on knowledge of communication that was first built around human interaction.”
BackgroundAlgorithms of Oppression: How Search Engines Reinforce Racism
Noble, S. U. (2020) · doi:10.2307/j.ctt1pwt9w5 · Cited by 2,709
Carlson, M. (2015) · Digital Journalism · doi:10.1080/21670811.2014.976412 · Cited by 542
The “Concept” of Communication
Dance, F. E. X. (1970) · Journal of Communication · doi:10.1111/j.1460-2466.1970.tb00877.x · Cited by 211
Voices in and of the machine: Source orientation toward mobile virtual assistants
Guzman, A. L. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.08.009 · Cited by 199
Self-citation
Research works printed but not linked: 66
- Allen JF, 2003, Encyclopedia of Computer Science, 1218
- 10.1177/0162243915606523 · DOI · Paper page
- 10.7551/mitpress/11022.001.0001 · DOI · Paper page
- Calo R (2015) Robotics and the lessons of cyberlaw. California Law Review 103(3): 512–563. Available at: http://www.californialawreview.org/print/2robotics_cyberlaw/
- 10.1080/17512786.2014.883116 · DOI · Paper page
- 10.1111/j.1468-2885.1999.tb00355.x · doi:10.1111/j.1468-2885.1999.tb00355.x · Paper page
- Dautenhahn K, 2004, Agent Culture: Human-Agent Interaction in a Multicultural World, 45
- 10.1080/21670811.2015.1096748 · DOI · Paper page
- Edwards A, 2018, Human-Machine Communication: Rethinking Communication, Technology, and Ourselves, 29
- 10.1016/j.chb.2016.06.005 · DOI · Paper page
- 10.1093/acprof:oso/9780190608071.001.0001 · doi:10.1093/acprof:oso/9780190608071.001.0001
- 10.1111/j.1559-1816.2012.00937.x · DOI · Paper page
- 10.1145/2818717 · DOI · Paper page
- Fortunati L, 2017, New Media & Society, 20, 1
- 10.1017/CBO9781139046855 · DOI · Paper page
- Gehl RW, 2017, Socialbots and Their Friends: Digital Media and the Automation of Sociality
- Goffman E, 1959, The Presentation of Self in Everyday Life
- 10.1177/1464884916641269 · DOI · Paper page
- Grudin J, 2012, The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies, and Emerging Applications
- Gunkel DJ, 2012, Communication+1, 1, 1
- 10.7551/mitpress/8975.001.0001 · DOI · Paper page
- Gunkel DJ, 2018, Human-Machine Communication: Rethinking Communication, Technology, and Ourselves, 221
- Guzman AL (In Review) Emotional Minds, Rational Tools: Ontological Differences Between People and Computers and the Implications for Human-Machine Communication.
- Guzman AL, 2017, Socialbots and Their Friends: Digital Media and the Automation of Sociality, 69
- 10.3726/b14399 · DOI
- IEEE Standards Association (2019) The IEEE global initiative on ethics of autonomous and intelligent systems. Available at: https://standards.ieee.org/industry-connections/ec/autonomous-systems.html
- Innis HA, 2008, The Bias of Communication
- 10.1386/macp.10.3.245_1 · doi:10.1386/macp.10.3.245_1
- Jones S, 2019, A Networked Self and Human Augmentics, Artificial Intelligence, Sentience, 5, 201
- Kelly GA, 1955, Psychology of Personal Constructs, 1
- 10.1080/21670811.2014.927986 · DOI · Paper page
- 10.1080/21670811.2019.1577147 · DOI · Paper page
- 10.1177/1077699018755983 · DOI
- 10.1080/13691180802660651 · DOI
- McLuhan M, 1994, Understanding Media: The Extensions of Man
- Marconi, Siegman A and Machine Journalist (2017) The future of augmented journalism: a guide for newsrooms in the age of smart machines. Report, Associated Press, New York. Available at: https://insights.ap.org/uploads/images/the-future-of-augmented-journalism_ap-report.pdf (accessed 30 May 2019).
- 10.1080/08821127.1990.10731302 · doi:10.1080/08821127.1990.10731302
- 10.7208/chicago/9780226516608.001.0001 · DOI · Paper page
- 10.1080/21670811.2016.1209083 · DOI · Paper page
- 10.1016/j.chb.2017.02.067 · DOI · Paper page
- Nass C, 2005, Wired for Speech: How Voice Activates and Advances The Human-Computer Relationship
- 10.1145/191666.191703 · DOI · Paper page
- 10.18574/nyu/9781479833641.001.0001 · DOI · Paper page
- Papacharissi Z, 2019, A Networked Self and Human Augmentics, Artificial Intelligence, Sentience, 5
- 10.17645/mac.v6i3.1596 · DOI
- 10.7208/chicago/9780226922638.001.0001 · DOI · Paper page
- Poster WR, 2016, Invisible Labor: Hidden Work in the Contemporary World, 87
- 10.1080/21670811.2014.927987 · DOI
- Reeves B, 1998, The Media Equation
- 10.1080/14791420.2015.1108450 · DOI
- 10.7208/chicago/9780226720838.001.0001 · DOI
- Rogers EM, 1986, Communication Technology: The New Media in Society
- Rogers EM, 1997, A History of Communication Study: A Biographical Approach
- 10.1016/j.chb.2018.08.047 · DOI
- 10.1057/9781137468376 · doi:10.1057/9781137468376
- Schramm W, 1972, The Process and Effects of Mass Communication, 3
- Schramm W, 1988, The Story of Human Communication: Cave Painting to Microchip
- 10.1145/642611.642661 · DOI
- 10.1016/j.chb.2018.09.014 · DOI
- Suchman LA, 2009, Human-Machine Reconfigurations: Plans and Situated Actions
- Sundar SS, 2008, Digital Media, Youth, and Credibility, 73
- 10.1177/009365000027006001 · DOI · Paper page
- Turkle S, 1984, The Second Self
- 10.1002/9781118540190.wbeic0182 · doi:10.1002/9781118540190.wbeic0182
- Weil P, 2017, Socialbots and Their Friends: Digital Media and the Automation of Sociality, 19
- 10.1177/1461444806061951 · DOI
Cited by
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326
“Hence, human oversight in the process is indispensable and critical for the mitigation of bias and beneficial application of large language models in education.”
BackgroundBorges, A. F. S., Laurindo, F. J. B., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102225 · Cited by 760
Kim, J., Lee, H., & Cho, Y. H. (2022) · Education and Information Technologies · doi:10.1007/s10639-021-10831-6 · Cited by 669
My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship
Brandtzaeg, P. B., Skjuve, M., & Følstad, A. (2022) · Human Communication Research · doi:10.1093/hcr/hqac008 · Cited by 490
Laestadius, L., Bishop, A., Gonzalez, M., Illenčík, D., & Campos-Castillo, C. (2024) · New Media & Society · doi:10.1177/14614448221142007 · Cited by 420
Cultural bias and cultural alignment of large language models
Tao, Y., Viberg, O., Baker, R. S., & Kizilcec, R. F. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae346 · Cited by 383
“How language is produced and transmitted has changed drastically as a result of digital communication technologies and applications of artificial intelligence (AI) [20], especially emerging generative AI applications such as ChatGPT [2].”
BackgroundChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda
Paul, J., Ueno, A., & Dennis, C. (2023) · International Journal of Consumer Studies · doi:10.1111/ijcs.12928 · Cited by 345
Future directions for chatbot research: An interdisciplinary research agenda
Følstad, A., Araujo, T., Law, E. L.-C., Brandtzaeg, P. B., Papadopoulos, S., Reis, L., Baez, M., Laban, G., McAllister, P., Ischen, C., et al. (2021) · Computing · doi:10.1007/s00607-021-01016-7 · Cited by 247
Voice in Human–Agent Interaction: A Survey
Seaborn, K., Miyake, N. P., Pennefather, P., & Otake-Matsuura, M. (2022) · ACM Computing Surveys · doi:10.1145/3386867 · Cited by 249
Hu, B., Mao, Y., & Kim, K. J. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107760 · Cited by 228
Hermann, E. (2022) · New Media & Society · doi:10.1177/14614448211022702 · Cited by 214
Deceitful Media: Artificial Intelligence and Social Life after the Turing Test
Natale, S. (2021) · doi:10.1093/oso/9780190080365.001.0001 · Cited by 209
Huang, S., Lai, X., Ke, L., Li, Y., Wang, H., Zhao, X., Dai, X., & Wang, Y. (2024) · Psychology Research and Behavior Management · doi:10.2147/prbm.s440889 · Cited by 196
Gil de Zúñiga, H., Goyanes, M., & Durotoye, T. (2024) · Political Communication · doi:10.1080/10584609.2023.2290497 · Cited by 198
Stamer, T., Steinhäuser, J., & Flägel, K. (2023) · Journal of Medical Internet Research · doi:10.2196/43311 · Cited by 160
Finding Love in Algorithms: Deciphering the Emotional Contexts of Close Encounters with AI Chatbots
Li, H., & Zhang, R. (2024) · doi:10.31219/osf.io/xd4k7 · Cited by 150
Zhang, R., Duan, W., Flathmann, C., McNeese, N., Freeman, G., & Williams, A. (2023) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3610072 · Cited by 134
Hong, J.-W., Wang, Y., & Lanz, P. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1785693 · Cited by 130
Li, T., Ji, Y., & Zhan, Z. (2024) · Asia Pacific Journal of Education · doi:10.1080/02188791.2024.2305163 · Cited by 133
Generative AI and the politics of visibility
Gillespie, T. (2024) · Big Data & Society · doi:10.1177/20539517241252131 · Cited by 122
Social interactions and relationships with an intelligent virtual agent
Lee, S. K., Kavya, P., & Lasser, S. C. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102608 · Cited by 108
Schlicker, N., Langer, M., Ötting, S. K., Baum, K., König, C. J., & Wallach, D. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106837 · Cited by 106
Hong, J. W., Peng, Q., & Williams, D. (2021) · New Media & Society · doi:10.1177/1461444820925798 · Cited by 102
ChatGPT: perspectives from human–computer interaction and psychology
Liu, J. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1418869 · Cited by 99
Is artificial intelligence more persuasive than humans? A meta-analysis
Huang, G., & Wang, S. (2023) · Journal of Communication · doi:10.1093/joc/jqad024 · Cited by 95
Dehnert, M., & Mongeau, P. A. (2022) · Human Communication Research · doi:10.1093/hcr/hqac006 · Cited by 86
Rizomyliotis, I., Kastanakis, M. N., Giovanis, A., Konstantoulaki, K., & Kostopoulos, I. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.08.035 · Cited by 85
AI ‘Everywhere and Nowhere’: Addressing the AI Intelligibility Problem in Public Service Journalism
Jones, B., Jones, R., & Luger, E. (2022) · Digital Journalism · doi:10.1080/21670811.2022.2145328 · Cited by 84
A User-centered Framework for Human-AI Co-creativity
Moruzzi, C., & Margarido, S. (2024) · Extended Abstracts of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613905.3650929 · Cited by 68
“Sorry, it was my fault”: Repairing trust in human-robot interactions
Zhang, X., Lee, S. K., Kim, W., & Hahn, S. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103031 · Cited by 62
Perrotta, C., Selwyn, N., & Ewin, C. (2024) · New Media & Society · doi:10.1177/14614448221075296 · Cited by 59
Huang, Y., & Huang, H. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2426029 · Cited by 58
Syvänen, S., & Valentini, C. (2020) · Journal of Communication Management · doi:10.1108/jcom-11-2019-0145 · Cited by 56
Measuring Voice UX Quantitatively: A Rapid Review
Seaborn, K., & Urakami, J. (2021) · Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411763.3451712 · Cited by 55
Meng, X., & Liu, J. (2025) · Online Information Review · doi:10.1108/oir-06-2024-0375 · Cited by 46
Academic Integrity and Artificial Intelligence: An Overview
Kumar, R., Eaton, S. E., Mindzak, M., & Morrison, R. (2024) · Springer International Handbooks of Education · doi:10.1007/978-3-031-54144-5_153 · Cited by 46
Greilich, A., Bremser, K., & Wüst, K. (2025) · International Journal of Consumer Studies · doi:10.1111/ijcs.70108 · Cited by 47
Hanson, K. R., & Bolthouse, H. (2024) · Socius: Sociological Research for a Dynamic World · doi:10.1177/23780231241259627 · Cited by 42
“Indeed, some argue that the point of Replika is that the interactions do not require a “real” person behind the screen and instead, the app replaces the need for another human participant for novel sexual encounters (Guzman and Lewis 2019; Laestadius et al. 2022).”
Anthropotropism: Searching for Recognition in the Scandinavian Gig Economy
Newlands, G. (2022) · Sociology · doi:10.1177/00380385211063362 · Cited by 39
Academic Integrity and Artificial Intelligence: An Overview
Kumar, R., Eaton, S. E., Mindzak, M., & Morrison, R. (2023) · Handbook of Academic Integrity · doi:10.1007/978-981-287-079-7_153-1 · Cited by 35
Heng, S., & Zhang, Z. (2025) · Psychology Research and Behavior Management · doi:10.2147/prbm.s531805 · Cited by 31
Posthuman Creativity: Unveiling Cyborg Subjectivity Through ChatGPT
Yan, D. (2025) · Qualitative Inquiry · doi:10.1177/10778004241231923 · Cited by 28
Normative Evaluation of Large Language Models with Everyday Moral Dilemmas
Sachdeva, P., & van Nuenen, T. (2025) · Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency · doi:10.1145/3715275.3732044 · Cited by 26
“Good” and “Bad” Machine Agency in the Context of Human-AI Communication: The Case of ChatGPT
Brandtzaeg, P. B., You, Y., Wang, X., & Lao, Y. (2023) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-48057-7_1 · Cited by 25
Li, E. C.-Y., & Pang, K.-W. (2024) · European Journal of Cultural Studies · doi:10.1177/13675494241236146 · Cited by 24
Reframing deception for human-centered AI
Umbrello, S., & Natale, S. (2024) · International Journal of Social Robotics · doi:10.1007/s12369-024-01184-4 · Cited by 23
Evolving Coagency between Artists and AI in the Spatial Cocreative Process of Artmaking
Nordström, P., Lundman, R., & Hautala, J. (2023) · Annals of the American Association of Geographers · doi:10.1080/24694452.2023.2210647 · Cited by 17
Examining Agency, Expertise, and Roles of AI Systems in AI-Mediated Communication
Mieczkowski, H., & Hancock, J. (2022) · doi:10.31219/osf.io/asnv4 · Cited by 16
Characterizing Communication Patterns between Audiences and Newsbots
Gómez-Zará, D., & Diakopoulos, N. (2020) · Digital Journalism · doi:10.1080/21670811.2020.1816485 · Cited by 15
Liu, D. (2025) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2024.0532 · Cited by 14
Co-creating humanistic AI AgeTech to support dynamic care ecosystems: a preliminary guiding model
Hwang, A. S., Tannou, T., Nanthakumar, J., Cao, W., Chu, C. H., Zeytinoglu Atici, C., Scane, K., Yu, A., Tsang, W., Chan, J., et al. (2024) · The Gerontologist · doi:10.1093/geront/gnae093 · Cited by 13
Centeno-Martín, H., Toledano-Buendía, S., & Ardévol-Abreu, A. (2023) · Anuario ThinkEPI · doi:10.3145/epi.2023.sep.02 · Cited by 10
Li, Z., & Shi, Z. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2025.2584725 · Cited by 9
Humanity’s Evolving Conversations: AI as Confidant, Coach, and Companion
Wiederhold, B. K. (2024) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2024.0387 · Cited by 6
Gnewuch, U., & Reinkemeier, F. (2026) · MIS Quarterly · doi:10.25300/misq/2025/18742 · Cited by 5
Yang, X. (2026) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2026.2618548 · Cited by 3
Metadata
- Authors
- Guzman, A. L., & Lewis, S. C.
- Venue
- New Media & Society
- Publisher
- SAGE Publications
- Published
- 2019-07-04
- Volume
- 22
- Issue
- 1
- Pages
- 70-86
- ISSN
- 1461-4448 (ISSN-L)
- 1461-7315
- OpenAlex
- W2955088691
- Cited by
- 951
- References
- 6
- FWCI
- 67.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.