Examining Agency, Expertise, and Roles of AI Systems in AI-Mediated Communication
Open accessAbstract
AI-Mediated Communication (AI-MC) occurs when an AI system operates on behalf of an individual in communication between people. Building on literature in psychology and human-machine communication, the following three studies answered two overarching questions: 1) how do people perceive their own agency when an AI system operates on their behalf in interpersonal communication? and 2) how do people perceive the role(s) of the system under these circumstances? Through a think-aloud study, as well as an online experiment, these studies revealed that people strive to maintain their agency in AI-MC, and devise various strategies to do so. Additionally, even though AI systems can play many roles in a communication task, people view them as social actors instead of as extensions of the self, which impacts how they attribute agency and behave in human-AI interactions. Lastly, people show increased reliance on the AI system when they lack expertise, which underlies decreased feelings of agency as well as changes in collaboration dynamics. Taken together, these studies apply research on agency and human-AI interaction to the nascent study of AI-MC. The results provide insights into the effects of AI-involvement and topical expertise on people’s perceptions and behaviors, as well as how they think about the roles of AI systems.
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In visual co-creation, AI agency often starts as reactive to the initial prompt but may shift towards co-operative during refinement.
“AI agency often starts as reactive to the initial prompt [60, 112] (AI-centric locus) but may shift towards co-operative during refinement (shared locus) [118].”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [118]
Section 3.4.1 (page unavailable) reports bidirectional influence: participants shaped AI output, and AI output affected what they wrote next. That supports a cooperative aspect, but the paper studies message writing, not visual co-creation, and does not establish a shift from reactive prompting to cooperative agency during refinement. (LLM, conf=0.82)
Experimental — accuracy not yet measuredIn the textual pathway, AI commonly exhibits cooperative agency, functioning as an active partner.
“The AI commonly exhibits cooperative agency, functioning as an active partner [118].”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [118]
A more comprehensive understanding of views surrounding authenticity in human-AI co-creation remains elusive.
“Recent work on AI use for writing has begun to explore relevant constructs, such as ownership, authorship, and agency [11,13,39,49], but a more comprehensive understanding of views surrounding authenticity in human-AI co-creation remains elusive.”
From 'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models (2025) · cites this paper as [49]
Integration of autonomous AI into co-creation could significantly restructure creative industries and labor markets, raising questions about intellectual property, fair compensation, and skill requirements.
“Societally, the integration of autonomous AI into co-creation could significantly restructure creative industries and labor markets [18, 60, 118], raising critical questions about the future of creative work, including intellectual property [83], fair compensation [82], and evolving skill requirements [202].”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [118]
The citation is attached to the proposed restructuring of creative industries and labor markets; the sentence cites other sources for intellectual property, compensation, and skills. Section 6.2 (Future Directions; page unavailable) discusses workplace consequences of attributing agency and credit or blame in human–AI collaboration, but does not address industry or labor-market restructuring. Searches for creative industries, labor markets, employment, and displacement found no passage establishing that point. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Yoonseo Choi, Eun Jeong Kang, and Juho Kim. 2021. How Does Netflix "Understand" Me?: Exploring End-user Needs to Design Human-centered Explanations. In 35th Conference on Neural Information Processing Systems. Sydney, Australia. https://kixlab.github.io/website-files/2021/neurips2021-workshop-HumanCenteredExplanation-paper.pdf
- Ada S Chulef, Stephen J Read, and David A Walsh. 2001. A hierarchical taxonomy of human goals. Motivation and Emotion 25, 3 (2001), 191-232.
- Haiyi Zhu, Bowen Yu, Aaron Halfaker, and Loren Terveen. 2018. Value-sensitive algorithm design: Method, case study, and lessons. Proceedings of the ACM on human-computer interaction 2, CSCW (2018), 1-23.
Cited by
Sorted by citations
Draxler, F., Werner, A., Lehmann, F., Hoppe, M., Schmidt, A., Buschek, D., & Welsch, R. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3637875 · Cited by 186 · see citing works
A Design Space for Intelligent and Interactive Writing Assistants
Lee, M., Gero, K. I., Chung, J. J. Y., Shum, S. B., Raheja, V., Shen, H., Venugopalan, S., Wambsganss, T., Zhou, D., Alghamdi, E. A., et al. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642697 · Cited by 136 · see citing works
Kobiella, C., Flores López, Y. S., Waltenberger, F., Draxler, F., & Schmidt, A. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3641964 · Cited by 57 · see citing works
Sahebi, S., & Formosa, P. (2025) · Synthese · doi:10.1007/s11229-025-04963-2 · Cited by 69 · see citing works
'It was 80% me, 20% AI': Seeking Authenticity in Co-Writing with Large Language Models
Hwang, A. H.-C., Liao, Q. V., Blodgett, S. L., Olteanu, A., & Trischler, A. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3711020 · Cited by 42 · see citing works
The Role of Inclusion, Control, and Ownership in Workplace AI-Mediated Communication
Kadoma, K., Aubin Le Quere, M., Fu, X. J., Munsch, C., Metaxa, D., & Naaman, M. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642650 · Cited by 24 · see citing works
Navigating the AI era: university communication strategies and perspectives on generative AI tools
Henke, J. (2024) · Journal of Science Communication · doi:10.22323/2.23030205 · Cited by 16 · see citing works
AI in the Writing Process: How Purposeful AI Support Fosters Student Writing
Siddiqui, M. N., Feliciano, V. A., Pea, R. D., & Subramonyam, H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-98459-4_14 · Cited by 6 · see citing works
Beulen, E., & Dans, M. A. (2026) · Information · doi:10.3390/info17040336 · Cited by 6 · see citing works
Zindulka, T., Goller, S., Lehmann, F., & Buschek, D. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713890 · Cited by 3 · see citing works
Nguyen, H. T. M., Thắng, P. C., Trang, T. T. N., & Le, L. B. (2026) · Telematics and Informatics Reports · doi:10.1016/j.teler.2026.100331 · Cited by 2 · see citing works
“My AI must have been broken”: How AI Stands to Reshape Human Communication
Naaman, M. (2022) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3523227.3555724 · Cited by 2 · see citing works
Enhancing qualitative inquiry: AI-assisted focus group data collection
Chen, H., Wang, Y., & Chang, C. (2025) · Qualitative Research Journal · doi:10.1108/qrj-04-2025-0145 · Cited by 3 · see citing works
Bhatti, M., Isa, F. M., & Muhammad, D. N. M. N. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4503480
AI as a Third Party in Coaching: Balancing Trust, Ethics, and Innovation
Diller, S. J., & Smith, W. (2026) · SpringerBriefs in psychology · doi:10.1007/978-3-032-23332-5_9
Theorising authenticity vis-à-vis, not versus, syntheticity in the age of AI
Bradley, A. F., & Marín, M. D. M. P. (2026) · AI-Linguistica Linguistic Studies on AI-Generated Texts and Discourses · doi:10.62408/ai-ling.v4i1.39
Hmood, S. S. (2026) · Research Journal in Advanced Humanities · doi:10.58256/b0qayg59
Generative AI, Augmenting Remote Contact
Jeffares, S. (2026) · Springer eBooks · doi:10.1007/978-3-032-27796-1_10
- Authors
- Mieczkowski, H., & Hancock, J.
- Affiliations
- Stanford University (US)
- Publisher
- Center for Open Science
- Published
- 2022-07-15
- OpenAlex
- W4285591746
- Cited by
- 16
- References
- 100
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 3 · 2023: 2 · 2022: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- OSF Preprints (OSF Preprints) (submittedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- AI-mediated communication, agency, expertise, AI roles, human-AI interaction, interpersonal communication, AI reliance, collaboration dynamics, think-aloud
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