When Human-AI Interactions Become Parasocial: Agency and Anthropomorphism in Affective Design
Open accessAbstract
With the continuous improvement of large language models (LLMs), chatbots can produce coherent and continuous word sequences that mirror natural human language. While the use of natural language and human-like conversation styles enables the use of chatbots within a range of everyday settings, these usability-enhancing features can also have unintended consequences, such as making fallible information seem trustworthy by emphasizing friendliness and closeness. This can have serious implications for information retrieval tasks performed with chatbots. In this paper, we provide an overview of the literature on parasociality, social affordance, and trust to bridge these concepts within human-AI interactions. We critically examine how chatbot “roleplaying” and user role projection co-produce a pseudo-interactive, technologically-mediated space with imbalanced dynamics between users and chatbots. Based on the review of the literature, we develop a conceptual framework of parasociality in chatbots that describes interactions between humans and anthropomorphized chatbots. We dissect how chatbots use personal pronouns, conversational conventions, affirmations, and similar strategies to position the chatbots as users’ companions or assistants, and how these tactics induce trust-forming behaviors in users. Finally, based on the conceptual framework, we outline a set of ethical concerns that emerge from parasociality, including illusions of reciprocal engagement, task misalignment, and…
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Users feel more comfortable disclosing sensitive information to LLMs than to other people and may use them to offload emotional burdens.
“Prior work finds that users feel more comfortable disclosing sensitive information to LLMs than to other people (Maeda and Quan-Haase, 2024) and may use them to offload emotional burdens (Eliot, 2024).”
From Sycophantic AI decreases prosocial intentions and promotes dependence (2026) · cites this paper as Maeda and Quan-Haase, 2024
Users typically have limited visibility into how characters are created, tuned, or moderated; which training data informs their emotional styles; or how conversational boundaries are algorithmically enforced.
“Users typically have limited visibility into how characters are created, tuned, or moderated; which training data informs their emotional styles; or how conversational boundaries are algorithmically enforced”
From Toward an Ethic of Synthetic Relationality: Identity, Intimacy, and Risk in AI-Mediated Roleplay Environments (2025) · cites this paper as Maeda and Quan-Haase 2024
Hallucinations can be exacerbated by LLM-CBs' tendencies to attempt to cover up errors or when users trust LLM-CBs due to their anthropomorphic design or established emotional connections.
“Furthermore, hallucinations can be exacerbated by LLM-CBs’ tendencies to attempt to cover up errors37 or when users trust LLM-CBs due to their anthropomorphic design or established emotional connections48,54.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [54]
LLM-CBs' design principles further risk the provision of sensitive data: emotional connections and LLM-CBs' anthropomorphism and sycophancy may increase user trust, risking the disclosure of sensitive health information.
“Furthermore, LLM-CBs’ design principles further risk the provision of sensitive data: emotional connections43 and LLM-CBs’ anthropomorphism and sycophancy54 may increase user trust, risking the disclosure of sensitive health information.”
From A scoping review on the mental health harms of LLM-based chatbots (2026) · cites this paper as [54]
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Sycophantic AI decreases prosocial intentions and promotes dependence
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A scoping review on the mental health harms of LLM-based chatbots
Diel, A., Torous, J., Cuijpers, P., Kleesiek, J., Nensa, F., Weber, N., Faust, F., Lalgi, T. J., Mellis, F. S., Teufel, M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03054-x · Cited by 4 · see citing works
“Furthermore, hallucinations can be exacerbated by LLM-CBs ’ tendencies to attempt to cover up errors 37 or when users trust LLM-CBs due to their anthropomorphic design or established emotional connections 48,54 .”
- Authors
- Maeda, T., & Quan-Haase, A.
- Affiliations
- Western University (CA)
- Faculty of Information and Media Studies, Western University, Canada and Department of Sociology, Western University, Canada
- Venue
- The 2024 ACM Conference on Fairness, Accountability, and Transparency
- Publisher
- ACM
- Published
- 2024-06-03
- Pages
- 1068-1077
- OpenAlex
- W4399362852
- Cited by
- 144
- References
- 18
- Citations by year
- 2026: 103 · 2025: 41
- FWCI
- 96.51
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Conference on Fairness, Accountability, and Transparency (FAccT) (publishedVersion, cc-by-nc-sa)
- 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
- chatbots, anthropomorphism, human-AI interaction, trust in chatbots, role play, social affordances, large language models, affective design, personal pronouns, ethical considerations, information retrieval, positive feedback loop
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