As people increasingly turn to AI for personal deliberation beyond task-oriented assistance, concerns about sycophancy in these value-laden contexts have grown. Unlike human flattery, which is intentional and self-interested, AI sycophancy emerges as a byproduct of RLHF’s reward structure for user-preference alignment. Yet the observable behavior is similar: both produce responses that preserve what users want to hear. Focusing on this phenomenon through Goffman’s face-work framework, we operationalize AI sycophancy as excessive face-saving, either active (preserving positive face through agreement) or passive (preserving negative face by withholding challenge). In a mixed-methods study (N=31), participants engaged with AI across three moral dilemmas under these conditions and a non-sycophantic neutral baseline. Sycophantic responses increased decision confidence but reduced open-minded thinking; participants felt supported yet found the conversations unproductive. Neutral responses, though initially uncomfortable, promoted cognitive flexibility and meaningful deliberation. These findings reveal a confidence-competence trade-off in AI-mediated moral reasoning and suggest that effective AI for personal deliberation requires calibrated friction, not unconditional agreement.

There is an ongoing discussion on what makes humans more engaged when interacting with conversational agents. However, in the area of language processing, there has been a paucity of studies on how people react to agents and share interactions with others. We attack this issue by investigating the user dialogues with human-like agents posted online and aim to analyze the dialogue patterns. We construct a taxonomy to discern the users’ self-disclosure in the dialogue and the communication authenticity displayed in the user posting. We annotate the in-the-wild data, examine the reliability of the proposed scheme, and discuss how the categorization can be utilized for future research and industrial development.

Recently, with the advent of high-performance generative language models, artificial agents that communicate directly with the users have become more human-like. This development allows users to perform a diverse range of trials with the agents, and the responses are sometimes displayed online by users who share or show-off their experiences. In this study, we explore dialogues with a social chatbot uploaded to an online community, with the aim of understanding how users game human-like agents and display their conversations. Having done this, we assert that user postings can be investigated from two aspects, namely conversation topic and purpose of testing, and suggest a categorization scheme for the analysis. We analyze 639 dialogues to develop an annotation protocol for the evaluation, and measure the agreement to demonstrate the validity. We find that the dialogue content does not necessarily reflect the purpose of testing, and also that users come up with creative strategies to game the agent without being penalized.

WARNING: This article contains contents that may offend the readers. Strategies that insert intentional noise into text when posting it are commonly observed in the online space, and sometimes they aim to let only certain community users understand the genuine semantics. In this paper, we explore the purpose of such actions by categorizing them into tricks, memes, fillers, and codes, and organize the linguistic strategies that are used for each purpose. Through this, we identify that such strategies can be conducted by authors for multiple purposes, regarding the presence of stakeholders such as ‘Peers’ and ‘Others’. We finally analyze how these strategies appear differently in each circumstance, along with the unified taxonomy accompanying examples.