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.

Generative information retrieval (GenIR) enables users to obtain synthesized information through iterative interaction with LLMs, fundamentally reshaping how AI-generated content is produced and consumed. Within this shift, users may encounter AI-generated informational content through two primary pathways: actively creating it themselves or consuming content generated by others. We examine whether authorship biases evaluation---whether users judge AI output from their own interactions more favorably than equivalent output from others. In a mixed-methods experiment (N=28, 2×2 within-subjects), participants interacted with an AI system to retrieve and craft information, then evaluated both their own result and equivalent output generated through the same process but framed as someone else's. Results reveal a selective authorship bias: participants rated self-obtained information significantly higher in quality, but showed no corresponding difference in trust. This pattern suggests that hallucination-aware skepticism constrained trust judgments, but could not prevent quality-driven selection behavior, even in the presence of information conflicts. Given that iterative interactions are inherent to GenIR, diverse interventions seem needed to support users' critical evaluation.

Conversational shopping agents powered by large language models are increasingly used for online product exploration, yet the role of interaction style in shaping shopping behavior and user experience remains underexplored in shopping IR. To address the gap, we conducted two studies in experience-goods domains. Study 1 involved 24 participants and compared a neutral conversational agent with a traditional product search interface, confirming functional adequacy and identifying two unmet needs, self-reflective preference structuring and socially grounded relational guidance. Study 2 involved 30 participants and evaluated two personas derived from these needs, Self-Mirroring and Relational Peer, against the same neutral agent in a within-subjects design with information availability held constant. Self-Mirroring increased critical thinking scores and sustained follow-up questioning of retrieved content, whereas Relational Peer increased social presence while reducing explicit verification behaviors such as comparing alternatives and checking conditions. The discussion outlines implications for AI shopping agents that adapt interaction style to decision context, balancing efficient exploration with user-led evaluation.

Conversational argument search systems influence how users access diverse perspectives but are prone to selective exposure. To address this, we propose two strategies: an interface-level multi-agent framework that structures perspective presentation and an interaction-level questioning strategy that encourages deeper engagement. We evaluate these strategies through a 2 x 2 factorial user study, examining their impact on selective exposure. Results show that the multi-agent setup facilitates broader perspective comparison, while agent-initiated questioning fosters deeper reflection; together, they promote more balanced argument access. Based on these findings, we discuss conversational search systems to mitigate selective exposure by implementing multi-agent interactions and questioning mechanisms.

The self-reference effect is a psychological phenomenon where information relating to oneself is processed more deeply and remembered more effectively than other information. We propose "self-referential reviews," crafted by merging personal information with existing reviews using the novel "Self-Referential ReviewMaker" prototype, which leverages Large Language Models (LLMs). The essence of the "self-referential review" lies in harnessing the self-reference effect, making the readers feel as if they are the protagonist of the review. To validate the efficacy of self-referential reviews, we conducted a user study focusing on online reviews with thirty-four participants. The contributions of our paper are centered around self-referential reviews, highlighting (1) the creation of these reviews using our new prototype, Self-Referential ReviewMaker, (2) their effectiveness in enhancing review helpfulness through the self-reference effect, and (3) the identification of additional factors influencing the self-reference effect with further discussion on enhancing user-focused review systems.