Recent adoption of conversational information systems has expanded the scope of user queries to include complex tasks such as personal advice-seeking. However, we identify a specific type of sought advice-a request for a moral judgment (i.e. "who was wrong?") in a social conflict-as an implicitly humanizing query which carries potentially harmful anthropomorphic projections. In this study, we examine the reinforcement of these assumptions in the responses of four major general-purpose LLMs through the use of linguistic, behavioral, and cognitive anthropomorphic cues. We also contribute a novel dataset of simulated user queries for moral judgments. We find current LLM system responses reinforce implicit humanization in queries, potentially exacerbating risks like overreliance or misplaced trust. We call for future work to expand the understanding of anthropomorphism to include implicit userside humanization and to design solutions that address user needs while correcting misaligned expectations of model capabilities.
Agentic AI systems are changing how people seek and use information. Yet, the research community has not fully adapted – methods for studying, building, and assessing such systems often remain static, missing the interactive, temporal, and evidence-driven dynamics that characterize real information seeking. This hands-on tutorial equips the CHIIR community with a concise, practice-oriented methodology for leveraging and evaluating information-seeking agents. We define a shared vocabulary for agents and connect it to user-centered IR constructs; we show how to design agentic workflows that elicit effective evidence seeking under temporal change (planning, tool choice, grounding); and we introduce log-based rubrics that score correctness, evidence support, adequacy, and cost. Short case studies and optional demonstrations using open frameworks (for example, Perplexica, local LLMs via Ollama, and metasearch engines such as SearXNG) illustrate how these ideas map to real systems. Attendees receive reusable materials, including slides and selected supplemental resources (e.g., example traces and optional demo notebooks), suitable for research and teaching. The tutorial assumes familiarity with core IR concepts but does not require prior experience with agent frameworks.
