CORTEXA
← Browse
arxivcs.HC2026-06-27

"If I Can See You": Understanding Spatially Situated Virtual Embodiment in Close Human-AI Relationships

Yulin Chen, Yang Zhan, Qiao Jin

AI companions are increasingly used for emotional support, companionship, and intimate interaction. While prior work has examined text- and voice-based AI companionship and emerging XR companion designs, less is known about how users with existing close AI companion relationships expect those relationships to change when companions become virtually embodied and spatially situated in everyday environments. To address this gap, we conducted a qualitative study with 17 AI companion users recruited from Reddit AI companion communities. We frame spatially situated virtual embodiment as a form of relational escalation: embodiment can make AI companionship more present, socially legible, and risk-sensitive in everyday life. Our findings show that: (1) embodiment creates tensions between support and intrusion, concreteness and imaginative openness, and growth and consistency; (2) embodiment can turn private AI companionship into a socially legible relational arrangement, requiring visibility, form, interaction style, and mode of access to be negotiated across social contexts; and (3) embodiment can intensify risks of emotional dependence, sensitive disclosure, social judgment, and misguided spatial action by increasing the companion's perceived relational presence, intimacy, public legibility, and spatial authority. We argue that future system design should first consider when embodiment is warranted, how embodied presence should be staged, how visibility and role boundaries should be negotiated, and how embodied companionship can remain safe. This work contributes to HCI research on human-AI intimacy by showing how virtual embodiment can transform close AI companionship into a spatial, socially visible, and risk-sensitive relationship.

View free PDFSource page

Related papers

arxivcs.HC2026-07-24

The machine can say it but cannot hear it. Designed affective patterns and the expressive-sensing asymmetry in human-machine communication

Jan K. Argasinski

Affect-adaptive systems increasingly act as communicators that sense a user's emotion and respond with events meant to change it, closing an affective loop. This vision assumes both that a machine's affective messages are received and that the bodily channel it monitors carries a…

View free PDFSource page
arxivcs.HCcs.AI2026-07-24

Beyond Perspectives: A Trio-Ethnography of Interpretation Evolution in LLM-Supported Programming Education

Jennie Ren, Jordan H. McDowell, Kyrie Zhixuan Zhou

Generative AI is reshaping programming education, yet educators often infer students' AI-supported learning from classroom observations alone. This experience report presents a trio-ethnography involving two computing educators with different teaching philosophies and one undergr…

View free PDFSource page
arxivq-bio.QMcs.HC2026-07-24

Loom: Multi-Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories

Siyuan Zhao, Nafiul Nipu, Hossein Fathollahian, Olga Karginova, Hao Chen, Ameen Salahudeen, et al.

We present Loom, a spatial transcriptomics (ST) visual computing system to support the analysis of pseudo-temporal trajectories, comparative investigation across samples and regions of interest, and the examination of spatially structured processes within local microenvironments.…

View free PDFSource page
arxivcs.HCcs.CL2026-07-24

Towards Reducing Foreign Language Anxiety Using Level-Appropriate Embodied Conversational Agents

Krishan Rajaratnam, Wenbin Gan, Yuan Sun

Foreign language anxiety (FLA) can be a major barrier to second language acquisition (SLA), especially in conversational contexts. With the proliferation of large language models (LLMs) throughout all areas of life, recent work suggests that interacting with LLM agents can be ins…

View free PDFSource page
arxivcs.HCcs.AIcs.LGcs.MM2026-07-24

Unboxing Diffusion Models for the Arts: Interactive Model Bending and Practice-Based Explainability

Ahmed M. Abuzuraiq, Philippe Pasquier

Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug models as part of making Yet largescale texttoimage diffusion systems are typically presented as opaque endtoend tools limiting this k…

View free PDFSource page