CORTEXA
← Browse
arxivcs.HC2026-07-17

Understanding Fortunetelling with Large Language Models in China: User Practices, Perceptions, and Impacts on Beliefs and Decisions

Xueer Lin, Chenyu Li, Shuai Ma, Yuhan Lyu, Zhenhui Peng

Fortunetelling is a cultural practice for navigating uncertainty, often associated with people's beliefs and decisions. Fortunetelling with recent large language models (LLMs) introduces new opportunities and risks. This paper conducts qualitative studies to understand users' practices, perceptions, and impacts of LLM fortunetelling in China. We first analyze 1,045 posts on Chinese social media, yielding a comprehensive taxonomy of the diverse foretold topics (e.g., career, romance), emotion reactions (e.g., surprise, worry), and perceived credibility (e.g., doubt, trust) of LLM fortunetelling. Then, we conduct interviews with 20 users of LLM fortunetelling. The findings indicate that users treat LLM fortunetelling as a tool less for accurate prediction but more for emotional support. While the fortunetelling results rarely change users' initial beliefs or decisions, they are associated with subtle mindset shifts, with some users reporting small behavioral adjustments. We discuss implications for gaining benefits from LLM fortunetelling.

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