Chifang Chou | Search arXiv

arXiv2026

Vibe-GUIDE: A Graph-based User Interface in IDEs for Oversight in Vibe Coding

In agentic coding, developers shift from implementing changes themselves to specifying intent, evaluating the agent's work, and making approval decisions. However, delegating implementation can introduce cognitive debt that erodes project comprehension over time, constraining developers' ability to provide oversight. In this work, we investigate the role of persistent shared representations in supporting project comprehension and oversight of coding agents. We present Vibe-GUIDE, an agentic coding interface built around a structural, live, manipulable, and adaptive graph representation organized by functional modules. We evaluated our interface in a randomized between-subjects study comparing how 16 developers completed three cumulative coding tasks using our interface or a Chat-only baseline. We found that Vibe-GUIDE can support project comprehension and sustained task performance while keeping developers cognitively involved in oversight. These findings show how persistent shared representations can complement natural-language interaction and help developers maintain the understanding needed to oversee agent-generated changes as projects evolve.

cs.HC↗

arXiv2026

Vibe Visualizing: How Visualization Novices Try (and Fail) to Generate and Interpret Visualizations with Conversational AI

Conversational AI has enabled users to generate and interpret visualizations through natural language, significantly lowering the technical barrier to entry. The increased accessibility brings visualization novices into data visualization, but also exposes them to misinformation and misinterpretations. We are motivated to examine what issues can arise in interactions with current conversational AI, whether visualization novices can recognize such issues, and how they respond to them. To examine these questions, we conducted a user study on ChatGPT with 20 visualization novices, collecting their conversation logs, semi-structured interview transcripts, and Likert-scale questionnaire responses. Through thematic analysis, we developed a codebook that covers AI execution compliance, issues of AI-generated visualizations, patterns of AI responses, and prompting patterns of users. We summarized four themes, including the quality of outcomes, recurring errors from ChatGPT, misuse by users, factors that affect user trust, confidence, and verification behavior, and human-AI collaboration dynamics. To demonstrate the generalizability of our codebook and findings, we replayed the initial user prompts on Gemini and Claude and compared the outcomes, which revealed distinct failure modes for each model. Based on the results of all analyses, we derive a set of design recommendations for future AI-assisted visualization systems. We conclude with discussions on literacy gaps, diverse human-AI collaboration dynamics, and implications for agentic visualization.

cs.HC↗