arXiv:2608. 08617v1 Announce Type: new Abstract: Group discussion-based teaching is widely used to foster collaborative learning, yet teachers in physical classrooms often struggle to simultaneously monitor multiple groups and quickly diagnose a target group before intervening.
arXiv:2606. 09831v1 Announce Type: cross Abstract: As classroom cohorts expand, team teaching is increasingly used to integrate the expertise and pedagogical perspectives of multiple teachers.
arXiv:2606. 11835v1 Announce Type: cross Abstract: Collecting participants' lived experiences is central to design research.
arXiv:2606. 12419v1 Announce Type: cross Abstract: Several educational domains rely heavily on diagrams and visual cues, yet most existing tutoring datasets are limited to text-only interactions.
arXiv:2606. 09239v1 Announce Type: new Abstract: While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely confined to standalone applications and offer limited support for transitioning their visual analytics solutions into interactive web environments.
arXiv:2608.24580v1 Announce Type: new Abstract: Understanding social interactions from non-verbal visual data is important for behavior analysis and activity monitoring. We propose an interpretable c...
arXiv:2607. 19209v1 Announce Type: cross Abstract: This full paper in the research-to-practice track presents methods for assessing student teams in tabletop exercises (TTXs).
Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static charts and overlook the interactive dashboards commonly used for real-world data exploration. We introduce Dashboard2Code, a novel task that requires a model to proactively explore an interactive dashboard, acquire and integrate feedback from its own interactions (e.
arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).
The paper introduces a computational framework that detects dynamic team‑process phases in collaborative virtual reality (VR) by analyzing timestamped dialogue. It uses late chunking, penalized Gaussian‑kernel change‑point detection, TF‑IDF, NMF, and a locally deployed large language model to identify semantic transitions and generate interpretable phase descriptions. The detected phases are aligned with interaction logs, demonstrating that transcript‑derived phases correspond to task‑action patterns and thus provide a transparent, transferable method for studying temporal changes in teamwork.
arXiv:2607. 04501v1 Announce Type: cross Abstract: The ability to automatically infer analytic intent from user interaction histories could enable interactive AI systems to proactively assist users during exploratory data analysis.
The paper introduces CanvasConvo, a system that presents large language model (LLM) conversations in two synchronized views: a traditional linear chat for ongoing dialogue and a spatial canvas that visualizes the conversation’s branching structure. In a five‑day field study with 24 participants, users tended to switch between the views rather than replace chat entirely; chat remained the primary interaction mode while the canvas was used for overview, revisiting, and exploring alternative paths. The results highlight challenges such as entrenched chat habits, smooth transitions between representations, and understanding branch context, offering insights for designing LLM interfaces that blend linear and non‑linear conversation representations.
Conversational DNA is a visual language and interactive atlas designed to explore human and AI dialogue by mapping speaker strands, communicative bases, and directed pairings. It visualizes speaker switching, response distance, and contribution length through adjustable helix geometry, and covers 151,489 episodes across eight corpora totaling 1.57 million source records. The system improves precision@5 on Molweni motif queries from 58.8% to 77.2% and demonstrates how annotation coverage affects perceived collection differences.