We present AgentCoach, an LLM-powered system that provides adaptive feedback for motor skill learning from tutorial videos. The system works by extracting key coaching points (CPs) and compiling CP-specific evaluators that map each cue to measurable kinematic parameters. This process allows AgentCoach to connect high-level semantic meaning with low-level postural estimation for accurate, context-aware evaluation. During practice, learners receive concise visual diagnostics of their mistakes paired with prescriptive verbal feedback that adapts based on their performance history. We technically validate the CP extraction and evaluator compilation across a wide range of common sports and exercise videos. A user study confirms the system's usability and shows the system's potential effectiveness of its adaptive feedback across multiple skills.
Output-centric programming paradigms such as Direct Manipulation Programming, Programming By Demonstration, and Programming By Example enable users to author programs by constructing an intended output. However, sometimes the purpose of a programming interaction is to discover an "intended output'' in the first place (e.g., exploratory data analysis, improvisational creative coding, early-stage prototyping). We argue that one role for output-centric programming here is scaffolding the user in demonstrating their next program editing step by selecting among possible modifications to their current program. We call this Programming By Scaffolded Demonstration (PBSD). To explore PBSD, we built Perpend, a programming environment for p5.js. In a user study with nine artists, we juxtapose Perpend with an existing Direct Manipulation editor, exploring how participants used Perpend to situate themselves within a space of possible programs, shift focus between program text and visual output, and shape their exploration by modifying their program structure.
Recent work identified clarity as one of the top quality attributes that notebook users value, but notebooks lack support for maintaining clarity throughout the exploratory phases of the notebook authoring workflow. We propose always-clear notebook authoring that supports both clarity and exploration, and present a Jupyter implementation called Tidynote. The key to Tidynote is three-fold: (1) a scratchpad sidebar to facilitate exploration, (2) cells movable between the notebook and the scratchpad to maintain organization, and (3) linear execution with state forks to clarify program state. An exploratory study (N=13) of open-ended data analysis tasks shows that Tidynote features holistically promote clarity throughout a notebook's lifecycle, support realistic notebook tasks, and enable novel strategies for notebook clarity. These results suggest that Tidynote supports maintaining clarity throughout the entirety of notebook authoring.
Visualization authoring is an iterative process requiring users to adjust parameters to achieve desired aesthetics. Due to its complexity, users often create defective visualizations and struggle to fix them. Many seek help on forums (e.g., Stack Overflow), while others turn to AI, yet little is known about the strengths and limitations of these approaches, or how they can be effectively combined. We analyze Vega-Lite debugging cases from Stack Overflow, categorizing question types by askers, evaluating human responses, and assessing AI performance. Guided by these findings, we design a human-AI co-debugging system that combines LLM-generated suggestions with forum knowledge. We evaluated this system in a user study on 36 unresolved problems, comparing it with forum answers and LLM baselines. Our results show that while forum contributors provide accurate but slow solutions and LLMs offer immediate but sometimes misaligned guidance, the hybrid system resolves 86\% of cases, higher than either alone.
Augmented Reality (AR) tutorials enhance procedural task learning by providing situated, step-by-step guidance. Yet, creating such tutorials requires AR authoring expertise, posing a significant entry barrier. To lower this barrier, we introduce ARify, an authoring system that semi-automatically transforms narrated instructional videos into AR tutorials. To guide system design, we conducted a content analysis of video tutorials and derived a design space of instructional intents, tactics, and AR representations. Building on this, ARify generates AR tutorials by integrating a vision–language model to plan tutorial structures and an AR builder to configure AR representations, and offers interfaces that allow users to refine and customize the results. A numerical study on three machine tasks and a user study with 18 participants showed that ARify achieves promising performance across task types, and allows novices to author effective AR tutorials, validating its effectiveness and usability.
Visual design instructors often provide multi-modal feedback, mixing annotations with text. Prior theory emphasizes the importance of actionable feedback, where “actionability” lies on a spectrum—from surfacing relevant design concepts to suggesting concrete fixes. How might creativity tools implement annotations that support such feedback, and how does the actionability of feedback impact novices’ process-related behaviors, perceptions of creativity, learning of design principles, and overall outcomes? We introduce VizCrit, a system for providing computational feedback that supports the actionability spectrum, realized through algorithmic issue detection and visual annotation generation. In a between-subjects study (N=36), novices revised a design under one of three conditions: textbook-based, awareness-centered, or solution-centered feedback. We found that solution-centered feedback led to fewer design issues and higher self-perceived creativity compared with textbook-based feedback, although expert ratings on creativity showed no significant differences. We discuss the implications for AI in Creativity Support Tools, including the potential of calibrating feedback actionability to help novices balance productivity with learning, growth, and developing design awareness.
Short-form video (SFV) platforms are increasingly popular, yet the rapid context switching and their potential effects on children’s cognitive functions are not well understood. In this work, we conducted a between-subjects experiment ($N = 180$) to examine how YouTube Shorts affects young children’s short-term memory (STM) and working memory (WM), measured using the forward and backward digit span tasks. The study focused on two core platform features of SFV: the easy-to-use swipe interface and the recommendation system. Using a 2$\times$2 factorial design, we compared four SFV group conditions that varied by interaction mode and content source, complemented by two long-form video baseline conditions (one with constant context switching and one without). Our results show that the feature combinations and the baseline comparisons were not associated with changes in STM or WM. However, swipe interaction increased video switching, while recommendation-based content increased category switching. The higher combined levels of video and category switching across participants were associated with marginal effects on working memory performance, while STM remained unaffected.