Category: Computer Science & AI
Visual Lyrics: Generating Animated Text for Music Lyric Videos with an Augmented Text Editor
System for generating animated lyric videos using augmented text editor interface and multimodal music analysis with LLM-driven animation synthesis.
P2P: From Prompt to Prototype – Functional UI Design with LLMs and MCP
Tutorial addressing the implementation gap in HCI research through LLM and MCP-enabled functional UI design, bridging conceptual design and deployable interfaces while maintaining accessibility.
Narrative Scaffolding: A Narrative-First Framework for Data-Driven Sensemaking
Narrative Scaffolding positions narrative construction as the primary interface for data-driven exploration, enabling deeper reflection and broader investigation patterns while preserving.
Beyond the Conversational Paradigm: Models, Design Principles, and Tools for Advancing the Spectrum of Human-LLM Interaction
Explore how LLM interfaces extend beyond conversations to integrate graphical elements. Research reveals user preferences vary by task, expertise, and context, enabling adaptive hybrid interface.
CodeVoyager: Integrating Interactive Visual Aids with LLMs for Code Comprehension
Study integrating LLMs with interactive visual aids for code comprehension, evaluating improved understanding and user trust through multimodal interaction design.
CoLyricist: Enhancing Lyric Writing with AI through Workflow-Aligned Support
AI-assisted lyric writing tool leveraging workflow-stage analysis to provide tailored support for experienced and novice lyricists across theme, ideation, drafting, and melody-fitting phases.
Smarter Together: Enhancing Human-AI Collaborative Grading With Teacher-Cognition Multi-Agent LLM Framework
TC-MAG framework uses multi-agent AI to improve automated grading of open-ended student responses with explainable partial credit assessment and teacher oversight capabilities.
Neural Transparency: Mechanistic Interpretability Interfaces for Anticipating Model Behaviors for Personalized AI
Neural transparency interface for LLM chatbot design enabling users to anticipate model behaviors through mechanistic interpretability visualization of behavioral trait vectors.
No Code, No Cloud: On-Device Mockup-to-Code with Lightweight Vision-Language AI
Lightweight on-device vision-language model generating HTML from design mockups without cloud infrastructure, supporting private prototyping with 235M parameters achieving competitive results.
Assessing the Critical Failure Factors of AI Chatbots for Research Using ISM Approach
Study using interpretive structural modeling to analyze critical failure factors of AI chatbots in academic research, revealing researcher knowledge deficiency as the primary constraint.
