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Jiaye Leng

Publications and source records attributed to Jiaye Leng.

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Spatial Balancing: Designing an LLM-Powered Spatial Externalization Interface for Iterative Science Communication Writing

Science communication revision requires writers to dynamically balance scientific exposition and narrative engagement - a process where writers often struggle with competing directions. Existing LLM-assisted tools help with co-writing, but offer limited support for navigating this iterative, multi-directional revision process. To address this gap, we designed Spatial Balancing, an exploratory revision environment that maps rhetorical goals and revision strategies onto a two-dimensional spatial canvas for experienced science communication creators with domain expertise but lacking formal professional training. By building a design space of communication strategies and embedding them into a spatial exploratory canvas, our system treats feedback as navigational cues rather than prescriptive judgments. Our findings show that this integrated revision environment helps writers stay focused on writing goals, reason about revision as trajectories, and explore alternatives, which supports greater metacognitive control and confidence without increasing workload. This work highlights the value of spatially externalized revision environments for supporting iterative, reflective thinking during LLM-assisted writing.

cs.HC

MoGraphGPT: Creating Interactive Scenes Using Modular LLM and Graphical Control

Creating interactive scenes often involves complex programming tasks. Although large language models (LLMs) like ChatGPT can generate code from natural language, their output is often error-prone, particularly when scripting interactions among multiple elements. The linear conversational structure limits the editing of individual elements, and lacking graphical and precise control complicates visual integration. To address these issues, we integrate an element-level modularization technique that processes textual descriptions for individual elements through separate LLM modules, with a central module managing interactions among elements. This modular approach allows for refining each element independently. We design a graphical user interface, MoGraphGPT , which combines modular LLMs with enhanced graphical control to generate codes for 2D interactive scenes. It enables direct integration of graphical information and offers quick, precise control through automatically generated sliders. Our comparative evaluation against an AI coding tool, Cursor Composer, as the baseline system and a usability study show MoGraphGPT significantly improves easiness, controllability, and refinement in creating complex 2D interactive scenes with multiple visual elements in a coding-free manner.

cs.HC