ECHO: Explainable Co-editing with Human-in-the-loop Operations for Presentation Refinement

Abstract

Authoring and refining presentation slides is a highly time-consuming core task in academic and business domains. While generative AI tools have lowered the barrier for creating initial drafts, their "black-box, one-way generation" paradigm severely deprives users of fine-grained control. Through a formative study (N=10), we identified "trial-and-error anxiety" and "inconsistent cross-page formatting" as primary bottlenecks in human-AI co-creation. Consequently, we present ECHO, an interactive system based on multimodal intent grounding and explainable operation plans. ECHO enables precise local edits via a "natural language + visual selection" paradigm, utilizing a decoupled "Plan-Confirm-Execute" loop and dynamic memory mechanisms to transform implicit AI intents into highly controllable layout co-creation. To systematically evaluate document refinement, we propose the CoEdit-Eval framework. Objective evaluations across multiple foundation models (e.g., GPT-5, GLM-4.7) demonstrate that while baselines uniformly fail in intent mapping (0% accuracy) and spatial grounding (0% Hit@1), the ECHO architecture boosts Target Hit@1 to 55%--85% depending on the base model. Furthermore, integrating Vision-Language Models (VLMs) effectively resolves spatial ambiguities -- achieving significant win rates in LLM blind evaluations -- and our Undo mechanism guarantees 100% physical file consistency (MD5 hash). Finally, a controlled study with 14 participants shows that ECHO significantly reduces cognitive workload (NASA-TLX scores dropped by 20.8%, from 82.6 to 65.4) and reveals the dynamic evolution of human control allocation across different cognitive tasks.