InfoAlign: A Human-AI Co-Creation System for Storytelling with Infographics

2026-02-26

AI Summary

This work addresses the challenge that existing infographics authoring tools struggle to maintain narrative coherence and align with users’ storytelling goals during the design process. To bridge this gap, the authors propose a narrative-centered, three-stage human-AI collaborative framework—encompassing story construction, visual encoding, and spatial layout—and implement it in a system called InfoAlign. By integrating natural language processing, semantic alignment–based design recommendations, and parametric layout generation, InfoAlign enables the transformation of unstructured text into a coherent narrative, recommends visually consistent encodings, and produces layout blueprints—all while preserving full user control throughout the workflow. InfoAlign represents the first approach to explicitly align story semantics with visual expression, significantly enhancing both narrative coherence and the collaborative experience in data storytelling.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Natural Language Processing: Information Extraction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data visualization

Abstract

Storytelling infographics are a powerful medium for communicating data-driven stories through visual presentation. However, existing authoring tools lack support for maintaining story consistency and aligning with users' story goals throughout the design process. To address this gap, we conducted formative interviews and a quantitative analysis to identify design needs and common story-informed layout patterns in infographics. Based on these insights, we propose a narrative-centric workflow for infographic creation consisting of three phases: story construction, visual encoding, and spatial composition. Building on this workflow, we developed InfoAlign, a human-AI co-creation system that transforms long or unstructured text into stories, recommends semantically aligned visual designs, and generates layout blueprints. Users can intervene and refine the design at any stage, ensuring their intent is preserved and the infographic creation process remains transparent. Evaluations show that InfoAlign preserves story coherence across authoring stages and effectively supports human-AI co-creation for storytelling infographic design.

Problem

• storytelling infographics
• story consistency
• design alignment
• human-AI co-creation
• visual storytelling

Innovation

• human-AI co-creation
• storytelling infographics
• narrative-centric workflow
• semantic alignment
• interactive design