Data Visualization, Data Storytelling, and Artificial Intelligence for Human-Centered Data Communication

Over the past decades, the proliferation of digital technologies has led to the large-scale production and collection of data from a wide variety of sources and devices. However, the mere accumulation of data is not sufficient to generate value. To derive meaningful insights and support informed decision-making, it is crucial to analyze, interpret, and effectively communicate data to relevant audiences. Data visualization and data storytelling play a critical role in this process by translating complex datasets into narratives and visual formats that are accessible and cognitively engaging. Concurrently, advances in artificial intelligence (AI) are reshaping the way data is processed, interpreted, and presented. The intersection of AI with data visualization and storytelling offers promising opportunities for developing adaptive, intelligent, and human-centered systems for data communication. As the demand for clarity, personalization, and actionable insight grows, exploring how AI can enhance both the visual and narrative dimensions of data communication represents a timely and impactful area of research.

This Research Topic aims to investigate how AI can be meaningfully integrated with data visualization and storytelling techniques to improve human comprehension, engagement, and decision-making. While data storytelling provides a narrative layer that contextualizes insights, and visualization offers intuitive access to patterns, AI has the potential to personalize, automate, and scale these processes. However, this integration also raises questions about authorship, bias, transparency, and ethical design. We aim to explore both the opportunities and challenges of utilizing AI to develop more effective and human-centered data communication tools. By fostering interdisciplinary dialogue, we strive to advance theoretical understanding and practical applications in this evolving space.

We invite original research, conceptual papers, design studies, system demonstrations, case studies, and critical reflections. Relevant topics include, but are not limited to:

- AI-generated or AI-assisted data stories
- Adaptive and personalized visual narratives
- Natural Language Generation for Data Explanation
- Interactive visual analytics with machine learning support
- Visual storytelling for explainable AI and model transparency
- Ethical, cognitive, and perceptual dimensions of AI-augmented storytelling
- Human-in-the-loop frameworks for narrative visualization design
- Applications in journalism, education, healthcare, climate, finance, and public policy
- AI-assisted Audience analysis in data storytelling and visualization

We encourage interdisciplinary contributions spanning computer science, human-computer interaction, data journalism, cognitive psychology, design, and communication. Submissions should offer novel insights into the integration of AI with visual and narrative techniques for enhanced data storytelling.