20 Introduction

In academic research, how you present your findings can be just as important as what you discovered. While rigorous analysis forms the backbone of scholarly work, clear and effective visual communication ensures that your insights reach and resonate with your intended audience. This is where the intersection of data visualization and artificial intelligence becomes a powerful force.

In this part, we explore how researchers can enhance their visual communication by combining best practices in visual design with the affordances of AI-powered tools. The section is organized into two main chapters.

Chapter 12 lays the conceptual and practical groundwork. Drawing from cognitive science, design research, and scientific communication, it explains why visuals matter and how they help in simplifying complex ideas and in improving retention, engagement, and ethical clarity. This chapter offers evidence-based guidelines on how to design effective academic visuals, covering everything from choosing the right chart type and writing meaningful captions to avoiding chartjunk and ensuring accessibility.

Chapter 13 builds on that foundation by turning to practice: AI tools that can help bring your data to life. You’ll learn how to use generative AI models like ChatGPT, Claude, and Julius AI to create charts and graphs from simple prompts. We also walk you through the AI capabilities embedded in familiar platforms like Excel (with Copilot) and Google Sheets (with Gemini), as well as a suite of user-friendly tools like Canva, Visme, Piktochart, Adobe Express, and Figma. These platforms now offer AI-assisted features that democratize design, allowing researchers, regardless of background, to create polished, data-driven visuals.