I’m now in the final stretch of finishing my book on the use of AI in academic research. It’s been a year-long journey, intense, unpredictable, and full of surprises, especially with how fast AI has been evolving. So much has happened during this time, and I’ve done my best to capture those shifts and explore what they mean for research and academia.
In the book, I cover a wide range of topics, from how AI can support data collection and analysis to how it can assist in writing and visualization.
Today’s post comes directly from the chapter on data visualization. In that chapter, I argue that visuals are not just decorative, they are essential tools for communicating research clearly and effectively.
I also walk through a number of AI tools that can help researchers create high-quality visuals, including AI chatbots, Julius, and several others worth exploring.
This visual pulls together some of the key ideas from that chapter, especially around the concept of visual literacy, what it means, why it matters, and how to put it into practice.
What is Visual Literacy?
Visual literacy is the skill of making sense of images and using visuals to express ideas. It’s like reading, but with pictures; you interpret, analyze, and communicate through what you see.
Why Visual Literacy Matters
- Visuals are just as important as words in research communication.
- Well-designed graphics reduce cognitive load and improve retention.
- Without visuals, your work risks being overlooked—especially in today’s fast-paced digital world.
What the Research Says
- Dual-Coding Theory: Visuals + text = better comprehension and memory (Paivio, 1971, 1986, 1991).
- Cognitive Load Theory: Structured visuals ease mental processing (Perra & Brinkman, 2021).
- Visuals improve engagement and clarity in academic and public-facing work (Houts et al., 2006; Khoury et al., 2019).
Best Practices at a Glance
- Choose the Right Format
Match your visual (e.g., table, graph) to your data’s message. - Keep It Simple and Clear
Avoid clutter. Highlight what matters. - Don’t Repeat the Text
Add value—don’t echo your paragraphs. - Use Effective Captions
A good caption explains, not just labels. - Design for Independence
Visuals should make sense on their own. - Know Your Audience
Tailor complexity to who’s reading. - Cut the Chartjunk
Every element should serve a purpose. - Test and Iterate
Ask: Can others understand this at a glance?
Ethical & Inclusive Design
- Be transparent about limitations or omitted data.
- Use colorblind-friendly palettes and readable fonts.
- Include alt-text for online visuals.
- Avoid manipulative scaling or misleading emphasis.
- Never distort data to fit a narrative.
- Credit original sources of visual content.
- Avoid using fear-based or emotionally manipulative imagery.
- Make sure visuals don’t reinforce stereotypes or bias.
- Ensure consent for any visuals involving people or personal data.
Final thoughts
I believe visual literacy is no longer a nice-to-have for researchers, it’s a core skill. Being able to design clear, honest, and meaningful visuals strengthens the impact of your work and helps your ideas reach a wider audience. With the support of AI tools, this process is now more accessible than ever.
References
- Houts, P. S., Doak, C. C., Doak, L. G., & Loscalzo, M. J. (2006). The role of pictures in improving health communication: A review of research on attention, comprehension, recall, and adherence. Patient Education and Counseling, 61(2), 173-190. https://doi.org/10.1016/j.pec.2005.05.004
- Khoury, C. K., Kisel, Y., Kantar, M., Barber, E., Ricciardi, V., Klirs, C., Kucera, L., Mehrabi, Z., Johnson, N., Klabin, S., Valiño, Á., Nowakowski, K., Bartomeus, I., Ramankutty, N., Miller, A., Schipanski, M., Gore, M. A., & Novy, A. (2019). Science–graphic art partnerships to increase research impact. Communications Biology, 2(295). https://doi.org/10.1038/s42003-019-0516-1
- Paivio, A. (1971). Imagery and verbal processes. Holt, Rinehart, and Winston.
- Paivio, A. (1986). Mental representations: A dual coding approach (Vol. 9). Oxford University Press.
- Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian Journal of Psychology/Revue canadienne de psychologie, 45(3), 255–287. https://doi.org/10.1037/h0084325
- Perra, M., & Brinkman, T. (2021). Seeing science: Using graphics to communicate research. Ecosphere, 12(10), e03786. https://doi.org/10.1002/ecs2.3786
