Data Comics and Generative AI: Revolutionizing Data Storytelling

Primary supervisor

Roberto Martinez-Maldonado

The aim of this project is to probe, conceptualize, and assess the fusion of generative AI with data comics to augment their narrative potency, user comprehension, and user engagement. Data comics, an innovative blend of data and visual artistry, effectively portray intricate narratives in an accessible comic strip format. By integrating generative AI, we can automate and enrich the storytelling process, further enhancing the visual and narrative depth of data comics. The primary areas of research encompass AI, data visualisation, and user experience. Projects can be applied to various sectors, but we are focusing on education and healthcare, given the wide-ranging implications of data comics.

Student cohort

Double Semester

Aim/outline

This project will grapple with the following upgraded set of inquiries:

How can we leverage generative AI in the creation process of data comics to magnify narrative potency and reader comprehension?

What influence does the implementation of generative AI have on user engagement levels and comprehension within the context of data comics?

What guiding principles or heuristic devices could we utilize to optimize the design of AI-enhanced data comics?

How might users interact with, customize, and derive value from the AI components embedded in data comics?

How can we empirically assess the efficacy and impact of these AI-enhanced data comics (e.g., through user experience studies, A/B testing, engagement analytics)?

What are the conceptual and pragmatic implications of the "one data story per comic strip" approach, and how can it be further enhanced through AI?

How can we establish effective feedback mechanisms between users and the generative AI to enable ongoing improvements and personalization?

URLs/references

These articles provide valuable insights into this research field:

Wang, Z., Ritchie, J., Zhou, J., Chevalier, F., & Bach, B. (2020). Data comics for reporting controlled user studies in human-computer interaction. IEEE Transactions on Visualization and Computer Graphics, 27(2), 967-977.

Wang, Z., Wang, S., Farinella, M., Murray-Rust, D., Henry Riche, N., & Bach, B. (2019, May). Comparing effectiveness and engagement of data comics and infographics. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-12).

Required knowledge

Skills and dispositions required:

  • Analytical, innovative, and creative problem-solving capabilities
  • Strong interest in AI, data visualization, and user experience design
  • Proficient programming skills in relevant languages (e.g., Python, JavaScript, Java, etc.)
  • Experience with generative AI models and systems such as DALL-E, Midjourney & Stable Diffusion.
  • Prior exposure to visualisation or comic design tools will be advantageous.