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Integrating Data Comics and Generative AI in Education

Primary supervisor

Roberto Martinez-Maldonado

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This project aims to enhance student engagement and comprehension by combining Data Comics with Generative AI. Data Comics present complex information in an engaging, accessible format, and by leveraging AI, we seek to automate their creation, making the process efficient and scalable. This project involves a human-centred design approach with students and teachers to ensure the content is relevant and pedagogically sound. The collaboration will tailor Data Comics to meet the needs of learners, while AI will enable the rapid generation of personalized educational materials.

Additionally, the project can explore ethical implications such as data privacy, bias, and the authenticity of AI-generated content, potentially developing a framework to address these concerns. By investigating design opportunities, including real-time feedback mechanisms and personalised learning experiences, the project aims to bridge the gap between innovative educational tools and practical classroom applications, ensuring the integration of AI and Data Comics is both effective and ethically sound.


Student cohort

Double Semester


The project can be scoped to cover 1 or more of the following areas:

Potential Objectives:

  1. Impact Assessment: Evaluate the educational impact of Data Comics on student engagement, comprehension, and retention of complex information.
  2. Co-Design Process: Collaborate with students and teachers in the co-design of Data Comics, ensuring that the content is relevant, engaging, and pedagogically sound.
  3. Generative AI Implementation: Develop and implement Generative AI algorithms to automate or semi-automate the creation of Data Comics, making the process more efficient and scalable.
  4. Ethical Exploration: Investigate the ethical implications of using Generative AI in educational content creation, focusing on issues such as data privacy, bias, and the authenticity of AI-generated materials.
  5. Design Opportunities: Identify and explore design opportunities that arise from the integration of AI and Data Comics, including personalisation of learning materials and real-time feedback mechanisms.

Potential Methodologies:

  • Literature Review: Conduct a comprehensive review of existing research on Data Comics, Generative AI, and their applications in education.
  • Workshops and Interviews: Organise workshops and interviews with educators and students to gather insights and feedback for the co-design process.
  • Prototyping and Testing: Develop prototypes of AI-generated Data Comics and test them in classroom settings to assess their effectiveness and gather user feedback.
  • Ethical Framework Development: Develop a framework to address the ethical considerations related to AI-generated educational content.

Potential Final Outcomes:

  • Enhanced understanding of the potential of Data Comics in education.
  • A co-designed collection of Data Comics that reflect the needs and preferences of both students and teachers.
  • An AI-driven tool for creating Data Comics, reducing the time and effort required for their production.
  • A set of ethical guidelines for the use of Generative AI in educational content creation.
  • Identification of new design opportunities that can further enhance the learning experience.


These articles can provide valuable insights into this research field:

Bach, B., Wang, Z., Farinella, M., Murray-Rust, D., & Henry Riche, N. (2018, April). Design patterns for data comics. In Proceedings of the 2018 chi conference on human factors in computing systems (pp. 1-12).

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 Graphics27(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.