Supervising Practice-Led Heuristic PhD Projects Integrating Creative Intuition and Artificial Intelligence

Keywords: Artificial intelligence, artistic research, heuristic inquiry, PhD supervision, practice-led research

Abstract

Heuristic inquiry, rooted in experiential and iterative exploration, plays a central role for both artistic research and artificial intelligence (AI) research. This article provides a guide for supervising PhD projects that incorporate heuristic inquiry in their creative and computational transdisciplinary contexts. Drawing on transdisciplinary literature and research examples, it addresses how supervisors can support students through to approach AI for their creative projects with an informed approach. Key topics include embracing uncertainty, heuristic iteration, intuitive problem-solving, supporting interdisciplinary learning, and navigating the epistemological challenges using heuristic methods. By mapping structural and methodological convergences between artistic and AI domains, this article aims to equip supervisors with adaptable tools to nurture complex, innovative, and practice-led doctoral projects.

Author Biographies

Hossein Najafi, Hong Kong Polytechnic University of Technology

Hossein Najafi is an media, animation, visual effects and drawing Associate Professor of Practice at School of Design in Hong Kong Polytechnic University. He is a practicing artist, and a researcher specializing in design, creativity, practice-led and heuristic research. After founding a production studio and working with various companies in London, Istanbul and Auckland, he brought a wealth of industry experience to his academic career.  Hossein holds a PhD in Art and Design and in his doctoral thesis, he investigated identity loss and transition through Persian illuminationist approaches that interfaced with heuristics. Hossein is recently researching on the creative heuristic processes in AI models.

Marcos Mortensen Steagall, Auckland University of Technology

Marcos Mortensen Steagall is an Associate Professor in the Department of Communication Design at Auckland University of Technology (AUT). In his research and professional pursuits, Dr. Mortensen Steagall explores the intersection of visual semiotics and practice-oriented methodologies in Art, Design, Communication, and Technology. His artistic practice, primarily centred on lens-based and digital image-making, serves as a method for knowledge production.  Dr. Mortensen Steagall's work is characterised by an interdisciplinary approach that merges academic research with artistic practice, highlighting the significance of embracing diverse cultural narratives and knowledge systems in Design. Additionally, he is the editor of the academic journal LINK Praxis and chairs the LINK International Conference, focusing on Practice-led Research and the Global South.

References

Alpaydin, E. (2020). Introduction to machine learning (4th ed.). MIT Press.

Baldwin, E. (2023). Embodying non-binary: A heuristic inquiry into identity and expression [Unpublished doctoral thesis]. AUT University.

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://doi.org/10.48550/arXiv.1702.08608

Douglass, B. G., & Moustakas, C. (1985). Heuristic inquiry: The internal search to know. Journal of Humanistic Psychology, 25(3), 39–55. https://doi.org/10.1177/0022167885253004

Ghobakhlou, A., & Najafi, H. (2024). A comparative study of heuristic inquiry in AI and artistic research. In W. Ings & K. Tudor (Eds.), Heuristic enquiries: Disciplinary inquiries, interdisciplinary engagement (pp. 135–161). Routledge. https://doi.org/10.4324/9781003507758-12

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2020). A survey of methods for explaining black box models. ACM Computing Surveys, 51(5), 1–42. https://doi.org/10.1145/3236009

Ings, W. (2011). Talking pictures: A heuristic inquiry into visual narrative practice [Unpublished doctoral thesis]. Auckland University of Technology.

Ings, W. (2014). Creative research in the academy: A case for practice-based doctoral education. Cambridge Scholars Publishing.

Ings, W. (2018). Sensory methodologies: Creative practice and heuristic inquiry. In W. Ings (Ed.), Research and design in creative practice (pp. 97–114). Springer.

Jünger, M., Reinelt, G., & Rinaldi, G. (1995). The traveling salesman problem. In M. Grötschel, C. L. Monma, & G. M. Nemhauser (Eds.), Handbooks in operations research and management science (Vol. 7, pp. 225–330). Elsevier.

LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539

Matai, R., Singh, M., & Mittal, M. L. (2010). Traveling salesman problem: An overview of applications, formulations, and solution approaches. In M. J. B. H. Zaheer-Ud-Din Babar (Ed.), Advances in computing and information technology (pp. 137–146). Springer.

Moussaoui, L., & Benslimane, R. (2023). Survey on reinforcement learning and its applications. Journal of Artificial Intelligence Research, 67(2), 101–123.

Moustakas, C. (1990). Heuristic research: Design, methodology, and applications. Sage Publications.

Najafi, H., Tudor, K., & Ings, W. (2024). A review of the evolution of heuristic inquiry. In W. Ings & K. Tudor (Eds.), Heuristic enquiries: Disciplinary inquiries, interdisciplinary engagement (pp. 5–34). Routledge. https://doi.org/10.4324/9781003507758-3

Panaita, E. (2018). Heuristic inquiry and graphic novel: An autobiographic narrative research [Unpublished doctoral thesis]. Auckland University of Technology.

Russell, S., & Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Pearson.

Sinfield, D. (2020). Typography and place: A heuristic inquiry into typographic voice in site-specific environments [Unpublished doctoral thesis]. Auckland University of Technology.

Zheng, A., & Casari, A. (2018). Feature engineering for machine learning: Principles and techniques for data scientists. O’Reilly Media.

Published
2025-06-17