Supervising Practice-Led Heuristic PhD Projects Integrating Creative Intuition and Artificial Intelligence
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.
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 Embodying non-binary: A heuristic inquiry into identity and expression [Unpublished doctoral thesis].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 Talking pictures: A heuristic inquiry into visual narrative practice [Unpublished doctoral thesis].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.
Copyright (c) 2025 Hossein Najafi; Marcos Mortensen Steagall (Translator)

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors submitting articles for publication warrant that the work is not an infringement of any existing copyright and will indemnify the publisher against any breach of such warranty. By publishing in LINK PRAXIS Journal, the author(s) agree to the dissemination of their work through the LINK PRAXIS Journal.
By publishing in LINK PRAXIS Journal, the authors grant the Journal a Creative Commons nonexclusive worldwide license (CC-BY 4.0): Creative Commons Attribution-NonCommercial 4.0 International License) for electronic dissemination of the article via the Internet, and, a nonexclusive right
to license others to reproduce, republish, transmit, and distribute the content of the journal. The authors grant the Journal the right to transfer content (without changing it), to any medium or format necessary for the purpose of preservation.
Authors agree that the Journal will not be liable for any damages, costs, or losses whatsoever arising in any circumstances from its services, including damages arising from the breakdown of technology and difficulties with access.