Michelle Dutoit

Research Interests

My research investigates how Generative AI can be reframed as a creative tool that empowers artists, rather than replacing them. To do this, I focus on improving the explainability and controllability of GenAI systems, making them more transparent and responsive to creative intent. I ground my work in Research through Design and Participatory Design, collaborating directly with artists and users to ensure these tools reflect real creative needs and workflows. Feel free to reach out, if you are interested in collaborating!

Academic Background

• 2026-present PhD student at the Media Informatics Group, LMU Munich
• 2023-2024 Human-Computer Interaction (MSc), Université Paris-Saclay
• 2022-2023 Interaction Technology (MSc), University of Twente
• 2018-2022 Media Informatics (BSc), LMU Munich

Teaching

• WS26/27Practical Information Visualization
• WS26/27Information Visualization
• SS26Practical Creative Coding
• SS26Computer Graphics 1

Publications

2026
Download	Michelle Dutoit, Baptiste Caramiaux Exploring Normativity in Stable Diffusion: Insights for XAI in the Arts C&C 2026, Proceedings of Explainable AI for the Arts Workshop 2026 (XAIxArts 2026), 13-16th July 2026, London, UK (bib)
2022
Download	Yanhong Li, Meng Liang, Julian Preissing, Nadine Bachl, Michelle Dutoit, Thomas Weber, Sven Mayer, Heinrich Hussmann A Meta-Analysis of Tangible Learning Studies from the TEI Conference In Sixteenth Interna- tional Conference on Tangible, Embedded, and Embodied Interaction (TEI 22), February 13–16, 2022, Daejeon, Republic of Korea. ACM, New York, NY, https://doi.org/10.1145/3490149.3501313
Download	Yanhong Li, Nadine Bachl, Michelle Dutoit, Thomas Weber, Sven Mayer, Heinrich Hussmann Designing Tangible Tools to Engage Silent Students in Group Discussion The 8th International Symposium on Educational Technology (ISET 2022) (bib)

Theses (Bachelor/Master)

Open

In Progress

Accomplished