Human–AI Exploration
by Design
I am an HCI researcher at Inria, where I explore new forms of human-AI interaction and study how people ideate with these systems, adapt them into their work practice, and change their own process along the way. Alongside that, I work also on the impact of AI use on the individual, on society, and on the environment.
I am a permanent researcher in the Loop team at Inria Lille (since 2024), and in addition will join Université Paris-Saclay as Professeur Attaché starting fall 2026.
My research interests lie in collaborative artificial intelligence for exploratory creative tasks. I define, study and evaluate the interaction that lets people construct ideas and concepts together with intelligent systems. With the belief that computers and humans have distinct skills to add to such a collaboration, I use knowledge from psychology, design and machine learning to enable systems to meaningfully align with human practices.
I serve as Chair of the Executive Committee of the Hybrid Human-AI Conference (HHAI) and co-organize the GT Human-Centered AI working group in France (CNRS GDR IHM).
Creativity support tools have begun to incorporate GenAI for exploring ideas. However, our preliminary study with nine designers showed that current GenAI tools lack explicit support for iteratively evolving, reflecting upon and tracking design alternatives. We developed DesignTrace, an early-stage GenAI design tool that allows designers to experiment with semantically relevant visual variations in an interactive design space. Its representation captures the progression of designers’ visual and semantic ideas through command histories, state tracking, and an interactive branching structure. A study of twelve professional designers shows that DesignTrace’s palette helps express, explore, and reflect on design intentions. Its interactive branching structure helps them maintain visual consistency across design iterations; remember and revisit earlier design decisions; and see connections across ideas. Our work shows how re-envisioning GenAI-based interfaces around explicit design traces enable designers to benefit from generative capabilities while maintaining control as they explore design variants.
Although current generative AI (GenAI) enables designers to create novel images, its focus on text-based and whole-image interaction limits expressive engagement with visual materials. Based on the design concept of deconstruction and reconstruction of digital visual attributes for visual prompts, we present FusAIn, a GenAI prompt composition tool that lets designers create personalized pens by loading them with objects or attributes such as color or texture. GenAI then fuses the pen’s contents to create new images. Extracting and reusing inspirational material matches designers’ existing work practices, making GenAI more contextualized for professional design. A study with 12 designers shows how FusAIn improves their ability to define visual details at different levels that are difficult to express with current GenAI prompts. Pen-based interaction lets them maintain fine-grained control over generated results, increasing GenAI image’s editability and reusability. We discuss the benefits of “composition as prompts” and directions for future research.
Professional designers create mood boards to explore, visualize, and communicate hard-to-express ideas. We present ImageSense, an intelligent, collaborative ideation tool that combines individual and shared work spaces, as well as collaboration with multiple forms of intelligent agents. In the collection phase, ImageSense offers fluid transitions between serendipitous discovery of curated images via ImageCascade, combined text- and image-based Semantic search, and intelligent AI suggestions for finding new images. For later composition and reflection, ImageSense provides semantic labels, generated color palettes, and multiple tag clouds to help communicate the intent of the mood board. A study of nine professional designers revealed nuances in designers’ preferences for designer-led, system-led, and mixed-initiative approaches that evolve throughout the design process. We discuss the challenges in creating effective human-computer partnerships for creative activities, and suggest directions for future research.
I advise PhD students working on topics spanning generative AI, human-AI collaboration, and sustainable machine learning. I have also supervised Master theses (2019–2025) and Bachelor theses (2017–2019).
Let’s connect
Interested in collaborating, discussing human-AI creativity, or applying to the group? I’d love to hear from you.