Alexis Jacq

About

Alexis Jacq is a leading researcher in human-robot interaction, with a focus on child-robot collaboration and educational robotics. His work centers on how robots can engage children in learning activities, particularly through the innovative "learning by teaching" paradigm, where children teach robots tasks like handwriting. This approach, explored in his highly cited 2016 paper (83 citations), has shown promise in supporting children with visuoconstructive deficits, blending cognitive development with interactive robotics. Jacq’s major contributions include formalizing "with-me-ness"—a concept for measuring engagement in real-time interactions (64 citations)—and designing long-term child-robot interactions that sustain motivation by calibrating robot learning rates (55 citations). He has also studied the impact of spatial arrangements on attention and perception in teaching contexts (27 citations), and explored mutual understanding in human-robot collaborations through Theory of Mind architectures. With over 230 total citations, Jacq’s work is foundational for creating empathetic, adaptive robots that enhance learning and therapy, making him a key figure in socially assistive robotics and child-centered AI.

Research Focus

Key Achievements

Top Papers

1. 1
   Learning by Teaching a Robot: The Case of Handwriting

   83 citations · 2016
2. 2
   From real-time attention assessment to “with-me-ness” in human-robot interaction

   64 citations · 2016
3. 3
   Building successful long child-robot interactions in a learning context

   55 citations · 2016
4. 4
   Child-robot spatial arrangement in a learning by teaching activity

   27 citations · 2016
5. 5
   Sensitivity To Perceived Mutual Understanding In Human-Robot Collaborations

   2 citations · 2018

Key Collaborators

Contact & Links