Kara McElvaine
Papers
1
Total Citations
10
H-Index
1
About
Kara McElvaine’s research lies at the intersection of human-robot interaction, educational technology, and artificial intelligence, with a focus on designing autonomous systems that can sustain meaningful engagement in collaborative learning environments. Her most cited work, “Autonomous disengagement classification and repair in multiparty child-robot interaction” (2016), introduces a novel algorithm that monitors engagement in small groups of children and triggers timely interventions when disengagement is detected. This contribution is foundational for the development of robotic tutors capable of adapting to the dynamic social and attentional needs of young learners. With 10 citations, this paper has influenced subsequent studies on real-time affect recognition and adaptive feedback in child-robot interaction. McElvaine’s work is notable for its practical approach to a pressing challenge in educational robotics: ensuring that robots can not only teach but also recognize and respond to waning student interest. Her research continues to shape how autonomous systems are designed for multiparty, real-world classroom settings, making her a key voice in the effort to create more responsive and effective robotic learning companions.
Research Focus
Key Achievements
Top Papers
- 1