Melissa Donnermann
Papers
14
Total Citations
286
H-Index
7
About
Melissa Donnermann is a researcher specializing in human-robot interaction, social robotics, and technology-enhanced learning, with a particular focus on deploying social robots in real-world educational and service environments. Her most influential work explores how social robots can support learners in higher education — a context traditionally underserved by robotic research, which has historically centered on children. Her 2021 study on social robots and gamification for learning (103 citations) demonstrated the potential of combining robotic agents with game-based motivation strategies to meaningfully boost student engagement. Building on this, her longitudinal investigations into adaptive robotic tutors at universities address the challenges of self-directed learning and limited instructor availability, contributing both empirical evidence and design frameworks to the field. Beyond education, Donnermann has extended her inquiry into hospitality and service contexts, examining how robots can function as hotel concierges and assessment tools, and how speech design shapes user perception. Her work on emotion modeling for robot storytellers and investigations into the uncanny valley through the Mere Exposure Effect reflect a sophisticated interest in the psychological dimensions of human-robot interaction. With over 270 cumulative citations, her research meaningfully advances understanding of how social robots can be thoughtfully integrated into everyday human environments.
Research Focus
Key Achievements
Top Papers
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- 4Integrating a Social Robot in Higher Education – A Field Study39 citations · 2020
- 5Towards Adaptive Robotic Tutors in Universities: A Field Study10 citations · 2021
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- 10Using a Social Robot as a Hotel Assessment Tool4 citations · 2023