Alexandru Litoiu

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Short et al. found that in a game between a human participant and a humanoid robot, the participant will perceive the robot as being more agentic and as having more intentionality if it cheats than if it plays without cheating. However, in that design, the robot that actively cheated also generated more motion than the other conditions. In this paper, we(More)
As robots are increasingly integrated into daily life, one of the most important roles they will assume is that of collaboratively helping us perform physical tasks. Be it helping us put together furniture, transporting materials, or assisting with food preparation, a system's ability to assess its (and others') skill level regarding the performance of(More)
This paper describes an extended (6-session) interaction between an ethnically and geographically diverse group of 26 first-grade children and the DragonBot robot in the context of learning about healthy food choices. We find that children demonstrate a high level of enjoyment when interacting with the robot, and a statistically significant increase in(More)
In intelligent tutoring systems, one fundamental problem that limits learning gains is the unproductive use of on-demand help features, namely overuse or aversion, resulting in students misusing the system rather than engaging in active learning. Social robots as tutoring agents have the potential to mitigate those behaviors by actively shaping productive(More)
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