1,335 research outputs found
The impact of robot tutor nonverbal social behavior on child learning
Several studies have indicated that interacting with social robots in educational contexts may lead to a greater learning than interactions with computers or virtual agents. As such, an increasing amount of social human–robot interaction research is being conducted in the learning domain, particularly with children. However, it is unclear precisely what social behavior a robot should employ in such interactions. Inspiration can be taken from human–human studies; this often leads to an assumption that the more social behavior an agent utilizes, the better the learning outcome will be. We apply a nonverbal behavior metric to a series of studies in which children are taught how to identify prime numbers by a robot with various behavioral manipulations. We find a trend, which generally agrees with the pedagogy literature, but also that overt nonverbal behavior does not account for all learning differences. We discuss the impact of novelty, child expectations, and responses to social cues to further the understanding of the relationship between robot social behavior and learning. We suggest that the combination of nonverbal behavior and social cue congruency is necessary to facilitate learning
Social robot tutoring for child second language learning
An increasing amount of research is being conducted
to determine how a robot tutor should behave socially in educa- tional interactions with children. Both human-human and human- robot interaction literature predicts an increase in learning with increased social availability of a tutor, where social availability has verbal and nonverbal components. Prior work has shown that greater availability in the nonverbal behaviour of a robot tutor has a positive impact on child learning. This paper presents a study with 67 children to explore how social aspects of a tutor robot’s speech influences their perception of the robot and their language learning in an interaction. Children perceive the difference in social behaviour between ‘low’ and ‘high’ verbal availability conditions, and improve significantly between a pre- and a post-test in both conditions. A longer-term retention test taken the following week showed that the children had retained almost all of the information they had learnt. However, learning was not affected by which of the robot behaviours they had been exposed to. It is suggested that in this short-term interaction context, additional effort in developing social aspects of a robot’s verbal behaviour may not return the desired positive impact on learning gains
Nonverbal immediacy as a characterisation of social behaviour for human-robot interaction
An increasing amount of research has started
to explore the impact of robot social behaviour on the
outcome of a goal for a human interaction partner, such
as cognitive learning gains. However, it remains unclear
from what principles the social behaviour for such robots
should be derived. Human models are often used, but
in this paper an alternative approach is proposed. First,
the concept of nonverbal immediacy from the communication
literature is introduced, with a focus on how it
can provide a characterisation of social behaviour, and
the subsequent outcomes of such behaviour. A literature
review is conducted to explore the impact on learning
of the social cues which form the nonverbal immediacy
measure. This leads to the production of a series
of guidelines for social robot behaviour. The resulting
behaviour is evaluated in a more general context, where
both children and adults judge the immediacy of humans
and robots in a similar manner, and their recall of
a short story is tested. Children recall more of the story
when the robot is more immediate, which demonstrates
an e�ffect predicted by the literature. This study provides
validation for the application of nonverbal immediacy
to child-robot interaction. It is proposed that nonverbal
immediacy measures could be used as a means of
characterising robot social behaviour for human-robot
interaction
Integrating Flow Theory and Adaptive Robot Roles: A Conceptual Model of Dynamic Robot Role Adaptation for the Enhanced Flow Experience in Long-term Multi-person Human-Robot Interactions
In this paper, we introduce a novel conceptual model for a robot's behavioral
adaptation in its long-term interaction with humans, integrating dynamic robot
role adaptation with principles of flow experience from psychology. This
conceptualization introduces a hierarchical interaction objective grounded in
the flow experience, serving as the overarching adaptation goal for the robot.
This objective intertwines both cognitive and affective sub-objectives and
incorporates individual and group-level human factors. The dynamic role
adaptation approach is a cornerstone of our model, highlighting the robot's
ability to fluidly adapt its support roles - from leader to follower - with the
aim of maintaining equilibrium between activity challenge and user skill,
thereby fostering the user's optimal flow experiences. Moreover, this work
delves into a comprehensive exploration of the limitations and potential
applications of our proposed conceptualization. Our model places a particular
emphasis on the multi-person HRI paradigm, a dimension of HRI that is both
under-explored and challenging. In doing so, we aspire to extend the
applicability and relevance of our conceptualization within the HRI field,
contributing to the future development of adaptive social robots capable of
sustaining long-term interactions with humans
Designing the future of education:From tutor robots to intelligent playthings
Robots exhibiting social behaviors have shown promising effects on children’s education. Like many analogue and digital educational devices in the past, robotic technology brings concerns along with opportunities for innovation. Tutor robots in the classroom are not meant to replace teachers, but to complement existing curricula with personalized learning experiences and one-on-one tutoring. The educational paradigm of tutor robots have insofar limited to replicate models from formal education, but many are the technical, ethical and de- sign challenges to bring this paradigm forward. Moreover, the educational paradigm of tutor robots de-facto perpetuates the exclusion of playful learning by doing with peers and objects, which is arguably the most important aspect of children’s upbringing and, yet, themost overlooked in formal education. Increasingly, robotics applications to children’s education are shifting from tutor-like paradigm to an intelligent playthings paradigm: to promote active, open-ended and independent learning through play with peers. This article is an invitation to reflect on the role that robotic technology, especially tutor robots and intelligent playthings, could play for children’s learning and development. The complexity of designing for children’s learning highlights the necessity to start a trans-disciplinary discussion to shape the future of education and foster a positive societal impact of robots for children’s learning
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