Robots have started to show real potential as learning or teaching companions for children in classrooms or at home, for elderly people to help maintain their cognitive and physical abilities, and for learners with deficiencies by adapting content to their capabilities. Robots show the potential to improve individual adaptation by learning from and with the user. Several research projects have aimed to apply HRI to education and learning in order to teach broader disciplines than just STEM, such as languages or handwriting. Robots also have the potential to enhance learning via kinaesthetic interaction, as well as enabling users to improve their self-esteem and providing adaptive empathic feedback. Robots can thus be a means to engage the learner and to motivate him in the learning task.
While robots for learning is quite an applied topic of HRI, we found that the context of learner-robot interaction is one of the most challenging and interesting for research, while having the potential to be very impactful. Aiming to go beyond individual interfaces or projects, this Research Topic aims to attract contributions that enable the generation of guidelines and principles for the design of learner-robot interaction.
This Research Topic focuses on social robotics research, showcasing novel algorithms and computational modeling that are applied within the context of learning. Special focus will be given to contributions proposing novel theories, models, and methods for learning with robots. We will also welcome original technical contributions presenting robot-focused systems, algorithms, and computational methods that are tailored for learner-robot interaction. We are particularly interested in contributions demonstrating the specificities of learner-robot interaction compared to classical human-robot interaction systems.
Below is a non-exhaustive list of topics of interest
? Adaptive mechanisms for robot tutors
? Theories and methods for robot tutoring (pedagogical and language acquisition)
? Design of autonomous systems for tutoring interactions
? Designing student models and assessing student’s learning and motivation in robot-learner
interaction
? Engagement in educational human-robot interaction
? Gain in learning vs fun in learning with a robot
? Kinaesthetic learning in human-robot interaction
? Impact of robot embodiment on learning
? Shared knowledge and knowledge modeling in HRI
? Technical innovation in learning or teaching robots
? The role of teachers in child-robot interactions
? Human-robot collaborative learning
? Human-robot knowledge sharing
Robots have started to show real potential as learning or teaching companions for children in classrooms or at home, for elderly people to help maintain their cognitive and physical abilities, and for learners with deficiencies by adapting content to their capabilities. Robots show the potential to improve individual adaptation by learning from and with the user. Several research projects have aimed to apply HRI to education and learning in order to teach broader disciplines than just STEM, such as languages or handwriting. Robots also have the potential to enhance learning via kinaesthetic interaction, as well as enabling users to improve their self-esteem and providing adaptive empathic feedback. Robots can thus be a means to engage the learner and to motivate him in the learning task.
While robots for learning is quite an applied topic of HRI, we found that the context of learner-robot interaction is one of the most challenging and interesting for research, while having the potential to be very impactful. Aiming to go beyond individual interfaces or projects, this Research Topic aims to attract contributions that enable the generation of guidelines and principles for the design of learner-robot interaction.
This Research Topic focuses on social robotics research, showcasing novel algorithms and computational modeling that are applied within the context of learning. Special focus will be given to contributions proposing novel theories, models, and methods for learning with robots. We will also welcome original technical contributions presenting robot-focused systems, algorithms, and computational methods that are tailored for learner-robot interaction. We are particularly interested in contributions demonstrating the specificities of learner-robot interaction compared to classical human-robot interaction systems.
Below is a non-exhaustive list of topics of interest
? Adaptive mechanisms for robot tutors
? Theories and methods for robot tutoring (pedagogical and language acquisition)
? Design of autonomous systems for tutoring interactions
? Designing student models and assessing student’s learning and motivation in robot-learner
interaction
? Engagement in educational human-robot interaction
? Gain in learning vs fun in learning with a robot
? Kinaesthetic learning in human-robot interaction
? Impact of robot embodiment on learning
? Shared knowledge and knowledge modeling in HRI
? Technical innovation in learning or teaching robots
? The role of teachers in child-robot interactions
? Human-robot collaborative learning
? Human-robot knowledge sharing
