Can a Social Robot Encourage Childrenxe2x80x99s SelfStudy? This paper presents a robot behavioral model designed to support children during selfstudy. In particular, we want to investigate how a robot could increase the time children keep concentration. The behavioral model was developed by observing children during selfstudy and by collecting information from experienced tutors through interviews. After observing the children, we decided to consider three states corresponding to different levels of concentration. The child can be smoothly performing the task (xe2x80x9clearningxe2x80x9d state), encountering some difficulties (xe2x80x9cstuckxe2x80x9d state) or distracted (xe2x80x9cdistractedxe2x80x9d state). The behavioral model was designed to increase the time spent concentrating on the task by implementing adequate behaviors for each of these three states. These behaviors were designed using the advices collected during the interview survey of the experienced tutors. A selfstudy system based on the proposed behavior model was implemented. In this system, a small robot sits on the table and encourages the child during selfstudy. An operator is in charge of determining the state of the child (Wizard of Oz) and the behavioral model triggers the appropriate behaviors for the different states. To demonstrate the effectiveness of the proposed behavioral model, a user study was conducted: 22 children were asked to solve problems alone and to solve problems with the robot. The children spent significantly (p  0.024) more time in the xe2x80x9clearningxe2x80x9d state when studying with the robot.

Teaching and learning with children: Impact of reciprocal peer learning with a social robot on childrenxe2x80x99s learning and emotive engagement Abstract   Pedagogical agents are typically designed to take on a single role: either as a tutor who guides and instructs the student, or as a tutee that learns from the student to reinforce what heshe knows. While both agentrole paradigms have been shown to promote student learning, we hypothesize that there will be heightened benefit with respect to studentsxe2x80x99 learning and emotional engagement if the agent engages children in a more peerlike way xe2x80x94 adaptively switching between tutortutee roles. In this work, we present a novel active roleswitching (ARS) policy trained using reinforcement learning, in which the agent is rewarded for adapting its tutor or tutee behavior to the childxe2x80x99s knowledge mastery level. To investigate how the three different childxe2x80x93agent interaction paradigms (tutee, tutor, and peer agents) impact childrenxe2x80x99s learning and affective engagement, we designed a randomized controlled betweensubject experiment. Fiftynine children aged 5xe2x80x937 years old from a local public school participated in a collaborative wordlearning activity with one of the three agentrole paradigms. Our analysis revealed that childrenxe2x80x99s vocabulary acquisition benefited from the robot tutorxe2x80x99s instruction and knowledge demonstration, whereas children exhibited slightly greater affect on their faces when the robot behaves as a tutee of the child. This synergistic effect between tutor and tutee roles suggests why our adaptive peerlike agent brought the most benefit to childrenxe2x80x99s vocabulary learning and affective engagement, as compared to an agent that interacts only as a tutor or tutee for the child. This work sheds light on how fixed role (tutortutee) and adaptive role (peer) agents support childrenxe2x80x99s cognitive and emotional needs as they play and learn. It also contributes to an important new dimension of designing educational agents xe2x80x94 actively adapting roles based on the studentxe2x80x99s engagement and learning needs.