Robot tutors provide new opportunities for education. However, they also introduce moral challenges. This study reports a systematic literature re-view (N = 256) aimed at identifying the moral considerations related to ro-bots in education. While our findings suggest that robot tutors hold great potential for improving education, there are multiple values of both (special needs) children and teachers that are impacted (positively and negatively) by its introduction. Positive values related to robot tutors are: psychological welfare and happiness, efficiency, freedom from bias and usability. However, there are also concerns that robot tutors may negatively impact these same values. Other concerns relate to the values of friendship and attachment, human contact, deception and trust, privacy, security, safety and accountability. All these values relate to children and teachers. The moral values of other stakeholder groups, such as parents, are overlooked in the existing literature. The results suggest that, while there is a potential for ap-plying robot tutors in a morally justified way, there are imported stake-holder groups that need to be consulted to also take their moral values into consideration by implementing tutor robots in an educational setting. (from Narcis.nl)
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Maintaining the child-robot relationship after a significant break, such as a holiday, is an important step for developing sustainable social robots for education. We ran a four-session user study (n = 113 children) that included a nine-month break between the third and fourth session. During the study, participants practiced math with the help of a social robot math tutor. We found that social personalization is an effective strategy to better sustain the child-robot relationship than the absence of social personalization. To become reacquainted after the long break, the robot summarizes a few pieces of information it had stored about the child. This gives children a feeling of being remembered, which is a key contributor to the effectiveness of social personalization. Enabling the robot to refer to information previously shared by the child is another key contributor to social personalization. Conditional for its effectiveness, however, is that children notice these memory references. Finally, although we found that children's interest in the tutoring content is related to relationship formation, personalizing the topics did not lead to more interest in the content. It seems likely that not all of the memory information that was used to personalize the content was up-to-date or socially relevant.
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This paper analyses the impact of two structural context factors on mathematics teacher students. First, the Netherlands is coping with a massive mathematics teacher shortage. Second, the Dutch knowledge-economy feeds the private tutoring sector. The impact on young teacher-students is tremendous; they start working as a teacher too early. Besides successful studying, broader professionalization and quality of mathematics education are in jeopardy. A quick-fix for mathematics education might do more damage than foreseen.
