Sense and Sensibility: What makes a social robot convincing to high-school students? Abstract --This study with 40 high-school students demonstrates the high influence of a social educational robot on students' decision-making for a set of eight true-false questions on electric circuits, for which the theory had been covered in the students' courses. The robot argued for the correct answer on six questions and the wrong on two, and 75% of the students were persuaded by the robot to perform beyond their expected capacity, positively when the robot was correct and negatively when it was wrong. Students with more experience of using large language models were even more likely to be influenced by the robot's stance - in particular for the two easiest questions on which the robot was wrong - suggesting that familiarity with AI can increase susceptibility to misinformation by AI. We further examined how three different levels of portrayed robot certainty, displayed using semantics, prosody and facial signals, affected how the students aligned with the robot's answer on specific questions and how convincing they perceived the robot to be on these questions. The students aligned with the robot's answers in 94.4% of the cases when the robot was portrayed as Certain, 82.6% when it was Neutral and 71.4% when it was Uncertain. The alignment was thus high for all conditions, highlighting students' general susceptibility to accept the robot's stance, but alignment in the Uncertain condition was significantly lower than in the Certain. Post-test questionnaire answers further show that students found the robot most convincing when it was portrayed as Certain. These findings highlight the need for educational robots to adjust their display of certainty based on the reliability of the information they convey, to promote students' critical thinking and reduce undue influence. Educational robots are becoming more common and they have significant potential in, e.g., STEM (science, technology, engineering and mathematics) education [46, 69, 17], offering students realistic and natural interactions, not the least by employing Large Language Models (LLMs), as demonstrated in several recent studies [41, 68, 67]. However, it is also well-known that while the LLMs' linguistic proficiency is often astonishing, their factual "knowledge" in STEM subjects is flawed, and incorrect statements occur frequently [34, 60]. Since robots can exert high informational social influence [38, 24, 25, 55, 56] and students will align with the robot's views to large extents [27], the positive as well as negative effects of learning with a social robot need to be considered: Students need to use critical thinking to decide if they should accept the robot's propositions [63]. Educators need to understand which students are more at risk of being misled by a robot presenting incorrect STEM facts, to provide in-time support.

Parent-child interaction is critical for child development, yet parents may need guidance in some aspects of their engagement with their children. Current research on educational math robots focuses on child-robot interactions but falls short of including the parents and integrating the critical role they play in children's learning. We explore how educational robots can be designed to facilitate parent-child conversations, focusing on math talk, a predictor of later math ability in children. We prototyped capabilities for a social robot to support math talk via reading and play activities and conducted an exploratory Wizard-of-Oz in-home study for parent-child interactions facilitated by a robot. Our findings yield insights into how parents were inspired by the robot's prompts, their desired interaction styles and methods for the robot, and how they wanted to include the robot in the activities, leading to guidelines for the design of parent-child-robot interaction in educational contexts.

The work presented in this paper was carried out in the context of the project Girls and boys: one day at university promoted by the City of Turin together with the University of Turin. We were responsible for two educational activities on robotics and coding hosted at the Computer Science Department, which made one of its laboratories available for this kind of lesson. At the conclusion of the lab's sessions, children compiled the Attribution of Mental State (AMS) questionnaire, which is a measure of mental states that participants attribute to robots, namely the user's perception of the robot's mental qualities as compared to humans. We distributed the questionnaires both to children attending the educational robotics lab and to children performing coding activities. Results show that the first group attributed higher mental qualities to the robots, compared to the attribution given by children that did not have a direct experience with a robot.

During the 2017-18 academic year we carried out a series of coding activities, lasting about 3 months, in a third of the Giulia Falletti primary school in Barolo in Turin (Gena et a., 2020). These activities aimed to teach students not only the basics of programming, but also to introduce a new language and a new way of thinking and solving problems: computational thinking. The class consisted of 25 pupils: 14 males and 11 females, which we then operationally divided into two working groups (13 + 12) to make coding lessons more manageable and provide better childcare. The lessons lasted one hour and were conducted, in the presence of one of the teachers, by a computer teacher assisted by a student / facilitator. At the end of the three months of this positive experience, we realized that having an educational robot that can perform the same kind of actions that virtual robots do, like those of code.org, is very useful for children, especially to help them to solve orientation problems. Therefore, since no commercial robot had the characteristics we wanted, we decided to create an educational robot from scratch, equipped with social, interactive and emotional skills, able to involve children and establish an emotional bond with them in order to increase their learning and involvement. We decided from the beginning to design the robot as an open source project, made at a low cost, proposing a kit that can be easily reproduced and improved by anyone who wishes.

Covid-19 has accelerated the transition to online teaching, raising questions about the role of robots in classrooms.

High cost of labor, expansions of scientific research, food and beverages industry demand, improved efficiency and reduced workspace are major growth drivers. In the modern competitive business, the role of robots is becoming significant for industrial applications.

When his daughters were young, Nader Hamda says, they were really into apps and computers. But now that they're a little older, their interest is waning. "They're not an exception," he says. According to numerous studies, young girls are moving away from computer science, not towards it. And Hamda says this is why his company, Ozobot, is now offering an educational robot called Evo.

Computing jobs are growing at twice the national rate of other types of employment. By 2020, the Bureau of Labor Statistics says, the US will have 1 million more computer science-related jobs than graduates qualified to fill them. In December, President Obama announced the Computer Science for All Initiative, pledging 4 billion in funding for computer science education in the nation's schools. Yet all kinds of dysfunction keeps the country from closing the deficit in computer science talent, according to a survey by Google and Gallup. Yes, school budgets are a problem, and teachers have a limited time to devote to additional classes.