Social robots for education: A review <s> Robot appearance <s> This paper investigates situated utterance generation in human-robot interaction. In addition, we study the achievement of joint attention because a person must have joint attention with a robot to identify the object indicated by a situated utterance description generated by the robot. This paper proposes a joint attention mechanism to achieve such joint attention as well as a speech generation system named Linta-III. By using the joint attention mechanism, Linta-III can omit obvious information in the situation from an utterance description. The joint attention mechanism employs eye contact and attention expression functions. These functions are the robot's physical expressions, and they allow the joint attention mechanism to draw the person's attention to the same sensor information as that noticed by the robot. We have also conducted a psychological experiment to evaluate the joint attention mechanism. The results indicated that the eye contact and attention expression functions were effective methods in the development of joint attention. <s> BIB001 </s> Social robots for education: A review <s> Robot appearance <s> Engaging storytelling is a necessary skill for humanoid robots if they are to be used in education and entertainment applications. Storytelling requires that the humanoid robot be aware of its audience and able to direct its gaze in a natural way. In this paper, we explore how human gaze can be modeled and implemented on a humanoid robot to create a natural, human-like behavior for storytelling. Our gaze model integrates data collected from a human storyteller and a discourse structure model developed by Cassell and her colleagues for human-like conversational agents (1994). We used this model to direct the gaze of a humanoid robot, Honda's ASIMO, as he recited a Japanese fairy tale using a pre-recorded human voice. We assessed the efficacy of this gaze algorithm by manipulating the frequency of ASIMO's gaze between two participants and used pre and post questionnaires to assess whether participants evaluated the robot more positively and did better on a recall task when ASIMO looked at them more. We found that participants performed significantly better in recalling ASIMO's story when the robot looked at them more. Our results also showed significant differences in how men and women evaluated ASIMO based on the frequency of gaze they received from the robot. Our study adds to the growing evidence that there are many commonalities between human-human communication and human-robot communication <s> BIB002
Because the positive learning outcomes are driven by the physical presence of the robot, the question remains of what exactly it is about the robot's appearance that promotes learning. A wide range of robots have been used in the surveyed studies, from small toylike robots to full-sized android robots. Figure 2A shows the most used robots in the published studies. The most popular robot in the studies we analyzed is the Nao robot, a 54-cm-tall humanoid by Softbank Robotics Europe available as having 14, 21, or 25 degrees of freedom (see Fig. 4B ). The two latter versions of Nao have arms, legs, a torso, and a head. They can walk, gesture, and pan and tilt their head. Nao has a rich sensor suite and an on-board computational core, allowing the robot to be fully autonomous. The dominance of Nao for HRI can be attributed to its wide availability, appealing appearance, accessible price point, technical robustness, and ease of programming. Hence, Nao has become an almost de facto platform for many studies in robots for learning. Another robot popular as a tutor is the Keepon robot, a consumergrade version of the Keepon Pro research robot. Keepon is a 25-cm-tall snowman-shaped robot with a yellow foam exterior without arms and legs (see Fig. 4C ). It has four degrees of freedom to make it pan, roll, tilt, and bop. Originally sold as a novelty for children, it can be used as a research platform after some modification. Nao and Keepon offer two extremes in the design space of social robots, and hence, it is interesting to compare learning outcomes for both. Comparing Keepon with Nao, the respective cognitive learning gain is d = 0.56 (N = 10; 95% CI, 0.532 to 0.58) and d = 0.76 (N = 8; 95% CI, 0.52 to 1.01); therefore, both show a medium-sized effect. However, we note that direct comparisons between different robots are difficult with the available data, because no studies used the same experimental design, the same curriculum, and the same student population with multiple robots. Furthermore, different robots have tended to be used at different times, becoming popular in studies when that particular hardware model was first made available and decreasing in usage over time. Because the complexity of the experimental protocols has tended to increase, direct comparison is not possible at this point in time. What is clear from surveying the different robot types is that all robots have a distinctly social character [except for the Heathkit HERO robot used in (60)]. All robots have humanoid features-such as a head, eyes, a mouth, arms, or legs-setting the expectation that the robot has the ability to engage on a social level. Although there are no data on whether the social appearance of the robot is a requirement for effective tutoring, there is evidence that the social and agentic nature of the robots promotes secondary responses conducive to learning BIB001 BIB002 . The choice of robot very often depends on practical considerations and whether the learners feel comfortable around the robot. The weighted average height of the robots is 62 cm; the shortest robot in use is the Keepon at 25 cm, and the tallest is the RoboThespian humanoid at 175 cm. Shorter robots are often preferred when teaching young children.