Team closeness provides the foundations of trust and communication, contributing to teams' success and viability. However, newcomers often struggle to be included in a team since incumbents tend to interact more with other existing members. Previous research suggests that online communication technologies can help team inclusion by mitigating members' perceived differences. In this study, we test how virtual reality (VR) can promote team closeness when forming teams. We conducted a between-subject experiment with teams working in-person and VR, where two members interacted first, and then a third member was added later to conduct a hidden-profile task. Participants evaluated how close they felt with their teammates after the task was completed. Our results show that VR newcomers felt closer to the incumbents than in-person newcomers. However, incumbents' closeness to newcomers did not vary across conditions. We discuss the implications of these findings and offer suggestions for how VR can promote inclusion.

Robotics and human-robot interaction (HRI) are growing fields that may benefit from an expanded perspective stimulated by more interdisciplinary contributions. One way to achieve this goal is to attract non-traditional students from the social sciences and humanities into these fields. This present paper describes two educational initiatives that focused on teaching non-engineering students about robotics and HRI. In one initiative, a group of younger students, including those with autism spectrum disorder (ASD), received hands-on experience with robotics in a context that was not overly technical, while in the other initiative, college students in the social sciences and humanities learned about basic HRI concepts and developed robotics applications. Themes common to both initiatives were to reach non-technical students who are not traditional targets for robotics education and to focus their learning on creating interactive sequences for robots based on key HRI design considerations rather than on the underlying mechanical and electrical details related to how those sequences are enacted inside the robot. Both initiatives were successful in terms of producing desired learning outcomes and fostering participant enjoyment.

This report describes the development of a prototypical Wizard of Oz, graphical user interface to wirelessly control a small, humanoid robot (Aldebaran Nao) during a therapy session for children with Autism Spectrum Disorders (ASD). The Dynamically Operated Manually Executed Robot interface (DOMER) enables an operator to initiate pre-developed behavior sequences for the robot as well as access the text-to-speech capability of the robot in real-time interactions between children with ASD and their therapist. Preliminary results from a pilot study suggest that the interface enables the operator to control the robot with sufficient fidelity such that the robot can provide positive feedback, practice social dialogue, and play the game, "Simon Says" in a convincing and engaging manner.

There is recent evidence that males and females view robots differently, from the way robots are conceptualized, to the way humans respond when they interact with them. In this paper, we further explore gender-based differences in human-robot interaction. Moreover, we provide the first available evidence for sex-related differences in reactions to gendered synthetic voices that are either disembodied or physically embodied within a robot. Results indicate that physical embodiment and perceived entity gender may interact with human sex-related characteristics and pre-experimental attitudes in determining how people respond to artificial entities.

The paper describes a novel application of neural networks to model the behavior of students in the context of an intelligent tutoring system. Self-organizing feature maps are used to capture the possible states of student knowledge from an existing test database. The trained network implements a universal student knowledge model that is compatible with knowledge space theory approaches to student assessment and computer aided instruction. The student model can be applied to rapidly assess the knowledge of any given student, and chart a path from lower to higher states of expertise. The authors illustrate the concept on an aircraft fuel management domain, demonstrating its noise-tolerance and insensitivity to feature map parameter values. An approach to determining the correct feature map size is also described.>