Millions of students worldwide have adopted online learning due to the isolation restrictions imposed by the Covid-19 pandemic. In this context, video conferencing platforms have garnered immense popularity as tools for teaching. However, these tools have several limitations compared to real-world encounters, especially in activities involving collaboration and teamwork. A growing number of researchers and educators have turned to avatar-based communication platforms, such as Mozilla Hubs, as alternatives that can complement video conferencing in social and teaching activities. Several previous research efforts have focused on developing tools that implement avatar-based communication systems or have explored creating activities in these 3D virtual spaces, such as poster sessions in scientific conferences or the classroom environment. In this work, we describe our semester-long efforts to develop Mozilla Hubs rooms toward promoting interaction and communication to help students self-form teams in the context of an introductory virtual reality course at the University of Florida. We describe hands-on activities to prepare students to use Mozilla Hubs effectively, including teaching them skills to customize and create avatars. We describe the implementation of three virtual rooms developed based on researchers' observations and students' survey responses. By observing students' behavior and communication patterns in those rooms, we propose a set of guidelines for building virtual rooms that can promote communication, interaction, and teamwork. We discuss the rooms' design, students' attendance, and avatar choices. Our findings suggest that highly detailed, small, closed spaces are preferred over large, open spaces with few details when promoting interaction and collaboration among students.

In the context of mental wellness support, trust and intimacy between a counselor and a patient are necessary to converge healing processes positively. However, convincing students to trust a virtual human for topics regarding mental wellness is a complex problem that requires understanding students' experiences. Based on research that discusses mental health as a concerning topic regarding Computer Science (CS) students, this paper investigates how undergraduate computing-related students perceive virtual humans' expertise on mental wellness support based on demographic resemblance on spoken accent and gender. Four virtual human counselors were developed to conduct the study, as 58 undergraduate computing-related students from two North American universities were recruited and assessed. Our findings suggest that students were less inclined to interact with a male virtual human than a female one. Also, that spoken accents can impact students' perceptions of expertise under students' multilingualism.

Virtual humans (i.e., embodied conversational agents) have the potential to support college students’ mental health, particularly in Science, Technology, Engineering, and Mathematics (STEM) fields where students are at a heightened risk of mental disorders such as anxiety and depression. A comprehensive understanding of students, considering their cultural characteristics, experiences, and expectations, is crucial for creating timely and effective virtual human interventions. To this end, we conducted a user study with 481 computer science students from a major university in North America, exploring how they co-designed virtual humans to support mental health conversations for students similar to them . Our findings suggest that computer science students who engage in co-design processes of virtual humans tend to create agents that closely resemble them demographically—agent-designer demographic similarity. Key factors influencing virtual human design included age, gender, ethnicity, and the matching between appearance and voice. We also observed that the demographic characteristics of virtual human designers, especially ethnicity and gender, tend to be associated with those of the virtual humans they designed. Finally, we provide insights concerning the impact of user-designer demographic similarity in virtual humans’ effectiveness in promoting mental health conversations when designers’ characteristics are shared explicitly or implicitly. Understanding how virtual humans’ characteristics serve users’ experiences in mental wellness conversations and the similarity-attraction effects between agents, users, and designers may help tailor virtual humans’ design to enhance their acceptance and increase their counseling effectiveness.

Inputting text is a fundamental task users perform in immersive virtual reality (VR) environments. This preliminary study quantifies the ‘gorilla arm’ effect regarding the required torque in the upper limb joints of an individual during a VR text entry task via ray-casting. This type of fatigue can be caused by the weight of the users’ arms, depending on the arm pose maintained during a task. We used motion capture technology to compare the required arm joints’ torque in two typical arm poses during VR text entry tasks. The results suggest that extending the arm higher and in front of the body towards the virtual keyboard causes about 3.77 times more net torque on the shoulder joint and 2.00 times more on the elbow joint, compared to when the arm is in a more relaxed and lowered position. These findings can support future VR text entry studies concerned with physical ergonomics.

Virtual humans (VHs) have the potential to support mental wellness among college computer science (CS) students. However, designing effective VHs for counseling purposes requires a clear understanding of students’ demographics, backgrounds, and expectations. To this end, we conducted two user studies with 216 CS students from a major university in North America. In the first study, we explored how students co-designed VHs to support mental wellness conversations and found that the VHs’ demographics, appearance, and voice closely resembled the characteristics of their designers. In the second study, we investigated how the interplay between the VH’s appearance and voice impacted the agent’s effectiveness in promoting CS students’ intentions toward gratitude journaling. Our findings suggest that the active participation of CS students in VH design leads to the creation of agents that closely resemble their designers. Moreover, we found that the interplay between the VH’s appearance and voice impacts the agent’s effectiveness in promoting CS students’ intentions toward mental wellness techniques.

Virtual conversations are increasingly utilized in healthcare education to enhance verbal empathic communication skills through tailored feedback on trainees’ responses. These conversations, supported by modalities such as speech, animation, and gestures, are highly customizable and accessible via the internet, bypassing the need for head-mounted displays (HMDs). However, training models to accurately evaluate empathic responses and generate human-like language with actionable suggestions remains a challenge. The advent of large language models (LLMs) provides new solutions to these challenges. This paper examines the impact of GPT-4-generated feedback on the empathic expressions of health professions trainees during virtual conversations. We enrolled 72 students from nursing and dental disciplines in an Interprofessional Collaborative Care course at the University of Florida. Participants were randomly assigned to one of two groups: one received feedback and suggestions from GPT-4 during conversations, while the other did not. We collected data on the perceived accuracy and helpfulness of the feedback from the intervention group. Using the Empathic Communication Coding System (ECCS) and GPT-4 Turbo, we assessed and compared the empathy levels of participants between the two groups. A Mann-Whitney U test was used to determine differences in average empathy levels. Results showed that participants receiving GPT-4 feedback had significantly higher median empathy levels than those without feedback. Feedback’s accuracy and utility were also affirmed by the participants. This study highlights the effectiveness of integrating LLMs like GPT-4 into virtual conversations for enhancing training outcomes in healthcare education.