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Do students learn from video lessons presented by pedagogical agents of different racial and gender types equivalently to those delivered by a real human instructor? How do the race and gender of these agents impact students’ learning experiences and outcomes? In this between-subject design study, college students were randomly assigned to view a six 9-minute video lesson on chemical bonds, presented by pedagogical agents varying in gender (male, female) and race (Asian, Black, White), or to view the original lesson with a real human instructor. In comparing learning with a human instructor versus with a pedagogical agent of various races and genres, ANOVAs revealed no significant differences in learning outcomes (retention and transfer scores) or learner emotions, but students reported a stronger social connection with the human instructor over pedagogical agents. Students reported stronger positive emotions and social connections with female agents over male agents. Additionally, there was limited evidence of a race-matching effect, with White students showing greater positive emotion while learning with pedagogical agents of the same race. These findings highlight the limitations of pedagogical agents compared to human instructors in video lessons, while partially reflecting gender stereotypes and intergroup bias in instructor evaluations. 

Creating believable virtual agents has long been the focus of artists and scientists. A believable agent enables the audience to be emotionally involved in a narrative, willingly suspending their disbelief for the pleasure of appreciating literacy, drama, film, etc. In the past few decades, believability has become the goal of virtual reality developers and researchers. In the realm of virtual reality, it is commonly accepted that a believable virtual agent should have personality, emotion, agency, intelligence, and more. Despite its seemingly complicated requirements, believability is a fragile product that can be easily jeopardized by missing one or more of these elements. In this paper, we review the questionnaires that past researchers have used on the topic of virtual agents' believability. Based on the prevailing questions identified in the relevant studies, we propose a scale-aiming at standardizing one-for measuring the believability of virtual agents. We recommend that future research involving virtual agents refer to this scale to evaluate the level of their believability. 

This study examined how well people can recognize and relate to animated pedagogical agents of varying ethnicities/races and genders. For both Study 1 (realistic-style agents) and Study 2 (cartoon-style agents), participants viewed brief video clips of virtual agents of varying racial/ethnic categories and gender types and then identified their race/ethnicity and gender and rated how human-like and likable the agent appeared. Participants were highly accurate in identifying Black and White agents but were less accurate for Asian, Indian, and Hispanic agents. Participants were accurate in recognizing gender differences. Participants rated all types of agents as moderately human-like, except for White agents. Likability ratings were lowest for White and male agents. The same pattern of results was obtained across two independent studies with different participants and different onscreen agents, which indicates that the results are not solely due to one specific set of agents. Consistent with the Media Equation Hypothesis and the Alliance Hypothesis, this work shows that people are sensitive to the race/ethnicity and gender of onscreen agents and relate to them differently. These findings have implications for how to design animated pedagogical agents for improved multimedia learning environments in the future and serve as a crucial first step in highlighting the possibility and feasibility of incorporating diverse onscreen virtual agents into educational computer software.