Recognizing and Relating to the Race/Ethnicity and Gender of Animated Pedagogical Agents

Fangzheng Zhao,Richard E. Mayer,Nicoletta Adamo-Villani,Christos Mousas, Minsoo Choi, Luchcha Lam, Magzhan Mukanova, Klay Hauser

JOURNAL OF EDUCATIONAL COMPUTING RESEARCH(2023)

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摘要
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.
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gender,agents,race/ethnicity,race/ethnicity
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