(Ir)relevance of Gender? on the Influence of Gender Stereotypes on Learning with a Robot

Education research has documented a trend that reflects gender-based differences in the choice of fields of study. This, in turn, contributes to an imbalance in the representation of men and women in particular professions: In educational contexts, female teachers predominantly teach stereotypically female areas of study like social sciences and humanities, whereas male teachers are mainly represented in stereotypically male domains like mathematics. Research further provides evidence for the fact that this gender-stereotyped division of labor in education significantly impacts students' learning and motivation. Would gender-related stereotypes also bias learning processes with robots? This is plausible in light of the fact that social robots become more and more popular in learning settings. Thus, should the next generation of educational robots be `gendered' and what impact would robot gender have on task performance, particularly in the context of a gender-stereotypical human-robot interaction (HRI) task? To investigate these issues, we examined the influence of robot gender on learning when completing either stereotypically female or stereotypically male learning tasks. 120 university students (60 females and 60 males) completed either stereotypically female or stereotypically male tasks with the support of a male vs. female instructor robot. The manipulation check indicated that participants recognized the robot's alleged gender correctly. Importantly, our results suggest that prevailing gender stereotypes associated with learning do not apply to robots that perform gender-stereotypical tasks. Interestingly, our findings indicated that a mismatch of robot gender and gender typicality of the respective task led to increased willingness to engage in prospective learning processes with the robot. We discuss these results with respect to future research on HRI and learning, and with regard to practical implications associated with the introduction of robots into higher education.