No abstract

Mindset has been shown to have a large impact on people’s academic, social, and work achievements. A growth mindset, i.e., the belief that success comes from effort and perseverance, is a better indicator of higher achievements as compared to a fixed mindset, i.e., the belief that things are set and cannot be changed. Interventions aimed at promoting a growth mindset in children range from teaching about the brain’s ability to learn and change, to playing computer games that grant brain points for effort rather than success. This work explores a novel paradigm to foster a growth mindset in young children where they play a puzzle solving game with a peer-like social robot. The social robot is fully autonomous and programmed with behaviors suggestive of it having either a growth mindset or a neutral mindset as it plays puzzle games with the child. We measure the mindset of children before and after interacting with the peer-like robot, in addition to measuring their problem solving behavior when faced with a challenging puzzle. We found that children who played with a growth mindset robot 1) self-reported having a stronger growth mindset and 2) tried harder during a challenging task, as compared to children who played with the neutral mindset robot. These results suggest that interacting with peer-like social robot with a growth mindset can promote the same mindset in children.

Background This study explored the use of interface agents, anthropomorphic, 3D‐animated computer characters that provide teaching or mentoring within a computer‐based learning environment, to encourage young Black and White women to pursue careers in engineering. Purpose (Hypothesis) We hypothesized that computer‐based models that matched young women in terms of their race and gender would be the most effective in positively influencing their interest, self‐efficacy, and stereotypes about engineering. Design/Method Eighty African American undergraduate female students in Experiment 1, and 39 White undergraduate female students in Experiment 2 interacted with a computer‐based agent that provided a narrative designed to encourage them to pursue engineering careers. The study employed a 2 × 2 between subjects factorial design (agent gender: male vs. female and agent race: Black vs. White). Results Across both studies we found that race and gender influenced the effectiveness of the agent for several key outcome measures. Computer‐based agents who matched the participants with respect to race and gender tended to be the most effective in improving the women's responses to engineering‐related fields. Nevertheless, the White male agent was actually significantly more influential than the White female agent for female Black participants. Conclusions Personalizing interface agent characteristics to match the target population can increase the effectiveness of a persuasive message to encourage young women to pursue engineering. Such an approach may contribute to the growth and inclusiveness of fields such as engineering.