Abstract
One-on-one mentoring is effective for helping novices with career development. However, traditional mentoring scales poorly. To address this problem, MentorPal emulates conversations with a panel of virtual mentors based on recordings of real STEM professionals. Students freely ask questions as they might in a career fair, while machine learning algorithms respond with best-match answers. MentorPal is researching rapid development of new virtual mentors, where training data will be sparse. In a usability study, 31 high school students reported (a) increased career knowledge and confidence, (b) positive ease-of-use, and that (c) mentors were helpful (87%) but seldom covered their preferred career (29%). These results demonstrate feasibility for virtual mentoring, but efficacy studies are needed to evaluate its impact, particularly for groups with limited STEM opportunities.
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