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.

抽象的

一对一的指导有效地帮助了新手职业发展。但是，传统的指导规模很差。为了解决这个问题，MentorPal根据真实STEM专业人士的录音模拟了与虚拟导师小组的对话。学生可以像在职业博览会上一样随意提问，而机器学习算法则以最匹配的答案做出回应。MentorPal正在研究新的虚拟导师的快速发展，在那里培训数据将是稀疏的。在一项可用性研究中，有31名高中学生报告（a）增加了职业知识和信心，（b）积极易用，并且（c）指导者很有帮助（87％），但很少涵盖他们喜欢的职业（29％ ）。这些结果证明了虚拟辅导的可行性，但是需要进行功效研究以评估其影响，