The growing field of machine learning (ML) and artificial intelligence (AI) presents a unique and unexplored case within persistence research, meaning it is unclear how past findings from engineering will apply to this developing field. We conduct an exploratory study to gain an initial understanding of persistence in this field and identify fruitful directions for future work. One factor that has been shown to predict persistence in engineering is belonging; we study belonging through the lens of confidence, and discuss how attention to social belonging confidence may help to increase diversity in the profession. In this research paper, we conduct a small set of interviews with students in ML/AI courses. Thematic analysis of these interviews revealed initial differences in how students see a career in ML/AI, which diverge based on interest and programming confidence. We identified how exposure and initiation, the interpretation of ML and AI field boundaries, and beliefs of the skills required to succeed might influence students' intentions to persist. We discuss differences in how students describe being motivated by social belonging and the importance of close mentorship. We motivate further persistence research in ML/AI with particular focus on social belonging and close mentorship, the role of intersectional identity, and introductory ML/AI courses.

中文翻译：

---

“一直在外面一点”：机器学习和人工智能学生的社会归属感信心和坚持

不断发展的机器学习 (ML) 和人工智能 (AI) 领域在持久性研究中提出了一个独特且未经探索的案例，这意味着目前尚不清楚过去的工程发现将如何应用于这个发展中的领域。我们进行了一项探索性研究，以初步了解该领域的持久性，并为未来的工作确定富有成果的方向。已被证明可以预测工程学持久性的一个因素是归属感。我们从信心的角度研究归属感，并讨论对社会归属信心的关注如何有助于增加职业的多样性。在这篇研究论文中，我们对 ML/AI 课程的学生进行了少量访谈。对这些访谈的主题分析揭示了学生对 ML/AI 职业的看法存在初步差异，这些差异基于兴趣和编程信心。我们确定了接触和启动、对 ML 和 AI 领域边界的解释以及对成功所需技能的信念如何影响学生坚持的意图。我们讨论了学生如何描述社会归属感激励的差异以及密切指导的重要性。我们鼓励对 ML/AI 进行进一步的持久性研究，特别关注社会归属感和密切指导、交叉身份的作用以及介绍性 ML/AI 课程。