[Context] Interviewing stakeholders is the most popular requirements elicitation technique among multiple methods. The success of an interview depends on the collaboration of the interviewee which can be fostered through the interviewer's preparedness and communication skills. Mastering these skills requires experience and practicing interviews. [Problem] Practical training is resource-heavy as it calls for the time and effort of a stakeholder for each student which may not be feasible for a large number of students. [Method] To address this scalability problem, this paper proposes RoboREIT, an interactive Robotic tutor for Requirements Elicitation Interview Training. The humanoid robotic component of RoboREIT responds to the questions of the interviewer, which the interviewer chooses from a set of predefined alternatives for a particular scenario. After the interview session, RoboREIT provides contextual feedback to the interviewer on their performance and allows the student to inspect their mistakes. RoboREIT is extensible with various scenarios. [Results] We performed an exploratory user study to evaluate RoboREIT and demonstrate its applicability in requirements elicitation interview training. The quantitative and qualitative analyses of the users' responses reveal the appreciation of RoboREIT and provide further suggestions about how to improve it. [Contribution] Our study is the first in the literature that utilizes a social robot in requirements elicitation interview education. RoboREIT's innovative design incorporates replaying faulty interview stages and allows the student to learn from mistakes by a second time practicing. All participants praised the feedback component, which is not present in the state-of-the-art, for being helpful in identifying the mistakes. A favorable response rate of 81% for the system's usefulness indicates the positive perception of the participants.

翻译：[背景] 在多种需求获取技术中，访谈利益相关者是最常用的方法。访谈的成功依赖于受访者的协作，而受访者的协作可通过访谈者的准备程度与沟通技巧得到增进。掌握这些技能需要经验积累与实践练习。[问题] 实践培训资源消耗巨大，因为每位学生都需要利益相关者投入时间与精力，这对于大量学生而言可能难以实现。[方法] 为解决这一可扩展性问题，本文提出RoboREIT——一种用于需求获取面试培训的交互式机器人导师。RoboREIT的人形机器人组件能够对访谈者的问题做出回应，而访谈者则从针对特定场景预设的备选方案中选择问题。在访谈环节结束后，RoboREIT会向访谈者提供关于其表现的上下文反馈，并允许学生检查自身错误。RoboREIT支持通过多种场景进行扩展。[结果] 我们进行了一项探索性用户研究以评估RoboREIT，并展示了其在需求获取面试培训中的适用性。对用户反馈的定量与定性分析揭示了用户对RoboREIT的认可，并提出了进一步改进的建议。[贡献] 本研究是文献中首次将社交机器人应用于需求获取面试教育领域。RoboREIT的创新设计引入了对错误访谈阶段的回放功能，使学生能够通过二次实践从错误中学习。所有参与者均高度评价了反馈组件——该组件在现有技术中尚不存在——认为其有助于识别错误。81%的系统有用性正面应答率表明了参与者的积极看法。