Requirements elicitation interviews are a widely adopted technique, where the interview success heavily depends on the interviewer's preparedness and communication skills. Students can enhance these skills through practice interviews. However, organizing practice interviews for many students presents scalability challenges, given the time and effort required to involve stakeholders in each session. To address this, we propose REIT, an extensible architecture for Requirements Elicitation Interview Training system based on emerging educational technologies. REIT consists of two phases: the interview phase, wherein students act as interviewers while the system assumes the role of an interviewee, and the feedback phase, during which the system assesses students' performance and offers contextual and behavioral feedback to enhance their interviewing skills. We demonstrate the applicability of REIT through two implementations: RoREIT with a physical robotic agent and VoREIT with a virtual voice-only agent. We empirically evaluated both instances with a group of graduate students. The participants appreciated both systems. They demonstrated higher learning gain when trained with RoREIT, but they found VoREIT more engaging and easier to use. These findings indicate that each system has its distinct benefits and drawbacks, suggesting that \gensys{} can be configured for various educational settings based on preferences and available resources.

翻译：需求获取访谈是一种广泛采用的技术，其成功与否很大程度上取决于访谈者的准备程度和沟通技能。学生可通过模拟访谈提升这些能力。然而，为大量学生组织模拟访谈面临规模化挑战，因为每次访谈都需要投入时间和精力邀请利益相关方参与。为此，我们提出REIT——一种基于新兴教育技术、可扩展的需求获取访谈培训系统架构。REIT包含两个阶段：访谈阶段，学生担任访谈者而系统扮演受访者角色；反馈阶段，系统评估学生表现并提供情境化与行为反馈以提升其访谈技能。我们通过两种实现验证了REIT的适用性：采用实体机器人代理的RoREIT和虚拟纯语音代理的VoREIT。我们以一组研究生为对象对两种实例进行了实证评估。参与者对两个系统均持肯定态度。使用RoREIT训练时，他们展现出更高的学习收益，但认为VoREIT更具吸引力且更易使用。这些发现表明每个系统都有其独特的优势与不足，提示REIT可根据偏好与可用资源针对不同教育场景进行配置。