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 has components to support both 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 distinct benefits and drawbacks, suggesting that REIT can be realized 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，访谈的成功很大程度上取决于访谈者的准备程度和沟通技能。学生可以通过实践访谈来提升这些能力。然而，为众多学生组织实践访谈面临可扩展性挑战，因为每次会话都需要投入时间和精力邀请利益相关者参与。为解决这一问题，我们提出REIT——一种基于新兴教育技术的可扩展需求获取访谈培训系统架构。REIT包含支持访谈阶段和反馈阶段的组件：在访谈阶段，学生扮演访谈者角色，系统则充当被访谈者；在反馈阶段，系统评估学生表现并提供情境化与行为化的反馈以提升其访谈技巧。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和采用纯语音虚拟代理的VoREIT。我们以一组研究生为对象对两个实例进行了实证评估。参与者对两个系统均表示认可。他们在使用RoREIT训练时表现出更高的学习收益，但认为VoREIT更具互动性且更易使用。这些发现表明每个系统各有独特优缺点，同时提示REIT可根据偏好与可用资源在不同教育场景中实现。