[Context] Requirements elicitation interviews are the most widely used elicitation technique. The interviewer's preparedness and communication skills play an important role in the quality of interaction, therefore, the interview's success. Students can develop their skills through practice interviews. [Problem] Arranging practice interviews for many students is not scalable, as the involvement of a stakeholder in each interview requires a lot of time and effort. [Principal Idea] To address this problem, we propose REIT, an extensible architecture for Requirements Elicitation Interview Trainer system based on emerging technologies for education. It has two separate phases. The first is the interview phase, where the student acts as an interviewer and the system as an interviewee. The second is the feedback phase, where the system evaluates the student's performance and provides contextual and behavioral feedback to enhance their interviewing skills. [Results/Contribution] We demonstrate the applicability of REIT by implementing two instances: RoREIT with an embodied physical robotic agent and VoREIT with a virtual voice-only agent. We empirically evaluated these two instances with a target user group consisting of graduate students. The results reveal that the students appreciated both systems. The participants 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 our generic architecture REIT can be configured for various educational settings based on preferences and available resources.

翻译：[背景] 需求获取访谈是最广泛使用的需求获取技术。面试官的准备程度和沟通技能对交互质量乃至访谈成功具有重要影响。学生可通过模拟访谈练习提升自身技能。[问题] 为众多学生安排模拟访谈练习缺乏可扩展性，因为每次访谈都需要利益相关方投入大量时间和精力。[核心思想] 针对此问题，我们提出基于新兴教育技术的需求获取访谈训练系统可扩展架构REIT。该架构包含两个独立阶段：访谈阶段，学生担任面试官角色，系统则作为受访者；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巧。[成果/贡献] 我们通过实现两个实例验证REIT的适用性：采用实体物理机器人代理的RoREIT，以及纯语音虚拟代理的VoREIT。以研究生群体为目标用户进行实证评估，结果显示学生对两个系统均给予积极评价。接受RoREIT训练的参与者展现出更高的学习增益，但VoREIT被认为更具吸引力且更易使用。这些发现表明两个系统各有优劣，证明我们的通用架构REIT可根据偏好与可用资源，为不同教育场景进行配置。