Requirements elicitation interviews are a widely adopted technique where the interview success 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 leveraging technologies such as robots and voice systems. 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 and 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 educators can customize REIT for various settings, considering preferences and available resources. We publicly share our system implementation and study materials in https://doi.org/10.5281/zenodo.7861906.