RoboREIT: An interactive robotic tutor with instructive feedback component for requirements elicitation interview training

Interviewing stakeholders is the most popular technique for eliciting requirements. The success of an interview depends on the interviewer's theoretical knowledge, preparedness, and communication skills. Practice interviews allow students to apply their knowledge and improve their skills through experience. This practical training is resource-intensive, requiring the time and effort of a stakeholder for each student, which may not be feasible for a large number of students. This paper introduces RoboREIT, an interactive Robotic tutor for Requirements Elicitation Interview Training. RoboREIT addresses the scalability problem of practice sessions with a robotic tutor acting as a stakeholder during the interview and providing feedback after the interview. 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. Our study is the first in the literature that utilizes a social robot in requirements elicitation interview education. RoboREIT's 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.