Elicitation interviews are the most common requirements elicitation technique, and proficiency in conducting these interviews is crucial for requirements elicitation. Traditional training methods, typically limited to textbook learning, may not sufficiently address the practical complexities of interviewing techniques. Practical training with various interview scenarios is important for understanding how to apply theoretical knowledge in real-world contexts. However, there is a shortage of educational interview material, as creating interview scripts requires both technical expertise and creativity. To address this issue, we develop a specialized GPT agent for auto-generating interview scripts. The GPT agent is equipped with a dedicated knowledge base tailored to the guidelines and best practices of requirements elicitation interview procedures. We employ a prompt chaining approach to mitigate the output length constraint of GPT to be able to generate thorough and detailed interview scripts. This involves dividing the interview into sections and crafting distinct prompts for each, allowing for the generation of complete content for each section. The generated scripts are assessed through standard natural language generation evaluation metrics and an expert judgment study, confirming their applicability in requirements engineering training.

Abstract 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.