Teaching Agile Requirements Engineering: A Stakeholder Simulation with Generative AI

TL;DR

This paper presents a pedagogical approach using Generative AI in stakeholder simulations to teach agile requirements engineering, enhancing student practical skills and understanding of AI limitations.

Contribution

It introduces a novel educational method employing AI Personas and meta-prompts for agile requirements elicitation in higher education.

Findings

Students gain hands-on experience with agile practices.

The approach helps students understand AI's technical and ethical limitations.

Meta-prompts offer flexibility across different AI models.

Abstract

Context: The active involvement of users and customers in agile software development remains a persistent challenge in practice. For this reason, it is important that students in higher education become familiar with good practices in Agile Requirements Engineering during their studies. Objective: Our objective is to enable students to learn how to interact with Generative Artificial Intelligence (GenAI) through the use of a stakeholder simulation with AI Personas, while also developing an understanding of the limitations of AI tools in practical contexts. Method: In our courses, we employ a stakeholder simulation using GenAI, in which students conduct interviews with AI Personas through a provided meta-prompt. Based on the outcomes of these interviews, students apply agile practices (e.g., story mapping or impact mapping) to document requirements. The use of GenAI is subsequently reflected upon in a structured group discussion. Results: Through this approach, students gain practical experience by applying state-of-the art agile practices for requirements elicitation and documentation while simultaneously developing an understanding of the technical and ethical limitations associated with the use of generative AI. Conclusion: We have applied this approach over several terms and found that using a meta-prompt provides flexibility, allowing us to remain independent of specific large language model providers.