Analysis of Student Perceptions and Learning Impact of Large Language Models in Requirements Engineering Education

Nowadays, large language models (LLMs) are increasingly used in software engineering education. However, evidence regarding their pedagogical value for improving Requirements Engineering (RE) education remains scant. Past research has primarily relied on small-scale or tool-centric evaluations, which are limited in their ability to provide meaningful insights into students' perceptions and learning impact, necessary to guide the systematic integration of LLMs into curricula. This work investigates the impact of using LLMs as co-analysts on student learning outcomes, student perceptions, and pedagogical feasibility in undergraduate RE education. This repository contains the experimental package, including a sample of RE artefacts developed by the students, the LLM-based requirement assistant tool, prompts, source code, pipelines and additional materials for replication.