Students' Perception of LLM Use in Requirements Engineering Education: An Empirical Study Across Two Universities

2025-09-07

AI Summary

This study investigates the impact of integrating large language models (LLMs) into requirements engineering (RE) education on students’ learning experience, perceived benefits, and challenges in practice. Employing a cross-institutional empirical design, comparative experiments were conducted concurrently at two universities, embedding LLM-assisted instruction in both individual assignments and team-based agile projects, and evaluating outcomes via mixed-method analysis—including surveys and qualitative thematic analysis. Its key contribution is the first systematic comparison of LLM integration across distinct pedagogical contexts, yielding a novel “Contextualized AI Integration Framework.” Results indicate that LLMs significantly enhance students’ conceptual understanding and practical efficiency in requirements elicitation and documentation; however, they also expose critical challenges—including academic integrity risks, diminished critical thinking, and overreliance on AI. Accordingly, the study proposes pedagogical design principles and evidence-informed practices that balance AI augmentation with foundational competency development.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

Problem

• Evaluating LLM impact on Requirements Engineering education
• Assessing student perceptions of benefits and challenges
• Comparing individual versus team-based AI integration contexts

Innovation

• Guided LLM integration in coursework
• Empirical study across two universities
• Individual versus team-based instructional formats