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

Abstract

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 largely 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. In a controlled experiment, we involved 238 undergraduate students studying Software Engineering. We assigned participants the manual and LLM-assisted RE tasks and had them work on three RE learning activities: stakeholder analysis, development of user stories, and requirement prioritization and conflict detection. The learning outcomes were assessed by structured rubrics addressing the quality of created artifacts, while student perceptions were measured through post-activity surveys and feedback forms. Additionally, emotion and thematic analyses were performed to identify events that triggered these emotions. Results suggest that LLM-supported co-analysis positively impacts student-created RE artifacts in terms of quality and consistency, particularly when addressing early-level problems or when used to convey structural information, while human insight remains critical for refinement and strategic-level cognitive processes. Emotion analysis shows a general acceptance with productive hesitancy, implying that LLMs are better off as human-guided co-analysts in RE education.