一句话：通过238名本科生的对照实验，评估LLM作为协同分析者对需求工程教育学习成果与学生认知的影响。
LLM在软件工程教育中日益普及，但其对需求工程（RE）教育的教学价值证据仍不足。本文研究LLM作为协同分析者对本科RE教育的学习成果、学生感知和教学可行性的影响。作者对238名软件工程本科生开展对照实验，比较人工与LLM辅助的利益相关者分析、用户故事编写、需求优先级排序与冲突检测。学习成果由结构化量规评估，学生感知通过问卷、反馈表及情感与主题分析测量。结果表明，LLM辅助可提升RE产出的质量与一致性，但人类洞察对精炼和战略性认知仍关键。情感分析显示总体接受但存在生产性犹豫，表明LLM更适合作为人工引导的协同分析者。
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.
@inproceedings{KarimiTR26,
title = {An Analysis of Student Perceptions and Learning Impact of Large Language Models in Requirements Engineering Education},
author = {Mohammed Ammar Karimi and Saurabh Tiwari and Santosh Singh Rathore},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805784}
}