一句话：提出基于 RAG 的 LLM 教学助手 GRIA，将初学者自然语言请求映射为 GNU Radio 领域知识与操作序列，显著降低教学与学习成本。
工业通信软件（如 GNU Radio）是连接无线通信理论与工程实践的关键桥梁，但其复杂度高、API 庞大，初学者在“领域理论”与“软件实现”之间存在巨大认知鸿沟；传统教学支持受限于文档的静态性与通用编程工具缺乏领域知识，难以提供有效的实时指导。本文提出由大语言模型增强的智能教学工具 GRIA，创新性地利用检索增强生成（RAG）将初学者的自然语言请求映射到精确的领域知识与软件操作序列，以解决工业软件教学中常见的 API 误用与配置困难问题。作者在真实教学场景中开展对照实验，结果显示 GRIA 使教学所需总时间减少超过 65%，教师重复处理低层调试任务的比例下降 69.4%，学习者学习时间约减少 70%，为 LLM 在复杂工业软件工程教育中的应用提供了新范式。
Industrial communication software (such as GNU Radio) serves as a critical bridge connecting wireless communication theory with engineering practice, occupying a core position in the modern radio industry. However, such software is typically highly complex and has a vast API, leaving beginners with a significant cognitive gap between "domain theory" and "software implementation." Existing teaching support is often limited by the static nature of documentation and the lack of specific domain knowledge in general programming tools, making it difficult to provide effective real-time guidance. To address this challenge in industrial software education, this paper proposes an intelligent teaching tool enhanced by Large Language Models—GRIA. GRIA innovatively leverages Retrieval-Augmented Generation (RAG) to map beginners' natural language requests to precise domain knowledge and software operation sequences, aiming to address the specific problems of API misuse and configuration difficulties in industrial software teaching. Through controlled experiments in real teaching scenarios, results show that GRIA can reduce the total time required for teaching by over 65%, decrease instructors' repetition rate on low-level debugging tasks by 69.4%, and significantly reduce learning time by approx. 70%. The successful practice of GRIA provides a new paradigm for the application of Large Language Models in complex industrial software engineering education.
@inproceedings{ZhangQDJXBLC26,
title = {Bridging the Gap in Industrial Software Education: A LLM-Based Intelligent Assistant for GNU Radio},
author = {Daiyang Zhang and Yuanye Qiu and Yu Duan and Zhiping Jiang and Yueshen Xu and Gairui Bai and Rui Li and Di Cui},
booktitle = {FSE},
year = {2026},
doi = {10.1145/3803437.3805780}
}