大模型时代的光纤通信技术研究生课程教学改革与探索:基于P-MASE模型的视角
Teaching Reform and Exploration in a Postgraduate Optical Communication Technology Course in the Era of Large Language Models: A P-MASE Perspective
摘要: 大模型在知识问答和代码生成方面的能力,正在改变研究生获取知识和解决专业问题的方式,并对光纤通信技术课程的教学过程与评价方式提出新的要求。本文以天津理工大学硕士研究生课程《光纤通信技术》为对象,从政策要求、学科发展和实施条件等方面分析大模型融入课程的必要性与可行性。在此基础上,依据P-MASE模型将课程教学组织分为引入问题、寻找方法、科学分析、有效解决和效果评价五个环节,并以长距离波分复用(WDM)光链路性能分析与优化为代表性问题,构建教师、学生与大模型协同参与的教学过程。改革方案利用大模型的知识问答和代码辅助支持学生学习,通过教师的方法指导组织专业错误核验与方案比较,并设置过程评价量规,以培养学生分析和解决复杂工程问题的能力。课程评价进一步关注学生获得结论的过程,旨在促进专业知识、工程能力与人工智能素养的协同培养。本研究可为大模型融入通信工程类研究生课程提供参考。
Abstract: Large language models (LLMs) support question answering and code generation, changing how postgraduate students acquire knowledge and solve disciplinary problems and creating new demands for the teaching process and assessment methods of the Optical Communication Technology course. Focusing on optic communication technology, a postgraduate course at Tianjin University of Technology, this study examines the necessity and feasibility of integrating LLMs in terms of policy requirements, disciplinary developments, and implementation conditions. The proposed teaching design follows the five stages of the P-MASE model: problem introduction, method exploration, scientific analysis, effective problem solving, and evaluation. The representative task of analyzing and optimizing the performance of long-distance wavelength-division multiplexing (WDM) optical links; is used to construct a collaborative teaching process involving teachers, students, and LLMs. LLMs support learning through question answering and coding assistance, while teachers guide students in checking for technical errors and comparing technical solutions. A process-assessment rubric is designed to support the development of students’ ability to analyze and solve complex engineering problems. Assessment emphasizes how students reach their conclusions, with the aim of integrating disciplinary knowledge, engineering competence, and artificial intelligence literacy. This design offers a reference for integrating LLMs into postgraduate communication engineering courses.
文章引用:童峥嵘, 张爱玲, 潘洪刚, 王雪, 李建锋. 大模型时代的光纤通信技术研究生课程教学改革与探索:基于P-MASE模型的视角[J]. 社会科学前沿, 2026, 15(11): 18-25. https://doi.org/10.12677/ass.2026.1511894

参考文献

[1] 倪晓玉, 袁敏, 孙师丹. 研究生教育赋能新质生产力: 核心要素、现实境遇与实践路径[J]. 研究生教育研究, 2024(4): 12-18.
[2] 林蕙青. 充分发挥科技创新的引领驱动作用实现高等教育高质量可持续发展[J]. 中国高教研究, 2024(11): 1-3.
[3] 项秋实, 王淼, 谢东辰, 等. 浅析光纤通信技术的原理及发展趋势[J]. 数字通信世界, 2024(3): 173-175.
[4] 戴岭, 赵晓伟, 祝智庭. 智慧问学: 基于ChatGPT的对话式学习新模式[J]. 开放教育研究, 2023, 29(6): 42-51+111.
[5] Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., et al. (2023) ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, Article 102274.
https://doi.org/10.1016/j.lindif.2023.102274
[6] Bastani, H., Bastani, O., Sungu, A., Ge, H., Kabakcı, Ö. and Mariman, R. (2025) Generative AI without Guardrails Can Harm Learning: Evidence from High School Mathematics. Proceedings of the National Academy of Sciences, 122, e2422633122.
https://doi.org/10.1073/pnas.2422633122
[7] 梁俊斌, 谢贇, 唐振华. 生成式人工智能赋能教育数字化转型的机理、挑战与对策[J]. 现代教育管理, 2025(11): 15-23.
[8] 韩杰, 张伟刚. 基于P-MASE的拔尖人才培养模式改革与实践[J]. 高等理科教育, 2023(1): 63-69.
[9] 张伟刚, 严铁毅, 张严昕. 基于P-MASE模型的研究性教学与素质教育[C]//河南科技大学. 素质教育: 让未来更美好——2020年大学素质教育高层论坛论文集. 北京: 高等教育出版社, 2020: 166-173.
[10] 李钦, 钱泉, 胡坤. 人工智能赋能高等教育拔尖创新人才培养的模式探索[J]. 豫章师范学院学报, 2026, 41(3): 45-50.
[11] Fan, Q., Zhou, G., Gui, T., Lu, C. and Lau, A.P.T. (2020) Advancing Theoretical Understanding and Practical Performance of Signal Processing for Nonlinear Optical Communications through Machine Learning. Nature Communications, 11, Article No. 3694.
https://doi.org/10.1038/s41467-020-17516-7
[12] Abdelli, K. (2026) Prompt Once, Manage All: A Unified LLM Framework for Multi-Task Optical Link Management. Journal of Lightwave Technology, 44, 2869-2879.
https://doi.org/10.1109/jlt.2026.3660177
[13] Rismanchian, S., Babar, E.T.R. and Doroudi, S. (2026) What Undergraduate Students Need to Know and Actually Know about Generative AI. Computers and Education: Artificial Intelligence, 10, Article 100554.
https://doi.org/10.1016/j.caeai.2026.100554
[14] Lewis, P., Perez, E., Piktus, A., et al. (2020) Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.
[15] Yao, S., Zhao, J., Yu, D., et al. (2023) ReAct: Synergizing Reasoning and Acting in Language Models. 2023 International Conference on Learning Representations, Kigali Convention Centre, 1-5 May 2023, 1-33.