ARTICLE

Volume 2,Issue 6

Cite this article
6
Citations
15
Views
20 June 2026

基于Agent Skill的高校课程项目选题研究—— 以《数字图像处理与计算机视觉》为例

智慧 李1 咏梅 刘1
Show Less
1 哈尔滨工程大学计算机科学与技术学院, 中国
EIR 2026 , 2(6), 112–115; https://doi.org/10.61369/EIR.2026060032
© 2026 by the authors. Licensee Art and Technology, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

针对《数字图像处理与计算机视觉》课程中学生选题困难、易选现成项目的问题,本文提出基于Agent Skill的自动化选题Topic-select Skill。该Skill通过双策略方式选题,策略A通过检索小众开源论文、分析论文中的缺陷从而发现改进空间;策略B通过搜索SOTA方法的应用空白领域、评估跨域迁移可行性以生成创新选题。系统对候选项目进行难度可行性、代码量、可改进空间、可复现性四维度综合评分,最后输出排序的推荐列表。实验以5道选题为验证案例,验证了方法的有效性与实用性。

Keywords
Agent Skill
课程项目选题
计算机视觉
深度学习教学
References

[1] 张剑平, 陈仕品. 人工智能赋能教育变革:机遇、挑战与路径[J]. 中国电化教育, 2020, (1): 1-8.
[2] 余胜泉. 人工智能教师的未来角色[J]. 开放教育研究, 2018, 24(1): 16-28.
[3] Kasneci E, et al. ChatGPT for good? On opportunities and challenges of large language models for education[J]. Learning and Individual Differences, 2023, 103: 102274.
[4] Chu Z, Wang S, Xie J, et al. LLM agents for education: Advances and applications[J]. arXiv preprint arXiv:2503.11733, 2025, 2.
[5] 黄荣怀, 周伟, 杜静, 等. 面向智能教育的三个基本计算问题[J]. 开放教育研究, 2019, 25(5): 11-22.
[6] Chen P, et al. KnowEdu: A system to construct knowledge graph for education[J]. IEEE Access, 2018, 6: 31553-31563.
[7] Ghosh A, et al. The future of assessments in the age of generative AI[J]. Journal of Computer Assisted Learning, 2024, 40(3): 1012-1025.
[8] Leinonen J, et al. Comparing code explanations created by students and large language models[C]//ITiCSE 2023: 124-130.
[9] Papers with Code. Machine learning papers with code, evaluation tables and benchmarks[EB/OL]. https://paperswithcode.com/, 2024.
[10] Zhongwei V. Hermes Edu Skills: Agent Skills for Chinese education scenarios[EB/OL]. https://github.com/zhongweiv/hermes-edu-Skills, 2025.

Share
Back to top