实验技术与管理

大模型的广泛应用为高校计算机实验教学带来了新挑战。以大模型时代计算机实验教学为研究对象，旨在解决传统教学目标和模式无法满足大模型时代人才需求的问题。通过调研IT行业对人才能力的新需求，剖析大模型的优势与局限性，明确大模型时代人机的协同模式，以及学生在大学阶段应该培养的关键能力。基于以上分析，提出基于大模型辅助的项目式学习实验教学体系，详细介绍如何借助大模型辅助开展人机协作教学，并设计由大模型驱动的智能机器人综合实验项目。实践结果表明，该教学模式显著提升了学生在复杂问题解决与创新设计等方面的能力。该研究为高校在人工智能背景下推进实验教学改革、构建人机协同人才培养机制提供了有益参考和思路。

[Objective] The widespread adoption of large language models(LLMs) has presented notable challenges to traditional paradigms of experimental computer teaching in universities. Traditional educational objectives and teaching models no longer align with the talent development needs of the LLM era. Recent studies have focused on the application of LLMs in experimental computer teaching. However, existing research often lacks an in-depth analysis of the limitations of LLMs and fails to sufficiently explore the evolving competency requirements for computer professionals or human–artificial intelligence(AI) collaboration patterns. Therefore, this paper thoroughly investigates the new competency requirements of the current job market for computer science students, redefines the goals of experimental computer teaching, and constructs a novel experimental computer teaching system centered on human–AI collaboration. The goal is to develop individuals equipped with innovative thinking, complex problem-solving skills, and the ability to collaborate with AI to meet the opportunities and challenges brought about by the LLM era. [Methods] To gain insight into industry-aligned competencies, a questionnaire-based survey was conducted targeting recent graduates from Beihang University who were employed in the computer industry. The survey explored the practical use of LLMs in their work, including how LLMs were applied in programming tasks, their strengths and limitations, and the extent to which programmers could be substituted. Based on the survey results, this paper analyzes the emerging competency requirements in the job market and clarifies the evolving goals of experimental computer teaching. Furthermore, it examines the opportunities and challenges caused by industrial transformation. Based on the analysis, this paper systematically designs corresponding teaching content and instructional models. Specifically, it proposes a project-based experimental teaching system supported by LLMs and designs a comprehensive project for intelligent robots driven by LLMs, thereby providing a detailed explanation of how to implement human–AI collaborative teaching. [Results] The novel experimental computer teaching system was officially implemented at Beihang University and was supported by an integrated teaching and research platform named “Xiji”. This system establishes a tripartite collaborative teaching model involving teachers, LLMs, and students. Based on this platform, a comprehensive LLM-driven project on intelligent robots has been adopted as a core experimental component of AI training for third-and fourth-year undergraduates from the School of Computer Science and Engineering, the School of Artificial Intelligence, and the School of Mechanical Engineering and Automation. It is also integrated into graduate-level courses such as Intelligent Control of Robots. Practical results indicate that the project has achieved positive outcomes in real-world teaching, facilitating the deep integration of LLMs effectively with engineering practice and developing students' competency in human–AI collaboration and comprehensive innovation. [Conclusions] This paper presents a systematic review and in-depth analysis of the current applications of LLMs in computer science, the evolving demands of the job market under new competency structures, and the advantages and innovative practices of project-based learning. In the future, universities should actively promote a structural transformation of experimental computer teaching, shifting from the traditional teacher–student binary relationship to a tripartite collaborative model involving teachers, LLMs, and students. By introducing LLM-assisted project-based learning, students can be guided effectively to develop capabilities in active exploration, deep thinking, and systematic innovation. This teaching model provides a practical and feasible pathway to support the transformation and advancement of talent cultivation in universities in the LLM era.