这篇论文拿五个主流AI模型去考大学OOP期末题，结果都比学生平均分高，但抽象类和图形题还是会翻车，想看AI编程能力真实段位的可以读读。
该研究评估了ChatGPT-5.2、DeepSeek-V3、Gemini 2.5 Flash、Claude Sonnet 4.5和M365 Copilot五个生成式AI系统，使用某大学入门OOP课程的编程测试和考试任务。AI生成的代码按与学生相同的评分标准评判，并与历史学生结果及上一年度发现对比。所有AI系统均获得高于学生平均分的成绩，在较长编程任务上常拿满分，但偶尔产生无法编译的代码，在接口、抽象类和部分继承相关任务上仍表现不佳。与上一年度相比，各系统在多数测评中明显进步，但存在若干重复性错误模式。
Revisiting the Performance of Generative Artificial Intelligence on Introductory Object-Oriented Programming Assessments: Insights from 2026
Recent advances in Generative Artificial Intelligence (GenAI) have substantially improved the ability of large language models (LLMs) to generate and explain source code. However, their performance on authentic object-oriented programming (OOP) assessments remains insufficiently understood. This study evaluates five widely used GenAI systems, ChatGPT-5.2, DeepSeek-V3, Gemini 2.5 Flash, Claude Sonnet 4.5, and M365 Copilot, using programming tests and examination tasks from an introductory university OOP course. The generated solutions were assessed using the same grading criteria applied to students and compared with historical student results from the same course, as well as findings from the previous year. Common errors were also analyzed to identify recurring limitations across models. All evaluated GenAI systems achieved higher scores than the average student cohort and frequently obtained full marks on longer programming tasks. Nevertheless, they occasionally produced non-compiling code and continued to struggle with advanced OOP concepts, particularly interfaces, abstract classes, and certain inheritance-related tasks. Performance was also limited on graphics-related questions involving image interpretation. Compared with the previous year, the evaluated systems demonstrated noticeable improvements across most assessments while exhibiting several recurring error patterns. The findings provide an updated evaluation of the capabilities and limitations of contemporary GenAI systems on authentic introductory OOP assessments. They also offer evidence that can inform the design of programming assessments, the responsible integration of GenAI tools into software engineering education, and future studies evaluating the evolution of AI-assisted programming.