Much research has highlighted the impressive capabilities of large language models (LLMs), like GPT and Bard, for solving introductory programming exercises. Recent work has shown that LLMs can effectively solve a range of more complex object-oriented programming (OOP) exercises with text-based specifications. This raises concerns about academic integrity, as students might use these models to complete assignments unethically, neglecting the development of important skills such as program design, problem-solving, and computational thinking. To address this, we propose an innovative approach to formulating OOP tasks using diagrams and videos, as a way to foster problem-solving and deter students from a copy-and-prompt approach in OOP courses. We introduce a novel notation system for specifying OOP assignments, encompassing structural and behavioral requirements, and assess its use in a classroom setting over a semester. Student perceptions of this approach are explored through a survey (n=56). Generally, students responded positively to diagrams and videos, with video-based projects being better received than diagram-based exercises. This notation appears to have several benefits, with students investing more effort in understanding the diagrams and feeling more motivated to engage with the video-based projects. Furthermore, students reported being less inclined to rely on LLM-based code generation tools for these diagram and video-based exercises. Experiments with GPT-4 and Bard's vision abilities revealed that they currently fall short in interpreting these diagrams to generate accurate code solutions.

翻译：大量研究已凸显大型语言模型（LLM），如GPT和Bard，在解决基础编程练习方面的惊人能力。近期工作表明，LLM能有效解决一系列基于文本描述的复杂面向对象编程（OOP）练习。这引发了对学术诚信的担忧，因为学生可能不道德地利用这些模型完成作业，忽视程序设计、问题解决与计算思维等重要技能的发展。针对这一问题，我们提出一种创新方法，通过使用图表和视频来制定OOP任务，旨在促进问题解决能力，并阻止学生在OOP课程中采用简单的“复制-提示”方式。我们引入了一套用于描述OOP作业的新颖符号系统，涵盖了结构性和行为性要求，并在整个学期的课堂环境中评估其使用效果。通过一项问卷调查（n=56），我们探讨了学生对这一方法的看法。总体而言，学生对图表和视频反应积极，其中基于视频的项目比基于图表的练习更受欢迎。这种符号系统似乎具有多重优势：学生投入更多精力理解图表，并对参与视频项目表现出更强的动力。此外，学生报告称，对于这些基于图表和视频的练习，他们倾向于减少对基于LLM的代码生成工具的依赖。针对GPT-4和Bard视觉能力的实验表明，它们目前尚无法有效解释这些图表以生成准确的代码解决方案。