超越简单的输入-输出评估任务：利用自动化编程评估支持非平凡课程
Beyond Simple Input-Output Assessment Tasks: Leveraging Automated Programming Assessment for Non-Trivial Courses
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中文总结 AI 辅助
本文提出将机器学习问题视为输入-输出评估任务，利用现有自动化编程工具（如VPL、Codeforces）支持非平凡AI课程，以促进理论与实践结合的教学创新。
中文摘要 AI 辅助
人工智能（AI）的公众可见度正在迅速增长，这得益于其在不同知识领域应用的积极影响。在这一新篇章中，涵盖AI和机器学习基础的课程对于理解其在当代社会中的作用和潜力至关重要。因此，在AI课程中，通过理论与实践紧密结合来理解基本概念和基础算法至关重要。本文报告了我们在为编程自动化评估工具设计机器学习练习方面的经验。需要指出的是，我们并非开发一种新型的自动评分系统。相反，我们提出了一种将机器学习问题视为输入-输出评估任务的视角。从这个视角出发，每个练习都有唯一且确定性的答案，并使得自动化编程评估工具（如Moodle的VPL、Codeforces和MOJ）能够有效支持AI教育。我们相信，本文能够鼓励教师通过采用更具动态性和互动性的AI课程方法（融合理论与实践）来促进教育创新。重要的是，本文并未引入AI用于教育的创新，而是引入了一种改进AI（特别是机器学习）学习的创新方法。
英文摘要
The public visibility of Artificial Intelligence (AI) is growing rapidly, driven by the positive impact of its applications across diverse fields of knowledge. In this new chapter, courses that cover the foundations of AI and machine learning become essential for understanding their role and potential in contemporary society. Therefore, understanding fundamental concepts and elementary algorithms through the close integration of theory with practice is essential in AI courses. In this essay, we report our experience designing machine learning exercises for automated assessment tools in programming. It is worth mentioning that we are not developing a novel form of automated grading system. Instead, we propose a perspective that frames machine learning problems as input-output assessment tasks. From this perspective, each exercise admits a unique and deterministic answer and enables automated programming assessment tools (e.g., VPL for Moodle, Codeforces, and MOJ) to effectively support AI education. We believe this essay can encourage instructors to foster educational innovation by adopting more dynamic and interactive approaches to AI courses that integrate theory and practice. Importantly, this essay does not introduce an innovation in the use of AI for education; rather, it introduces an innovative approach to improving the learning of AI, particularly, machine learning.
