Date
Publisher
arXiv
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.
Study at a glance
- Question: Can machine learning fundamentals be effectively structured and evaluated using automated input-output programming assessments?
- Setting and data: University courses in machine learning and operating systems, 2025 to 2026; student survey feedback and comments.
- Method: Design and deployment of input-output exercises, automated grading, LLM submission inspection, and student survey analysis.
Why it matters
Understanding foundational artificial intelligence and machine learning concepts requires closely integrating theory with practice. However, translating complex, stochastic machine learning algorithms into automated grading tasks remains challenging.
Key findings
- More than 60% of surveyed students fully agreed that the automated grading exercises improved the integration of theory and practice.
- Over 91% of students gave the highest rating to the importance of including input-output examples in exercise statements.
- Students exhibited a clear preference for automated assessment over purely human grading, driven primarily by receiving fast feedback.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Who age?
Why use AI?
Study design
