Automation of Learning Workflows for 3D Modeling Skills in Engineering Education

Abstract

This paper presents a novel platform for automated self-paced learning in Computer-Aided Design (CAD) courses within engineering education. The platform fully automates the entire learning cycle, including exercise generation, submission, scheduling, test design, and grading. The central hypothesis posits that complete automation reduces repetitive tasks for instructors, allowing them to dedicate more time to individualized student support. The system also provides key advantages: it generates unique exercises for each student to prevent plagiarism while maintaining comparable complexity; it delivers instantaneous grading and feedback to enhance motivation; and it enables students to work with almost any CAD software, as the evaluation relies on physical properties rather than commercial tools. After several years of successive testing and refinement, the tool can generate and accurately grade frequent activities in large student cohorts, providing abundant data points. Their statistical analysis, via multiple approaches, confirms that the system reliably produces individualized exercises, reduces grading errors, and offers prompt, consistent feedback, thereby supporting a more efficient and engaging learning process.