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October 23, 2025Computer Applications in Engineering Education0 citationsOpen Access

CADuBoost: Enhancing Education in Mechanical 3D CAD Modeling Through Automated Grading and Feedback System

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YYYeongjun YoonYJYeseong JeonJKJaeyeon Kim

Key Points

  • Automated grading improves modeling proficiency in mechanical CAD education, and enhances self-directed learning capabilities.
  • The system processes geometric and non-geometric data to compare student models against reference models effectively.
  • Evaluation includes shape assessment through point cloud comparison and analysis of design history via the CAD system's API.
  • Supporting automated grading may significantly enhance educational quality while improving instructor efficiency.

Abstract

ABSTRACT 3D CAD modeling technology has become an essential tool for product design across various industries, including machinery, aerospace, automotive, architecture, and healthcare. Consequently, numerous educational institutions offer training programs and certification exams to enhance and evaluate the modeling proficiency of 3D CAD system users. However, the manual grading process currently employed in 3D CAD modeling exams reveals several limitations, such as excessive time and effort, and challenges in maintaining consistency in evaluations. In mechanical CAD systems, in particular, users can create the same model using different features, making precise grading criteria essential. Additionally, the lack of self‐directed learning capabilities among learners has emerged as a pressing issue, highlighting the need for more effective educational solutions. To address these challenges, this study introduces CADuBoost, an automated grading and feedback system for 3D CAD modeling education in mechanical engineering. CADuBoost compares student‐submitted 3D CAD models with reference models through a comprehensive evaluation framework that processes both geometric and non‐geometric data. Shape evaluation is conducted using neutral formats such as STEP and STL through point cloud comparison, multi‐view image analysis, and dimensional accuracy measurement. Non‐geometric evaluation is performed by extracting and analyzing design history and constraint information via the 3D CAD system's API. Furthermore, by providing visual feedback through color‐coded geometric differences and detailed design history analysis, the system delivers personalized feedback that effectively fosters self‐directed learning. The effectiveness of CADuBoost was validated through experiments in real educational settings, showing possibilities to improving students' modeling proficiency and self‐directed learning abilities. This system is expected to enhance instructors' efficiency and improve the overall quality of education.

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Cite This Study

Yoon et al. (2025) studied this question.

synapsesocial.com/papers/68f9bad7d7353cfcfc68f541https://doi.org/10.1002/cae.70096
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