# A Python-Based Automation Script to Mark Computer-Aided Design Assessments
## 4 Discussion
The tool's ability to provide consistent and objective feedback on technical aspects of CAD models can be complemented by instructor-led discussions on design philosophy, problem-solving strategies, and industry best practices. Furthermore, the integration of such automated tools into the curriculum necessitates a re-evaluation of assessment strategies. Traditional grading rubrics may need to be adapted to incorporate both automated and manual evaluation components, ensuring a comprehensive assessment of student skills [12]. This hybrid approach can lead to more nuanced and holistic evaluations of student work, potentially improving the overall quality of CAD education. The introduction of automated assessment tools can significantly impact student learning experiences. By providing rapid feedback, students can engage in more iterative design processes, fostering a culture of continuous improvement [6; 16]. This aligns with the principles of formative assessment, where ongoing feedback is used to guide and enhance learning throughout the course [6; 16]. Moreover, the consistency of automated feedback can help students develop a more systematic approach to CAD modelling. By repeatedly encountering objective evaluations of their work, students may become more attuned to the technical requirements and best practices in CAD. This can lead to improved self-assessment skills and a deeper understanding of CAD principles [1]. Despite the promising results, several challenges and limitations must be addressed to optimise the advantage of the tool for both instructor and student. As students progress to more complex designs, the automated tool may need to evolve to handle advanced features. The tool's reliance on specific software versions, file formats, and APIs may pose challenges in maintaining long-term compatibility and accessibility. Expanding the tool to work with various CAD software packages, each with unique features and modelling approaches, presents a significant technical challenge but would enhance its utility in diverse educational settings. Additionally, while the tool excels in evaluating quantitative metrics, its capacity to assess more subjective aspects of design, such as creativity or aesthetic quality, remains limited. The focus was on geometric properties due to their standardisation. Unfortunately, the Solid Edge API could not incorporate checks for model tree quality, constrained sketches, and material properties, as.par files are a proprietary format. Other CAD software APIs may be able to incorporate these parameters. This highlights a limitation with proprietary software for generalisability and open science. The CG location relative to the coordinate system provides a proxy for ensuring students can use the relationship features in Solid Edge. To address these challenges and further enhance the capabilities of automated CAD assessment tools, several avenues for future research and development can be explored. Incorporating machine learning algorithms could enable the tool to assess more subjective aspects of design, such as aesthetics or ergonomics [22]. Future research should explore the integration of machine learning techniques to assess these qualitative factors, further expanding the scope of automated grading in CAD education. Developing a more versatile tool that can interact with multiple CAD software platforms (e.g., Solid Works, AutoCAD, or CATIA) would increase its applicability across different educational settings. Applying the automated marking approach to similar software such as finite element analysis, signal processing, or multibody modelling could further expand the scope of automated grading in engineering education [23, 24, 25]. Integrating features that support peer review and collaborative design processes could enhance the tool's educational value [6, 22]. Virtual reality [26], augmented reality [27], and generative artificial intelligence [28, 29] provide an exciting opportunity for education enhancement in engineering, particularly around design and manufacturing [30, 31].