# A Python-Based Automation Script to Mark Computer-Aided Design Assessments
## 4 Discussion  
The findings of this study underscore the significant advantages of integrating automation into CAD education. The Python-based automation tool demonstrated perfect reliability in both inter-marker and intra-marker assessments, highlighting its reliability and accuracy in grading CAD submissions. These results align with previous research emphasising the reliability of automated tools in educational contexts, particularly in minimising human error and bias in grading processes [12, 13, 14]. The development and implementation of this Python-based automation tool represent a significant step forward in enhancing CAD education. By streamlining the assessment process and providing consistent, objective feedback, the tool addresses key challenges in engineering education [4, 10]. The perfect reliability and substantial time-saving capabilities demonstrate the potential for automation to transform educational practices in technical fields. As the field moves forward, it is crucial to continue refining and expanding the capabilities of such tools while maintaining a balanced approach that values both technological efficiency and human insight. By doing so, the field can create more effective, engaging, and industry-relevant educational experiences for future engineers, preparing them for the challenges and opportunities of an increasingly digital and automated professional landscape [19]. The tool's ability to achieve 100% agreement with manual assessments indicates that it can serve as a reliable substitute for traditional evaluation methods, enabling instructors to focus on more qualitative aspects of feedback [4, 10]. This is particularly important in CAD education, where understanding the rationale behind design decisions often requires nuanced, human insight [1, 9]. By automating the grading of objective metrics such as volume, surface area, and CG location, instructors can allocate more time to addressing complex design principles and fostering creative problem-solving skills among students. Another outcome of this study is the dramatic reduction in grading time. The automation tool's ability to process 75 CAD files in 22 min, compared to 213 min for manual grading, represents a nearly 90% improvement in efficiency. This time-saving benefit is consistent with prior studies on the adoption of automation in education, which report similar enhancements in workflow efficiency [12, 13, 14]. Such improvements are particularly valuable in large classes, where the grading workload can be overwhelming for instructors. From a pedagogical perspective, the immediate feedback provided by the automation tool has the potential to significantly enhance student learning outcomes [12, 13, 14]. Prompt feedback is a critical component of effective teaching, allowing students to identify and address errorsin their work more efficiently [12; 13; 14]. By incorporating this tool into the curriculum, instructors can provide students with detailed, timely evaluations, fostering a more iterative and reflective learning process. This aligns with learning theories which emphasise the importance of active engagement and self-assessment in skill development [4]. The implications of this research extend beyond education. The automation of CAD evaluation reflects broader trends in engineering practice, where automation and digital tools are increasingly used to streamline workflows and enhance productivity [19]. By familiarising students with these technologies, educators can better prepare them for the demands of the modern engineering workplace, bridging the gap between academic instruction and professional practice [19]. The implementation of automated assessment tools in CAD education also raises important considerations regarding the role of technology in shaping pedagogical approaches. While the efficiency gains are evident, it is crucial to maintain a balance between automated and human-led instruction.