Leveraging AI for 2D technical drawing analysis, feedback and assessment in higher education

Technical drawing is a core skill for any Mechanical Engineering program, intended to equip students with the fundamentals of technical design communication while assimilating key concepts such as views, perspectives, dimensioning, tolerancing and materials selection. Technical drawings are manually-graded by the lead instructor by assessing the drawing's proficiency and quality based on a set of standards and marking criteria. For large student cohorts, this becomes a time-consuming activity, potentially leading to ‘marking fatigue’, usually producing highly variable grades and feedback. By using a dataset of 32 student drawings and a five-criterion rubric, we compare AI-generated grades with historic human-marker scores through error analysis, ANOVA and Kruskal–Wallis tests. Results suggest there is no significant statistical difference between the marks/grades awarded by AI and a human marker. This, however, will depend on the prompting engineering techniques applied, together with additional practices such as role-setting and context-setting. The study also identifies limitations—such as OCR-induced hallucinations, variability between LLM platforms, and lack of batch-processing capabilities—that currently constrain full automation.