In this paper, we build on the idea that specialized instruction improves the overall quality of CAD documents by guidingstudents into selecting the most suitable modeling strategies and approaches. To this end, automatic assessment tools canbe used to detect errors and provide feedback, thus relieving instructors from routine checks and allowing them to addressquality errors and modeling aspects of higher semantic level. A representative commercial Model Quality Testing (MQT)solution was selected as a case study to determine whether these tools may become automated assistants for studentevaluation and feedback. As a result, a new taxonomy of modeling aspects that can be automatically checked is proposed.We claim that current MQT tools can supplement the learning of quality concepts, but require significant tuning and onlyprovide limited testing and tutoring capabilities. Extending the capabilities of these tools (through macros or dedicatedAPI’s), or even developing entirely new MQT tools specifically aimed at instruction purposes, is an essential requirement todevelop automated teaching-assistants based on MQT techniques.
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