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Freehand sketching is a powerful skill in engineering design [1, 2]. Freehand sketching empowers designers in the early stages of design to express ideas, communicate with stakeholders, and evaluate concepts at a rapid pace. However, teaching sketching in engineering education poses unique challenges for the classroom. Sketching in other domains is often taught in studio-style courses where instructors can provide personalized feedback on technique. This type of feedback is not possible in typical large entry-level engineering graphics courses. To address this problem, Sketchtivity was developed as an intelligent tutoring software to aid instructors in providing feedback on sketching. Using a tablet and smart pen, learners receive real-time personalized feedback on sketching practice. The main goals of this project are to improve sketching instruction methods, understand the educational efficacy of Sketchtivity, and work towards improving the feedback and content of Sketchtivity. 

Introductory engineering courses at large universities often number over a hundred students, while online classes can have even larger enrollments, significantly constraining instructors’ ability to provide feedback on homework, including the free-body diagrams (FBDs). Most online homework systems do not provide feedback on FBDs if the systems even allow the submission, and instructors often lack time or resources to provide this. A few systems have been developed that use a menu-based system allowing students to creative FBDs. There is a growing concern amongst engineering educators that student lacks critical sketching skills and the ability to idealize a real-world system as a free body diagram (FBD). A sketch-recognition based tutoring system, Mechanix, allows learners to hand-draw solutions just as they would with pencil and paper, while also providing iterative real-time personalized feedback. Sketch recognition algorithms use artificial intelligence to identify the shapes, their relationships, and other features of the sketched student drawing. Other AI algorithms then determine if and why a student’s work is incorrect, enabling the tutoring system to return immediate and iterative personalized feedback facilitating student learning that is otherwise not possible in large classes. Preliminary results using Mechanix, a sketch-based statics tutoring system built at Texas A&M University suggest that a sketch-based tutoring system increases homework motivation in struggling students and is as effective as paper-and-pencil-based homework for teaching method of joints truss analysis. The current project implements Mechanix at five different universities obtaining Pre/Post Concept Inventory, homework, and exam scores. It is compared against either the university's current online system or paper-based homework. Focus groups provide further insight into the students’ perceptions about the impact of Mechanix on their learning.