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Protests against racial injustice have been increasing in the United States. Universities often rapidly respond to acts of injustice through public statements about their position to uphold the equality of all people. To gauge the desires and concerns around discussing events causing social unrest in engineering classrooms specifically, the engineering education faculty chair of a large university conducted discussions with both students and faculty regarding its place in their classrooms. This paper describes the emerging themes from survey responses using coding and grounded theory. Reactions from students and faculty were decidedly different. Most students stressed the importance of discussing such topics in class with their engineering faculty, while most faculty emphasized their concerns with doing so due to their lack of training to effectively handle such topics. This paper describes the evaluation of student and faculty responses and its implications for supporting diversity and inclusion in the engineering classroom. 

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. 

Introductory engineering courses within large universities often have annual enrollments exceeding several hundreds of students, while online classes have even larger enrollments. It is challenging to achieve differentiated instruction in classrooms with class sizes and student diversity of such great magnitude. In such classes, professors assess whether students have mastered a concept through multiple-choice questions, marking answers as right or wrong with little feedback, or using online text-only systems. However, in these scenarios the feedback is of a mostly binary nature (right or wrong) with limited constructive feedback to scaffold learning. A growing concern among engineering educators is that students are losing both the critical skill of sketched diagrams and the ability to take a real system and reduce it to an accurate but simplified free-body diagram (FBD). A sketch-recognition based tutoring system, called Mechanix, allows students 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. To observe the effectiveness of this system, it has been implemented into various courses at three universities, with two additional universities planning to use the system within the next year. Student knowledge is measured using Concept Inventories based in both Physics and Statics, common exam questions, and assignments turned in for class. 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. In focus groups, students believed the system enhanced their learning and increased engagement. Keywords: sketch recognition; intelligent user interfaces; physics education; engineering education