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Visual thinking with diagrams is a crucial skill for learning and problem-solving in STEM subjects. To improve in this area, students need a variety of visual problems for deliberate practice. However, in our interviews, educators shared that they struggle to create these practice exercises because of limitations of existing tools. We introduce Edgeworth, a tool designed to help educators easily create visual problems. Edgeworth works in two main ways: firstly, it takes a single diagram from the user and systematically alters it to produce many variations, which the educator can then choose from to create multiple problems. Secondly, it automates the layout of diagrams, ensuring consistent high quality without the need for manual adjustments. To assess Edgeworth, we carried out case studies, a technical evaluation, and expert walkthrough demonstrations. We show that Edgeworth can create problems in three domains: geometry, chemistry, and discrete math. These problems were authored in just 15 lines of Edgeworth code on average. Edgeworth generated usable answer options within the first 10 diagram variations in 87% of authored problems. Finally, educators gave positive feedback on Edgeworth's utility and the real-world applicability of its outputs. 

Certified orientation and mobility specialists (COMS) work with clients who are blind or visually impaired (BVI) to help them travel independently with confidence. Part of this process involves creating a narrative description of a route and using specific techniques to help the client internalize it. We focus on the problem of automatically generating a narrative description of an indoor route based on a recording from a smartphone. These automatically generated narrations could be used in cases where a COMS is not available or to enable clients to independently practice routes that were originally learned with the help of a COMS. Specifically, we introduce Clew3D, a mobile app that leverages LIDAR-equipped iOS devices to identify orientation and mobility (O&M) landmarks and their relative location along a recorded route. The identified landmarks are then used to provide a spoken narration modeled after traditional O&M techniques. Our solution is co-designed with COMS and uses methods and language that they employ when creating route narrations for their clients. In addition to presenting Clew3D, we report the results of an analysis conducted with COMS regarding techniques and terminology used in traditional, in-person O&M instruction. We also discuss challenges posed by vision-based systems to achieve automatic narrations that are reliable. Finally, we provide an example of an automatically generated route description and compare it with the same route provided by a COMS.