PI: James Landay

Abstract

Millions of people participate in the design process on a daily basis, however many are design novices, having little to no knowledge of successful design principles. Our research has demonstrated conclusively that while novices are at a disadvantage in creating good design, they are able to ​recognize good design almost on par with experts. We seek to use big data, machine learning and computer vision to understand and capture successful design practices of experts to create tools that allow novices to reuse these practices and artifacts in a manner that relies on novices recognizing and appropriating good design rather than creating it from scratch.

Through both qualitative and quantitative experiments we will assess the ability of design tools (including our proposed system, EDNA) to understand and capture the successful design practices of experts and leverage them for use by novices. We will focus on design systems, which are informal design guidelines created by experts, their effect on novice abilities, and their impact on design time and the self-efficacy that novices experience. Furthermore, we propose to explore and test the ability of novices to give meaningful feedback to experts through the creation and implementation of a meaningful design scale.