Designing Data-informed Intelligent Systems to Create Positive Impact

Abstract

This paper explores several approaches for designing data-informed intelligent systems to create a positive impact. Two contrasting case studies in K12 education are used to illustrate design methods, questions and recommendations. The first case study addresses the poverty achievement gap in America and shows how product data can be used to identify areas of inequity in digital education. The second case study looks at the unintended consequences of automating data-driven optimization in the context of a digital math game. Together, the two case studies reveal generalizable knowledge that supports the design of intelligent feedback loops to create a positive impact. Further, this paper considers both the benefits and limitations of data feedback in complex social-technical systems.