Master of Graphic Design (MGD) students collaborated with the Laboratory of Analytic Sciences (LAS) here at NC State to explore the potential of a Tailored Daily Report (TLDR)—an intelligent, interactive interface that might assist intelligence analysts as they begin their day.

The core research question of the project:

How might the design of an interface use the affordances of machine learning to provide a personalized user experience (ML) so that the analyst might quickly and knowledgeably enter the day’s workflow?

Students moved through a human-centered research process to determine current analyst needs and opportunities and then prototyped interfaces exploring the potential of ML to assist analysts in a variety of scenarios, from a critical hostage situation to a language analyst assisting with ongoing diplomatic efforts.

This research is part of a larger initiative led by LAS, the Summer Conference on Applied Data Science (SCADS). This conference presents a multi-year challenge: to develop and optimize a TDLR for thousands of individual needs and interests. The project brings together expertise from academia, industry, and government in order to collaboratively address this data science challenge. 

MGD students will share their prototypes during the initial conference to inspire possible features for the TLDRs created by SCADs participants.

Final Prototypes

Designers: Amanda Williams, Liz Chen, Riley Walman. ©NCSU MGD, College of Design
Designers: Elizabeth Gabriel, Brian Sekelsky, Jillian Swaim. ©NCSU MGD, College of Design
Designers: Katie Denson, Jeff Wilkinson, Jacob Williams. ©NCSU MGD, College of Design

The Human-Centered Design Process

Personas & Use Cases
Nyah: a search and discovery analyst called in to surge on a crisis situation
Chloe: an analyst reporter working on a familiar topic
Ron: a language analyst asked to assist with an unfamiliar topic
images of documents that map out personas and scenarios for each of the three student groups: Ron, Chloe and Nyah. Documents highlight important characteristics of each personas.
Mapping the Datasets: Diagrams of the data provided to each student group. The language analyst group, for example, was provided with data form the Nixon Reports to use as real data in their prototype.
As Is User Journey Maps: each group diagrammed the current user experience for their persona. This slides shares those diagrams that detail the experience step by step.
Identifying Pain Points: Using MIRO, students diagrammed the user pain points that they wished to address in their prototypes. This slide shows a pain point diagram for each of the groups.
Benchmarking: each group was asked to gather existing examples of data visualization and intelligent interface design. This slide shows a collection of data visualizations.
Sketches: slides shows lots of sketches made by students early in the design process. This includes sketches of interactive circular diagrams, hive-like interface structures and wearable watch-like interface.
Three Different Task Flows: Sketches. Each team then selected three approaches and sketched out how their interface might look frame by frame. This slide shows that each group presented three such sketched wireframes in the first critique.
Round Two: Two task flows. This slide shows more high definition interface ideas, frame by frame. These were created using prototyping software rather than just sketches.

Ultimately, students created hi-fi prototypes and scenario vidoes. This slide provides a detail from each of the final hi fidelity prototypes.The first is a circular interactive interface; the second is a modular interface using a card-like structure; the third uses hexagonal shapes to suggest a hive-like interface.