top of page
Screen Shot 2020-10-23 at 1.14.54 AM.png

Rotman Design Challenge

Role: research, strategy, and facilitation

Contributors: Andrea Noble, Bjorg Flygenring, Brendan Raftery, Silvia Garuti, and Yasmine Abuzeid 

Timing: 6 weeks, Feb - March 2020

In early 2020, I joined a team of six graduate students at Parsons School of Design that won first place in the Rotman Design Challenge hosted by the University of Toronto.  Over the course of 6 weeks, we researched, ideated, tested, prototyped and formed a business model for our solution, Earshot.


The Challenge:



The brief asked one concise (yet loaded) question: 

Screen Shot 2020-10-23 at 12.43.02

In focusing on this brief, we knew that we needed to define each of the key terms in the proposed question. What is relief? Who are “those impacted”? What was immediate? Which natural disasters? We aligned by defining these terms and aligning as a team with a more narrow understanding of our scoper as we entered the research process.


Screen Shot 2020-10-23 at 12.37.51

We interviewed 18 people - varying from survivors of disasters to experts and volunteers. We learned more about the complexity of disaster relief and the challenge of making sure communities are set up to build resilience long after the disaster. We decided to focus on US populations, as we felt we had the best leverage and understanding of ongoing emergency systems to create a meaningful solution. 

Screen Shot 2020-10-23 at 12.53.03

Research: (3).gif

Things We Heard:

"What we needed was information more than supplies"

- Survivor

"I felt like I was playing a bad zombie video game because I didn't know what was going on around me"

- Survivor

"You have to know what people on the ground need. You might think they need boots and flashlights but they don't"

- Volunteer


In our research, we gained a better understanding of the system of aid relief - learning that organizations providing relief had to work quickly, and often had to assume what would be needed as they worked in the days following a disaster. This often created a misalignment between the relief needed and the relief provided - causing survivors of disasters to feel unheard and ignored. 

We also learned how much misinformation these difficult and crucial moments carried, causing additional frustration and sometimes dangerous situations for survivors. Some felt that news sources were susceptible to "media hype" and often felt that the impact of an upcoming disaster might be overplayed, survivors to sometimes be underprepared. 

Key Insights:

  • Because there is not a clear feedback system, aid often operates on assumptions and can be misaligned with immediate needs. 

  • Bottom-up solutions are more efficient than government and NGO responses due to local knowledge and proximity.

  • Resilience planning is strongest when it is locally informed.

  • Following a disaster, there is no single source of reliable information.

Design Principles: 

Screen Shot 2020-10-24 at 10.57.16

Make people

feel heard

Screen Shot 2020-10-24 at 10.57.20

Provide swift and accurate relief

Screen Shot 2020-10-24 at 10.57.25

Make information accessible

Reframed Challenge:

Screen Shot 2020-10-25 at 12.23.03

The Solution

Our solution, Earshot democratizes information for both those impacted by a natural disaster, and for the organizations and governments that serve them.

At the center of our solution is an Ai driven chatbot named Quinn. In the current system of aid, relief is distributed based on hypotheses and previous knowledge. Earshot’s database uses machine learning from every message and phone call to create actionable insights for governments and organizations to deliver better, faster, and more accurate aid. Organizations will be able to use Earshot to both figure out what the users need today and to predict what they may need if a scenario like this happens again in the future. 

QUINN gif.gif
Screen Shot 2020-10-23 at 1.08.47 AM.png
Screen Shot 2020-10-23 at 1.08.40 AM.png

The insights we gather are used in two ways:

How It Works:

Quinn uses them so that it can better answer questions that people have when they text it, making information available to the public faster.



We send those insights to governments and aid organizations so that they know what areas need aid the most and exactly what kind of aid is needed. Organizations can also use these insights in the future for resilience planning and predictive analytics.

earshot 3.gif

Full Video Submission

One of my first projects in my undergraduate career was a product design challenge examining hurricane relief. At that time, our final concept was a giant ziplock bag made of pool liner I fused together myself using a hair straightener in my college dorm (yes, the fumes were bad!) Revisiting this subject 8 years later represented my growth as a design strategist and systems thinker - learning to critically examine leverage points and areas of intervention, alongside careful consideration of stakeholders and human need. 


Completing the project during the rise of COVID-19 was surreal. As we submitted our final video, we lived through same wave of misinformation and confusion we found in our own research weeks before. We learned just how delicate and helpless these moments could feel, and how much prior learning and anticipation of needs could have prevented shortages and panic. We believe that Earshot should exist in the world, and are examining ways to bring it to life. 


bottom of page