Unlocking Developer Potential with A.I. Powered Tools
and Intuitive Education
BrainStation x MongoDB
24 Hour Tech Sprint
UX Design Lead
1 Project Manager, 3 Designers,
2 Engineers, 1 Data Scientist
Figma
Project Overview
MongoDB Atlas provides developers at all levels with training resources to effectively learn and use MongoDB. Despite this, users often turn to secondary resources like GitHub and ChatGPT.
Strategized with the six-member team, leading the UX strategy. Conducted heuristic and usability tests and built a final prototype that enhances the onboarding & learning tool access with an AI chatbot.
Problem Statement
Users struggle to navigate MongoDB’s education resources and often resort to third parties (e.g., GitHub, ChatGPT) which may be unreliable and is inconvenient.
How Might We
HMW empower developers of all levels by reimagining MongoDB resources as a powerfully simple place to start creating and disrupting industries?
The Solution
Enhance MongoDB's educational resources through AI enhancements, improved accessibility and smooth UI design, empowering developers of all skill levels.
Let's dive right in! Here's the final prototype with refreshed
onboarding, UI features, and the A.I. chatbot.
Step 1: Empathize
Our team of designers encountered a challenge when working on a project for developers, as some members were unfamiliar with MongoDB. After aligning our understanding of the platform, we set goals for and a timeline.
Target User | Data Analysis
With our Data Scientist leading the way, our analysis identified a key user group:
Young Professional Developers Aged 18-34. In 2024, 67.7% of respondents that want to work with MongoDB fell within this age bracket, and notably, 46.8% of are active users of Chat GPT.
We also pulled data from Stack Overflow and discovered that MongoDB ranks as the second most popular platform for learning to code in 2024.
Product Evaluation | Heuristic and Usability Tests
We conducted heuristic evaluations of the process to get started and interviewed 3 new MongoDB users to identify opportunities and pain points. We also spoke to an experienced professional familiar with MySQL, MongoDB and Oracle to gain a broader perspective.
Interviews | 4 Participants
Participant 1, 28 years old, Engineer in New York
Affinity Mapping | 4 Themes
Next, we organized and analyzed our data, extracting relevant insights and themes that revealed our target users’ needs, pains and preferences.
Refined HMW
Based on new insights from our data analysis and user research, we reframed the question...
Step 2: Define
Based on our primary research, we created a young developer's persona, Manny Cooper. His needs and challenges were to crucial to guide our design and development process.
To unlock developer potential, we explored the problem from multiple angles, devising a holistic solution.
#1 Build a MongoDB In-House A.I. ChatBot
#2 Improve Intuitiveness of the Get Started Process
#3 Embrace MongoDB's Strength of Good Design
Step 3: Ideate
We quickly distributed tasks within the team, scheduled a 2-hour check-in with the PM, and stayed in close contact with our developers.
I was responsible for reworking the first screen new developers land on, addressing user onboarding and form clarity issues identified during testing, while also exploring UI inspiration and pop-up concepts.
Step 4: Prototype
Here are process examples with highlighted areas for enhanced user experience. Due to time constraints, we used screenshots instead of recreating the foundation!
After trouble-shooting, problem-solving, and discussing, we finally finished our prototype with 30 minutes left on the clock.
Reflection
We successfully tackled cross-functional challenges through open communication, questions, and regular check-ins. We held each other accountable!
Towards the end of the design sprint, we all found ourselves rushing, scrambling, and tensions were high; however, thanks to out PM’s leadership we were able to stay on track.
Next Steps
Given the time constraints, we were unable to conduct usability tests with our target user. Our judges provided us with our first round of feedback.
Data Scientist Vivas suggested enhancing text classification techniques to better understand user questions, enabling more effective responses and an improved user experience
The next step for the ChatBot could be helping developers work together with AI on coding tasks so they can troubleshoot on the spot and improve efficiency.
designs
