iNeuron Support
System Design
A collaborative help platform where learners can solve doubts together and get immediate support from an AI chatbot, Megatron, when needed
Role
Associate UI/UX Designer
Timeline
November - December 2023
Team
Design Lead
3 Associate Designers
Deliverables
Research, Conceptualization,
UI Flows, Visual Design, Interaction Design, User Testing
Impact Summary
32%
Course Completion Rate
25%
Students Drop-off
30%
Daily Active Users
20%
Monthly Active Users
By combining chat, voice, and video calls into a single interface, the redesigned system enhances peer-to-peer communication, reduces platform-switching fatigue, and boosts overall efficiency
Problem Scenario
To lay the foundation for our research, we began by understanding the real-world situations our users faced scenarios that directly inspired the need for a Support System
1
?
While studying on an online platform, Rohit suddenly stumbled upon a doubt, leaving him puzzled.
2
Rohit reached out to the admin for help, but no reply came—frustration set in
3
!!!
Rohit struggled to reach his batchmates and felt demotivated without their contact details.
Design Problem
Through careful review of real user situations, we filtered out the most critical pain points our design needed to solve
01
Students Struggled with Delayed Query Responses
From our research, we learned that the existing system couldn’t meet student needs. Due to a lack of support staff, students often had to wait a long time to get help with their queries
02
Community Interaction Was Missing for Students
Research revealed that users were actively looking for a community space to interact with peers and form study groups right after enrollment
03
Slow Support Highlighted the Need for Automation
Users needed faster support, so our challenge was to automate the system to resolve queries quickly and keep them engaged
Project Goal
To improve the student support experience by automating responses, reducing long wait times caused by limited staff, and analyzing user interactions to make the system smarter. The aim was to increase user satisfaction and boost platform engagement, measured through higher Daily Active Users (DAU) and Monthly Active Users (MAU)
iNeuron Support
System Design
A collaborative help platform where learners can solve doubts together and get immediate support from an AI chatbot, Megatron, when needed
Role
Associate UI/UX Designer
Timeline
November - December 2023
Team
Design Lead
3 Associate Designers
Deliverables
Research, Conceptualization,
UI Flows, Visual Design, Interaction Design, User Testing
Impact Summary
32%
Course Completion Rate
30%
Daily Active Users
25%
Students Drop-off
20%
Monthly Active Users
iNeuron Support
System Design
A collaborative help platform where learners can solve doubts together and get immediate support from an
AI chatbot, Megatron, when needed
Role
Associate UI/UX Designer
Timeline
November - December 2023
Team
Design Lead
3 Associate Designers
Deliverables
Research, Conceptualization,
UI Flows, Visual Design, Interaction Design, User Testing
Impact Summary
32%
Course Completion Rate
30%
Daily Active Users
25%
Students Drop-off
20%
Monthly Active Users
By combining chat, voice, and video calls into a single interface, the redesigned system enhances peer-to-peer communication, reduces platform-switching fatigue, and boosts overall efficiency
Problem Scenario
To lay the foundation for our research, we began by understanding the real-world situations our users faced scenarios that directly inspired the need for a Support System
1
?
While studying on an online platform, Rohit suddenly stumbled upon a doubt, leaving him puzzled.
2
Rohit reached out to the admin for help, but no reply came—frustration set in
3
!!!
Rohit struggled to reach his batchmates and felt demotivated without their contact details.
Design Problem
Through careful review of real user situations, we filtered out the most critical pain points our design needed to solve
01
Students Struggled with Delayed Query Responses
From our research, we learned that the existing system couldn’t meet student needs. Due to a lack of support staff, students often had to wait a long time to get help with their queries
02
Community Interaction Was Missing for Students
Research revealed that users were actively looking for a community space to interact with peers and form study groups right after enrollment
03
Slow Support Highlighted the Need for Automation
Users needed faster support, so our challenge was to automate the system to resolve queries quickly and keep them engaged
Project Goal
the most critical pain points our
To improve the student support experience by automating responses, reducing long wait times caused by limited staff, and analyzing user interactions to make the system smarter. The aim was to increase user satisfaction and boost platform engagement, measured through higher Daily Active Users (DAU) and Monthly Active Users (MAU)
By combining chat, voice, and video calls into a single interface, the redesigned system enhances peer-to-peer communication, reduces platform-switching fatigue, and boosts overall efficiency
Problem Scenario
To lay the foundation for our research, we began by understanding the real-world situations our users faced scenarios that directly inspired the need for a Support System
1
?
While studying on an online platform, Rohit suddenly stumbled upon a doubt, leaving him puzzled.
2
Rohit reached out to the admin for help, but no reply came—frustration set in
3
!!!
Rohit struggled to reach his batchmates and felt demotivated without their contact details.
Design Problem
Through careful review of real user situations, we filtered out the most critical pain points our design needed to solve
01
Students Struggled with Delayed Query Responses
From our research, we learned that the existing system couldn’t meet student needs. Due to a lack of support staff, students often had to wait a long time to get help with their queries
02
Community Interaction Was Missing for Students
Research revealed that users were actively looking for a community space to interact with peers and form study groups right after enrollment
03
Slow Support Highlighted the Need for Automation
Users needed faster support, so our challenge was to automate the system to resolve queries quickly and keep them engaged
Project Goal
To improve the student support experience by automating responses, reducing long wait times caused by limited staff, and analyzing user interactions to make the system smarter. The aim was to increase user satisfaction and boost platform engagement, measured through higher Daily Active Users (DAU) and Monthly Active Users (MAU)
Design Brief
This project focused on improving the student support experience. Due to limited staff and a growing user base, students often faced long wait times when seeking help
Proposed Solutions
We wanted to create an automated support system to answer student queries faster, make the experience better, and reduce the support
team’s workload
01
Community Interaction Was Missing for Students
We added a chatbot called Megatron to answer common questions instantly. This helped handle most queries and reduced the need for support staff
02
Voice / video call integration
We added voice and video call features to help users easily connect with their batchmates. This made it faster and more comfortable for them to share doubts and get help
03
Community Platform
Created a forum where users can ask questions, talk with batchmates, and form study groups. This helped them get quick support from peers
Design Process
We spoke with users and stakeholders to understand their needs and align them with business goals
Note: Due to privacy policies, research details can’t be shared, but all decisions were based on user insights.
Research Phase
01
Data Analysis
We analyzed support team data to identify frequently asked questions and the busiest times for help requests. This helped us understand user behavior and improve the support experience
02
User Interviews
We interviewed users to learn about their support experience. Many were frustrated with delays and wanted a community space to discuss course-related questions with peers
03
Competitive Analysis
We explored platforms like Stack Overflow and Reddit to see how they help users solve problems. This helped us learn what users expect and find ways to build a better experience for them
04
Created User Persona
Based on our research, we created empathy maps and identified two key user groups: new enrollees who needed guidance, and active learners looking for quick support. By understanding their pain points, goals, and behaviors, we developed user personas that guided our design decisions to better meet their specific needs
Design Goals
From the research insights, we identified key user needs and pain points. These informed clear design goals that guided the solution to align with real user expectations.
01
Reduce Response Time
Design the system to deliver instant support using an AI-powered chatbot, reducing response time and minimizing user frustration during high-traffic periods
02
Reusability
Users expect a familiar experience across the platform. To meet this need, we focused on designing reusable components that ensure consistency and speed up development.
03
Efficiency
Design the system to reduce response delays and deliver faster support, addressing user frustration identified during research
04
Visual Aesthetics
Users preferred clean and visually engaging interfaces. To meet this need, we aimed to elevate the visual design for a more appealing and enjoyable experience
Visualization
After analyzing our research insights, we moved to the design phase by creating low and high-fidelity wireframes to bring the solution to life
Sketch, Design, Prototype & Test
01
Sketching and Alteration
We conducted pen-and-paper tests and stakeholder workshops to refine early sketches. Through continuous feedback and iteration, we shaped a usable and effective structure for the support system.
02
Mid-Fidelity Designs
We created mid-fidelity wireframes to define the structure and features of the support system. Based on usability testing with focus groups, we iterated the designs to better align with user needs and behaviors.
03
High-Fidelity Prototypes
We built high-fidelity prototypes to represent the final design of the community platform and chatbot. Through usability testing, we refined the chatbot’s responses and improved key community features based on user feedback.
04
Testing
We conducted heuristic evaluation and usability testing to assess the design's performance. Based on the findings, we made necessary improvements to create a more effective and user-friendly product.
Explore the final screens
Let’s walk through the final UI screens of the support system, crafted based on insights from user research. Each screen is designed to solve specific user pain points and improve the overall support experience. ( Includes only relevant screens )
Design Brief
This project focused on improving the student support experience. Due to limited staff and a growing user base, students often faced long wait times when seeking help
Proposed Solutions
We wanted to create an automated support system to answer student queries faster, make the experience better, and reduce the support
team’s workload
01
Automated chat-based responses
( Megatron )
We added a chatbot called Megatron to answer common questions instantly. This helped handle most queries and reduced the need for support staff
02
Voice / video call integration
We added voice and video call features to help users easily connect with their batchmates. This made it faster and more comfortable for them to share doubts and get help
03
Community Platform
Created a forum where users can ask questions, talk with batchmates, and form study groups. This helped them get quick support from peers
Design Process
We spoke with users and stakeholders to understand their needs and align them with business goals
Note: Due to privacy policies, research details can’t be shared, but all decisions were based on user insights.
Research Phase
01
Data Analysis
We analyzed support team data to identify frequently asked questions and the busiest times for help requests. This helped us understand user behavior and improve the support experience
02
User Interviews
We interviewed users to learn about their support experience. Many were frustrated with delays and wanted a community space to discuss course-related questions with peers
03
Competitive Analysis
We explored platforms like Stack Overflow and Reddit to see how they help users solve problems. This helped us learn what users expect and find ways to build a better experience for them
04
Created User Persona
Based on our research, we created empathy maps and identified two key user groups: new enrollees who needed guidance, and active learners looking for quick support. By understanding their pain points, goals, and behaviors, we developed user personas that guided our design decisions to better meet their specific needs
Design Goals
From the research insights, we identified key user needs and pain points. These informed clear design goals that guided the solution to align with real user expectations.
Research Phase
01
Reduce Response Time
Design the system to deliver instant support using an AI-powered chatbot, reducing response time and minimizing user frustration during high-traffic periods
02
Reusability
Users expect a familiar experience across the platform. To meet this need, we focused on designing reusable components that ensure consistency and speed up development.
03
Efficiency
Design the system to reduce response delays and deliver faster support, addressing user frustration identified during research
04
Visual Aesthetics
Users preferred clean and visually engaging interfaces. To meet this need, we aimed to elevate the visual design for a more appealing and enjoyable experience
Visualization
After analyzing our research insights, we moved to the design phase by creating low and high-fidelity wireframes to bring the solution to life
Sketch, Design, Prototype & Test
01
Sketching and Alteration
We conducted pen-and-paper tests and stakeholder workshops to refine early sketches. Through continuous feedback and iteration, we shaped a usable and effective structure for the support system.
02
Mid-Fidelity Designs
We created mid-fidelity wireframes to define the structure and features of the support system. Based on usability testing with focus groups, we iterated the designs to better align with user needs and behaviors.
03
High-Fidelity Prototypes
We built high-fidelity prototypes to represent the final design of the community platform and chatbot. Through usability testing, we refined the chatbot’s responses and improved key community features based on user feedback.
04
Testing
We conducted heuristic evaluation and usability testing to
assess the design's performance. Based on the findings, we
made necessary improvements to create a more effective and
user-friendly product.
We conducted heuristic evaluation and usability testing to assess the design's performance. Based on the findings, we made necessary improvements to create a more effective and user-friendly product.
Explore the
final screens
the most critical pain points our
Let’s walk through the final UI screens of the support system, crafted based on insights from user research. Each screen is designed to solve specific user pain points and improve the overall support experience.
( Includes only relevant screens )
Design Brief
This project focused on improving the student support experience. Due to limited staff and a growing user base, students often faced long wait times when seeking help
Proposed Solutions
We wanted to create an automated support system to answer student queries faster, make the experience better, and reduce the support
team’s workload
01
Automated chat-based responses ( Megatron )
We added a chatbot called Megatron to answer common questions instantly. This helped handle most queries and reduced the need for support staff
02
Voice / video call integration
We added voice and video call features to help users easily connect with their batchmates. This made it faster and more comfortable for them to share doubts and get help
03
Community Platform
Created a forum where users can ask questions, talk with batchmates, and form study groups. This helped them get quick support from peers
Design Process
We spoke with users and stakeholders to understand their needs and align them with business goals
Note: Due to privacy policies, research details can’t be shared, but all decisions were based on user insights.
Research Phase
01
Data Analysis
We analyzed support team data to identify frequently asked questions and the busiest times for help requests. This helped us understand user behavior and improve the support experience
02
User Interviews
We interviewed users to learn about their support experience. Many were frustrated with delays and wanted a community space to discuss course-related questions with peers
03
Competitive Analysis
We explored platforms like Stack Overflow and Reddit to see how they help users solve problems. This helped us learn what users expect and find ways to build a better experience for them
04
Created User Persona
Based on our research, we created empathy maps and identified two key user groups: new enrollees who needed guidance, and active learners looking for quick support. By understanding their pain points, goals, and behaviors, we developed user personas that guided our design decisions to better meet their specific needs
Design Goals
From the research insights, we identified key user needs and pain points. These informed clear design goals that guided the solution to align with real user expectations.
01
Reduce Response Time
Design the system to deliver instant support using an AI-powered chatbot, reducing response time and minimizing user frustration during high-traffic periods
02
Reusability
Users expect a familiar experience across the platform. To meet this need, we focused on designing reusable components that ensure consistency and speed up development.
03
Efficiency
Design the system to reduce response delays and deliver faster support, addressing user frustration identified during research
04
Visual Aesthetics
Users preferred clean and visually engaging interfaces. To meet this need, we aimed to elevate the visual design for a more appealing and enjoyable experience
Visualization
After analyzing our research insights, we moved to the design phase by creating low and high-fidelity wireframes to bring the solution to life
Sketch, Design, Prototype & Test
01
Sketching and Alteration
We conducted pen-and-paper tests and stakeholder workshops to refine early sketches. Through continuous feedback and iteration, we shaped a usable and effective structure for the support system.
02
Mid-Fidelity Designs
We created mid-fidelity wireframes to define the structure and features of the support system. Based on usability testing with focus groups, we iterated the designs to better align with user needs and behaviors.
03
High-Fidelity Prototypes
We built high-fidelity prototypes to represent the final design of the community platform and chatbot. Through usability testing, we refined the chatbot’s responses and improved key community features based on user feedback.
04
Testing
We conducted heuristic evaluation and usability testing to
assess the design's performance. Based on the findings, we
made necessary improvements to create a more effective and
user-friendly product.
Explore the
final screens
Let’s walk through the final UI screens of the support system, crafted based on insights from user research. Each screen is designed to solve specific user pain points and improve the overall support experience. ( Includes only relevant screens )
Project Result
Post-launch feedback showed positive impact on user engagement and business outcomes. It Includes ;
01
DAU Increased by 30% & MAU Increased by 20%
DAU rose by 30% and MAU by 20%, showing increased user engagement. The community platform emerged as a key space for course-related discussions
02
Student Drop-Off Reduced by 25%
Student drop-off from the iNeuron platform decreased by 25%, indicating improved retention
and user satisfaction
03
32% Boost in Course Completion Rates
Compared to previous months, the average course completion rate saw up to 32% increase, reflecting higher learner engagement and improved platform effectiveness
04
Improved User Satisfaction
Users gave positive feedback on the new system, valuing Megatron’s instant support, faster doubt resolution, and easy peer interaction
Conclusion & Challenges
Post-launch feedback showed positive impact on user engagement and business outcomes. It Includes ;
01
Building Trust in the Community Platform
Users often contacted iNeuron admins via email instead of engaging with peers, showing low trust in community responses. We aim to strengthen trust within the platform to encourage peer-to-peer support
02
Improving Visual Query Support
Megatron couldn't process image-based queries due to lack of text detection. Enhancing this will be a focus in future updates
03
Secure Peer Communication
Compared to previous months, the average course completion rate saw up to 32% increase, reflecting higher learner engagement and improved platform effectiveness
04
Improved User Satisfaction
To prevent misuse of voice and video interactions, users highlighted the need for stronger privacy and security measures. this will be a focus in future updates
Let’s keep the momentum going next up
Learn More
Behance
Linked In
Twittter
Project Result
Post-launch feedback showed positive impact on user engagement and business outcomes. It Includes ;
01
DAU Increased by 30% & MAU Increased by 20%
DAU rose by 30% and MAU by 20%, showing increased user engagement. The community platform emerged as a key space for course-related discussions
02
Student Drop-Off Reduced by 25%
Student drop-off from the iNeuron platform decreased by 25%, indicating improved retention and user satisfaction
03
32% Boost in Course Completion Rates
Compared to previous months, the average course completion rate saw up to 32% increase, reflecting higher learner engagement and improved platform effectiveness
04
Improved User Satisfaction
Users gave positive feedback on the new system, valuing Megatron’s instant support, faster doubt resolution, and easy peer interaction
Conclusion &
Challenges
Due to time constraints, not all features were launched. In the next phase, we aim to enhance the system for better efficiency and impact.
01
Building Trust in the Community Platform
Users often contacted iNeuron admins via email instead of engaging with peers, showing low trust in community responses. We aim to strengthen trust within the platform to encourage peer-to-peer support
02
Improving Visual Query Support
Megatron couldn't process image-based queries due to lack of text detection. Enhancing this will be a focus in future updates
03
Secure Peer Communication
Compared to previous months, the average course completion rate saw up to 32% increase, reflecting higher learner engagement and improved platform effectiveness
04
Improved User Satisfaction
To prevent misuse of voice and video interactions, users highlighted the need for stronger privacy and security measures. this will be a focus in future updates
Let’s keep the momentum going next up
Behance
Behance
Linked In
Linked In
Twittter
Twittter
Project Result
Post-launch feedback showed positive impact on user engagement and business outcomes. It Includes ;
DAU Increased by 30% & MAU Increased by 20%
DAU rose by 30% and MAU by 20%, showing increased user engagement. The community platform emerged as a key space for course-related discussions
Student Drop-Off Reduced by 25%
Student drop-off from the iNeuron platform decreased by 25%, indicating improved retention and user satisfaction
32% Boost in Course Completion Rates
Compared to previous months, the average course completion rate saw up to 32% increase, reflecting higher learner engagement and improved platform effectiveness
Improved User Satisfaction
Users gave positive feedback on the new system, valuing Megatron’s instant support, faster doubt resolution, and easy peer interaction
Conclusion &
Challenges
Due to time constraints, not all features were launched. In the next phase, we aim to enhance the system for better efficiency and impact.
01
Building Trust in the Community Platform
Users often contacted iNeuron admins via email instead of engaging with peers, showing low trust in community responses. We aim to strengthen trust within the platform to encourage peer-to-peer support
02
Improving Visual Query Support
Megatron couldn't process image-based queries due to lack of text detection. Enhancing this will be a focus in future updates
03
Secure Peer Communication
To prevent misuse of voice and video interactions, users highlighted the need for stronger privacy and security measures. this will be a focus in future updates