Reading Assistant: Leveraging AI to make learning more accessible.
Reimagining the learning experience for kids with Learning Disabilities (LD).
As a Product Designer passionate about creating inclusive digital experiences, I’ve always been drawn to projects that make a meaningful difference in people’s lives.
This is the story behind Reading Assistant, an AI-powered tool I designed for Intelligent Machine Labs, an edtech startup based in Boston.
The Challenge
Children with special needs face unique obstacles in developing reading skills.
Traditional methods lack adaptability for diverse learning styles, while existing tools often fail to maintain attention with their uninspiring interfaces. Parents desperately need better progress insights, and standard speech recognition technology rarely accommodates the unique speech patterns these children exhibit. These combined barriers create a significant gap in educational resources for this vulnerable population.
The opportunity was clear: create an AI-powered solution that genuinely enhances learning outcomes while providing an inclusive, supportive experience.
My Design Process
1. User Research
Understanding the users’ needs required comprehensive stakeholder engagement.
I conducted in-depth interviews with children experiencing various special needs, capturing their frustrations and aspirations directly from the source. Parents and caregivers shared critical observations about home reading challenges, while speech therapists offered clinical perspectives on common speech development patterns. Special education teachers provided invaluable insights about classroom implementation requirements that would make the tool truly practical in educational settings.
These diverse perspectives revealed patterns of need that would guide every subsequent design decision.
2. Information Architecture
Accessibility drove every aspect of the information architecture.
I developed intuitive navigation patterns that accommodated varying cognitive abilities, ensuring children could move through the application with confidence and independence. Clear, consistent interface elements reduced cognitive load, while progressive disclosure prevented the overwhelming sensation many special needs children experience with feature-heavy applications. The content structure followed validated reading development sequences, creating a foundation that respected educational best practices while allowing for individual learning paths.
The resulting framework provided structure without rigidity — a critical balance for special needs learners.
3. UX Design
Adaptability formed the core of my UX design approach.
The system dynamically adjusts difficulty based on performance metrics, keeping children in their optimal learning zone without manual intervention from parents or teachers. Immediate, encouraging feedback mechanisms reinforce progress while carefully avoiding negative reinforcement that could discourage continued effort. Game-like elements maintain engagement without distracting from core learning objectives, while extensive customization options accommodate individual sensory preferences and learning styles.
Every interaction was designed to build confidence alongside reading skills.
4. Visual Design
Visual accessibility requires thoughtful consideration beyond standard design principles.
I researched and implemented a color palette specifically chosen to accommodate various visual processing differences, including color blindness and sensory sensitivities common in neurodivergent children. The interface features consistent, clear iconography that communicates function without relying solely on text, while carefully selected dyslexia-friendly typography improves readability for struggling readers. Animations reinforce learning concepts without triggering sensory overload, and the visual reward system celebrates progress without creating dependency on external validation.
The result is a visually engaging experience that serves rather than hinders the learning process.
Key Features
The final product included several innovative features that directly addressed the needs identified in my research:
1. AI-Powered Speech Recognition
I designed an interface for the speech recognition system that could identify and provide gentle correction for common pronunciation challenges.
2. Adaptive Learning Path
I created a visual system to represent progress and dynamically adjust difficulty based on performance, ensuring that children always worked at their optimal challenge level.
3. Multi-Sensory Learning Tools
I incorporated visual, auditory, and interactive elements to support different learning styles and reinforce concepts through multiple channels.
4. Educator Dashboard
I designed an intuitive dashboard that translated complex performance data into actionable insights, helping adults provide better support.
Impact of Results
The Reading Assistant transformed learning outcomes for children with special needs.
Test scores showed measurable improvements in reading fluency and comprehension after consistent use, while engagement metrics revealed significantly longer reading sessions compared to traditional methods.
Parents reported valuable insights gained from the progress-tracking features, enabling more targeted support outside the app environment.
The adaptive technology successfully served children across the spectrum of needs, from mild reading difficulties to significant speech and processing challenges.
These results validate the power of thoughtful design to create meaningful educational change.
Lessons Learned
This project reinforced several important design principles that I’ll carry forward into future work:
Key Takeaways:
- Inclusive design benefits everyone — Features designed for accessibility often improve the experience for all users.
- Testing with actual users is irreplaceable — The insights gained from observing children interact with prototypes were invaluable.
- Technology should adapt to humans, not vice versa — The success of the speech recognition component came from training it on diverse speech patterns rather than expecting children to conform to rigid pronunciation standards.
- Collaboration is essential — Working closely with education specialists ensured the design was pedagogically sound while still being engaging.
Conclusion
The Reading Assistant project represents what I find most fulfilling about product design: the opportunity to solve complex problems and create experiences that genuinely improve lives.
By leveraging AI technology with thoughtful, human-centered design principles, we created a tool that meets children where they are and helps them develop critical skills for lifelong success.
I’m proud of the impact this project has had and the design thinking that went into creating an experience that’s not just functional but empowering for children who often face significant barriers to learning.
This case study highlights my work on the Reading Assistant project. For more information about my design work, visit my portfolio at yashraut.com
