Pitching in for a16z in accordance to U.S. DoE guidelines 2025
Conversational AI
Introduction
When we began Eduwave AI, we weren’t just building another ed-tech product.
We were asking a harder question:
What would learning feel like if a child had a personal tutor who understood them; not just their grades?
Role: Founding Product Designer
Team: Project Manager, Design, Engineer
Constraints: Architecting a hyper-personalized experience within the strict boundaries of DoE safety guidelines at a high-speed 0→1 startup pace.
Problem
One-Size Doesn't Fit All. Traditional EdTech platforms function like "digital worksheets" static, robotic, and cold.
The Gap: Most tools focus on data tracking for admins rather than engagement for students.
The Consequence: High drop-off rates and "Learning Fatigue" in children aged 5–12.
Constraint: Designing a highly personalized AI within the strict legal boundaries of the 2025 DoE safety guidelines.
Solution
We delivered a multi-sided ecosystem designed for students, parents, and teachers:
For Students: The Magic Whiteboard & Reward Island
Magic Whiteboard: An infinite canvas where children co-create with AI, visualizing concepts through conversation. It enables a student to get an answer for every appearing WHY!
Result: 18-minute average session time (3x the industry standard).
Reward Island: A world that grows as children learn, unlocking new areas based on interests. Badges like Explorer, Curious Mind, and Helper Hero celebrate learning moments, curiosity, and persistence.
Playful Onboarding: A conversational, avatar-led setup that replaced static forms and boosted Activation by +4.2 points.
For Parents: Growth in Real Time
Parents get real-time insights into performance and exactly how their child perceives the world. We replaced "random charts" with a narrative-driven overview of what they learned today.
For Teachers: Smarter Teaching
Built for classrooms that thrive on curiosity, Eduwave lets teachers assign pre-learning tasks, personalize lessons, and track each student’s individual pace.
Testing & Results
From MVP to a16z.
Validation: Usability tests revealed a flaw in the initial "hint" logic. We corrected this to ensure students felt "scaffolded" rather than "stuck."
Traction: Took the product to SF Tech Week and the a16z Final Round.
Business Results: Secured Active Investment Deals and achieved +28% Parent Retention and 70% Overall Engagement.
Reflections
Designing for Children is a Responsibility. This project taught me that when designing for AI and children, Safety is the primary UI.
Takeaway: Successful 0→1 products aren't just about "Cool AI"; they are about building trust with parents and curiosity in children.
Future Vision: Investigating how adaptive interfaces can further reduce the "Digital Divide" in early education.
