Overview
- Role
- UX Lead and Lovable Developer
- Team
- 5, me as UX Lead in a cross-functional team
- Organization
- NYEdTech Hackathon 2026, 3rd Place
- Timeline
- 2026, 4-week sprint
- Live app
- ai-for-everyman.lovable.app
- Tools
- React, TypeScript, Tailwind CSS, Lovable
The Problem
89% of small business owners tried AI. Most stopped.
We surveyed 9 small business owners across 7 industries. 67% named privacy and security as the top barrier to continued AI use. Not cost. Not relevance. Fear of exposing sensitive data.
Existing AI education platforms teach what AI is. None teach how to use AI safely. Coursera's AI For Everyone offers passive video with no safety training. Google's Make AI Work For You covers only Google tools with no privacy content. SBDC and Google's AI U depends on local availability with no self-serve option. No platform combines industry personalization, safety simulation, and the specific gap stopping small business owners from continuing.
"Can AI make employees more efficient without risking information leaks?"
— Survey respondent, small business owner
What We Learned From Users
13-question survey. 9 small business owners. 7 industries.
Critical insight: zero respondents named cost or "doesn't apply to me" as a barrier. This contradicts typical market research on small business technology adoption. Privacy is the primary barrier.
Meet Maya Chen
Safety-First AI Literacy
8 to 10 minute sessions. Built on React, TypeScript, and Tailwind CSS.
Personalized onboarding
A quiz matches you to your industry, team size, comfort level, and top concerns. The platform generates a custom learning path. Real estate agents get real estate scenarios. Nonprofits get fundraising scenarios. Nothing generic.
Traffic light safety framework
Users classify data into Green (safe), Yellow (caution), and Red (never share) through interactive simulations. The platform gives real-time feedback on data exposure risks. You practice before you touch an AI tool.
Industry-specific modules
Every scenario matches your field. 15 modules span sales, marketing, finance, HR, legal, IT security, and operations. Each module has three difficulty tiers: beginner, intermediate, and advanced.
How It Works
- Select industry. You pick the area you work in.
- Share goals. You share your AI goals so the training fits your specific needs.
- Personalized plan. The platform generates custom focus areas and a Safe Start path.
- Safe practice. You run through simulations to build confidence with real-world scenarios.
Learning Science Behind The Platform
We used the 4C/ID framework by van Merriënboer and Kirschner, a proven model for complex skill acquisition.
Learning task. A real business scenario. Draft a personalized cold email for a new lead using AI. Whole-task practice in authentic context.
Supportive info. Conceptual knowledge. AI predicts human tone through sentiment analysis patterns. Mental models that transfer across tasks.
Just-in-time info. Step-by-step guidance. Use the Persona, Problem, Solution prompt template. Delivered at the moment of need.
Part-task practice. Focused safety checks. Spot the hallucinated company facts in this AI draft. Repeated until automatic.
Results
- 3rd Place at NYEdTech Hackathon 2026
- 67% of surveyed users willing to test the prototype
- 78% want periodic updates, which validates the subscription model
- 33% interested in certificates, a revenue expansion signal
The hackathon validated our core hypothesis. Small business owners respond to AI education when you lead with safety, meet them in their industry context, and get them to hands-on practice fast. Four beta testers gave us their emails during the research phase.
Growth Path
MVP. Real estate vertical. Demonstrated at hackathon. High data-privacy stakes make safety training immediately valuable.
Next. Expand to additional industries. Curriculum mapped across 15 business functions. The architecture scales to every vertical.
Scale. Move from individual to peer learning cohorts. Add team accounts, collaborative exercises, and industry-specific certification.
What I Learned
This project changed how I think about AI education. Our research surprised us. Cost and relevance were not barriers at all. Zero respondents named those. The real blocker is fear of exposing sensitive data. That finding reshaped the entire product direction toward safety-first design.
Leading UX on a 5-person team during a 4-week sprint forced sharp prioritization. The persona, Maya Chen, kept every decision grounded. When a feature idea came up, we filtered through one question: does this help Maya feel safe using AI with her client data? If not, we cut it.
The 4C/ID instructional design framework gave us a research-backed structure that translated directly into the UI. Learning science and UX design reinforced each other. The pedagogy shaped the interface, not the other way around.
Team
AI for Everyman was built by a five-person team over a 4-week sprint.
- Kate Borgen — UX Lead
- Linda Paredes — Team Coordinator
- Evan Fram — Business Development
- Anastasiia Emelianova — Project Manager
- Sam Goje — AI Education Content