Designing an AI-Powered Learning Experience

Despite the platform’s capabilities across courses, community, resources, and live training, new users were often unclear about what to do when they first entered the Labs.

The platform had strong content and a trusted founder-led brand, but the learning experience felt fragmented. Users could access many parts of the product, but there was no clear path connecting them.

At first, the work seemed like a UI and feature design challenge. But through product audits, community feedback, and platform analysis, the deeper issue became clear.

The platform did not need to only help users browse more content. It needed to help users understand what to do next.

Through founder conversations, community feedback, product audits, and platform analysis, I mapped the audience into four learner types.

This helped me see that the platform could not rely on one generic onboarding path. Users came in with different levels of urgency, confidence, and AI fluency.

The first onboarding concept was designed as a structured assessment. It captured role, goals, AI familiarity, tool interests, learning preferences, and time commitment.

The goal was to build a comprehensive learner profile and use that information to recommend better courses.

But the experience quickly revealed a weakness: it asked users to define themselves before helping them make progress.

Users did not need to describe every attribute about themselves. They needed faster clarity on what to do next.

I redesigned the onboarding logic around user goals instead of user categories.

Rather than asking users, “What type of learner are you?” the flow began with a more direct question: “What are you trying to achieve with AI?”

From there, the system could ask smarter follow-up questions and translate the user’s intent into a recommended learning path.

This made onboarding feel more actionable, reduced decision fatigue, and gave users a clearer first step.

A major insight from the platform was that users joined because they trusted Julia’s expertise, AI workflows, and teaching style.

They were not only buying access to courses. They wanted guidance on how to apply AI in real business situations.

I helped define Ask Julia as a learning companion, not a generic chatbot. The goal was to help users think through course content, business questions, workflow decisions, and next steps.

Early concepts explored multiple ways to access Ask Julia: a dedicated page, floating assistant, text chat, voice, and video.

But too many entry points risked making the feature feel unclear. Users needed to quickly understand whether Ask Julia was a chatbot, support tool, course search, or separate product.

I simplified the experience around a persistent assistant model.

Voice and video were treated as future extensions, not the core mental model.

Onboarding and Ask Julia were the two core feature directions, but the broader platform also needed to support a more connected learning journey.

I worked across key product surfaces to improve clarity, hierarchy, and continuity.

Each improvement supported the same product goal: helping users move through the platform with less friction and more direction.

The project helped First Movers shift from a content-heavy platform into a more guided AI learning experience.

The biggest lesson was that AI only creates value when its role is clearly defined.

Ask Julia became stronger once it was positioned as a learning companion instead of another generic assistant. The onboarding experience also became stronger once it focused on what users wanted to achieve, not just who they were.