LinkedIn Learning · Web Design
AI-powered learning for professionals
In the era of AI, how can we best support professional's career development through innovative learning experiences?
Overview
LinkedIn Learning key feature is career development. It is an enterprise tools for our enterprise customers to support their employees' career development. Learners onboard to the platform with invitation from their company's L&D or their managers. On the platform, they set their career goals, and start their learning plan.
In this project, I redesigned the existing career development features end to end, by addressing a few key problems with the introduction of AI. It was presented at LinkedIn Talent Connect 2024, and the MVP was launched in 2024 and received positive feedback.
My Approach
I use different approaches based on the nature of the project. For this project, there are somethings unique about it:
First, LinkedIn Learning team is different from other teams at LinkedIn, in that it operates very much like a start-up. We figure things out as we go, and changes are very common here. Therefore, we worked on this project with quick iteration, frequent reviews, and less linear process. Instead of moving from research → explorations → wireframe → hi-fidelity designs, we design, iterate and align at all levels (strategy, flow, interaction, visuals) simultaneously.
On the other hand, in order to drive clarity and move the project forward in a productive way in such environment, I leveraged a lot of design thinking tools (visualization, framework) to help drive alignment and facilitate decision making.
Lastly, it's an enterprise facing project, which means user experience is being considered as well as other business complexities. Therefore I spent tons of time trying to understand the business and operation aspects of this project, to make sure I understand all the stakeholder needs when pushing for better user experience.
Problem & Opportunities
Enterprise learning context
Over 90% of LinkedIn Learning users come from enterprise environments, where learning is often driven by extrinsic motivations—such as manager expectations or L&D requirements—rather than intrinsic curiosity.
While learners recognize the high quality and relevance of content, they often struggle to stay engaged due to limited time and competing work priorities.
However, the deeper challenge is not just time or motivation—it's that current learning experiences are not well aligned with how real career growth happens, which is often nuanced, non-linear, and highly personal.
Current experience gaps
Despite strong content, the current Career Development experience falls short in supporting meaningful progress:
1. Rigid and shallow goal setting
Learners can only select predefined roles or skills, which oversimplifies real career goals. In reality, goals are often more specific, contextual, and evolving.
2. Limited personalization
Today, learners with the same goal receive nearly identical learning plans, regardless of their background, experience level, or intent. This leads to low relevance and poor engagement.
3. Low-quality recommendations
Existing plan generation relies heavily on SERP logic, which can result in mismatched or overly generic content (e.g., irrelevant or too basic courses).
4. Lack of contextual understanding
The system does not account for key factors such as:
- Learner's current role
- Years of experience
- Distance to target role
This leads to plans that don't reflect where the learner actually is in their journey.
5. Unclear outcomes and weak sense of progress
Learners lack clarity on what they will achieve after completing a plan, reducing motivation and perceived value.
Emerging opportunity: AI + Learning Coach
The launch of Learning Coach — an AI-powered assistant embedded within LinkedIn Learning — surfaced a strong signal:
- Career development is one of the most frequently asked topics
- It also drives the highest WoW retention among Coach users
At the same time, learners are explicitly asking for:
- More structured guidance
- More personalized plans aligned with their goals
This reveals a clear gap in the current experience — and a strong opportunity:
Leverage AI (LLM + Learning Coach) to move from static, generic learning plans → dynamic, personalized, and context-aware career guidance
"Discussion to create a plan to help prepare for the job position I am attempting to interview for and prepare for the current trends."
"I would like to get more accurate career guidance if possible so i could follow the courses and learning path …"
Theoretical insight: what makes goals effective
I leveraged an independent research I did back in graduate school on goal-setting theory to help guide the design and inform the team. What I've learned was that effective learning goals tend to have a few key characteristics:
- Specific, not vague — clear and actionable rather than abstract
- Challenging but achievable — motivating without being unrealistic
- Proximal, not purely long-term — broken into near-term milestones
- Self-directed, not only assigned — allowing learners to shape their own goals
- Learning-oriented, not just outcome-driven — focusing on growth, not just results
Design opportunity
Reimagine Career Development as:
- Goal-driven, not content-driven
- Personalized, not one-size-fits-all
- Context-aware, not role-only
- Interactive and adaptive, not static plans
By capturing richer learner intent (free-form goals, experience, context) and using AI to generate tailored plans, we can better support how people actually grow in their careers.
Design Process
Lead with Vision
At LinkedIn, we often lead with design vision, and in this project, we do the same. We started broad and tried to brainstorm as many ideas as possible. We spent two weeks putting together our 1-year vision for this project. You can view the presentation below, or through this link.
Here are a few main ideas from the flow:
1. Ask probing questions to understand learners and help them set better goals.
✅ Adopted
This is where AI can bring the most user value, and it also aligns with our overall strategy. But we need to balance free conversation vs structure in goal setting.
2. Coach works as a co-pilot to guide you to navigate Next role explorer to set your goal.
❌ Was not adopted
This could add more noise than value in this case. I did some systematic thinking for the coach pattern here.
3. Improve learning plan to add more modality, including role-play, reflection, job search.
✅ Adopted
They feel very well suited for enterprise learning scenarios. Some features are being passed over to other teams. Role-play is already live now.
4. Coach proactively prompt you next actions in the homepage banner.
💡 Influenced another team
This informed the design of the search banner.
5. Learning plan is being updated based on learner's progress and recent activities.
🔄 Adopted, but went on a different direction
6. Manager involvement
❌ Not adopted
Everyone is excited about it, but we are not there yet. In a vision project that I did later, I've included this idea.
Dive Deeper
After aligning on the main focus of the redesign, we started to dive deeper. I was leading the onboarding/goal setting flow, collaborating with another designer who focused on Learning plan redesign.
A few key questions that I explored for Onboarding/Goal setting are:
How free the conversation should be? To balance structure and freedom in coach conversation when trying to collect user info and help user set goals.
First, I divided the onboarding flow into 3 main parts: profile completion, intent collection, plan introduction. I've explored different options along the spectrum of being fully free vs very rigid. What I ended up was somewhere in between: for the profile completion part, we follow a pre-set path to collect user info, and for the intent collection, we used free text input combined with some pre-set path/branches. Plan introduction was done lightly and we saved the time for plan adjustment in the learning plan page.
How many steps should the onboarding flow be?
Asking more questions may lead to friction, but it can also improve the quality and relevance of the plan we generated. What we ended up with was:
- Profile related: yes. Try to make sure we have enough profile information for plan customization.
- Goal related: minimal. We think user may have new ideas or inspirations along the way. The key is to help them get to the smart plan quickly.
- Other (time commitment, preferred modality): no.
How much plan information do we show to user in the goal setting flow?
Highlight milestone, results in the flow. They'll see more information when they get to the plan page.
Visual Branding and Motion
Interaction patterns and systems
Created a framework to unify thoughts on how to use AI in the UI.
Coach patterns debated: Full screen, Co-pilot, Inline.
Plan creation pattern debated: WYSIWYG, Inline.
Interaction patterns debated: Controlled by user, Triggered by Coach.
Onboarding flow: Focused on the balance of friction vs commitment.
Final Design
Full Flow
Prototype
Scope was scaled down significantly for the MVP.
Focus: Revamping the onboarding experience and adding a little bit of customization to the learning plan.
Results & Impact
AI-Based Learning Plans drove overall positive learning engagement, indicating learners who are receiving a plan are getting more value out of Learning.
Learners exposed to AI-Based Learning Plans learned +18% more than those who weren't (+18% Triggered to Total Skill Credits; +2.0% SWI).
Further, we exceeded our Plan Starts target driving a +42% increase in Plan Start Rate (18% → 25%), indicating improved plan relevance.
We also exceeded our Coach WAU target, driving +36% SWI (+48K Coach WAU), introducing a new audience to our AI-based capabilities early in their learner journey.