In 2023, Meta’s Reality Labs came to us to design their employee learning portal. They wanted a central hub for all their training that would fulfil their employees needs from new hire onboarding to retirement. They needed something flexible that would evolve as the employee’s career developed.
We accomplished this by creating a system in two key ways: first with individual learning objects, each represented by a tile on the main learning hub page that provides all the important information of that resource at a glance. Second, we created two versions of the hub landing page, one for new hires, and the other for returning learners.
The onboarding experience at Reality Labs is programmatic and broken down first by the initial four weeks of work, and then by the second and third month. Each of these periods have key topics and training that are assigned to the learner. The hub is designed as an interactive timeline which enables the learner to see where they are in their onboarding journey, what’s up next, and maybe where they might have some catching up to do. Each learning tile tells them the name of the resource, a summary of the resource, tags which link to similar resources, estimated time for completion, and indicators for bookmarking and completion.
After the onboarding experience is finished the view of the hub changes. The experience is centered around trainings most relevant to their role while providing plenty of opportunity to discover other topics as well. In the onboarding timeline all the learning tiles are labeled as Core, as they are the core trainings for all Reality Labs employees. In the continued learning view there are more tile types, such as Recommended and Elective. Employees also have the opportunity to browse all training offering, right from the hub.
These are just a few of the highlights. Other features include a customizable search, a learning history timeline, the ability to return to the onboarding at any time in career, and a personal library of bookmarked resources. Together they make a robust package of features that make both guided and self-directed learning simple.