New tutor onboarding


project overview

Only 17 percent of newly approved tutors made it to their first lesson, so there was lots of room to guide new tutors to connect with new students.

The goal of this project was to help new tutors understand how the Chegg Tutors service works.

There was a lot of confusion around how to get started tutoring students on the platform and a lack of onboarding information. 

My role was interaction and visual design. I worked on this with the lead designer and researcher, with feedback from the design director. Stakeholders included product, community, and business. 


user research

We started out the project by shadowing newly approved tutors in our current system. The current system just consisted of an approval email and a 10-minute interactive PDF guide. 

We quickly found out that new tutors felt stranded on an island getting started with online tutoring. The biggest issue was how they get matched to new students, or what are called “opportunities” on the site.

The sparklines to the left illustrate that opportunities are a common low point in the new tutor journey.


current flow

The map below illustrates the current flow (before this project) of a newly approved tutor. The area in grey was the area we focused on since we hypothesized it would have the biggest impact on helping new tutors understand how to get students.


We looked at a variety of inspiration for this project, namely Intercom, Airbnb, Invision, Duolingo and Thumbtack. The big takeaway from these sites were giving users contextual information at the right moment. 




Quick sketching of lots different ideas helped us hone in on what was most crucial for new tutors to learn right away. 




Once we sketched out a flow that we thought was working, we wireframed and tested it with newly approved tutors. We actually delivered the good news that they had been approved during the UER sessions, which was exciting to see them experience that delightful moment. 

From the UER sessions, we confirmed three key areas that were crucial to tutors having successful first lessons. We decided to make those requirements before they could connect with students. 




version A

Iterated on different ways to group this information and communicate it to the tutors, namely clarifying what’s required versus recommended. 


version B

Below is another iteration considering payment as a requirement. This step was later chunked out as something the tutor could do while they’re waiting to be approved.



This is where we landed for the Minimal Viable Product: Chunking out the required steps and recommended ones in two different states. 


recommended tips

Once the new tutors completed the required steps, they’d get the recommended tips. 


low site activity

We heard from many new tutors that when they didn’t have opportunities immediately, they assumed there were no students on the site, which wasn’t accurate. An aspect of the project that didn’t make it into the MLP was visualizing site activity, so the tutor could see that students were online by location and subject.

This is a view of site activity on a December Tuesday at noon. 



Another area of the MVP was giving new tutors contextual tips while they were waiting for opportunities to tutor. 

Next steps

  • See if this initial post-approval state helps new tutors get into lessons
  • Iterate based on learnings in production 
  • Add areas of delight, such as motion transitions that could help better chunk information via timing, that got stripped from the engineering MLP
  • Follow up to prioritize other areas in the new tutor journey that will onboard over time