Eclipse

Eclipse

Eclipse

MY ROLE

Mia Thompson

Mia Thompson

The TL;DR

THE TEAM

Product Manager
Engineering Manager
BE and FE Engineers
Data Analyst

Product Manager
Engineering Manager
BE and FE Engineers
Data Analyst

THE TIMELINE

January–April 2026
Launched March 27, 2026

January–April 2026
Launched March 27, 2026

Check it out live! Studocu.com/ai

Check it out live! Studocu.com/ai

THE PROBLEM

Studocu’s library contains more than 50 million student documents, but only 1.8% of Studocu AI users added one to their projects. Relevant materials were hidden behind a three-step search flow, requiring students to know what they needed and actively look for it.

Studocu’s library contains more than 50 million student documents, but only 1.8% of Studocu AI users added one to their projects. Relevant materials were hidden behind a three-step search flow, requiring students to know what they needed and actively look for it.

THE SOLUTION

I connected Studocu’s content library to Studocu AI through search and recommendations, helping students overcome the cold-start problem while keeping them in control of their projects.

I connected Studocu’s content library to Studocu AI through search and recommendations, helping students overcome the cold-start problem while keeping them in control of their projects.

THE OUTCOMES

Documents added to AI projects

+37.9%
Documents added to AI projects

+37.9%

Documents added through Search

+14.7%

+14.7%
Documents added through Search

Users adding Studocu Documents

+27.4%

In guardrail metrics

No decline

+27.4%
Users adding documents

No decline
In guardrail metrics

Documents added to AI projects

+37.9%

Documents added through Search

+14.7%

Users adding Studocu Documents

+27.4%

In guardrail metrics

No decline

INTRO

INTRO

Studocu’s strongest asset was hidden

Studocu is a global study platform with more than 50 million student-contributed documents. In 2025, they made the strategic decision of launching Studocu AI, a product that allows students to turn their study materials into summaries, quizzes and mock exams.

The two experiences weren’t properly connected. Students often started an AI project without adding materials, leaving the system without context and themselves without an obvious next step. Meanwhile, Studocu’s existing library frequently contained exactly what they needed.

How might we anticipate students’ needs while keeping them in control of what enters their project?

WHAT SHIPPED?

WHAT SHIPPED?

One sidebar, two ways to add context

We redesigned the library panel to give students two complementary ways to enrich their projects: search when they knew what they needed, and recommendations when they didn’t. Recommendations used the student’s course and existing project materials as the most reliable signals, while leaving students in control of what was added.

OPPORTUNITIES

OPPORTUNITIES

Search when students know. Recommend when they don’t. 

Only 1.8% of users added documents from Studocu to their projects. Hotjar recordings showed that reaching search required several steps, creating avoidable friction. This first opportunity was straightforward: make searching for documents easier to reach, reducing clicks from 3 to 1.

Before

Before

After

After

But what if the student didn’t know what to search for? 

Together with the PM and EM, I explored proactive recommendations that could help students start with relevant context. Our hypothesis was: surfacing useful documents would lead to more context-rich projects, and ultimately better study sessions.

We explored a broad set of user and project signals, but prioritised the most reliable context available for the first version: the student’s course and the materials already in their project. 

Cold start

Cold start

Recommendations ready to go

Recommendations ready to go

KEY DESIGN DECISIONS

KEY DESIGN DECISIONS

1) One sidebar, two ways to add context

We redesigned the library panel to give students two complementary ways to enrich their projects: search when they knew what they needed, and recommendations when they didn’t. Recommendations used the student’s course and existing project materials as the most reliable signals, while leaving students in control of what was added.

AI helped me explore faster, not decide for me.

Explorations

Explorations

2) AI shouldn’t take over

The system could anticipate what might be useful, but it never added content automatically. Recommendations remained suggestions: students reviewed the documents and decided what belonged in their project.

3) When context is missing, ask for it.

The student’s course was our strongest signal for generating relevant recommendations. When it was missing, we turned the empty state into a clear next step: “Add a course to get recommendations”.


Selecting “Add a course” opened the course picker directly, giving the system enough context to generate relevant recommendations without taking students away from their project.

The student’s course was our strongest signal for generating relevant recommendations. When it was missing, we turned the empty state into a clear next step: “Add a course to get recommendations”.


Selecting “Add a course” opened the course picker directly, giving the system enough context to generate relevant recommendations without taking students away from their project.

KEY RESULTS

KEY RESULTS

More context-rich projects, without harming core behaviour

+37.9%

Studocu documents added to AI projects

21.8K treatment / 15.81K control

+27.4%

Unique users adding Studocu documents

8,580 treatment / 6,736 control

+14.7%

Studocu documents added through Search

17.43K treatment / 15.19K control

Guardrails remained stable

Guardrails remained stable

No statistically significant change in retention, activation, Core usage, trials or payments.

The test demonstrated stronger library discovery and project enrichment. Whether richer projects subsequently improve learning outcomes, retention or paid conversion remains the next question…

REFLECTION

REFLECTION

What's next?

While the initial results are successful, I would still want to explore: 

  • Does adding more relevant context lead to better study sessions and repeat usage? 

  • How should recommendations evolve as the system learns more about the student and their project?


There’s more behind the final flow: discarded directions, recommendation logic and technical trade-offs.

I’d be happy to unpack them in conversation

______________________________________________________________________________________________________________________________

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Credits

Credits

Product Manager

Engineer Manager

Backend Developer

Backend Developer

Front End Developer

Front End Developer

Data Analyst

Product designer

Olga Revyakina

Tairon Coelho

Nazila Najafi

Fernando Kuper Cardoso

Felipe Lourenço

Harman Singh

Yevhenii Matviichuk

Want to say hola?

I’d love to talk design! Send me an email at maquiadrian@gmail.com

Want to say hola?

I’d love to talk design! Send me an email at maquiadrian@gmail.com

Want to say hola?

I’d love to talk design! Send me an email at maquiadrian@gmail.com