Improving Usability of Maven Central and increasing awareness of Sonatype Nexus Respository

Improving Research at Larn

Role

Research, Design, Design Systems, Wireframing, Strategy.

Platform

Desktop, Mobile Responsive

Category

SAAS, Data & Security

Year

2025

Overview

In April 2025, I worked on the beta version of Larn, to help academic and industry researchers streamline their research process including literature review, data collection and insight generation. This product is currently in beta.

I led the product’s redesign, working closely with the CEO and engineering team to rethink the experience from the ground up with the aim of transforming Larn into a more focused, intuitive tool for researchers doing meaningful work.

Why It Mattered

What we wanted to achieve

The goal was to reduce the time and cognitive load spent on confirming the relevancy of a research article thus enabling researchers to focus on high-level thinking and innovation.

What is currently happening

With limited resources, our interviews focused solely on academic researchers and students, conducted through surveys and usage monitoring.
From earlier research we also know their primary goal is to publish research papers often as a means to secure future contracts.
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The global research software market (including tools for data analysis, lab management, and collaboration) is projected to grow steadily, with annual growth rates between 8–12%, depending on the segment.
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‍AI-powered tools for literature review, citation management, and research workflow (like Scite, ResearchRabbit, Elicit, Consensus AI, Anara) have raised significant funding and are seeing increased adoption among academics and corporate R&D teams.

Softwaere Research Market

AI - Powered Tools

What We Knew About Users

Type 1: Casual users: These users tried Larn once or twice and never returned. Typically students with no ongoing research needs.
Type 2: Occasional users: They used Larn 2–3 times bi-weekly, often for long sessions. These were typically students who occasionally needed research support.
Type 3: Core users: They used Larn consistently, 4–5 times in a row at short intervals. These were typically involved in primary research fields.

In the early stages, our approach was largely experimental. Instead of creating rigid personas, we categorized users based on how they used the current version of Larn.

We analyzed the workflow of Larn’s core users (Type 3) and observed a clear pattern: they used the chat to quickly judge a document’s relevance. Irrelevant documents were usually dismissed within 3–4 prompts, while the relevant ones led to 10–12 prompts.

User Types

Categorizing Relevance

The key challenge in this project was defining what made a document “relevant.” Since Larn caters to a wide range of users from researchers to students determining consistent criteria wasn’t straightforward.

To establish a baseline, I used ChatGPT to outline an initial framework for judging document relevancy across factors such as topic alignment, content depth, and citation quality.
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With these results, I consulted both the CEO and the Larn's users to further streamline the relevant criteria in a bid to make it concise and comprehensive.

Categorizing relevance

Protoyping

With the wireframes, I focused on creating prototypes focus on how users would interact with document results, understand their relevance at a glance, and refine their searches effortlessly.

Prototypes

Design

With a functional prototype in place, I shifted focus toward usability testing to validate whether the design matched real user expectations. The goal was to observe how users interpreted document relevance indicators and interacted with the novel features built.

By the final iteration, I achieved a balance between function and simplicity allowing users to intuitively gauge document quality without needing to overthink the process.

Style Guide

Dashboard

Seamless Onboarding

The onboarding was designed to be intuitive and value-driven, ensuring that new users could immediately grasp what Larn offers from their very first interaction.

Larn - Onboarding

Larn - Upload Article

Refining the Document Upload Experience

Alongside onboarding, I refined the document upload flow to make it seamless for users to contribute and organize their materials.

Larn - Project (No files)

Larn - Project (Grid /List)

Testing & Refining the Workflow

The new campaign tool was well received. Participants appreciated how structured and guided the process felt, especially when navigating through different relevance metrics.

However, tests also users prioritized certain metrics, such as topic similarity and citation strength, over others. In contrast, some metrics were either misunderstood or perceived as redundant, leading to confusion and slower task completion.

Larn - Create Campaign

Larn View Article - Select Metrics

Impact

While we improved Larn's overall user experience, we measured impact on by comparing the amount of time users spent on checking the relevancy of multiple documents before and after the changes was made (up to 5 documents).

With the campaign tool, users would spend significantly more time on the first document compared to a user just using the chatbot. However with consecutive tries with different documents, the time is significantly cut short.

The changes led to an overall average faster time in reducing the amount of time in which a user spends on checking the relevancy of a document by 496.01%. (approximately 500%).

Conclusion

Redesigning Larn was a process of learning through iteration and real user behavior. By focusing on our most engaged users, we built features that aligned with their research needs, simplified their workflows, and helped researchers judge each paper with clarity.

While there’s still more to explore, these early changes positioned Larn as a smarter, more focused tool for researchers doing meaningful work.

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