Knowledge Network: Designing Clarity for an AI-Driven Research Tool

Role: Product & UX Designer
Skills: Information design, AI interaction modeling, systems thinking, cross-functional collaboration, UX research, concept modeling
Timeline: Early-stage product design & later redesign reflections

Project Summary

We set out to design an AI-powered research system that could read large volumes of text, detect meaningful relationships (support, contradiction, cause/effect, analogy), and visualize how ideas connect across sources.

My role was to turn this evolving linguistic engine into an experience researchers could understand, navigate, and trust.

This case study walks through:

  • what the original system looked like

  • why it was difficult to use

  • what early design attempts revealed

  • how I reframed the problem

  • what I would do differently today

Background

Researchers face fragmented information: thousands of papers, datasets, articles, and no unified way to see how ideas relate.

Our founder imagined an AI system that could map those relationships automatically — revealing conceptual structure across texts.
The tool could extract meaningful relationships. The question was:
How do we design an interface that reveals insight instead of exposing raw algorithmic structure?

The Original System: What I Inherited

1. Input Experience — A Steep Learning Curve

The system required a multi-step query:

  • topic

  • subtopics

  • expansion terms

  • source selection

  • advanced filters

In usability sessions, nearly every participant typed a simple keyword into the first field and pressed Enter.

Insight:
Users expected search → results.
The system required instructions → configuration → parameters → results.

2. Output Experience — Dense, Abstract, and Hard to Interpret

Problems visible in this graphic:

  • too many nodes

  • overlapping labels

  • truncated text

  • edges labeled with abstract cognitive relationship types

  • no interactivity

Users couldn’t tell what anything meant — only that it was complicated.

Insight:
Graphical complexity ≠ conceptual clarity.

3. A Disconnect Between AI Logic and Human Meaning

[VISUAL 4: Original Relationship Taxonomy Diagram]

The system detected linguistically accurate relationship types (causal, supportive, contradictory, etc.).
But showing those labels directly to users overloaded them with abstraction.

Insight:
Users don’t need technical classifications; they need why this matters.

Understanding the System (Design Lens)

Behind the scenes, the engine detected relationships that were cognitively meaningful:

  • support

  • contradiction

  • cause/effect

  • analogy

  • attribution

[VISUAL 5: Diagram of Relationship Types]

This was powerful — but visualizing those relationships literally in a graph was overwhelming.
The design challenge became:

How do we translate machine logic into something people can understand and act on?

Early Attempts & What They Taught Me

Attempt 1: The Relationship Guide

Goal: clarify what each relationship type meant.
Outcome: users still had to remember abstract definitions.

Lesson:
Clear definitions do not equal clear understanding.

Attempt 2: Relationship Filters

Goal: reduce visual clutter by toggling relationship types on/off.
Outcome: fewer lines → but no improvement in comprehension.

Lesson:
Managing information ≠ communicating value.

Attempt 3: Concept Summary Boxes

Goal: make nodes readable by showing brief summaries.
Outcome: helped in isolation, but didn’t scale and made the layout fragmented.

Lesson:
Micro-clarity cannot compensate for macro-level abstraction.

Emerging Success: Showing the Sources

This feature grounded the system in something recognizable and credible.

Users reacted positively because:

  • they understood where information came from

  • it bridged abstraction with familiarity

  • it built trust in the AI model’s reasoning

Design Insight:
Anchoring abstract analysis in tangible sources builds trust.

Reflection: What I Would Do Differently Today

This project took place under typical early-stage constraints: evolving models, limited engineering resources, and pressure to deliver quickly.

My approach was thoughtful but not yet guided by a strong north-star vision. Today, I would:

  • define that vision up front

  • design interim steps that ladder toward it

  • reveal AI reasoning progressively, not literally

  • guide users through meaning, not mechanics

  • anchor abstractions in human-friendly explanations

  • use visual storytelling instead of raw graphs

Your existing visuals actually support this evolution beautifully — your diagrams show where the system succeeded and where it overwhelmed users.

Design Principles That Emerged

These now guide my work in AI UX:

  • Clarity is not simplification — it’s what makes complexity usable.

  • Surface meaning, not machine structure.

  • Ground abstraction in familiar artifacts (sources, excerpts).

  • Provide guided exploration, not raw output.

  • Translate reasoning steps into intuitive user flows.

Next Phase — Designing for Meaning

I’m currently working on a redesigned version that:

  • starts with user questions

  • presents insights as guided paths

  • surfaces system reasoning step-by-step

  • makes the graph feel like a narrative, not a map

This reflects the lesson that understanding emerges from context, not structure alone.

Final Summary

This case study shows how I design for complex AI systems:

  • turning internal linguistic logic into human meaning

  • transforming overwhelming structures into guided journeys

  • using UX to clarify, not simplify

  • building trust through transparency

  • designing under constraints with long-term structure in mind

  • combining systems thinking with human-centered interpretation