Lina Mohamadi — UX Researcher

UX Researcher & Product Designer · Systems Thinker

Designing clarity
for complex
systems.

I explore how people understand complex products, make decisions, and interact with technology. Through research and systems thinking, I turn scattered information into clear, evidence-based design decisions.

Understanding Complexity Connecting the Dots Research Before Assumptions Systems Thinking Human-Centered Design Evidence Over Opinions Designing for Clarity Understanding Complexity Connecting the Dots Research Before Assumptions Systems Thinking Human-Centered Design Evidence Over Opinions Designing for Clarity

I enjoy making sense
of complexity.

I didn't start my career in UX.

For more than a decade, I worked in industrial design and product development, collaborating with engineers, manufacturers, and cross-functional teams to bring products from concept to production. That experience taught me to see products as systems, where technology, people, and processes continuously influence one another.

When I moved into UX Research, I realized that the same way of thinking applies to digital products. Instead of understanding how physical components fit together, I became interested in how people make sense of complex systems, how they make decisions, and how thoughtful design can reduce uncertainty.

Today, I research complex products, uncover patterns in user behavior, and transform research into structures that help teams design with greater clarity and confidence.

Whether I'm evaluating an enterprise platform, improving a user workflow, or exploring Human-AI Interaction, I'm driven by the same question:

How can complexity become something people can confidently use?

Currently
UX Researcher
Working Student · TeamViewer
Göppingen, Germany
Focus Areas
UX Research Human-AI Interaction Systems Thinking Product Design Enterprise UX
Education
M.Sc. UX Management & Design
PFH Private Hochschule · 2025–
B.A. Industrial Design
Alzahra University · Tehran
Languages
EnglishC1
GermanB2
PersianNative

Professional
journey.

UX Research Intern
TeamViewer · Göppingen, Germany · Mar – May 2026

Evaluated an AI-supported knowledge system for source reliability and trust, then redesigned its configuration to make research insights more traceable and evidence-based.

Design & Product Development Specialist
KICO · Tehran, Iran · Jul 2020 – Mar 2025

Led cross-functional product development for automotive components, bridging design intent, engineering constraints, and production realities across complex team structures.

Industrial Designer
KICO · Tehran, Iran · Nov 2012 – Jul 2020

Designed mechanical components for automotive systems from initial concept through technical documentation, working closely with engineers and production teams throughout.

Let's work
together.

I'm currently seeking full-time UX Research or Human-AI Interaction roles in Germany and Europe. If you're building AI products that need a researcher who understands both systems and people. Let's talk.

/TeamViewer AI Agent← All work
TeamViewer · Enterprise AI · Mar–May 2026

AI-Supported Access to UX Research Knowledge

An internal AI agent giving cross-team stakeholders access to UX research insights, without reading full reports. The challenge: making it trustworthy, not just useful.

Role
UX Research Intern
Duration
3 months · Mar–May 2026
Methods
Scenario Evaluation, Config Analysis, Workflow Observation
Tools
Microsoft Copilot, Confluence
Deliverables
Revised Prompt v1.3, Evaluation Framework, Project Report

Research was being produced. But not used.

UX research reports were thorough, but their format didn't match how stakeholders engage with knowledge in fast-paced workflows. An AI agent was introduced to bridge this gap, connected to Confluence documentation and answering queries in plain language. The challenge: an agent can sound confident while being completely wrong.

"How reliably and effectively does an AI agent support cross-team access to UX research knowledge through source-grounded responses?"


Three angles on the same system.

01

Configuration Analysis

Reviewed the agent's prompt structure | identifying instruction conflicts, scope ambiguities, and conditions leading to hallucinated responses across multiple testing cycles.

02

Scenario-Based Evaluation

Seven representative stakeholder queries assessed against four criteria: source citation, Confluence traceability, response relevance, and knowledge boundary acknowledgment.

03

Workflow Observation

Direct exposure to stakeholder reactions revealed where the agent helped and where trust broke down in real usage.


Reliability is a design problem, not a model problem.

14
Sources cited across 6 of 7 scenarios · avg 2.3 per response
7/7
Scenarios returned relevant, contextually appropriate responses
100%
Knowledge boundary correctly acknowledged when no data existed
v1.3
Final prompt | retrieval-driven, not interpretation-driven

From multi-step search to a single trusted query.

Stakeholders retrieved source-linked excerpts in one interaction instead of navigating multiple reports | enabling research to influence fast-moving product decisions.

The most important insight: an agent that prioritises transparency over fluency builds more durable trust. Acknowledging knowledge limits is more valuable than generating confident-sounding answers.

If you would like to read the complete practical project report, you can open the full PDF below.

Open Full PDF Report
/Startklar← All work
Mobile App · Product Design · Apr–Jul 2025

Startklar · helping international students start well in Germany.

A full UX design process | from 4 qualitative interviews and 38 survey responses to dual-mode hi-fi prototype | for a mobile app easing the overwhelming first weeks of student life in Germany.

Role
UX Designer & Researcher
Duration
Apr – Jul 2025
Methods
Interviews, Survey, Persona, Journey Map, Usability Testing
Tools
Figma, Miro, OBS Studio
Deliverables
Hi-Fi Prototype (Guide + Explore modes), Research Report, Style Guide

Fragmented resources, institutional formats | students who need help right now.

International students arriving in Germany face bureaucracy, housing searches, university systems, and community-building | all simultaneously, in a new language. Existing tools are fragmented and institutional. Startklar provides task-based guidance with a community layer, meeting students at the moment they need help.


Startklar Case Study, Part 1
Startklar Case Study, Part 2
Startklar Case Study, Part 3
/ EDEKA Usability ← All work
EDEKA App · Usability Research · Dec 2025–Jan 2026

Solving usability challenges
in the EDEKA shopping app.

A moderated usability study with 8 participants using think-aloud protocol identified critical failure patterns across list management, barcode scanning, and price visibility — and proposed targeted redesigns for each.

03
Role
UX Researcher & Designer
Duration
Dec 2025 – Jan 2026
Participants
8 · Think-Aloud
Tools
OBS Studio · Figma · Miro
Deliverables
Usability Report · Redesigned Flows

What I did in this project.

Moderated Testing
Conducted usability sessions using think-aloud method, guiding participants through task-based scenarios
Technical Setup
Managed technical setup and session recording using OBS Studio
Analysis & Synthesis
Identified key pain points, gain points, and initial solution ideas through affinity mapping
UI Redesign
Translated research insights into concrete UI designs and solution concepts

A trusted brand with a usability gap.

EDEKA is one of Germany's largest grocery retailers. Its app is used daily by millions — but usability testing revealed a persistent gap between what users expected and what actually happened, particularly during list creation, product scanning, and purchase planning.

Usability issues in consumer apps are often trust issues in disguise. When users receive no feedback, they don't just get frustrated — they question whether the app is working at all.

How the study was conducted.

01
Survey & Quantitative Research
Distributed a survey to understand usage patterns and identify initial challenge areas before sessions. Responses shaped the task scenarios.
02
Moderated Think-Aloud Sessions
8 participants completed scenario-based tasks simulating real grocery shopping. Sessions recorded with OBS Studio for behavioral analysis.
03
Competitive Analysis
Qualitative comparison of 4 competing grocery apps (Rewe, Aldi, Lidl, kaufland) across key dimensions: navigation, features, search, and list management.
04
Feature Benchmark
Feature-by-feature comparison evaluating grocery guidance, step-by-step mode, AI features, scan-to-list, price check, and accessibility.
05
User Interviews
4 participants interviewed to understand motivations, mental models, and expectations around grocery planning behavior.

Three moments where trust breaks down.

1
List Entry — Wrong Default

Users landed directly on an existing list instead of a list overview, leading them to delete items and reuse the same list. The multi-list feature remained undiscovered throughout sessions.

"I thought I had to delete everything first before adding new items."
2
Scanning — Silent Failure

No feedback during barcode scanning caused repeated attempts and confusion. Users assumed the feature was broken rather than simply slow, reducing trust in the entire app.

"Is it working? I have no idea if it scanned anything."
3
Price Visibility — Planning Blocked

Price information was absent during list creation, making it impossible to plan spending in advance. Users returned to paper lists where they felt more in control.

"I can't plan my budget if I don't see prices while adding items."

What the research revealed.

Pain Points
  • Difficulty creating new shopping lists
  • Limited language support in search
  • Unclear interaction in special offers section
  • Confusing category structure
  • Broad and imprecise search results
  • Lack of feedback while scanning products
  • Missing quantity information in products
  • Low perceived value of homepage features
  • Missing price visibility
Gain Points
  • Intuitive shopping list editing
  • Valuable list sharing feature
  • Helpful special offers section
  • Smooth item adding flow
  • Intuitive swipe-to-remove interaction

How the challenges were addressed.

Creating New List

Introduce a list overview as the default entry point, allowing users to immediately view all existing lists instead of landing directly on a previously used one. This surfaces the multi-list feature and lowers cognitive load during task execution.

My Lists
+ New list
🛒 Weekly shopping4
🥗 Healthy meals7
🎂 Birthday party12
List overview as default entry
Scanning Artikel

Redesign the scanning experience to provide clear guidance and continuous feedback throughout the interaction. Show active scanning state, confirm when an item has been successfully added, and build confidence in the reliability of the feature.

Scan Item
Scanning… hold steady
Real-time scanning feedback
Price Check

Integrate price information directly into the shopping list experience, allowing users to view individual item prices while building their list and understand their overall spending. This enables informed decisions during the planning phase.

Meine Liste
Milch 1L1,05 €
Bananen 500g0,89 €
Tomaten1,49 €
Gesamt (3 Items)3,43 €
Inline price visibility during planning

What changes if we fix this.

List management becomes intuitive, reducing confusion and unnecessary actions
Builds user trust through clear and consistent system feedback during scanning
Enables informed decision-making by introducing price visibility and cost awareness
Reduces repeated actions and friction across key user flows, improving overall confidence

What this project taught me.

The most important insight from this project: usability issues in consumer apps are frequently trust issues in disguise. When the system provides no feedback, users don't just feel uncertain about the feature — they begin to doubt the entire product.

This project reinforced that feedback loops are not a nice-to-have. They are the foundation of a trustworthy experience. Every interaction without feedback is a missed opportunity to build user confidence.

/AI Chatbot UX← All work
Human-AI Interaction · Experiment Design · Apr–Jul 2025

AI Chatbot UX, measuring how explainability shapes trust.

A controlled experiment and survey measuring how AI explainability affects user trust, perceived usefulness, and intention to use conversational AI interfaces | applying constructs from HCI literature.

Role
UX Researcher · Group Project
Duration
Apr – Jul 2025
Methods
Controlled Experiment, LimeSurvey Questionnaire, Random Assignment
Tools
LimeSurvey, Figma, Miro
Deliverables
Experiment Design, Survey Instrument, Research Report

Does explaining an AI's reasoning change whether users trust it?

This project investigated how explainability in conversational AI interfaces affects user trust | testing whether users who receive transparent AI reasoning report higher trust, perceived usefulness, and intention to use compared to those receiving opaque responses.

"Explainability is not just a transparency feature | it's a trust mechanism that shapes whether users act on AI-generated information."


A controlled approach to measuring trust.

01

Construct Definition

Defined trust, perceived usefulness, and intention-to-use constructs from established HCI literature | ensuring measurement validity and theoretical grounding.

02

Experiment & Survey Design

Designed a controlled experiment with two conditions (explainable vs. non-explainable AI responses) and a LimeSurvey questionnaire to measure outcomes across constructs.

03

Participant Coordination

Coordinated participant recruitment, random assignment to conditions, and data collection | ensuring methodological rigor and clean condition separation.


Trust in AI is the thread connecting all my projects.

This experiment directly informed my thesis direction | examining how knowledge-boundary acknowledgment in AI systems shapes perceived credibility. The same trust mechanisms at play in conversational explainability also appear in enterprise AI knowledge agents, and in the prompt redesign work at TeamViewer.