Why is UX Research AI-Ethics?


“…the only thing that matters is whether people can use machines we design responsibly, with intention, that is the meaningful question, everything else is fantasy.” 


-Jaron Lanier, "Father of VR" | IQ2 Debate: Don't Trust The Promise Of Artificial Intelligence’


Stakeholder & Environmental Scan

Coordination with research scientists, marketing teams, designers, engineers and product managers to identify research questions and formalizing the shared goals and assumptions. These include: 

  • KPIs/SMART Goals 

  • T-Shirt Sized Research Goals/Questions

  • Stakeholder assumptions and testable hypotheses (may include hypothesized use cases.)

  • Evaluation for new or existing UX Metrics (ex: meeting, exceeding, not-met, unknown)

  • Target Market Demographics (if not specified in assumptions)

  • Commitment of resources (observation, design, participant compensation, tools, stimuli links, reporting collaborators etc.

Pre or Post Environmental Scan/Non-Experimental Research: 


  • Competitive Analysis

  • Market Research

  • Business & Social Science Literature Reviews

  • Meta-analysis

  • Heuristic Reviews

  • Cognitive Walkthroughs

  • Past meeting minutes

Scoping & Experimental Design

Quantitative and/or qualitative user study proposals include:

  • Main Research Questions 1-2, Formal Hypotheses, Assumptions model/Causal Diagrams

  • Participant Screening Logic, Questions, Consent Forms

  • Stimuli Tracking design

  • Discussion Guide Writing (Scenarios, Tasks, Questions)

  • Data Capture Design, Protocols (ex. Success/Struggle/Fail Criteria)

  • Stimuli Creation and Human Resource Requirements

  • Testing & Reporting Timelines

  • Mixed methods specification (examples): 

    • Moderated/Unmoderated/Remote/Live 

    • Usability Testing

    • Survey Design

    • Counterbalancing, randomization

    • SUS/validated indices

    • Card Sorts

    • Tree Tests

    • Ethnography

    • 1st Click Tests

    • 5 Second Value Prop Testing

    • Heat Maps

    • Persona Mining/Narrative Testing 

    • Diary Studies

    • Interviews

    • Concept Testing

    • A/B Testing

Observation & Data Analysis

Testing Environment Setup

  • For onsite studies: Testing multi-camera setup, two-way mirror labs, live observation streaming

  • Piloting session timing and between session debrief schedulesAssessor-Moderator syncing protocols 

Receiving, managing and analyzing data from user studies with: 

  • Null Hypothesis Testing & Interpretation with bivariate tests

    • Correlation, T-Tests, ANOVA, Predictive Analytics (OLS Multivariate Regression, Binary Interactions, Logistic Regression)

  • Qualitative Analysis

    • Grounded Theory/Text Analysis -

    • Open Coding

    • Axial Coding

    • Parent/Sibling Theming

    • Video Analysis

  • Tools:

    • STATA,

    • SPSS,

    • Qualtrics (including Stats IQ)

    • UserTesting.com

    • Chalkmark


& Collaboration

Identify, promote and share user testing best practices and techniques with the broader organization:

  • Executive summary

  • Methodology

  • Key findings

  • Hypotheses status: Proved, Needs Research

  • Results on UX Metrics (as defined in Proposal)

  • Storyboards/Customer Journey Maps

  • User Themes

  • New or updates to Personas or archetypes

  • Red Routes

  • Use Case Maps

  • Component/Design element pain points

  • Wireframes

Collaborate cross-functionally with design, engineering and product development groups​ 

  • Lo-Fi Prototyping (Invision, Axure, Sketch, PPT, Keynote)

  • Empathy Maps

  • Service Blue Printing

  • Highlight Reel Production

  • Design Workshops

Let's talk about stuff...

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