# UX Oracle Glossary

Use these terms when precision matters for teaching, stakeholder alignment, or artifact writing. Prefer plain language unless the audience benefits from formal UX terminology.

## Core UX terms

- `Affordance`: A cue that suggests how something can be used.
- `Mental model`: What a user believes will happen before they act.
- `Cognitive load`: The mental effort required to understand or complete a task.
- `Discoverability`: How easily users can find a feature without instruction.
- `Findability`: How easily users can locate content within an information architecture.
- `Learnability`: How quickly a new user can become competent.
- `Task completion rate`: The percentage of users who finish a task successfully.
- `Error rate`: The percentage of attempts that fail or contain mistakes.
- `Time on task`: How long it takes to complete a task.

## Research and measurement

- `Qualitative research`: Non-numerical evidence such as interviews, observations, and themes. Best for why and how.
- `Quantitative research`: Numerical evidence such as survey data, usage metrics, and benchmark scores. Best for how many and how often.
- `Think-aloud protocol`: Asking users to verbalize thoughts while completing tasks.
- `SUS`: A ten-item usability benchmark scored from 0-100.
- `NPS`: A likelihood-to-recommend score from -100 to 100 after promoter and detractor calculation.
- `First-click testing`: Measuring where users click first when attempting a task.
- `Benchmark study`: Repeating a consistent study design to compare performance over time.

## Methods and synthesis

- `Heuristic evaluation`: Expert review of an interface against usability principles.
- `Cognitive walkthrough`: Stepping through a task as a first-time user to test whether the next action is obvious.
- `Card sorting`: Asking participants to organize information into groups.
- `Tree testing`: Testing whether users can find items in a text-only structure.
- `Affinity mapping`: Grouping atomic observations into emergent themes.
- `Thematic analysis`: Identifying patterns across qualitative data.
- `JTBD`: Framing behavior around the job a user is trying to get done.
- `Opportunity mapping`: Translating insights into concrete areas for design or product action.

## Artifacts

- `Persona`: An evidence-based archetype built from shared behaviors, needs, and contexts.
- `Empathy map`: A structure for what users say, do, think, and feel.
- `Journey map`: A timeline of actions, thoughts, emotions, pain points, and opportunities across a scenario.
- `Service blueprint`: A journey map extended with backstage systems, roles, and dependencies.
- `Wireframe`: A low- to mid-fidelity representation of structure and layout.
- `Prototype`: A testable model of an experience, from sketch to interactive mockup.

## Decision and prioritization

- `MoSCoW`: Prioritization using must, should, could, and won't.
- `RICE`: Prioritization using reach, impact, confidence, and effort.
- `Kano`: Categorizing features by baseline expectations, performance effects, and delight.
- `Tradeoff`: A decision where improving one dimension costs another, such as rigor versus speed.

## Accessibility and ethics

- `WCAG`: The accessibility standard for digital experiences.
- `POUR`: Perceivable, operable, understandable, and robust.
- `Dark pattern`: A design that manipulates users against their own interests.
- `Universal access`: Designing for varied abilities, devices, and input modes.
- `HX`: Human experience, including dignity, safety, equity, and wellbeing beyond task success.

## Bias and reasoning risks

- `Confirmation bias`: Seeking evidence that supports an existing belief.
- `Anchoring`: Overweighting the first idea or data point.
- `Status quo bias`: Preferring the current solution because it is familiar.
- `Survivorship bias`: Learning only from users who made it through the flow.
- `Framing effect`: Different wording or presentation changes interpretation.
- `Empathy hallucination`: Presenting inferred thoughts or feelings as facts without evidence.

## Output reminders

- Use formal terms when the audience needs them.
- Translate jargon into plain language for mixed or non-UX audiences.
- Mark inference level whenever a term like `thinks`, `feels`, or `motivation` rests on partial evidence.
