AI answer evidence explorer

01 / Define

The problem to explore

A hypothetical assistant mixes unsupported claims with sourced passages. Explore an answer interface that makes evidence inspectable and helps users mark uncertainty before using a response in their work.

This is an original UXCharan practice scenario, not a commissioned client project or a validated research finding. Treat the problem as a hypothesis. Adapt it when your research points elsewhere.

Who to learn from

Knowledge workers checking an AI-generated research summary. Recruit consenting adults with relevant experience where possible; document recruitment limits and avoid claiming your sample represents everyone.

02 / Discover

Research before choosing a solution

Questions to investigate

What makes an answer worth trusting?
When do users check a source?

Start with a small set of interviews or contextual walkthroughs that fits your access and consent arrangements. Compare two existing approaches to the same task. Separate direct observations, participant statements and your own assumptions. If primary research is not possible, clearly label desk research and list what remains untested.

Map the experience

Trace the task from its trigger through completion and follow-up. Identify where information, responsibility or confidence is missing. Choose one opportunity you can justify with evidence.

03 / Design

A manageable project scope

Ask a question
inspect support
flag an unsupported claim

Choose one primary device and a realistic context of use. Explore at least two approaches before refining a flow. Build only the screens and states needed to demonstrate the selected task; a complete production system is outside this brief.

Include the difficult moments

Source contradicts answer
no supporting source

Document loading, empty, error and success states where relevant. Consider keyboard order, text enlargement, contrast, meaningful labels and alternatives to colour-only status. Use synthetic data and a fictional brand; do not collect real medical records, credentials or payment information.

Project-specific artefact

An evidence model and answer-review flow.

04 / Test & improve

What would make the design work?

Can users distinguish supported content from unverified output?

Turn this question into realistic prototype tasks. Record whether participants complete each task, where they hesitate, what they misunderstand and how much help they need. Choose success criteria before testing; report observed counts with sample size, rather than claiming statistically proven improvements.

Prioritise findings by impact on the task. Revise the flow and retest the most consequential change where possible. If you cannot retest, explain that limitation.

05 / Tell the story

Your submission checklist

Problem framing, intended users, scope and your role.
Research approach, anonymised evidence and assumptions that changed.
Journey or flow, alternatives explored and reasons for your decisions.
An evidence model and answer-review flow.
Viewable Figma prototype or a shareable document, including key edge cases.
Usability findings, before-and-after revisions and remaining limitations.
A concise portfolio narrative that labels this as a self-initiated concept.

Show what you learned and changed. Do not invent testimonials, interview quotes, launch metrics, employment relationships or business outcomes. Disclose material AI assistance and verify generated content.

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Useful methods for this project

These sources inform the learning process; they do not validate the fictional scenario or endorse UXCharan. Explore more tools in the resource library.