What does the research actually say about designing AI?
Plain-English takeaways from peer-reviewed papers, organised by the design decisions you face. Every claim links to its source: 12 questions, 17 papers so far.
Design questions
12 questionsShould the AI ask before it acts?
Auto-act on high-confidence, reversible steps; ask first on uncertain or costly ones; always offer a quick dismiss.
Should you show the AI's confidence?
Show confidence where it varies meaningfully case to case, and visually flag low-confidence answers.
Do explanations make people trust AI appropriately?
Only explain when the explanation is quick to verify; don't use explanations as decoration to boost acceptance.
How do you stop people from blindly accepting AI answers?
For high-stakes decisions, ask for the user's own take first, then reveal the AI suggestion.
Do inline AI suggestions cost users time?
Time suggestions to natural pauses, and make them quick to scan and to reject.
How should you set expectations for an imperfect AI?
State accuracy and typical failures plainly at first use, and pick the error type your users tolerate best.
Should users be able to tweak AI output?
Make every AI result editable or adjustable; even small controls boost adoption after errors.
Is a blank chat box enough?
Pair chat with examples, templates and structured controls instead of a lone empty prompt field.
What do people need to know before working with an AI?
Onboard with what the AI is good and bad at, plus what it was optimized for, not model internals.
Can AI writing suggestions change what users think?
Keep defaults neutral on contested topics and make it easy to see and change the AI's stance.
Do citations make AI answers trustworthy?
Link each claim to its source inline and make checking one click; don't treat a citation list as proof.
Who gets the most out of AI assistance?
Design for novices first: surface best-practice suggestions where newcomers struggle, and keep them optional for experts.
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