ux-research checked
Guidelines for Human-AI Interaction
Research-backed guidelines for AI experiences, including capability disclosure, uncertainty, efficient dismissal, correction, and control.
Pattern Decisions This Source Supports
| Pattern | Supported decision | Required contract | Claim note |
|---|---|---|---|
| Adaptive defaults | Choose adaptive defaults when the system proposes a starting value inside the current task based on context or learned behavior. | Opening the task shows the proposed value and why it was chosen before the user submits, exports, sends, schedules, or applies anything. | Microsoft Research supports contextual relevance, correction, dismissal, and user control for AI-infused behavior. |
| Agent plan preview | Choose agent plan preview when an AI agent's proposed multi-step execution needs review before it starts. | The preview names the objective, plan version, source context, model or workflow instructions, planned tools, and expected output before run. | Supports user control, correction, expectations, uncertainty, and capability boundaries. |
| Agent progress trace | Choose agent progress trace when an agent or automation run has started and users need live status across multiple steps or tools. | The trace belongs to one run ID, reviewed plan version, objective, actor, start time, current state, and final state. | Supports user control, feedback, correction, and expectation-setting during AI-assisted experiences. |
| AI agent acts without approval | Flag this anti-pattern when an AI agent or automation executes a high-impact side effect without showing and requiring approval for the exact action and payload first. | The agent distinguishes read-only steps, draft steps, reversible local changes, and external side-effect steps before execution. | Supports user control, capability disclosure, correction, and human oversight in AI experiences. |
| AI confidence shown as fake precision | Flag this anti-pattern when an AI or automated surface shows a precise score without calibration scope, decision threshold, freshness, uncertainty reason, or safe next action. | The interface distinguishes raw internal scores from user-facing calibrated confidence. | Supports AI uncertainty communication, capability limits, correction, and user control. |
| Confidence / uncertainty display | Choose confidence / uncertainty display when users need to judge prediction reliability before acting on AI or automation output. | The display names the task, prediction, source of the confidence estimate, calibration scope, and last calibration or update time when available. | Supports capability boundaries, uncertainty communication, correction, feedback, and control in AI experiences. |
| Correction feedback | Choose correction feedback when users need to correct AI output, source use, assumptions, recommendations, safety behavior, or answer quality after an AI response is shown. | A correction feedback action preserves answer ID, response version, claim span, source ID, user reason, expected correction, submitter, timestamp, and chosen scope. | Supports user correction, feedback, control, and calibrated trust for AI behavior. |
| Dangerous-action review | Choose dangerous-action review when the user is about to execute a high-impact action and needs to inspect the exact payload, risk, evidence, and side effects before it leaves the safe preview state. | The review is bound to a specific action ID, payload version, target, actor, permission scope, source context, evidence set, and policy trigger. | Supports human control, correction, uncertainty handling, and capability-boundary communication for AI-assisted high-impact actions. |
| Editable AI output | Choose editable AI output when the primary object is generated output after creation and users need to revise, review, save, copy, or apply that output. | The generated draft, user edits, tracked changes, source mappings, citations, review status, and final output state are modeled as distinct data rather than one mutable text blob. | Supports user correction, feedback, control, and capability-boundary disclosure in AI experiences. |
| Escalate to human | Choose escalate to human when users need a route from AI, automation, chatbot, self-service, or failed recovery to a human channel or queue. | The escalation action names the human destination and whether it is live, asynchronous, specialist, supervisor, emergency, or review-only. | Supports user control and appropriate human-centered recovery when AI is uncertain or inappropriate. |
| Human approval gate | Choose human approval gate when automation is paused at runtime and cannot execute the next step until an eligible human authorizes it. | The gate belongs to a specific automation run, step ID, payload version, model or workflow version, target object, and approver rule. | Supports human control, correction, uncertainty, and capability-boundary guidance. |
| Prompt suggestions | Choose prompt suggestions when the user needs help discovering useful AI requests, not when the task can be captured by a fixed form. | Selecting a suggestion populates or inserts editable prompt text instead of silently running the request. | Human-AI guidelines support capability clarity, user control, correction, dismissal, and appropriate reliance. |
| Recommendations | Choose recommendations when the system ranks a small set of likely useful items from behavior, context, similarity, popularity, rules, or model output. | Opening a recommendation navigates to that item or action and records the event according to the disclosed analytics and personalization rules. | Microsoft Human-AI guidelines support capability disclosure, uncertainty, efficient dismissal, correction, and user control. |
| Regenerate / retry | Choose regenerate / retry when the task is rerunning an AI response attempt, creating a new answer version, continuing a failed generation, or recovering from failed AI source or tool work. | Retry same prompt repeats the submitted prompt and visible context unless the UI explicitly shows which context changed. | Supports user correction, control, feedback, and appropriate reliance in AI experiences. |
| Scope clarification | Choose scope clarification when the AI needs a missing boundary before producing a reliable answer, plan, retrieval query, or tool action. | The system identifies what boundary is missing before asking the user to clarify. | Supports clarification, control, and avoiding inappropriate AI assumptions. |
| Tool-use visibility | Choose tool-use visibility when users need to inspect exact tool names, purposes, inputs, outputs, permissions, side effects, failures, or redactions. | Each tool-use item identifies one tool call or tool-call attempt with stable call ID, run ID, step ID, tool name, status, timestamp, and permission scope. | Supports user control, correction, feedback, and capability-boundary disclosure in AI experiences. |
Evidence Role
This source is treated as ux-research evidence. Use it to validate the decision rules above, not as a visual style reference.
Publisher: Microsoft Research. Last checked: .