cognitive-walkthrough

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Cognitive Walkthrough

认知走查（Cognitive Walkthrough）

This skill enables AI agents to perform a task-specific usability evaluation using the Cognitive Walkthrough method, a technique that simulates how users (especially novices) think through completing specific tasks in an interface.

Unlike broad heuristic evaluations, Cognitive Walkthrough provides deep analysis of particular user journeys, identifying where users get stuck, confused, or make errors.

Use this skill when you need granular, task-focused insights into learnability and ease of first use.

Combine with "Nielsen Heuristics" for general usability, "Don Norman Principles" for intuitive design, or "WCAG Accessibility" for inclusive access.

该技能使AI Agent能够使用认知走查（Cognitive Walkthrough）方法执行特定任务的可用性评估，这是一种模拟用户（尤其是新手）在界面中完成特定任务时思考过程的技术。

与宽泛的启发式评估不同，认知走查（Cognitive Walkthrough）针对特定用户旅程提供深度分析，识别用户遇到卡顿、困惑或出错的环节。

当你需要针对可学习性和首次使用便捷性获取细致、聚焦任务的洞察时，可使用该技能。

可结合「尼尔森启发式原则（Nielsen Heuristics）」进行通用可用性评估，结合「唐·诺曼设计原则（Don Norman Principles）」进行直观性设计评估，或结合「WCAG无障碍标准（WCAG Accessibility）」进行包容性访问评估。

When to Use This Skill

何时使用该技能

Invoke this skill when:

Analyzing specific user tasks (e.g., "complete checkout", "upload a file")
Evaluating learnability for first-time users
Identifying points of confusion in a flow
Debugging why users fail to complete tasks
Assessing onboarding or critical user journeys
Comparing alternative designs for the same task
Preparing for usability testing (hypothesis generation)

在以下场景调用该技能：

分析特定用户任务（例如：「完成结账」「上传文件」）
评估首次使用用户的可学习性
识别流程中的困惑点
排查用户无法完成任务的原因
评估新手引导或关键用户旅程
对比同一任务的不同设计方案
为可用性测试做准备（生成假设）

Inputs Required

所需输入

When executing this walkthrough, gather:

task_description: Specific task to evaluate (e.g., "Create a new account and add first item to wishlist") [REQUIRED]
user_persona: Target user type (novice/intermediate/expert, demographics, goals, prior experience) [REQUIRED]
interface_description: Description of the interface (web/mobile app, key features) [REQUIRED]
screenshots_or_prototype: Visual references of the interface [OPTIONAL but highly recommended]
starting_point: Where the task begins (e.g., "homepage", "logged-in dashboard") [OPTIONAL, defaults to common entry point]
success_criteria: How to know task is complete [OPTIONAL, inferred from task if not specified]

执行走查时，需收集以下信息：

task_description：待评估的特定任务（例如：「创建新账户并将首个商品加入心愿单」）【必填】
user_persona：目标用户类型（新手/中级/专家、人口统计特征、目标、过往经验）【必填】
interface_description：界面描述（网页/移动应用、核心功能）【必填】
screenshots_or_prototype：界面的视觉参考【可选但强烈推荐】
starting_point：任务起始位置（例如：「首页」「已登录的仪表盘」）【可选，默认常见入口点】
success_criteria：任务完成的判定标准【可选，若未指定则从任务中推断】

The Cognitive Walkthrough Method

认知走查方法

Cognitive Walkthrough evaluates four key questions at each step:

认知走查通过在每个步骤评估四个关键问题来开展：

For Each Action in the Task:

针对任务中的每个操作：

Q1: Will users try to achieve the right effect?

Do users understand what they need to do next?
Is the goal of this step clear?
Does it match their mental model of the task?

Q2: Will users notice that the correct action is available?

Is the control/action visible?
Can users find what they need to interact with?
Is it discoverable without hunting?

Q3: Will users associate the correct action with the effect they're trying to achieve?

Does the control's appearance/label suggest it will do what they want?
Is there a clear connection between action and goal?
Are affordances and signifiers clear?

Q4: If the correct action is performed, will users see that progress is being made?

Is there immediate feedback?
Does the system confirm the action succeeded?
Can users tell they're closer to their goal?

问题1：用户会尝试达成正确的效果吗？

用户是否明白下一步需要做什么？
此步骤的目标是否清晰？
它是否符合用户对该任务的心智模型？

问题2：用户会注意到正确操作是可用的吗？

控件/操作是否可见？
用户能否找到需要交互的元素？
无需刻意寻找就能发现它吗？

问题3：用户会将正确操作与他们试图达成的效果关联起来吗？

控件的外观/标签是否表明它能实现用户想要的效果？
操作与目标之间是否有清晰的关联？
功能可见性和标识是否清晰？

问题4：如果执行了正确操作，用户会看到进度正在推进吗？

是否有即时反馈？
系统是否会确认操作成功？
用户能否感知到自己离目标更近了？

Walkthrough Procedure

走查流程

Follow these steps systematically:

系统地遵循以下步骤：

Step 1: Define the Context (5 minutes)

步骤1：定义上下文（5分钟）

Identify the task:
- What is the user trying to accomplish?
- What is the success criteria?
- Example: "Add a product to cart and proceed to checkout"
Define the user:
- Experience level: First-time user / Occasional user / Expert
- Domain knowledge: Novice / Familiar / Expert
- Technical proficiency: Low / Medium / High
- Context: Desktop / Mobile / Tablet, Time pressure, Environment
- Motivation: Why are they doing this task?
Establish starting state:
- Where does the task begin? (homepage, search results, profile page)
- What do users already know?
- What are they thinking/feeling at the start?

确定任务：
- 用户试图完成什么？
- 成功标准是什么？
- 示例：「将商品加入购物车并进入结账流程」
定义用户：
- 经验水平：首次用户 / 偶尔用户 / 专家
- 领域知识：新手 / 熟悉 / 专家
- 技术熟练度：低 / 中 / 高
- 使用场景：桌面端 / 移动端 / 平板、时间压力、环境
- 动机：他们为什么要执行这个任务？
确定起始状态：
- 任务从哪里开始？（首页、搜索结果页、个人资料页）
- 用户已经知道什么？
- 他们在开始时的想法/感受是什么？

Step 2: Decompose the Task (10 minutes)

步骤2：分解任务（10分钟）

Break the task into atomic actions (smallest meaningful steps):

Example Task: "Create account and add item to wishlist"

Navigate to homepage
Find "Sign Up" or "Create Account" button
Click "Sign Up" button
Locate email field
Enter email address
Locate password field
Enter password
Click "Create Account" button
Wait for confirmation/redirect
Navigate to product page
Find "Add to Wishlist" button
Click "Add to Wishlist"
Confirm item was added

Key principle: Each action should be a single, observable user behavior.

将任务拆解为原子操作（最小的有意义步骤）：

示例任务：「创建账户并将商品加入心愿单」

导航至首页
找到「注册」或「创建账户」按钮
点击「注册」按钮
定位邮箱输入框
输入邮箱地址
定位密码输入框
输入密码
点击「创建账户」按钮
等待确认/重定向
导航至商品页
找到「加入心愿单」按钮
点击「加入心愿单」
确认商品已加入

核心原则： 每个操作都应是单一、可观察的用户行为。

Step 3: Walk Through Each Action (30-60 minutes)

步骤3：逐一走查每个操作（30-60分钟）

For each action, answer the 4 cognitive questions:

针对每个操作，回答4个认知问题：

Action Template:

操作模板：

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Action [N]: [Description]

User's Goal at this step: [What they're trying to accomplish] Current State: [What they see/where they are]

Q1: Will users try to achieve the right effect?

Analysis: [Will users know what to do next?]
Issues: [Problems if any]
Rating: ✅ Clear / ⚠️ Unclear / ❌ Confusing

Analysis: [Will users know what to do next?]
Issues: [Problems if any]
Rating: ✅ Clear / ⚠️ Unclear / ❌ Confusing

Q2: Will users notice the correct action is available?

Analysis: [Is the control visible/findable?]
Issues: [Problems if any]
Rating: ✅ Visible / ⚠️ Somewhat hidden / ❌ Hidden

Analysis: [Is the control visible/findable?]
Issues: [Problems if any]
Rating: ✅ Visible / ⚠️ Somewhat hidden / ❌ Hidden

Q3: Will users associate action with intended effect?

Analysis: [Does the control suggest what it does?]
Issues: [Problems if any]
Rating: ✅ Clear / ⚠️ Ambiguous / ❌ Misleading

Analysis: [Does the control suggest what it does?]
Issues: [Problems if any]
Rating: ✅ Clear / ⚠️ Ambiguous / ❌ Misleading

Q4: Will users see progress is being made?

Analysis: [Is there feedback after the action?]
Issues: [Problems if any]
Rating: ✅ Clear feedback / ⚠️ Delayed/weak / ❌ No feedback

Analysis: [Is there feedback after the action?]
Issues: [Problems if any]
Rating: ✅ Clear feedback / ⚠️ Delayed/weak / ❌ No feedback

Critical Issues Found:

[Issue 1]
[Issue 2]

[Issue 1]
[Issue 2]

Recommendations:

[Specific improvement 1]
[Specific improvement 2]

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[Specific improvement 1]
[Specific improvement 2]

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Step 4: Synthesize Findings (15 minutes)

步骤4：综合发现（15分钟）

After walking through all actions:

Identify failure points:
- Where did multiple questions get ❌ or ⚠️ ratings?
- Which steps are most likely to cause user confusion?
- Where might users give up?
Categorize issues:
- Critical blockers: Users likely can't complete task
- Major friction: Users struggle significantly but may succeed
- Minor issues: Small delays or confusion
- Cognitive load: Mental effort required
Calculate success likelihood:
- Estimate % of target users who would complete task on first try
- Identify most common failure modes
Prioritize improvements:
- Quick wins (easy fixes, high impact)
- Major redesigns (complex fixes, high impact)
- Nice-to-haves (easy fixes, low impact)

完成所有操作的走查后：

识别失败点：
- 哪些步骤有多个❌或⚠️评级？
- 哪些步骤最可能导致用户困惑？
- 用户可能在哪些环节放弃？
分类问题：
- 关键阻塞点：用户很可能无法完成任务
- 主要摩擦点：用户遇到明显困难但可能最终成功
- 次要问题：微小延迟或困惑
- 认知负荷：所需的脑力消耗
计算成功可能性：
- 估算目标用户首次尝试完成任务的百分比
- 识别最常见的失败模式
优先改进项：
- 快速优化（易实现、高影响）
- 重大重构（复杂修复、高影响）
- 锦上添花（易实现、低影响）

Step 5: Generate Report (10 minutes)

步骤5：生成报告（10分钟）

Create comprehensive walkthrough report (see format below).

创建完整的走查报告（格式如下）。

Report Structure

报告结构

markdown

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Cognitive Walkthrough Report

Task: [Task description] User Persona: [User type and characteristics] Interface: [System/app being evaluated] Date: [Date] Evaluator: [AI Agent]

Executive Summary

Task Success Prediction

Estimated Success Rate (First Attempt): [X]% of target users

Critical Findings

[Most critical issue]
[Second critical issue]
[Third critical issue]

[Most critical issue]
[Second critical issue]
[Third critical issue]

Overall Assessment

[2-3 sentence summary of learnability]

User Context

Target User Profile

Experience Level: [Novice/Intermediate/Expert]
Domain Knowledge: [Description]
Technical Proficiency: [Low/Medium/High]
Device/Context: [Desktop/Mobile, environment]
Motivation: [Why they're doing this]
Prior Experience: [What they already know]

Experience Level: [Novice/Intermediate/Expert]
Domain Knowledge: [Description]
Technical Proficiency: [Low/Medium/High]
Device/Context: [Desktop/Mobile, environment]
Motivation: [Why they're doing this]
Prior Experience: [What they already know]

Task Definition

Goal: [What user wants to accomplish] Success Criteria: [How to know they succeeded] Starting Point: [Where task begins]

Step-by-Step Walkthrough

Action 1: [Navigate to homepage]

User's Goal: Find where to start creating an account Current State: User just arrived at homepage

Q1: Will users try to achieve the right effect?

Analysis: Users typically look for "Sign Up", "Register", or "Create Account" in header/nav
Issues: None expected - standard mental model
Rating: ✅ Clear

Analysis: Users typically look for "Sign Up", "Register", or "Create Account" in header/nav
Issues: None expected - standard mental model
Rating: ✅ Clear

Q2: Will users notice the correct action is available?

Analysis: "Sign Up" button is in top-right corner of header (standard location)
Issues: Small text (12px), low contrast (#999 on #FFF = 2.8:1)
Rating: ⚠️ Somewhat hidden

Analysis: "Sign Up" button is in top-right corner of header (standard location)
Issues: Small text (12px), low contrast (#999 on #FFF = 2.8:1)
Rating: ⚠️ Somewhat hidden

Q3: Will users associate action with intended effect?

Analysis: "Sign Up" is standard terminology, clearly indicates account creation
Issues: None
Rating: ✅ Clear

Analysis: "Sign Up" is standard terminology, clearly indicates account creation
Issues: None
Rating: ✅ Clear

Q4: Will users see progress is being made?

Analysis: N/A - no action taken yet (just viewing)
Issues: N/A
Rating: N/A

Analysis: N/A - no action taken yet (just viewing)
Issues: N/A
Rating: N/A

Critical Issues:

Low contrast on "Sign Up" button - WCAG fail, hard to see
Button is small (24px height) - mobile users may struggle

Low contrast on "Sign Up" button - WCAG fail, hard to see
Button is small (24px height) - mobile users may struggle

Recommendations:

Increase contrast to 4.5:1 minimum (WCAG AA)
Increase button size to 44px (touch target guideline)
Consider more prominent placement or visual weight

[Continue for all actions...]

Increase contrast to 4.5:1 minimum (WCAG AA)
Increase button size to 44px (touch target guideline)
Consider more prominent placement or visual weight

[Continue for all actions...]

Failure Points Analysis

Critical Blockers (Users likely to fail)

1. Action 7: Create password with complexity requirements

Problem: Password requirements not shown until after submission fails
Impact: Users guess rules, get frustrated by repeated errors
Affected Users: 70-80% of novices
Severity: Critical
Fix Priority: P0 (Must fix)
Recommendation: Show requirements inline before user types

2. Action 12: Find "Add to Wishlist" button

Problem: Icon-only button (heart icon) with no label, not obvious
Impact: Users don't see it or don't understand what it does
Affected Users: 50-60% of first-time users
Severity: High
Fix Priority: P1 (Should fix)
Recommendation: Add text label "Add to Wishlist" next to icon

1. Action 7: Create password with complexity requirements

Problem: Password requirements not shown until after submission fails
Impact: Users guess rules, get frustrated by repeated errors
Affected Users: 70-80% of novices
Severity: Critical
Fix Priority: P0 (Must fix)
Recommendation: Show requirements inline before user types

2. Action 12: Find "Add to Wishlist" button

Problem: Icon-only button (heart icon) with no label, not obvious
Impact: Users don't see it or don't understand what it does
Affected Users: 50-60% of first-time users
Severity: High
Fix Priority: P1 (Should fix)
Recommendation: Add text label "Add to Wishlist" next to icon

Major Friction Points

[Continue...]

Minor Issues

[Continue...]

Success Probability by User Type

User Type	Estimated Success Rate	Time to Complete	Confidence
Novice	45%	8-12 minutes	Low frustration tolerance
Intermediate	75%	4-6 minutes	Moderate confidence
Expert	95%	2-3 minutes	High efficiency

Target: Novices should have ≥80% success rate with ≤5 minutes time.

Gap: Current design falls short for novices by 35 percentage points.

User Type	Estimated Success Rate	Time to Complete	Confidence
Novice	45%	8-12 minutes	Low frustration tolerance
Intermediate	75%	4-6 minutes	Moderate confidence
Expert	95%	2-3 minutes	High efficiency

Target: Novices should have ≥80% success rate with ≤5 minutes time.

Gap: Current design falls short for novices by 35 percentage points.

Cognitive Load Assessment

Memory Burden

Items to remember: [List what users must recall]
Rating: Low / Medium / High
Issue: [If high, explain why]

Items to remember: [List what users must recall]
Rating: Low / Medium / High
Issue: [If high, explain why]

Decision Points

Choices users make: [Number and complexity]
Rating: Low / Medium / High
Issue: [Unnecessary decisions increase cognitive load]

Choices users make: [Number and complexity]
Rating: Low / Medium / High
Issue: [Unnecessary decisions increase cognitive load]

Error Recovery

How easy to fix mistakes: [Analysis]
Rating: Easy / Moderate / Difficult
Issue: [Problems with undo/back/reset]

How easy to fix mistakes: [Analysis]
Rating: Easy / Moderate / Difficult
Issue: [Problems with undo/back/reset]

Prioritized Recommendations

Phase 1: Critical Fixes (1-2 weeks)

1. Show password requirements inline (Action 7)

Why: Eliminates #1 failure point
Impact: +25% success rate for novices
Effort: Low (4 hours)

2. Add text label to wishlist button (Action 12)

Why: Makes feature discoverable
Impact: +15% task completion
Effort: Low (2 hours)

3. Increase "Sign Up" button contrast (Action 1)

Why: Accessibility + discoverability
Impact: +10% users find starting point
Effort: Low (1 hour)

Total Phase 1 Impact: +50% novice success rate (45% → 67.5%)

1. Show password requirements inline (Action 7)

Why: Eliminates #1 failure point
Impact: +25% success rate for novices
Effort: Low (4 hours)

2. Add text label to wishlist button (Action 12)

Why: Makes feature discoverable
Impact: +15% task completion
Effort: Low (2 hours)

3. Increase "Sign Up" button contrast (Action 1)

Why: Accessibility + discoverability
Impact: +10% users find starting point
Effort: Low (1 hour)

Total Phase 1 Impact: +50% novice success rate (45% → 67.5%)

Phase 2: Major Improvements (1-2 months)

[Continue with medium priority items...]

Phase 3: Polish (3+ months)

[Continue with nice-to-have improvements...]

Alternative Design Suggestions

Based on walkthrough findings, consider these alternative approaches:

Alternative 1: Progressive Disclosure for Signup

Current: All fields shown at once Proposed: Step-by-step (email → password → confirm) Pros: Reduces cognitive load, clearer feedback per step Cons: More clicks, may feel slower Recommendation: A/B test with target users

Alternative 2: Social Sign-Up

Current: Email/password only Proposed: Add "Sign up with Google/Apple" Pros: Faster, no password to remember Cons: Privacy concerns, dependency on third-party Recommendation: Offer as option alongside email signup

[Continue with other alternatives...]

Comparison to Best Practices

Practice	Current Implementation	Recommendation
Password requirements visibility	Hidden until error	Show inline before typing
Button sizing (mobile)	24px	44px minimum
Color contrast	2.8:1 (WCAG fail)	4.5:1 (WCAG AA)
Error messages	Generic	Specific and actionable
Confirmation feedback	Weak	Clear success messages

Practice	Current Implementation	Recommendation
Password requirements visibility	Hidden until error	Show inline before typing
Button sizing (mobile)	24px	44px minimum
Color contrast	2.8:1 (WCAG fail)	4.5:1 (WCAG AA)
Error messages	Generic	Specific and actionable
Confirmation feedback	Weak	Clear success messages

Next Steps

Prioritize fixes: Start with Phase 1 critical items
Prototype improvements: Create clickable mockups with changes
User testing: Validate findings with 5-8 target users
Iterate: Run another cognitive walkthrough after changes
Monitor metrics: Track task completion rates, time-on-task, error rates

Prioritize fixes: Start with Phase 1 critical items
Prototype improvements: Create clickable mockups with changes
User testing: Validate findings with 5-8 target users
Iterate: Run another cognitive walkthrough after changes
Monitor metrics: Track task completion rates, time-on-task, error rates

Methodology Notes

Method: Cognitive Walkthrough (Wharton et al., 1994)
Evaluator: AI agent simulating UX expert
Perspective: Novice user (first-time, no training)
Limitations:
- Based on interface analysis, not actual user behavior
- Success rates are estimates, not measured data
- Should be validated with real user testing

Method: Cognitive Walkthrough (Wharton et al., 1994)
Evaluator: AI agent simulating UX expert
Perspective: Novice user (first-time, no training)
Limitations:
- Based on interface analysis, not actual user behavior
- Success rates are estimates, not measured data
- Should be validated with real user testing

References

Wharton, C., Rieman, J., Lewis, C., & Polson, P. (1994). "The Cognitive Walkthrough Method"
Nielsen, J. (1994). "Heuristic Evaluation"
Spencer, R. (2000). "The Streamlined Cognitive Walkthrough Method"

Version: 1.0 Date: [Date]

---

Wharton, C., Rieman, J., Lewis, C., & Polson, P. (1994). "The Cognitive Walkthrough Method"
Nielsen, J. (1994). "Heuristic Evaluation"
Spencer, R. (2000). "The Streamlined Cognitive Walkthrough Method"

Version: 1.0 Date: [Date]

---

Key Principles of Cognitive Walkthrough

认知走查的核心原则

1. Focus on Learnability

1. 聚焦可学习性

Emphasis on first-time use, not expert performance
"Can users figure it out?" vs. "Is it efficient?"

强调首次使用体验，而非专家操作效率
关注「用户能否弄明白？」而非「是否高效？」

2. Question-Driven Analysis

2. 基于问题的分析

The 4 questions provide structured evaluation
Every action is scrutinized from user perspective

4个问题提供结构化评估框架
从用户视角审视每个操作

3. Task-Specific

3. 特定任务导向

Evaluates actual user goals, not general interface
Deep dive vs. broad sweep

评估真实用户目标，而非通用界面
深度剖析而非宽泛扫描

4. Theory-Grounded

4. 理论支撑

Based on cognitive psychology (information processing theory)
Simulates human problem-solving process

基于认知心理学（信息处理理论）
模拟人类解决问题的过程

5. Predictive Method

5. 预测性方法

Identifies issues before user testing
Generates testable hypotheses

在用户测试前识别问题
生成可测试的假设

Common Walkthrough Findings

常见走查发现

Typical Issues Discovered:

典型问题：

Discoverability Problems:

Hidden buttons or controls
Unlabeled icons
Non-standard locations
Poor visual hierarchy

Unclear Affordances:

Links that don't look clickable
Buttons that look disabled
Confusing icon meanings
Misleading labels

Feedback Failures:

No confirmation after actions
Unclear error messages
No progress indicators
Silent failures

Mental Model Mismatches:

Unexpected behavior
Counter-intuitive flows
Inconsistent patterns
Violates conventions

Cognitive Load:

Too many choices
Requiring memory of previous screens
Complex multi-step processes
Unclear next steps

可发现性问题：

隐藏的按钮或控件
无标签图标
非标准位置
视觉层次混乱

功能可见性不清晰：

看起来不可点击的链接
看起来已禁用的按钮
图标含义模糊
标签误导

反馈缺失：

操作后无确认
错误信息不清晰
无进度指示器
静默失败

心智模型不匹配：

意外行为
违反直觉的流程
模式不一致
违反惯例

认知负荷过高：

选择过多
需要记忆之前页面的信息
复杂的多步骤流程
下一步操作不清晰

Success Metrics

成功指标

Measure walkthrough effectiveness:

Before Walkthrough:

Baseline task completion rate
Average time on task
Error rate
User satisfaction scores

After Implementing Fixes:

Improved completion rate (target: +20-40%)
Reduced time on task (target: -30-50%)
Lower error rate (target: -40-60%)
Higher satisfaction (target: +1-2 points on 5-point scale)

衡量走查的有效性：

走查前：

基准任务完成率
平均任务耗时
错误率
用户满意度得分

实施修复后：

任务完成率提升（目标：+20-40%）
任务耗时减少（目标：-30-50%）
错误率降低（目标：-40-60%）
满意度提升（目标：5分制下+1-2分）

Combining with Other Methods

与其他方法结合使用

Use cognitive walkthrough when:

You have specific tasks to evaluate
You need deep, granular insights
You're early in design (pre-user testing)
You want to predict first-use issues

Complement with:

Nielsen Heuristics: General usability issues
Don Norman Principles: Intuitive design assessment
WCAG Audit: Accessibility compliance
User Testing: Validate predictions with real users
Analytics: Quantitative validation (funnels, drop-off)

使用认知走查的场景：

你需要评估特定任务
你需要深度、细致的洞察
处于设计早期阶段（用户测试前）
你想预测首次使用的问题

可补充的方法：

尼尔森启发式原则（Nielsen Heuristics）：通用可用性问题
唐·诺曼设计原则（Don Norman Principles）：直观性设计评估
WCAG审计：无障碍合规性
用户测试：通过真实用户验证预测结果
数据分析：量化验证（漏斗分析、流失率）

Tips for Effective Walkthroughs

有效走查的技巧

Be specific about users: "Novice" is better than "user"; "65+ non-tech-savvy" is even better
Stay in character: Think like the user, not the designer
Document assumptions: What does the user know? What don't they know?
Be honest about issues: Don't rationalize or excuse bad design
Provide actionable recommendations: "Unclear" → "Add tooltip explaining X"
Quantify when possible: "50% of users" vs. "some users"
Consider context: Time pressure, mobile vs. desktop, distractions
Test assumptions: Validate findings with actual users

明确用户画像：「新手」比「用户」更具体；「65岁以上非技术用户」则更精准
代入用户角色：站在用户角度思考，而非设计师
记录假设：用户知道什么？不知道什么？
坦诚面对问题：不要为糟糕的设计找借口
提供可落地的建议：将「不清晰」改为「添加解释X的提示框」
尽可能量化：用「50%的用户」而非「部分用户」
考虑使用场景：时间压力、移动端vs桌面端、干扰因素
验证假设：通过真实用户测试验证发现

Version

版本

1.0 - Initial release

Remember: Cognitive Walkthrough is a predictive method. While it's highly effective at identifying learnability issues, always validate findings with real users through usability testing.

1.0 - 初始版本

注意： 认知走查是一种预测性方法。尽管它在识别可学习性问题方面非常有效，但始终需要通过可用性测试，结合真实用户的反馈来验证发现。