Open-Ended Interview Questions: 100+ Examples and How to Use Them

Open-ended interview questions are the engine of qualitative research. Unlike closed questions — which get yes/no or multiple-choice answers — open-ended questions invite participants to share experiences, perspectives, and feelings in their own words, revealing insights that surveys and checkboxes simply cannot capture.

What Are Open-Ended Interview Questions?

An open-ended question is any question that cannot be answered with a single word or a simple selection. They typically begin with:

• What — "What does your typical workflow look like?"
• How — "How do you currently handle this problem?"
• Tell me — "Tell me about the last time you experienced this."
• Walk me through — "Walk me through your decision-making process."
• Describe — "Describe what an ideal solution would look like for you."

Open-ended questions work because they give respondents agency to answer in their own frame of reference. You learn what matters to them, not just what you thought to ask about.

According to a study published in Field Methods journal, open-ended questions produce 40% more actionable insights than closed questions in qualitative research contexts, because they surface unexpected themes and priorities that researchers did not anticipate.

Why Open-Ended Questions Matter in Research

The gap between what people say they do and what they actually do is one of the oldest challenges in social research. Closed questions are particularly vulnerable to this problem — they anchor participants in your categories and assumptions.

Open-ended questions give participants room to tell you what is really happening. When a user says "I usually just work around it," that is more valuable than a 4-star satisfaction rating. When a customer explains their mental model for pricing, you understand something no survey scale could capture.

For product teams, open-ended interviews with real users are often the difference between building features people asked for and building features people will actually use. Tools like Koji take this further by conducting the interviews automatically — the AI asks your open-ended questions, probes based on participant responses, and extracts themes across hundreds of conversations without requiring a human moderator for each session.

100+ Open-Ended Interview Questions by Category

Product Discovery and User Research

Understanding current behavior:

• Walk me through how you currently solve [problem].
• What tools do you use for [task], and how did you end up choosing them?
• Describe a typical week in your role.
• What takes up more of your time than it should?
• Tell me about the last time you felt frustrated trying to accomplish [goal].
• What is your current workaround for [problem]?
• How have you changed how you handle this over the past year?

Understanding motivations and goals:

• What does success look like for you in [area]?
• What are you ultimately trying to achieve when you [task]?
• How do you measure whether [process] is working?
• What would need to be true for this to be considered a great solution?
• If you had a magic wand, how would this work?
• What would make you recommend this to a colleague?

Probing on pain points:

• What is the hardest part of [process] for you?
• What workarounds have you invented for this?
• Tell me about a time when [process] really broke down.
• What do you wish existed that does not?
• Who else in your organization feels this pain?
• How does this problem affect the rest of your work?

Exploring decision-making:

• Walk me through how you evaluated your options.
• What almost made you choose something different?
• Who else was involved in this decision?
• What would make you switch to a different solution?
• What were the biggest risks you considered?
• What information were you missing when you made this decision?

Customer Discovery Interviews

Problem validation:

• Tell me about the last time [problem area] created a real headache for you.
• How often does this come up in your work?
• Have you tried to solve this before? What happened?
• What is the cost of not solving this — in time, money, or frustration?
• Who else on your team deals with this?
• How has this problem changed over the past year?

Understanding the market:

• How do you currently stay informed about [topic]?
• What tools or services do you pay for to help with this?
• What would make you willing to pay for a solution?
• What makes you trust a new vendor in this space?
• Describe your ideal relationship with a vendor.
• What does your current vendor do well? What do you wish were different?

Jobs-to-Be-Done Questions

Functional jobs:

• What were you trying to get done when you first looked for this solution?
• Walk me through the situation that led you to search for [solution type].
• What job were you hiring [product or service] to do?
• What would you go back to doing if this solution did not exist?
• What does this tool make possible that was not possible before?

Emotional and social jobs:

• How do you want to feel after completing this task?
• What does your boss or team care about in how you handle this?
• What would make you proud of how you handled [situation]?
• How would solving this change how others see your work?
• What would a win look like for you personally?

User Experience and Usability

Task flow exploration:

• Walk me through how you typically do [task].
• Tell me about the last time you used [feature].
• What is your mental model of how this works?
• Where do you usually start when you need to [goal]?
• What do you look at first when you open this?

Feedback on interactions:

• What was confusing or unexpected about that?
• What would you expect to happen when you click that?
• If you were explaining this to a colleague, how would you describe it?
• What would make this feel more natural?
• Where did you get stuck?
• What would you change if this were your product?

Employee Experience and HR Research

Engagement and culture:

• Describe what a great day at work looks like for you.
• What makes you feel valued by your organization?
• Tell me about a time you felt proud of something you accomplished here.
• What would make you recommend this company to a friend?
• What is getting in the way of you doing your best work?

Change and transition:

• Walk me through how the recent changes have affected your daily work.
• What concerns do you have about [organizational change]?
• What information would help you feel more comfortable about the transition?
• How has this changed how you work with your team?
• What is working better than expected? What is harder than expected?

Market Research and Brand

Perception and awareness:

• What comes to mind when you think of [brand]?
• How would you describe [brand] to a friend who had never heard of it?
• What do you associate with [product category]?
• What makes a brand feel trustworthy to you in this space?
• How did you first become aware of [company]?

Purchase journey:

• Walk me through your research process before buying [product type].
• What almost stopped you from purchasing?
• What tipped the decision in [product]'s favor?
• What information were you looking for that you could not find?
• How has your experience been since you became a customer?

How to Write Your Own Open-Ended Questions

Good open-ended questions share three characteristics:

1. They are genuinely open. Avoid questions with obvious "right" answers or leading language. "Don't you find it frustrating when...?" is not truly open-ended — it is a leading question with an open-ended sentence structure.

2. They are specific enough to focus. "Tell me everything about your life" is technically open-ended but useless. "Tell me about your experience trying to [specific task] in the last month" is both open-ended and focused.

3. They ask for experience, not opinion. "What do you think of design X?" gets an opinion that may not reflect real behavior. "Tell me about the last time you used a design like this" gets experience that reveals actual behavior.

Common Mistakes to Avoid

Double-barreled questions: "What do you like and dislike about [product]?" forces participants to answer two questions at once. Split them into separate questions.

Hypothetical questions: "Would you use a feature that...?" Hypothetical answers are unreliable — people are optimistic about what they would do in theory. Ask about past behavior instead.

Jargon-loaded questions: If participants have to decode your question, they answer your words, not their reality. Use plain language.

Questions that confirm your hypothesis: "Can you tell me about how this problem wastes your time?" assumes the problem exists and wastes time. Let participants define the problem first.

Using Open-Ended Questions at Scale

One of the limitations of traditional surveys is that open-ended questions are labor-intensive to analyze. You can ask "What is your biggest frustration?" but then someone has to read 200 responses and find the patterns manually.

AI-native research platforms like Koji solve this. You design your core open-ended questions, and Koji's AI interviewer conducts real conversations with each participant — asking your questions, probing spontaneously based on responses, and following threads you would follow yourself. After all interviews are complete, Koji automatically extracts themes, sentiment, and key quotes across the full dataset.

This means you get the depth of open-ended qualitative research with the scalability of quantitative surveys. Teams using Koji have run 50-participant discovery studies in the time it used to take to complete five interviews manually. Every theme is backed by specific participant quotes, making it easy to share findings with stakeholders.

Key Things to Know

• Open-ended does not mean unstructured: You can use open-ended questions within a structured, systematic research design
• Probe after every open-ended question: The real insight is usually in the follow-up, not the first answer
• Record and transcribe: Open-ended responses need to be captured verbatim for reliable analysis
• Expect longer sessions: Open-ended questions take more time per question than closed ones — plan accordingly

Tips & Best Practices

• Start every interview with an easy, open-ended warm-up question to build rapport
• Use the "narrative elicitation" technique: "Tell me the story of the last time this happened"
• After participants answer, ask "What else?" or "Can you say more?" — you will be surprised what comes out
• Never answer your own question or suggest options if a participant pauses
• Sequence questions from broad context and background to specific experiences and opinions

Related Articles

• Semi-Structured Interviews: The Complete Guide
• Probing and Follow-Up Questions: Going Deeper
• How to Write Great Interview Questions
• Avoiding Bias in Research Interviews
• User Interview Guide Template

Frequently Asked Questions

Q: What is the difference between open-ended and probing questions? A: Open-ended questions initiate a topic; probing questions follow up to go deeper. "What is your current workflow?" is open-ended. "What specifically makes that step slow?" is a probe. Good interviews use both in sequence.

Q: How many open-ended questions should I have in an interview? A: For a 45–60 minute interview, 6–8 core questions with room to probe is ideal. More than 10 usually means you rush through without going deep enough on any single question.

Q: Can I use open-ended questions in surveys? A: Yes, but the analysis burden is high. For large samples, you either analyze a subset or use AI-assisted text analysis. Platforms like Koji turn open-ended interview conversations into structured insights automatically, making large-scale qualitative analysis practical.

Q: How do I avoid leading questions? A: Read each question aloud and ask yourself: "Does this question have an obvious correct answer?" If yes, rewrite it. Also check for emotionally loaded words like "frustrating" or "broken" that prime participants to respond negatively.

Q: How do AI interview tools handle open-ended questions? A: AI platforms like Koji ask your core open-ended questions and then probe dynamically based on what participants say — similar to how a skilled human moderator would. They also analyze all responses automatically, surfacing themes and sentiment without requiring manual coding.