In this post, we’ll break down when to use each type of method, so you can select the best approach to answer the research questions you have.
What is moderated testing? Moderated testing involves a researcher or moderator who guides participants through a set of tasks, asking questions and observing how they interact with a product or prototype. The moderator has control over the flow of the test, and they can ask follow-up questions in real-time based on participants' responses.
Benefits of moderated testing: Control over the session: Moderators can adapt the test on the fly, ask follow-up questions, and dig deeper into specific areas of interest.Rich qualitative insights: Since the moderator is present, they can explore the participant’s thinking, motivations, and behaviors in detail.Exploration of complex topics: Moderated testing allows for open-ended discussions, making it ideal for testing early concepts or understanding complex user needs.When to use moderated testing: Very open ended discovery: If you want complete control over each conversation with each participant, human-moderated methodologies are still (for now) better than AI at moderating very complex conversations.Less tech-savvy users: If your participants might struggle with the interface or require support, a moderator can guide them and help clarify tasks.Moderated testing works best when you need to dive deep into user motivations and behaviors, but it can be time-consuming and resource-heavy, making it difficult to scale for large studies.
What is unmoderated testing? Unmoderated testing, on the other hand, is more hands-off. Participants go through the tasks independently, without a researcher guiding them. This type of testing allows you to collect data at scale and is ideal for situations where quick feedback is needed.
Benefits of unmoderated testing: Scalability: Unmoderated tests can be completed by many participants at the same time, making it easier to gather a large volume of data quickly.Faster turnaround: Without the need to schedule sessions or have a moderator present, unmoderated testing can deliver results faster.Cost-effective: Since there’s no moderator, unmoderated testing is typically more budget-friendly, especially for large studies.Natural user behavior: Participants complete tasks in their own environment, which can result in more authentic, natural behavior.When to use unmoderated testing: Larger audiences: When you need to test with many participants or collect quantitative data from a wide range of users.Tight deadlines: If you’re on a strict timeline, unmoderated testing allows you to gather feedback without scheduling delays.High-fidelity prototypes: Unmoderated testing is useful when you want to observe how participants naturally interact with a product without the influence of a moderator.While unmoderated testing is great for scaling and fast feedback, it lacks the depth of moderated sessions. It’s harder to ask follow-up questions or explore the “why” behind a user’s actions without a researcher guiding the session.
What is AI-moderated testing? AI-moderated testing bridges the gap between the control of moderated testing and the scalability of unmoderated testing. In this approach, AI takes the role of the moderator, guiding participants through tasks, asking dynamic follow-up questions, and even translating conversations into multiple languages.
Benefits of AI-moderated testing: All the benefits of unmoderated testing: AI-moderated testing comes with all the same benefits as unmoderated testing—it's scalable, cost-effective, and can be completed asynchronously. You can test with many participants at the same time and gather quantitative data at scale.Multilingual support: Advanced generative AI enables AI-moderated testing to conduct interviews in over 50+ languages. This is especially useful for global research, where language barriers might otherwise be an issue.Dynamic follow-up questions: AI can understand participants' responses and formulate follow-up questions on the fly, diving deeper into the "why" behind a user's actions, much like a human moderator.Concurrent and asynchronous sessions: With AI, you can conduct multiple interviews at once, allowing you to collect insights much faster than with human moderation. Since participants don’t need to be scheduled, they can complete tasks on their own time.Scalable qualitative and quantitative insights: AI-moderated testing allows you to gather both types of data by scaling interviews and recording measurable outcomes from user interactions.Comfortable participant experience: Some users feel more at ease knowing they aren’t being watched by a human moderator, which can reduce bias in their responses and lead to more honest feedback.When to use AI-moderated testing: Need for scale and depth: When you need the flexibility and speed of unmoderated testing but want the depth of qualitative insights that come from follow-up questions and real-time reactions.Multilingual studies: If you’re conducting research with participants from different language backgrounds, AI-moderated testing can easily accommodate global studies.Large sample sizes: AI can manage hundreds of participant interviews at once, making it ideal when you need to conduct large-scale studies quickly.Reducing bias: When participants are likely to feel self-conscious with a human moderator, AI offers a more neutral, less intimidating option for gathering honest feedback.AI-moderated testing offers the best of both worlds: the rich insights of moderated testing combined with the scalability and speed of unmoderated approaches. It’s especially useful when conducting research across language barriers or at scale.
Comparing the three methods Each method has its strengths and trade-offs, and choosing the right one depends on your research goals and project timeline.
How to choose the right approach With so many options available, how do you know which testing method is right for your project? Here are a few key factors to consider:
Project goals: If you need deep, qualitative insights, moderated or AI-moderated testing is ideal. For large-scale usability studies, unmoderated testing might be more appropriate.Budget: Unmoderated testing is the most cost-effective option, while moderated testing requires more resources. AI-moderated testing sits in the middle and can offer significant value when you need both scale and depth.Timeline: Unmoderated testing is the fastest option, followed by AI-moderated testing, which allows for asynchronous participation. Moderated testing is the slowest due to scheduling requirements.Target audience: If your participants are less tech-savvy or need support during the test, moderated or AI-moderated testing may be necessary. If they are comfortable navigating tests on their own, unmoderated testing works well.Language needs: If you’re conducting global research, AI-moderated testing allows you to engage with participants in multiple languages, without needing a human translator.There’s no one-size-fits-all answer when it comes to choosing a user testing method. The right approach depends on your project needs, timeline, and the type of insights you’re looking to gather. Moderated testing provides rich, qualitative data, unmoderated testing offers speed and scalability, and AI-moderated testing brings together the best of both worlds—depth and scale.
AI-moderated testing, in particular, offers a powerful way to bridge the gap between the other two methods. It allows you to ask dynamic follow-up questions, scale your studies, and conduct research in multiple languages, making it an incredibly flexible option for today’s fast-paced product teams.
Haven’t tested AI-moderated research yet? Try AI-moderated testing with Wondering.com to see how it can streamline your research process and deliver actionable insights faster than ever before.
