How to run evaluative research on AI-generated content outputs to measure user trust and perceived accuracy

AI-generated content is now embedded in products across nearly every category—from writing assistants and search summaries to medical explanations and financial advice. As these outputs become more visible to end users, a critical question emerges for product teams: do users actually trust what the AI produces, and do they perceive it as accurate?

These are not engineering questions. They are research questions. And answering them well requires a deliberate evaluative research approach that goes beyond "does the model get the right answer?" to understand how users experience, interpret, and act on AI-generated content.

This guide walks through how to plan, design, and execute evaluative research focused on user trust and perceived accuracy of AI outputs.

Why trust and perceived accuracy matter for AI products

A language model can produce a technically correct answer that users still refuse to act on. Conversely, a fluent but subtly wrong response can be accepted without question. Both scenarios represent product failures, and neither is detectable through standard model evaluation metrics like precision or recall.

Trust and perceived accuracy are distinct constructs, but they interact in important ways:

Trust is a user's willingness to rely on AI-generated content when making decisions or completing tasks. It develops over time and is influenced by past experiences, interface design, and the stakes of the situation.
Perceived accuracy is a user's in-the-moment judgment of whether a specific piece of AI-generated content is correct and complete. It can shift from one output to the next.

A user might generally trust an AI assistant (high baseline trust) but doubt a specific output because it contradicts their domain knowledge (low perceived accuracy for that instance). Or they might have low general trust but find a particular answer convincing enough to use.

Understanding both constructs—and the gap between perceived and actual accuracy—helps product teams make better decisions about how to present AI outputs, when to add transparency features, and where human review is necessary.

Define what you are evaluating

Before selecting research methods, get specific about the AI-generated content you want to study. "AI content" is too broad to evaluate meaningfully. Narrow the scope by asking:

What type of output? A short summary, a long-form explanation, a recommendation, a data visualization caption, a code suggestion?
What domain? Health information carries different trust dynamics than recipe suggestions. The stakes shape user expectations.
What level of user expertise? A domain expert evaluates AI outputs differently than a novice. Your participant recruitment strategy needs to reflect this.
What is the user's task? Are they using the content to make a decision, learn something new, complete a workflow, or verify something they already know?

Document these parameters clearly. They will determine your study design, stimulus materials, and the trust dimensions most worth measuring.

Choose your research methods

Evaluative research on trust and perceived accuracy benefits from a mixed-methods approach. Quantitative measures give you comparable ratings across conditions. Qualitative methods reveal the reasoning and mental models behind those ratings.

Quantitative approaches

Trust and credibility scales—Established instruments exist for measuring trust in automated systems. The Trust in Automation scale (Jian et al., 2000) and the Credibility Assessment scale are commonly adapted for AI content evaluation. These use Likert-scale items that capture dimensions like perceived reliability, competence, and understandability. You can administer these after users interact with AI-generated content.

Perceived accuracy ratings—After reading each AI output, ask participants to rate how accurate they believe the content is on a defined scale. If you have ground-truth data, you can later compare perceived accuracy to actual accuracy to identify over-trust and under-trust patterns.

Behavioral measures—Track what users do after encountering AI content. Do they accept it as-is, edit it, fact-check it externally, or discard it? Time-on-task, edit rates, and override rates are all behavioral proxies that complement self-reported trust.

Comparative ratings—Show users the same information presented in different ways—with and without source citations, with and without an "AI-generated" label, with varying confidence indicators—and ask them to rate trust and accuracy for each variant.

Qualitative approaches

Think-aloud protocols—Ask participants to verbalize their thoughts as they read and evaluate AI-generated content. This surfaces real-time trust judgments: what makes them pause, what triggers skepticism, what they find reassuring. Think-alouds are particularly valuable for understanding how users assess accuracy when they cannot independently verify the information.

Semi-structured interviews—After task completion, explore participants' reasoning in depth. Ask what factors influenced their trust, whether they noticed anything that seemed wrong or suspicious, and how the experience compared to getting the same information from a human or a traditional source.

Diary studies—For products where trust develops over repeated interactions, diary studies capture how trust evolves. Participants log their reactions to AI outputs over days or weeks, noting moments of trust, doubt, surprise, or frustration. This longitudinal data reveals patterns that single-session studies miss.

Design your study stimuli

The content you show participants during the study needs careful preparation. Poorly constructed stimuli will produce noisy, uninterpretable data.

Use real outputs, not idealized ones

Resist the temptation to cherry-pick the model's best outputs. If your research only tests polished responses, you will learn nothing about how users react to the messy outputs they will inevitably encounter. Include a realistic range of output quality:

Outputs that are accurate and well-structured
Outputs that are accurate but awkwardly phrased
Outputs that contain minor factual errors
Outputs that are confidently wrong (hallucinations)

Control for confounding variables

If you are comparing trust across different presentation styles (e.g., with vs. without citations), hold the content itself constant. If the content differs between conditions, you cannot attribute trust differences to the presentation alone.

Establish ground truth

For perceived accuracy research, you need a reliable way to determine whether each output is actually correct. This might involve expert review, fact-checking against authoritative sources, or using outputs where the correct answer is unambiguous. Without ground truth, you can measure perceived accuracy but cannot assess calibration—whether users' accuracy judgments track reality.

Recruit the right participants

Trust and accuracy perceptions vary significantly by domain expertise, prior experience with AI tools, and individual disposition toward technology. Your recruitment criteria should reflect the user segments most relevant to your product.

Consider screening for:

Domain knowledge level—Experts and novices evaluate AI content through different lenses. Experts can spot factual errors; novices rely more on surface cues like fluency and formatting.
AI familiarity—Users who regularly interact with AI tools have different baseline trust levels than those encountering AI-generated content for the first time.
Task context—If your product serves multiple use cases, recruit participants whose goals match the scenarios you are testing.

Aim for 8–12 participants per segment for qualitative studies. For quantitative comparisons between conditions, target at least 30 participants per condition to detect moderate effect sizes.

Run the sessions

Structure for qualitative sessions

A typical session might follow this structure:

Brief introduction—Explain the session format without priming participants to focus on trust or accuracy. Saying "we want to understand how you use this tool" is better than "we are studying whether you trust AI."
Task scenarios—Give participants realistic tasks that require them to use AI-generated content. Observe their behavior and ask them to think aloud.
Rating scales—After each task, administer trust and perceived accuracy measures.
Debrief interview—Explore their experience in depth. Ask about specific moments where they hesitated, felt confident, or changed their mind.

Avoid leading questions

Questions like "Did you find the AI trustworthy?" push participants toward a binary answer. Instead, ask open-ended questions:

"Walk me through what you were thinking when you read that response."
"Was there anything about that output that stood out to you?"
"How would you describe the quality of what you just read?"
"What would you do next with this information in a real situation?"

Document everything

Record sessions (with consent), capture screen interactions, and take detailed notes on observable behaviors—facial expressions, hesitations, scrolling patterns, and moments where participants sought external verification.

Analyze and synthesize findings

Quantitative analysis

Calculate mean trust and perceived accuracy scores across conditions. Compare scores between content variants, user segments, and output quality levels. Look for patterns:

Do trust scores drop after encountering a single hallucination, and do they recover?
Are perceived accuracy ratings higher for content with citations than without?
Do domain experts show better calibration (closer alignment between perceived and actual accuracy) than novices?

Qualitative analysis

Code interview and think-aloud transcripts for recurring themes related to trust cues and accuracy judgments. Common themes in AI trust research include:

Fluency bias—Users equating smooth, well-written text with accuracy
Specificity as a trust signal—Detailed outputs being perceived as more credible than vague ones, regardless of correctness
Hedging language—How qualifiers like "may" or "it's possible" affect trust
Source attribution—Whether citing sources increases perceived accuracy
Contradiction with prior knowledge—The strongest trigger for distrust among experts
Error recovery—Whether the product's handling of errors (corrections, explanations) rebuilds or further erodes trust

Tools like Dovetail can help research teams tag, organize, and pattern-match across qualitative data from these sessions—particularly when working with large volumes of think-aloud transcripts and interview recordings across multiple participant segments.

Map the trust-accuracy gap

One of the most useful outputs of this research is a matrix that maps perceived accuracy against actual accuracy for each stimulus:

	Actually accurate	Actually inaccurate
Perceived as accurate	Calibrated trust	Over-trust (risk zone)
Perceived as inaccurate	Under-trust (missed opportunity)	Calibrated distrust

Over-trust is a safety concern: users are accepting wrong information. Under-trust is a product adoption concern: users are rejecting good information. Both require different design interventions.

Turn findings into design decisions

Evaluative research on trust and accuracy should produce concrete recommendations, not just insight reports. Map your findings to design levers:

Transparency features—If users over-trust fluent but incorrect outputs, consider adding confidence indicators, source links, or explicit uncertainty language.
User control—If expert users want to verify before acting, make it easy to inspect the reasoning or sources behind an AI output.
Error handling—If trust collapses after a single bad output and does not recover, invest in better error detection and graceful correction flows.
Onboarding and expectation setting—If new users have unrealistic expectations of AI accuracy, calibrate those expectations early through onboarding or contextual guidance.
Labeling and disclosure—Test whether "AI-generated" labels increase healthy skepticism or create blanket distrust. The answer varies by domain and audience.

Common pitfalls to avoid

Testing only good outputs. If every stimulus in your study is a polished, correct response, your findings will not generalize to real-world use.

Conflating trust with satisfaction. A user can be satisfied with an AI feature while maintaining appropriate skepticism. High trust is not always the goal—calibrated trust is.

Ignoring longitudinal effects. A single-session study captures first impressions. Trust in AI products changes significantly over repeated use, especially after encountering errors. Plan for longitudinal follow-up when possible.

Skipping domain-specific calibration. Trust dynamics for AI-generated medical information are fundamentally different from those for AI-generated marketing copy. Do not assume findings transfer across domains without testing.

Neglecting the comparison baseline. Users do not evaluate AI content in a vacuum. They compare it—consciously or unconsciously—to content from humans, search engines, or their own knowledge. Include comparison conditions in your study design where feasible.

Building a trust research practice

Measuring trust and perceived accuracy is not a one-time study. As AI models improve, as your product's AI features evolve, and as users gain more experience with AI tools broadly, trust dynamics will shift.

Build evaluative research on AI trust into your regular research cadence. Track trust metrics over time the same way you track usability metrics. Share findings across product, engineering, and content teams so that trust considerations inform model selection, prompt design, and interface decisions—not just UX copy.

Platforms like Dovetail can serve as a central repository for this ongoing research, making it easier to revisit past findings, compare across studies, and surface patterns as your AI product matures.

Trust is not a feature you ship once. It is a relationship you maintain through continuous attention to how users experience what your product produces.