As AI becomes more integrated into products, teams need ways to evaluate intelligent experiences. Traditional usability heuristics still apply, but AI introduces new behaviors that require additional considerations.

AI systems generate outputs, learn from feedback, and behave probabilistically. These characteristics affect how users interpret and interact with systems. Evaluating these experiences requires thinking beyond traditional usability.

AI UX heuristics help teams assess whether intelligent systems are understandable, trustworthy, and useful.

Visibility of AI Behavior

Users should be able to understand when AI is involved and how it influences outcomes. When AI operates invisibly, users may struggle to interpret results.

For example, recommendation systems in Spotify often indicate that content is recommended based on listening behavior. This context helps users understand why suggestions appear.

Research from Nielsen Norman Group has shown that visibility helps users build mental models. This principle applies to AI systems, where behavior may be less predictable.

When evaluating AI experiences, teams can ask whether users understand when AI is influencing results.

Appropriate Confidence

AI systems should communicate appropriate levels of confidence. Overly confident outputs may encourage over-reliance, while overly cautious outputs may reduce usefulness.

This balance influences user behavior.

Research from Stanford University has shown that users adjust reliance on AI based on perceived confidence. When confidence aligns with accuracy, outcomes improve.

Evaluating confidence involves assessing how outputs are framed and how users interpret them.

Support for Iteration

AI workflows are often iterative. Users refine inputs, review outputs, and adjust results.

Systems that assume one-shot interactions may frustrate users.

For example, generative tools such as ChatGPT support iterative conversations. Users refine prompts and adjust outputs. This interaction pattern supports exploration.

Evaluating AI experiences involves assessing whether iteration is easy and visible.

Human Control and Override

Users should be able to review and override AI outputs. Removing control can reduce trust.

Autocomplete in Google Docs allows users to accept or ignore suggestions. This pattern supports collaboration while preserving agency.

When evaluating AI experiences, teams can consider whether users maintain control.

Consistency Across AI Behaviors

AI systems often appear across multiple parts of a product. Consistent behavior helps users understand how intelligence operates.

Research from Microsoft Research has found that consistent patterns help users develop mental models of AI systems.

Evaluating AI experiences involves assessing whether behaviors remain consistent across contexts.

Support for Learning

Users often learn how to work with AI over time. Systems should support this learning process.

Recommendation systems such as those used by Netflix improve as users interact with them. Users learn how recommendations behave and adjust expectations.

Evaluating AI experiences involves considering how users develop understanding over time.

Handling Uncertainty

AI outputs may contain uncertainty. Systems should help users interpret and manage this uncertainty.

Research published in Nature Digital Medicine has found that users benefit from explanations or supporting information in uncertain scenarios.

Evaluating AI experiences involves assessing whether uncertainty is handled effectively.

AI UX Heuristics

These heuristics help teams evaluate AI experiences:

• Visibility of AI behavior

• Appropriate confidence

• Support for iteration

• Human control and override

• Consistency across AI behaviors

• Support for learning

• Handling uncertainty

These considerations extend traditional usability evaluation to intelligent systems.

As AI becomes more common, these heuristics help teams design experiences that users understand and trust.