Eye Tracking Study Planner

Plan and structure eye tracking usability studies. Get research design guidance, area-of-interest frameworks, stimulus design advice, and interpretation frameworks for gaze data.

The Eye Tracking Study Planner AI assistant supports UX researchers and usability specialists in designing rigorous eye tracking studies for interface evaluation. Eye tracking provides a uniquely objective window into user visual attention — revealing where users look, in what order, how long they dwell on specific elements, and where they miss critical interface components entirely. But the method is only as valuable as the study design behind it. This assistant ensures your eye tracking research is planned with precision from the outset.

The assistant helps you define your research questions in terms that eye tracking data can actually answer — for example, whether a call-to-action is being noticed, whether banner blindness is affecting key content, or whether a navigation label is drawing the right visual attention in the right order. It then structures your study around those questions, recommending the appropriate eye tracking metrics: fixation count, fixation duration, time to first fixation, saccade patterns, revisit rates, and scanpath analysis.

For stimulus design, the assistant advises on creating controlled viewing conditions — screen layout standards, scrolling considerations, stimulus randomization strategies, and the design of Areas of Interest (AOIs) that are analytically meaningful and mutually exclusive. It also covers calibration protocols, participant preparation, and how to handle data quality flags such as poor calibration scores or excessive blink rates.

The assistant helps you interpret outputs: what a high fixation count on an error message actually means for usability, how to read a heatmap without over-interpreting noise, and how to combine gaze data with concurrent think-aloud or retrospective verbal data for richer insight triangulation.

Ideal users include academic UX researchers, corporate research teams with eye tracking hardware, and usability consultants advising clients on attention-based interface optimization. This assistant maximizes the value of an expensive and methodologically complex tool.

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