ReviewJournal of eye movement research2026
Eye Tracking and AI-Generated Content: A Systematic Literature Review of Visual Attention, Cognitive Processing, and User Engagement.
Review in Journal of eye movement research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence increasingly produces text, images, feedback, summaries, advertisements, synthetic faces, and audiovisual content evaluated alongside human-produced material. This systematic review synthesized comparative eye-tracking evidence on visual attention, cognitive processing, and engagement with AI-generated content. Searches of Scopus, Web of Science, and PubMed yielded 896 records; 23 studies met the eligibility criteria and contributed 778 participants in the review-relevant eye-tracking components. The evidence covered textual, static visual, audiovisual, and interactive outputs. Across heterogeneous designs and tasks, no modality-independent gaze pattern emerged. AI-generated material sometimes attracted more focal inspection, sometimes received less task-relevant attention, and often redistributed gaze across interface elements. Where supported by task characteristics or complementary outcomes, longer viewing was more often associated with processing difficulty, uncertainty, or checking than with preference. Generated summaries supported learning in some settings, whereas realistic synthetic media remained difficult to identify despite focused inspection. Methodological appraisal identified recurrent limitations in sampling, stimulus matching, confounder control, eye-tracking reporting, and documentation of model versions, prompts, generation settings, and output selection. Observed gaze differences were context-dependent and varied with modality, task, comparator, source belief, expertise, output quality, and measurement choices. Standardized reporting and stronger links between gaze and functional outcomes are needed for cumulative inference.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.