Details

Eye tracking and AI-generated content : a systematic literature review of visual attention, cognitive processing, and user engagement
ID Resulbegoviq, Hakile (Author), ID Hlebec, Valentina (Author), ID Pejić Bach, Mirjana (Author), ID Stamatović, Irina (Author)

.pdfPDF - Presentation file, Download (951,44 KB)
MD5: 70428702595A04C9B63B0E78D435BDBA
URLURL - Source URL, Visit https://www.mdpi.com/1995-8692/19/5/97 This link opens in a new window

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.

Language:English
Keywords:eye tracking, generative artificial intelligence, AI-generated content, visual attention, cognitive processing, user engagement, synthetic media, systematic review
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FDV - Faculty of Social Sciences
Publication status:Published
Publication version:Version of Record
Publication date:04.09.2026
Year:2026
Number of pages:Str. 1-51
Numbering:vol. 19, no. 5, [article no.] 97
PID:20.500.12556/RUL-186911 This link opens in a new window
UDC:316.77:004.8
ISSN on article:1995-8692
DOI:10.3390/jemr19050097 This link opens in a new window
COBISS.SI-ID:290135811 This link opens in a new window
Publication date in RUL:07.09.2026
Views:122
Downloads:32
Metadata:XML DC-XML DC-RDF
:
Copy citation
Share:Bookmark and Share

Record is a part of a journal

Title:Journal of eye movement research
Shortened title:J. eye mov. res.
Publisher:European Group for Eye Movement Research, MDPI
ISSN:1995-8692
COBISS.SI-ID:3116656 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Back