Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
Repository of the University of Ljubljana
Open Science Slovenia
Open Science
DiKUL
slv
|
eng
Search
Advanced
New in RUL
About RUL
In numbers
Help
Sign in
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
)
PDF - Presentation file,
Download
(951,44 KB)
MD5: 70428702595A04C9B63B0E78D435BDBA
URL - Source URL, Visit
https://www.mdpi.com/1995-8692/19/5/97
Image galllery
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
UDC:
316.77:004.8
ISSN on article:
1995-8692
DOI:
10.3390/jemr19050097
COBISS.SI-ID:
290135811
Publication date in RUL:
07.09.2026
Views:
122
Downloads:
32
Metadata:
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Copy citation
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
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