Evidence map›Paper›PMID 42582304›Full record

ArticleFrontiers in psychology2026

Is generative artificial intelligence becoming addictive? An exploration among undergraduate students.

Onur Isbulan, Ozkan Ergene, Eda Demirhan

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Onur IsbulanDepartment of Instructional Technologies, Sakarya University, Sakarya, Türkiye.
Ozkan ErgeneDepartment of Mathematics and Science Education, Sakarya University, Sakarya, Türkiye.
Eda DemirhanDepartment of Mathematics and Science Education, Sakarya University, Sakarya, Türkiye.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to examine the multidimensional nature of Generative Artificial Intelligence (GAI) addiction among undergraduate students. For this purpose, firstly, the Generative Artificial Intelligence Addiction Scale (GAI-AS) was developed and administered to 919 undergraduate students. The GAI-AS was designed as a psychometrically valid instrument to assess addictive patterns of GAI use across four dimensions: salience, excessive dependency, mood modification, and functional impairment. The findings provide strong evidence of the scale's validity and reliability. Subsequently, the validated instrument was administered to 803 undergraduate students to investigate demographic and usage-related differences in GAI addiction tendencies. Findings revealed no significant differences in GAI-AS scores according to gender or type of GAI version used (free vs. paid). Grade-level differences were observed only in the dimension of excessive dependency, with freshman students reporting slightly lower scores than upper-class students. In contrast, frequency of GAI use emerged as a strong and consistent predictor, as daily users showed significantly higher scores across all four dimensions and on the total scale. Correlation analyses further demonstrated moderate positive associations among all GAI-AS sub-dimensions, supporting the conceptualization of GAI addiction as an interconnected behavioral and emotional phenomenon. These findings clarify GAI addiction as a psychological construct and provide a validated scale.

Indexed as

addictionGAI dependencygenerative artificial intelligencescale developmentundergraduatevalidation

Identifiers

PMID42582304
PMCPMC13457625

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.