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TRUVACE RECORD VERSION
record: TRV-2026-2592
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:59:24.663055Z
status: published
lens: p_space
sector: health
headline: The Role of Biological Rhythms in Generative AI Addiction: Associations with Chronotype and Sleep Quality
dek: Abstract The rapid diffusion of Generative Artificial Intelligence (GenAI) technologies among university students has introduced a novel domain of technology-related behavioral risk. Although prior research on digital addiction has predominantly focused on psychological determinants of internet and smartphone overuse, the potential role of circadian preference and sleep in problematic GenAI use remains underexplored. This study examined associations between chronotype, sleep quality, and GenAI addiction. Using a…
gain_title: (none)
problem_title: In a survey of 868 undergraduates at a public university, evening-type students scored higher on generative AI addiction than morning and intermediate types, and poorer sleep went with higher scores.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: In a survey of 868 undergraduates at a public university, evening-type students scored higher on generative AI addiction than morning and intermediate types, and poorer sleep went with higher scores.
problem_evidence: evening-type students reported higher GenAI addiction scores than morning and intermediate types | poor sleep quality was positively associated with GenAI addiction
quick_read: A cross-sectional survey of 868 undergraduates at a public university found that students who prefer evenings reported higher generative AI addiction scores than morning-type and intermediate-type students. Poorer sleep quality was also positively associated with higher scores.

The researchers measured addiction with the Artificial Intelligence Addiction Scale, circadian preference with the Composite Scale of Morningness and sleep with the Pittsburgh Sleep Quality Index. Earlier digital addiction research focused mostly on psychological drivers of internet and smartphone overuse, so body-clock factors in problematic AI use had been little studied.

In regression models controlling for age, gender and sleep quality, chronotype stayed associated with addiction scores, while gender was not statistically significant. The authors call for longitudinal and multi-method research in higher education settings.
limitation: The cross-sectional design at a single public university shows associations only, and the authors say longitudinal and multi-method research is still needed.
tag: Evidence-backed problem
key_points: The survey covered 868 undergraduate students at one public university. | Evening-type students had higher generative AI addiction scores than morning and intermediate types. | Poor sleep quality was positively associated with generative AI addiction scores. | Chronotype remained associated with addiction scores after controlling for age, gender and sleep quality. | Gender was not statistically significant in the regression models. | Three instruments were used: the Artificial Intelligence Addiction Scale, the Composite Scale of Morningness and the Pittsburgh Sleep Quality Index.
rundown: Researchers surveyed 868 undergraduates at a public university, using the Artificial Intelligence Addiction Scale for addiction, the Composite Scale of Morningness for chronotype and the Pittsburgh Sleep Quality Index for sleep.

Evening-type students reported higher generative AI addiction scores than morning and intermediate types, and poor sleep quality was positively associated with those scores.

Regression models controlling for age, gender and sleep quality still showed an association for chronotype, whereas gender was not significant. The authors describe circadian preference and sleep quality as relevant correlates of generative AI addiction.
sources:
- peer_reviewed | International Journal of Mental Health and Addiction | https://doi.org/10.1007/s11469-026-01679-3 | 2026-06-04
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