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Night-owl students scored higher on AI addiction in a survey of 868 undergraduates

Study title: The Role of Biological Rhythms in Generative AI Addiction: Associations with Chronotype and Sleep Quality

Evidence type
Survey
Sample
n = 868
Sources
1 sourcePeer-reviewed
Published
2 min read
Truvace record
TRV-2026-2592cite or check it

Study title: The Role of Biological Rhythms in Generative AI Addiction: Associations with Chronotype and Sleep Quality

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.

Read the study abstract

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…

The problem

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.

In brief

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.

Main points

  1. The survey covered 868 undergraduate students at one public university.
  2. Evening-type students had higher generative AI addiction scores than morning and intermediate types.
  3. Poor sleep quality was positively associated with generative AI addiction scores.
  4. Chronotype remained associated with addiction scores after controlling for age, gender and sleep quality.
  5. Gender was not statistically significant in the regression models.
  6. Three instruments were used: the Artificial Intelligence Addiction Scale, the Composite Scale of Morningness and the Pittsburgh Sleep Quality Index.

The full story

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

1 on file
  1. International Journal of Mental Health and Addictiondoi:10.1007/s11469-026-01679-3
    Peer-reviewed2026-06-04

Talk it over

Four questions to start onWho carries this cost, and what would have to change to prevent it?

Start with a question

Night-owl students scored higher on AI addiction in a survey of 868 undergraduateshttps://truvace.com/item/the-role-of-biological-rhythms-in-generative-ai-addiction-associations-with-chro

  1. Who carries this cost, and what would have to change to prevent it?

    The problem 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.

    Answer in the thread

  2. How do we know? What can this kind of evidence show, and what can’t it?

    The evidence This rests on a survey with a sample of 868, from International Journal of Mental Health and Addiction.

    Answer in the thread

  3. What does it leave open? What else would you want to know before acting on it?

    The record’s own caveat The cross-sectional design at a single public university shows associations only, and the authors say longitudinal and multi-method research is still needed.

    Answer in the thread

  4. Have you seen AI used this way where you study, work or live? Did it go the same way?

    Answer in the thread

The questions are the same on every record. The details under them are taken from this record as written.

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