Psychological correlates of dependence on generative and conversational AI: a systematic review and meta-analysis
- 1Turan University
Abstract
Background. Dependence on generative and conversational artificial intelligence (AI) is measured with diverse instruments spanning addiction criteria, task reliance, and emotional bonding. No meta-analysis has pooled its psychological correlates across populations.
Methods. We conducted a pre-registered three-level meta-analysis (Zenodo: 10.5281/zenodo.23420672) searching eight electronic databases and citations of instrument papers on 16 October 2026, with citation chasing. Zero-order correlations were pooled using correlated and hierarchical effects models on Fisher's z with cluster-robust inference (CR2). Scale type (T1 addiction-type, T2 functional, T3 emotional, T4 mixed) was the primary moderator. Certainty was rated via GRADE.
Results. We included 127 samples (123 studies; N = 74,492; 29 countries; 70% university students; 39% mainland China). AI dependence correlated moderately with anxiety (r = 0.37, 95% CI [0.25, 0.48]), stress (r = 0.35 [0.25, 0.43]), depression (r = 0.32 [0.21, 0.42]), social anxiety (r = 0.36 [0.17, 0.53]), loneliness (r = 0.28 [0.18, 0.38]), and procrastination (r = 0.26 [0.12, 0.40]), and weakly and inversely with self-esteem (r = −0.18 [−0.35, −0.00]). All 95% prediction intervals crossed zero. Addiction-type and mixed scales showed no detectable difference; functional and emotional scales were rarely paired with clinical outcomes. Certainty was low to very low.
Conclusions. AI dependence moderately correlates with distress and self-regulation difficulties, but prediction intervals cross zero and evidence certainty is low. Functional and emotional dependence have rarely been studied alongside clinical outcomes.
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BibTeX
@misc{rekhert2026psychological,
title = {{Psychological correlates of dependence on generative and conversational AI: a systematic review and meta-analysis}},
author = {Martin Rekhert},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23449099},
url = {https://zenodo.org/records/23449099},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Psychological correlates of dependence on generative and conversational AI: a systematic review and meta-analysis
AU - Martin Rekhert
PY - 2026
DA - 2026/10/18
PB - Zenodo
DO - 10.5281/zenodo.23449099
UR - https://zenodo.org/records/23449099
AB - Background. Dependence on generative and conversational artificial intelligence (AI) is measured with diverse instruments spanning addiction criteria, task reliance, and emotional bonding. No meta-analysis has pooled its psychological correlates across populations. Methods. We conducted a pre-registered three-level meta-analysis (Zenodo: 10.5281/zenodo.23420672) searching eight electronic databases and citations of instrument papers on 16 October 2026, with citation chasing. Zero-order correlations were pooled using correlated and hierarchical effects models on Fisher's z with cluster-robust inference (CR2). Scale type (T1 addiction-type, T2 functional, T3 emotional, T4 mixed) was the primary moderator. Certainty was rated via GRADE. Results. We included 127 samples (123 studies; N = 74,492; 29 countries; 70% university students; 39% mainland China). AI dependence correlated moderately with anxiety (r = 0.37, 95% CI [0.25, 0.48]), stress (r = 0.35 [0.25, 0.43]), depression (r = 0.32 [0.21, 0.42]), social anxiety (r = 0.36 [0.17, 0.53]), loneliness (r = 0.28 [0.18, 0.38]), and procrastination (r = 0.26 [0.12, 0.40]), and weakly and inversely with self-esteem (r = −0.18 [−0.35, −0.00]). All 95% prediction intervals crossed zero. Addiction-type and mixed scales showed no detectable difference; functional and emotional scales were rarely paired with clinical outcomes. Certainty was low to very low. Conclusions. AI dependence moderately correlates with distress and self-regulation difficulties, but prediction intervals cross zero and evidence certainty is low. Functional and emotional dependence have rarely been studied alongside clinical outcomes.
ER -