ArticleJournal of nursing management2025
Development and Validation of Artificial Intelligence Addiction Scale for Researchers: A Methodological Study.
Article in Journal of nursing management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Generative AI dependency on programming among university students: a scale development and validation study.BMC psychology · 2026Article
- A critical narrative synthesis of psychological correlates, measurement, and reported findings on conversational AI engagement and dependence-related constructs.Frontiers in psychology · 2026Review
- Validation and Application of the Chinese Integral Nursing Leadership Scale Among Clinical Nurses.Journal of nursing management · 2026Article
- Academic AI overreliance scale: development and preliminary psychometric validation in health sciences university students.Frontiers in digital health · 2026Article
- Adaptation and validation of the generative AI dependency scale: evidence from construct validity, reliability and measurement invariance.Frontiers in psychology · 2026Article
- Development and Validation of Artificial Intelligence Addiction Scale for Researchers: A Methodological Study.Journal of nursing management · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The integration of artificial intelligence (AI) tools into research has brought significant advancements, enhancing efficiency, innovation, and productivity across various academic disciplines. However, alongside these transformative benefits, the growing dependence on AI tools has raised concerns regarding overreliance and the potential for addictive behaviors among researchers. Despite the widespread adoption of AI among the researchers, there remains a notable gap in the availability of validated instruments specifically designed to assess AI addiction within this context. Objective: To develop a scale to measure AI addiction among researchers and evaluate its psychometric properties. Design: A methodological design was employed, consisting of two phases: scale development and psychometric evaluation. Methods: Items were generated through a comprehensive literature review and semistructured interviews to capture AI addiction attributes. The scale's psychometric properties-including content validity, face validity, construct validity, and internal consistency reliability-were assessed. Data from a convenience sample of 718 nursing researchers were randomly divided into two independent subsamples for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability was evaluated using Cronbach's alpha, McDonald's omega, split-half reliability, and corrected item-total correlations. Results: The finalized scale comprises 22 items across five dimensions: compulsive behavior, overdependency, functional impairment, withdrawal, and tolerance. EFA identified a five-factor structure explaining 73.66% of the variance. CFA validated the structure with robust fit indices for first order ( Conclusion: The researchers' AI addiction scale is a valid and reliable tool for assessing AI addiction among researchers, providing a robust framework to evaluate compulsive behavior, dependency, functional disruption, withdrawal symptoms, and tolerance.
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Registered trials
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.