Development and Validation of Artificial Intelligence Addiction Scale for Researchers: A Methodological Study

Area of Science:

• Psychology
• Research Methodology
• Digital Health

Background:

• Artificial intelligence (AI) integration enhances research efficiency but raises concerns about overreliance and addiction among researchers.
• A significant gap exists in validated instruments to assess AI addiction specifically within the research community.

Purpose of the Study:

• To develop and psychometrically evaluate a scale for measuring AI addiction in researchers.
• To provide a reliable tool for assessing the extent of AI dependency and its potential negative impacts.

Main Methods:

• A two-phase methodological design involved scale development and psychometric evaluation.
• Item generation via literature review and interviews, followed by exploratory and confirmatory factor analysis (EFA/CFA) on data from 718 nursing researchers.
• Reliability assessed using Cronbach's alpha, McDonald's omega, and split-half reliability.

Main Results:

• A 22-item scale was finalized, encompassing five dimensions: compulsive behavior, overdependency, functional impairment, withdrawal, and tolerance.
• EFA and CFA confirmed a robust five-factor structure explaining substantial variance, with excellent model fit indices.
• The scale demonstrated high internal consistency and reliability (Cronbach's alpha = 0.924, McDonald's omega = 0.870).

Conclusions:

• The developed AI addiction scale is a valid and reliable instrument for researchers.
• It offers a comprehensive framework for evaluating compulsive AI use, dependency, functional impairment, withdrawal, and tolerance.
• This scale addresses the need for quantitative assessment of AI addiction in academic settings.