Table of Contents
Abstract
The rapid integration of Artificial Intelligence into academic workflows has revolutionized how scholars conduct research, offering unprecedented efficiency in data analysis, literature synthesis, and manuscript preparation. However, this technological leap introduces a hidden psychological cost: the risk of Behavioral Addiction and cognitive overreliance. The Researcher Artificial Intelligence Addiction Scale (RAIAS) was developed to quantify this emerging phenomenon. It provides a structured, psychometrically sound method to evaluate when a researcher's use of AI transitions from a helpful augmentation of their skills to a detrimental cognitive crutch.
Designed specifically for the academic context, the RAIAS captures the unique vulnerabilities of scholars who may increasingly outsource critical thinking to automated systems. By offering a validated metric tailored to this specific population, the scale allows institutions, mentors, and individuals to monitor Academic Integrity and psychological well-being in the digital age. It serves not as a diagnostic label, but as a crucial screening tool to foster awareness and prompt self-reflection regarding digital habits in high-stakes intellectual environments.
📊 Psychometric Scorecard
22
Multidimensional
0.92
0.87
0.962
Authors
Purpose
Existing psychometric tools designed to measure technology addiction, such as those for general internet or smartphone overuse, are inadequate for the academic context. Research is inherently goal-directed and cognitively demanding, meaning that high engagement with digital tools is often necessary and productive. Applying generalized addiction scales to researchers risks overpathologizing normal, efficient behavior while missing the specific nuances of academic dependency.
The RAIAS fills a critical gap by specifically targeting the boundary between intensive, productive AI use and pathological dependency among scholars. It serves as a vital screening instrument for academic leaders and mental health professionals to identify researchers who may be compromising their intellectual autonomy or experiencing functional impairment due to excessive AI reliance. Ultimately, it provides a framework to safeguard the rigorous, independent thought processes that are foundational to scientific inquiry.
Construct
The theoretical foundation of the RAIAS is deeply rooted in established Behavioral Addiction frameworks, specifically drawing from Griffiths’ components model and the DSM-5 criteria for substance use disorders. The scale operationalizes AI addiction across five distinct but interrelated dimensions: compulsive behavior, overdependency, functional impairment, withdrawal, and tolerance.
Compulsive behavior captures the automatic, irresistible urge to use AI for routine tasks, while overdependency reflects a loss of scholarly self-efficacy, where researchers feel incapable of working without algorithmic assistance. Functional impairment measures the resulting decline in research quality, originality, or methodological rigor. Withdrawal assesses the psychological distress—such as anxiety or frustration—experienced when AI tools are unavailable. Finally, tolerance tracks the escalating need to rely on AI for increasingly complex cognitive tasks, moving from simple grammar checks to core hypothesis generation.
Validity
The psychometric validation of the RAIAS involved rigorous testing to ensure the tool accurately captures the intended constructs without capturing unrelated variance. The developers established strong content and face validity through comprehensive literature reviews and qualitative interviews, ensuring the items resonated with the actual, lived experiences of modern researchers.
Construct validity was thoroughly demonstrated through advanced structural equation modeling, confirming that the scale measures a distinct, multidimensional psychological phenomenon rather than just general technology use. The moderate interfactor correlations, ranging from 0.41 to 0.62, indicate that while the five dimensions are related and contribute to a global construct of AI addiction, they remain distinct enough to warrant separate measurement. These results indicate that the RAIAS meets high psychometric standards, making it a robust tool for both empirical research and institutional assessment.
Reliability
Reliability analyses revealed that the RAIAS possesses excellent internal consistency, meaning the items reliably measure the same underlying construct across different respondents and contexts. The scale demonstrated a high Cronbach's alpha of 0.924, which is well above the standard threshold for acceptable reliability and indicates excellent cohesion among the 22 items.
To provide a more robust estimate of reliability that does not assume tau-equivalence, the researchers also calculated McDonald's omega, which yielded a strong value of 0.870. Additionally, a Spearman-Brown split-half reliability coefficient of 0.814 further substantiates the instrument's precision. These comprehensive metrics assure researchers and clinicians that the RAIAS will yield stable, consistent, and dependable scores when assessing AI dependency behaviors in academic populations.
Factor Analysis
To uncover and confirm the underlying structure of the scale, the researchers employed a split-sample approach using both exploratory and confirmatory factor analyses. The exploratory factor analysis successfully extracted a five-factor model that accounted for an impressive 73.66% of the total variance. This empirical structure aligned perfectly with the theoretical dimensions of addiction proposed during the scale's conceptualization.
Subsequent confirmatory factor analysis on an independent sample verified this structure, yielding excellent fit indices. Both first-order and second-order models demonstrated strong fit, with a Comparative Fit Index of 0.962 and a Root Mean Square Error of Approximation of 0.06 for the first-order model. The second-order model performed similarly well, confirming that the five distinct subscales meaningfully load onto a single, higher-order overarching construct of AI addiction.
Subscales
| Subscale | Items | Description |
|---|---|---|
| Compulsive Behavior | Measures the automatic, irresistible urge to use AI tools for academic tasks, often bypassing conscious deliberation. | |
| Overdependency | Assesses the emotional and psychological belief that one cannot function effectively or produce quality research without AI assistance. | |
| Functional Impairment | Evaluates the negative impact of excessive AI use on academic performance, methodological rigor, and research originality. | |
| Withdrawal | Captures the emotional and cognitive distress, such as anxiety or frustration, experienced when AI tools are inaccessible. | |
| Tolerance | Measures the progressive escalation of AI use, where increasing intensity or frequency of engagement is needed to achieve the same scholarly outcomes. |
Instrument
| Test Type | Self-report questionnaire |
| Format | 22 items |
| Scoring | Scores are categorized using percentile-based thresholds: low risk (< 25th percentile), moderate risk (25th-75th percentile), and high risk (≥ 75th percentile). Higher total scores indicate a greater frequency of AI addiction behaviors. |
| Population | Adults, Healthcare professionals, Nurses |
| Administration | Self-administered |
Scoring & Interpretation Guidelines
| Cut-off Scores | Low risk: < 25th percentile; Moderate risk: 25th-75th percentile; High risk: ≥ 75th percentile |
Researcher Artificial Intelligence Addiction Scale Items
Items are currently not available
The individual items of this scale are not publicly available. Researchers interested in using this instrument should contact the original authors directly to request the scale materials.
Sample
The study utilized a convenience sample of 718 nursing researchers. The sample was randomly divided into two independent subsamples to conduct exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) separately.
Cite This Paper
Ahmed Abdelwahab Ibrahim El-Sayed, Samira Ahmed Alsenany, Maha Gamal Ramadan Asal, Ibrahim Alasqah (2025). Researcher Artificial Intelligence Addiction Scale. Journal of nursing management. https://doi.org/10.1155/jonm/8458533
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Cite this article
Mohammed looti (2026). Researcher Artificial Intelligence Addiction Scale. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/s/researcher-artificial-intelligence-addiction-scale/
Mohammed looti. "Researcher Artificial Intelligence Addiction Scale." PSYCHOLOGICAL SCALES, 13 Aug. 2026, https://scales.arabpsychology.com/s/researcher-artificial-intelligence-addiction-scale/.
Mohammed looti. "Researcher Artificial Intelligence Addiction Scale." PSYCHOLOGICAL SCALES, 2026. https://scales.arabpsychology.com/s/researcher-artificial-intelligence-addiction-scale/.
Mohammed looti (2026) 'Researcher Artificial Intelligence Addiction Scale', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/s/researcher-artificial-intelligence-addiction-scale/.
[1] Mohammed looti, "Researcher Artificial Intelligence Addiction Scale," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, August, 2026.
Mohammed looti. Researcher Artificial Intelligence Addiction Scale. PSYCHOLOGICAL SCALES. 2026;vol(issue):pages.