AI Addiction Scale (AIAS-21)

A 21-item instrument for assessing behavioral patterns of excessive AI use

published 03-Jun-2025

Author: Igor V. Pantić, MD, PhD, Psychiatry Specialist

University of Belgrade, Faculty of Medicine, Department of Medical Physiology, Višegradska 26/II, RS-11129, Belgrade, Serbia

Email: igor.pantic@med.bg.ac.rs; igorpantic@gmail.com

The AI Addiction Scale (AIAS-21) is a concise, self-administered instrument designed to evaluate patterns of potentially problematic or addictive engagement with artificial intelligence (AI)-based technologies. These technologies may include conversational agents, generative applications, virtual companions, and other interactive AI systems increasingly integrated into everyday activities.

The scale comprises 21 items organized across seven empirically grounded dimensions reflective of core behavioral addiction constructs: compulsive use, craving, tolerance, withdrawal, preoccupation, continued use despite harm, and functional impairment. The AIAS-21 offers a structured framework for evaluating the extent and impact of AI use on an individual’s emotional, cognitive, and functional well-being.

Proposed Core Constructs for AI Addiction Scale (AIAS-21)

The constructs are partially derived from diagnostic criteria for behavioural and other addictions from DSM-5 and ICD-11 and adapted to fit AI-specific interactions. The constructs are as follows:

1. Compulsive Use / Loss of Control

2. Craving

3. Tolerance

4. Withdrawal

5. Preoccupation / Salience

6. Continued Use Despite Harm

7. Functional Impairment

Below is a 21-item AIAS-21, with 3 items per construct, all rated on a 5-point Likert scale.

Instructions

This questionnaire is designed to help you reflect on how you’ve been using artificial intelligence (AI) tools like chatbots, virtual assistants, or content generators over the past 6 months.

Please read each statement below and choose the answer that best describes how much you agree with it. There are no right or wrong answers—just go with what feels most accurate for you.

Use the following scale to answer:

| 1 = Strongly Disagree

| 2 = Disagree

| 3 = Neutral / Not Sure

| 4 = Agree

| 5 = Strongly Agree

Answer each question honestly. Your responses can give you insight into your habits and help identify whether AI use might be affecting your daily life.

Scoring Instructions

Add up your responses for all 21 items.

The minimum possible score is 21 (if answered 1 for every item).

The maximum possible score is 105 (if answered 5 for every item).

Higher scores suggest a greater probability of problematic or addictive AI use.

Score Range Interpretation

21–41 - Minimal risk – low involvement with AI

42–62 - Moderate risk – signs of problematic use

63–83 - High risk – likely problematic use

84–105 - Very high risk – possible addictive behavior; consider further evaluation

These thresholds are provisional and subject to validation in future empirical studies.

Note: This tool is intended for educational and self-reflection purposes. It is not a diagnostic instrument. If your AI use is causing distress or interfering with your life, consider speaking to a mental health professional.

Copyright and Usage Statement

© 2025 Dr. Igor V. Pantić. All rights reserved. 

The AI Addiction Scale (AIAS-21) and its associated materials (items, structure, scoring rubric, and interpretive guidance) are the intellectual property of Dr. Igor V. Pantić and are protected under applicable national and international copyright laws.

Academic and research use:

Use of the AIAS-21 for non-commercial academic research purposes is permitted only with prior written permission from the copyright holder. Any publication, dissemination, or adaptation of the scale must include proper citation and acknowledgment of the author.

Commercial use:

Use of the AIAS-21 or its derivatives for commercial purposes, including but not limited to integration into digital platforms, clinical software, corporate wellness tools, or monetized educational content, is strictly prohibited without a formal licensing agreement.

To request permission for academic or commercial use, please contact:

Dr. Igor V. Pantić

Email: igorpantic@gmail.com

Any unauthorized reproduction, distribution, or modification of this material is prohibited and may result in legal action.

References

Pantic IV. AI Addiction Scale (AIAS-21). 2025. Available at: https://www.igorpantic.com/aias-21

Pantic IV. Introducing the AI addiction scale (AIAS-21): a screening tool for problematic AI use. European Child & Adolescent Psychiatry. 2026;35(1):297. https://doi.org/10.1007/s00787-025-02874-8

Independent Research Using or Adapting AIAS-21

Preliminary psychometric evidence (China, 2026)

An independent study involving Chinese university students reported satisfactory internal consistency (Cronbach's α = 0.854) for a four-item AI addiction measure derived from AIAS-21. These findings provide preliminary psychometric evidence for the abbreviated measure but do not constitute validation of the complete 21-item instrument.

Lan Y, Liu S, Chen H. Exploring the formation of learning burnout among college students in AI context: a serial mediation mechanism of AI dependence and addiction based on I-PACE model. Frontiers in Computer Science. 2026;8:1756441. https://doi.org/10.3389/fcomp.2026.1756441

Independent international adaptation and digital implementation (Indonesia, 2025)

An independent Indonesian study adapted AIAS-21 to develop a web-based expert system for assessing symptoms of problematic AI use. The researchers integrated the instrument's conceptual framework with psychological expert input and a Certainty Factor algorithm, demonstrating its applicability to digital assessment systems. This work represents an independent technological application of AIAS-21 rather than formal psychometric validation.

Putera BR, Nursakinah B. Implementasi Sistem Pakar Diagnosa Artificial Intelligance Addcition Berbasis Web Menggunakan Metode Certainty Factor Studi Kasus HIMAGIRI UNS. Jurnal Informatika Universitas Pamulang. 2025;10(4):143–152. https://doi.org/10.32493/jiup.v10i4.53717

Selected Publications Citing AIAS-21

1. Lan Y, Liu S, Chen H. (2026). Exploring the formation of learning burnout among college students in AI context: a serial mediation mechanism of AI dependence and addiction based on I-PACE model. Frontiers in Computer Science, 8, 1756441.

2. Gong G, Li G. (2026). The adaptive ecological trap: a grounded theory study of adolescent AI dependency. Frontiers in Psychology, 17, 1839672.

3. Chornomydz A, Lukaniuk M, Oleshchuk O. (2026). РОЗРОБКА ТА ПСИХОМЕТРИЧНА ВАЛІДИЗАЦІЯ ШКАЛИ СКРИНІНГОВОЇ ДІАГНОСТИКИ ЗАЛЕЖНОСТІ ВІД ШТУЧНОГО ІНТЕЛЕКТУ (AIAS-10+1 v4.0). Psychological Journal, 12(3), 7–33.

4. Altınışık MS. (2026). Perceived AI-related autonomy erosion in adults: scale development and associations with well-being, authenticity, and self-esteem. Current Psychology, 45, Article 1492. https://doi.org/10.1007/s12144-026-09999-2

5. Ou X, Lu M, Zhu J, Li P. (2026). Measuring university students' AI dependency in the school learning contexts: a non-addictive perspective. Psicologia: Reflexão e Crítica. https://doi.org/10.1186/s41155-026-00414-0

6. Putera BR, Nursakinah B. (2025). Implementasi Sistem Pakar Diagnosa Artificial Intelligance Addcition Berbasis Web Menggunakan Metode Certainty Factor Studi Kasus HIMAGIRI UNS. Jurnal Informatika Universitas Pamulang, 10(4), 143–152.

7. Aşık, R., Efetürk, N., & Tozak, F. (2026). Artificial intelligence addiction, technostress, and work-life balance among academics: An employee health perspective. Istanbul Gelisim University Journal of Health Sciences, 30, 784–802. https://doi.org/10.38079/igusabder.1914738.