# Development and validation of the AI dependence scale for Chinese undergraduates and a preliminary exploration

## TL;DR

This study created a reliable scale to measure overreliance on AI among Chinese undergraduates, identifying key risk factors and offering a tool for educators.

## Contribution

The paper introduces the AI Dependence Scale (AIDep-22), a validated instrument for assessing AI overreliance in higher education.

## Key findings

- The AIDep-22 demonstrated strong psychometric properties, including high internal consistency and validity.
- Male students, upper-year students, and frequent AI users showed higher AI dependence.
- The four-factor structure of the scale was confirmed through exploratory and confirmatory factor analyses.

## Abstract

With the proliferation of generative artificial intelligence (AI) in higher education, student overreliance has become a growing concern, potentially undermining critical thinking and autonomous learning. To address the lack of a comprehensive measurement tool, this study developed and validated the AI Dependence Scale (AIDep-22), a new instrument designed to assess this phenomenon across four hypothesized dimensions: emotional dependence, functional dependence, cognitive dependence, and loss of control.

The scale was constructed following a rigorous two-stage process, beginning with item generation and refinement through expert reviews and cognitive interviews, followed by psychometric evaluation with two independent samples of Chinese university students (N = 400 each).

An exploratory factor analysis (EFA) supported the four-factor structure, which was subsequently confirmed by a confirmatory factor analysis (CFA) on the second sample. The final 22-item scale demonstrated excellent internal consistency (Cronbach's alpha = 0.87), strong convergent and discriminant validity, and robust criterion-related validity. Preliminary analyses also identified key demographic risk factors, revealing that male students, upper-year students, those in applied majors, and more frequent AI users reported significantly higher dependence.

This study contributes a reliable and valid diagnostic tool that enables educators and researchers to identify and support students at risk, and to design targeted interventions that promote a more balanced human-AI relationship in higher education.