Development and validation of a scale for dependence on artificial intelligence in university students

Background: Artificial Intelligence (AI) has permeated various aspects of daily life, including education, specifically within higher education settings. These AI technologies have transformed pedagogy and learning, enabling a more personalized approach. However, ethical and practical concerns have also emerged, including the potential decline in cognitive skills and student motivation due to excessive reliance on AI. Objective: To develop and validate a Scale for Dependence on Artificial Intelligence (DIA). Methods: An Exploratory Factor Analysis (EFA) was used to identify the underlying structure of the DIA scale, followed by a Confirmatory Factor Analysis (CFA) to assess and confirm this structure. In addition, the scale’s invariance based on participants’ gender was evaluated. Results: A total of 528 university students aged between 18 and 37 years (M = 20.31, SD = 3.8) participated. The EFA revealed a unifactorial structure for the scale, which was subsequently confirmed by the CFA. Invariance analyses showed that the scale is applicable and consistent for both men and women. Conclusion: The DAI scale emerges as a robust and reliable tool for measuring university students’ dependence on AI. Its gender invariance makes it applicable in diverse population studies. In the age of digitalization, it is essential to understand the dynamics between humans and AI to navigate wisely and ensure a beneficial coexistence.