Generative AI dependency on programming among university students: a scale development and validation study

With the rapid integration of generative artificial intelligence into programming education, concerns have emerged regarding university students' potential dependency on such technologies. However, there is a lack of validated instruments to systematically measure generative AI Dependency on programming contexts. To address this gap, the present study aimed to develop and validate the Scale of Generative AI Dependency on Programming among University Students. The study further seeks to contribute to the conceptualization of GenAI dependency as a context-specific construct in programming education, rather than treating students' reliance on GenAI as a general or undifferentiated form of technology use. Drawing on prior research and theoretical frameworks related to technology dependency and learning behaviors, an initial item pool was constructed to capture students' dependency on generative AI across three dimensions: cognitive, affective, and behavioral dependency. Using a convenience sampling approach, data were collected from 1,295 university students with programming learning experiences involving generative AI. Item analysis was first conducted to examine item discrimination and reliability. Exploratory factor analysis was then employed to identify the underlying factor structure of the scale, followed by confirmatory factor analysis to test the model fit and construct validity. In addition, Pearson correlation analyses were performed to provide evidence of criterion-related validity. The results were generally consistent with a three-factor structure with satisfactory reliability and validity, indicating that the scale may serve as a useful instrument for assessing generative AI Dependency on programming among university students. Theoretically, this study extends the application of the ABC framework to AI-supported programming education and provides preliminary evidence that GenAI dependency may involve interrelated cognitive, affective, and behavioral components. Practically, this scale provides researchers and educators with a useful tool for investigating students' dependency on generative AI in programming and for informing instructional design and intervention strategies in programming education.