Development and validation of the generative AI engagement scale

As generative AI becomes more integrated into everyday life, understanding the behavioral impact of generative AI usage becomes increasingly important. However, research lacks validated tools for capturing both the frequency and quality of generative AI use. This study presents the Generative AI Engagement Scale (GAIES), a multidimensional instrument that was developed following best practices in scale construction and validation. GAIES consists of two subscales: the Use Frequency scale, which measures how often users interact with generative AI for self-interested and task-oriented purposes, and the Interaction Style scale, which assesses how users interact with generative AI through Questioningness, Expressiveness, and Preciseness. This study included 414 participants. Several psychometric evaluations were involved, including classical test theory, exploratory and confirmatory factor analyses, and item response theory. The subscales showed strong internal consistency, a clear factor structure, and a good fit. Besides validating GAIES, we demonstrated its practical utility through two case studies. An analysis of a structural equation model revealed that predictors from the Unified Theory of Acceptance and Use of Technology explained Self-interest- and Task-oriented usage differentially, indicating the predictability of the scale. Further, latent profile analysis revealed four distinct user subgroups, demonstrating the usefulness of the scale in identifying meaningful patterns of engagement. These findings establish GAIES as a psychometrically and theoretically sound method of measuring generative AI engagement. A key contribution of GAIES is its ability to go beyond generic usage metrics and offer a foundation for future research into the behavioral implications of generative AI usage.