New GenAI-RTS scale measures how students rely on AI in writing

Researchers Shahin Hossain and Tukhbita Afroz Nawmi introduced the GenAI Reliance Types Scale (GenAI-RTS), a validated 20-item instrument designed to measure how undergraduates rely on generative AI in academic writing—beyond simple usage frequency. The study surveyed 382 students at a U.S. Minority-Serving Institution and conducted 14 in-depth interviews. Confirmatory factor analysis supported a five-factor structure: Strategic Reliance split into Deliberate Use and Critical Evaluation, plus Instrumental, Dependent, and Dialogic factors. Fit indices were strong (CFI = .92, RMSEA = .08; with robust estimator CFI = .98, RMSEA = .07), and subscale reliability ranged from omega = .75 to .88.

Notably, the scale achieved scalar measurement invariance across gender, first-generation college status, and STEM versus non-STEM majors—the first such evidence for a GenAI reliance tool. Rasch analysis recommended a five-point response format for optimal category functioning. Strategic reliance was positively associated with AI literacy, and the four reliance types differentiated students across multiple writing process and outcome variables. The GenAI-RTS provides educators and researchers a theoretically grounded, psychometrically robust way to identify student reliance profiles and inform AI literacy interventions and academic integrity policies.