Modeling and measuring graduate students’ generative AI literacy: a study based on Marzano’s taxonomy

Background: Generative AI is rapidly reshaping graduate students' academic research practices. Existing research lacks both literacy models specific to graduate students and a unifying theoretical framework to explain each dimension's functional role and interrelationships, constraining its cultivation and evaluation. Methods: Semi-structured interviews with 14 professors were analyzed using grounded theory, with Marzano's taxonomy of educational objectives serving as the theoretical framework for selective coding. A corresponding scale was developed and empirically validated with 308 graduate students through exploratory factor analysis, confirmatory factor analysis, and reliability and validity tests. Results: Five first-order dimensions and 15 s-order indicators were identified and validated through grounded theory analysis and questionnaire testing, with the scale demonstrating acceptable reliability and validity. Grounded theory analysis revealed that the five dimensions functioned as an interconnected whole. Cognitive foundation provided the underlying knowledge base for AI tool use; higher-order thinking critically evaluate and deeply processed AI-generated content; operational skills served as the practical tool that translated cognitive and critical thinking into academic outputs; metacognitive reflection monitored and evaluated the AI usage process; and ethical responsibility offered value guidance and normative boundaries for the functioning of all dimensions. Conclusion: This study developed and initially validated a scale for measuring graduate students perceived generative AI literacy. These findings offer actionable insights for designing AI-integrated graduate education and literacy cultivation strategies.