Evidence map›Paper›PMID 42823997›Full record

ArticleFrontiers in psychology2026

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

Yaozheng Zhi, Wei Yang, Kaili Huang

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Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Yaozheng ZhiSchool of Education, Shanxi Normal University, Taiyuan, Shanxi, China.
Wei YangSchool of Chemical Engineering and Technology, Taiyuan University of Science and Technology, Taiyuan, Shanxi, China.
Kaili HuangCollege of Public Administration, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

generative AI literacygraduate studentsmeasurementmodel constructionscale development

Identifiers

PMID42823997
PMCPMC13627062

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.