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From Novice to Expert: Developing a Descriptive Competence Framework Guiding the Mastery of Large Language Models in Education

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Title: From Novice to Expert: Developing a Descriptive Competence Framework Guiding the Mastery of Large Language Models in Education
Language: English
Authors: XiaoShu Xu (ORCID 0000-0002-0667-4511), Jia Liu (ORCID 0000-0002-3806-4072), Wilson Cheong Hin Hong (ORCID 0000-0002-9858-2015), Shanshan Hao (ORCID 0009-0000-8460-5653), Xiuxuan Shi (ORCID 0009-0002-3591-7735)
Source: Asia-Pacific Journal of Teacher Education. 2026 54(3):308-329.
Availability: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 22
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Novices, Expertise, Artificial Intelligence, Natural Language Processing, Technology Integration, Competence, Educational Technology, Mastery Learning, Learning Trajectories
DOI: 10.1080/1359866X.2026.2675952
ISSN: 1359-866X
1469-2945
Abstract: This study introduces and validates the LLMs Expert User Competence Framework, a model for developing expertise in the educational use of Large Language Models (LLMs). Grounded in the Knowledge, Skills, and Attitudes (KSA) model, the framework outlines five progressive levels -- Novice, Beginner, Competent, Proficient, and Expert -- capturing the trajectory of user development. A multi-phase validation process included expert panel review (n = 8), pilot testing (n = 194), and a main survey (n = 502). Experts' qualitative and quantitative input led to key refinements, such as the inclusion of real-world applications, ethical considerations, and continuous professional learning. Quantitative results confirmed strong construct validity and internal consistency. Most participants identified as novices, particularly in operational skills, highlighting the need for structured training for teachers. Regression analyses showed that LLMs usage frequency and duration significantly predicted higher competence across all KSA domains. This study fills a critical gap in existing research by offering a comprehensive, scalable and descriptive framework to inform steps necessary for educators' responsible and progressive mastery of LLMs in educational contexts.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1508406
Database: ERIC
FullText Text:
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  Data: <searchLink fieldCode="AR" term="%22XiaoShu+Xu%22">XiaoShu Xu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0667-4511">0000-0002-0667-4511</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jia+Liu%22">Jia Liu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3806-4072">0000-0002-3806-4072</externalLink>)<br /><searchLink fieldCode="AR" term="%22Wilson+Cheong+Hin+Hong%22">Wilson Cheong Hin Hong</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9858-2015">0000-0002-9858-2015</externalLink>)<br /><searchLink fieldCode="AR" term="%22Shanshan+Hao%22">Shanshan Hao</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0000-8460-5653">0009-0000-8460-5653</externalLink>)<br /><searchLink fieldCode="AR" term="%22Xiuxuan+Shi%22">Xiuxuan Shi</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0002-3591-7735">0009-0002-3591-7735</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Asia-Pacific+Journal+of+Teacher+Education%22"><i>Asia-Pacific Journal of Teacher Education</i></searchLink>. 2026 54(3):308-329.
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  Data: Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Data: 1359-866X<br />1469-2945
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  Data: This study introduces and validates the LLMs Expert User Competence Framework, a model for developing expertise in the educational use of Large Language Models (LLMs). Grounded in the Knowledge, Skills, and Attitudes (KSA) model, the framework outlines five progressive levels -- Novice, Beginner, Competent, Proficient, and Expert -- capturing the trajectory of user development. A multi-phase validation process included expert panel review (n = 8), pilot testing (n = 194), and a main survey (n = 502). Experts' qualitative and quantitative input led to key refinements, such as the inclusion of real-world applications, ethical considerations, and continuous professional learning. Quantitative results confirmed strong construct validity and internal consistency. Most participants identified as novices, particularly in operational skills, highlighting the need for structured training for teachers. Regression analyses showed that LLMs usage frequency and duration significantly predicted higher competence across all KSA domains. This study fills a critical gap in existing research by offering a comprehensive, scalable and descriptive framework to inform steps necessary for educators' responsible and progressive mastery of LLMs in educational contexts.
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