Integration of artificial intelligence competencies into a holistic digital literacy framework for middle school education

The rise of artificial intelligence (AI) and its rapidly expanding tools is revolutionizing various sectors, with education standing out as a profoundly affected domain, creating an urgent need to incorporate AI competencies into education. This study proposes an AI-enriched digital Educational Framework (AIEDF) based on a newly developed Holistic Digital Literacy Framework (HDLF), synthesizing competencies from existing frameworks. It investigates the essential AI competencies for preparing young learners, specifically middle-school students, to prosper in an AI-driven world. Middle-school students are targeted due to their developmental readiness for abstract thinking, allowing effective early engagement with foundational AI concepts. Initially, four established digital education frameworks are analyzed and synthesized to create HDLF to be used as the foundation for incorporating AI competencies suitable for the cognitive level of the intended age group. The HDLF contains four core areas (Core Computational Concepts and Skills, Digital Citizenship and Ethical Competence, Impact of Computing on Society, and Digital Competence and Lifelong Learning). A literature review identified AI competencies essential for thriving in the 21st century. Using a systematic integration approach, these competencies were analyzed and mapped to the subtopics of the HDLF. The resulting AIEDF is then aligned with Bloom’s taxonomy, illustrating the progression from fundamental AI understanding (e.g., Machine Learning Basics) to more advanced tasks (e.g., designing simple AI applications). It is designed to support structured AI education, developmentally appropriate, and aligned with international standards, proposing a structured and adaptable foundation that requires empirical validation in future work.

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Publication Details

Journal
Scientific Reports
Published
2026-04-17
DOI
https://doi.org/10.1038/s41598-026-47133-1
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00

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article

Integration of artificial intelligence competencies into a holistic digital literacy framework for middle school education

Raghda Altamimi, Anas Abu Al-Haija’a
Scientific Reports
Teaching and Learning Programming
article

Integration of artificial intelligence competencies into a holistic digital literacy framework for middle school education

Raghda Altamimi, Anas Abu Al-Haija’a
article en

Abstract

The rise of artificial intelligence (AI) and its rapidly expanding tools is revolutionizing various sectors, with education standing out as a profoundly affected domain, creating an urgent need to incorporate AI competencies into education. This study proposes an AI-enriched digital Educational Framework (AIEDF) based on a newly developed Holistic Digital Literacy Framework (HDLF), synthesizing competencies from existing frameworks. It investigates the essential AI competencies for preparing young learners, specifically middle-school students, to prosper in an AI-driven world. Middle-school students are targeted due to their developmental readiness for abstract thinking, allowing effective early engagement with foundational AI concepts. Initially, four established digital education frameworks are analyzed and synthesized to create HDLF to be used as the foundation for incorporating AI competencies suitable for the cognitive level of the intended age group. The HDLF contains four core areas (Core Computational Concepts and Skills, Digital Citizenship and Ethical Competence, Impact of Computing on Society, and Digital Competence and Lifelong Learning). A literature review identified AI competencies essential for thriving in the 21st century. Using a systematic integration approach, these competencies were analyzed and mapped to the subtopics of the HDLF. The resulting AIEDF is then aligned with Bloom’s taxonomy, illustrating the progression from fundamental AI understanding (e.g., Machine Learning Basics) to more advanced tasks (e.g., designing simple AI applications). It is designed to support structured AI education, developmentally appropriate, and aligned with international standards, proposing a structured and adaptable foundation that requires empirical validation in future work.

Scientific Reports
Eötvös Loránd University (HU)
Eötvös Loránd Tudományegyetem
Quality Education
Openalex Percentile: Top 5%
Teaching and Learning Programming
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