ArticleScientific reports2026
Integration of artificial intelligence competencies into a holistic digital literacy framework for middle school education.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.