Address potential scalability challenges in implementing
Research gap analysis derived from 3 education papers in our local library.
The gap
The paper does not address potential scalability challenges in implementing AI literacy integration across diverse institutional contexts and resource levels.
Evidence profile
Sourced from the inline gaps and open questions and limitations of the source papers, classified as scalability gap, spanning 3 journals. Those papers have been cited 1 times in total.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Determination of Artificial Intelligence Literacy Levels of German Language Teacher Candidates (2026) · Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi · doi
The uneven distribution of literacy scores signals persistent gaps in curricular design, faculty training, and access to relevant tools, indicating that AI literacy risks becoming a peripheral outcome rather than a deliberately cultivated professional competency without institutional reform.
scalability gapinline gapsevidence 5/5Keywords: literacy uneven distribution scores signals persistent gaps curricular design faculty training access relevant tools indicating - The Impact of Artificial Intelligence (AI) Implementation on Students’ Mindset in The Era of The Fourth Industrial Revolution (2026) · Cerdika: Jurnal Ilmiah Indonesia · doi
The paper does not address potential scalability challenges in implementing AI literacy integration across diverse institutional contexts and resource levels.
scalability gapopen questionsevidence 5/5Keywords: address potential scalability challenges implementing literacy integration across diverse institutional contexts resource levels - CRAILF: A Zero-Cost Python-Based Gamified Framework for Enhancing AI Literacy Among Rural High School Students (2026) · Review of Artificial Intelligence in Education · cited 1× · doi
The framework achieved 28% AI literacy improvement (Cohen's d = 0.85) in a small sample (n=20) of rural high school students over 6 weeks, but the paper does not address whether this effect size would persist across larger rural populations, longer implementation periods (beyond 6 weeks), different grade levels, or contexts with varying initial AI exposure and curriculum integration models.
scalability gaplimitationsevidence 5/5Keywords: AI literacy improvement effect size rural high school students small sample longitudinal scalability
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