AI evidence extraction

Conceptualizing AI literacy: An exploratory review

Davy Tsz Kit Ng, Jac Ka Lok Leung, Samuel Kai Wah Chu, Shen Qiao · Computers and Education Artificial Intelligence · 2021

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

6/10

Relevance

1/4

Quality (LMQS)

Evidence

1,492

Citations

111.32

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1016/j.caeai.2021.100041

Methodology & findings

Study design

Exploratory literature review grounded in analysis of 30 peer-reviewed articles, conducting qualitative synthesis to conceptualize AI literacy and propose a multi-dimensional framework.

Sample

N = 30, 2 groups

Primary method

Qualitative content analysis and thematic synthesis of peer-reviewed literature; no quantitative statistical methods employed.

Main result

The study proposed "four aspects (i.e., know and understand, use and apply, evaluate and create, and ethical issues) for fostering AI literacy based on the adaptation of classic literacies." This consolidated framework establishes a theoretical foundation for understanding and teaching AI literacy across multiple dimensions.

Reports effect sizes.

Research paradigm

Interpretivist/constructivist

Author conclusions

The authors conclude that "this study sheds light on the consolidated definition, teaching, and ethical concerns on AI literacy, establishing the groundwork for future research such as competency development and assessment criteria on AI literacy." The framework provides a foundation for developing educational approaches to AI literacy.

Risk of bias

Selection bias in article choice (only 30 peer-reviewed articles selected); Potential publication bias (only peer-reviewed articles included); Lack of systematic search protocol stated; No inter-rater reliability reported for article evaluation; Selection bias: Limited to 30 peer-reviewed articles; search strategy and inclusion/exclusion criteria not explicitly detailed; Publication bias: Review limited to published literature, potentially excluding grey literature or unpublished perspectives; Interpretation bias: Single-reviewer or limited-reviewer qualitative synthesis may affect conceptualization; Selection bias in article inclusion (30 articles selected from unknown total population); Publication bias (peer-reviewed articles only); Potential language bias (not stated if non-English articles included); Author interpretation bias in thematic synthesis

Limitations

The paper notes that "public understanding of AI technologies and how to define AI literacy is under-explored," indicating that the field itself lacks comprehensive empirical grounding
As a narrative review synthesis, the study is limited by the scope and quality of the 30 articles reviewed and does not employ quantitative meta-analytic methods.

Open questions raised

The authors identify that future research should focus on competency development and assessment criteria for AI literacy, as well as the need for sound theoretical foundations to define, teach, and evaluate AI literacy across educational contexts.
Lack of empirical validation of the proposed AI literacy framework
Need for competency development standards for AI literacy
Assessment criteria and evaluation methods for AI literacy remain underdeveloped
Limited understanding of how AI literacy should be taught across different educational levels
Insufficient exploration of ethical dimensions in AI education

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