A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023)
Open accessAbstract
The explosion of AI across all facets of society has given rise to the need for AI education across domains and levels. AI literacy has become an important concept in the current technological landscape, emphasizing the need for individuals to acquire the necessary knowledge and skills to engage with AI systems. This systematic review examined 47 articles published between 2019 to 2023, focusing on recent work to capture new insights and initiatives given the burgeoning of the literature on this topic. In the initial stage, we explored the dataset to identify the themes covered by the selected papers and the target population for AI literacy efforts. We identified that the articles broadly contributed to one of the following themes: a) conceptualizing AI literacy, b) prompting AI literacy efforts, and c) developing AI literacy assessment instruments. We also found that a range of populations, from pre-K students to adults in the workforce, were targeted. In the second stage, we conducted a thorough content analysis to synthesize six key constructs of AI literacy: Recognize, Know and Understand, Use and Apply, Evaluate, Create, and Navigate Ethically. We then applied this framework to categorize a range of empirical studies and identify the prevalence of each construct across the studies. We subsequently review assessment instruments developed for AI literacy and discuss them. The findings of this systematic review are relevant for formal education and workforce preparation an…
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Almatrafi et al. (2024) identified AI literacy competencies as recognizing, knowing and understanding, using and applying, evaluating, creating, and navigating ethically.
“Almatrafi et al. (2024) identified AI literacy competencies as recognizing, knowing and understanding, using and applying, evaluating, creating, and navigating ethically.”
From A Competency Framework for AI Literacy: Variations by Different Learner Groups and an Implied Learning Pathway (2025) · cites this paper as Almatrafi et al., 2024
The few studies available on AI literacy among faculty tended to show low levels of self-reported AI literacy among faculty.
“the few studies available on this topic tended to show low levels of (self-reported) AI literacy among faculty (Almatrafi et al., 2024; Chiu et al., 2024; Long & Magerko, 2020; Pinski & Benlian, 2024).”
From Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs (2024) · cites this paper as Almatrafi et al., 2024
Models that systematically connect coursework with extracurricular learning in AI/data education in higher education remain rare.
“Yet, in the specific context of AI/data education in higher education, models that systematically connect coursework with extracurricular learning remain rare, and the constituent components of such programs have not been empirically identified through rigorous decision-making methodologies [18,19].”
From Toward Sustainable AI Literacy in Higher Education: A Delphi–AHP Approach to Identifying Key Components of AI/Data Extracurricular Programs (2026) · cites this paper as [19]
The constituent components of AI/data extracurricular programs have not been empirically identified through rigorous decision-making methodologies.
“Yet, in the specific context of AI/data education in higher education, models that systematically connect coursework with extracurricular learning remain rare, and the constituent components of such programs have not been empirically identified through rigorous decision-making methodologies [18,19].”
From Toward Sustainable AI Literacy in Higher Education: A Delphi–AHP Approach to Identifying Key Components of AI/Data Extracurricular Programs (2026) · cites this paper as [19]
The systematic design of curriculum-linked extracurricular programs-particularly through rigorous decision-making methodologies-has received scant attention.
“The systematic design of curriculum-linked extracurricular programs-particularly through rigorous decision-making methodologies-has received scant attention.”
From Toward Sustainable AI Literacy in Higher Education: A Delphi–AHP Approach to Identifying Key Components of AI/Data Extracurricular Programs (2026) · cites this paper as [19]
Current literature distinguishes generic AI literacy for non-experts from domain-specific AI literacy for specialists.
“This maturation has led to a distinction in the current literature: the separation between generic AIL for non-experts and domain-specific AIL for specialists (Almatrafi et al., 2024; Long and Magerko, 2020).”
From The AI literacy heptagon: A structured approach to AI literacy in higher education (2026) · cites this paper as Almatrafi et al., 2024
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“…a relatively new construct being discussed in higher education for teaching and learning and (b) the few studies available on this topic tended to show low levels of (self-reported) AI literacy among faculty (Almatrafi et al., 2024; Chiu et al., 2024; Long & Magerko, 2020; Pinski & Benlian, 2024).”
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“…AI literacy framework exists, research increasingly converges on recurring dimensions: understanding AI concepts, applying AI in practice, critically evaluating its use, navigating ethical implications, and engaging creatively (Almatrafi et al., 2024; Ng et al., 2021; Pinski & Benlian, 2023).”
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- Authors
- Almatrafi, O., Johri, A., & Lee, H.
- Affiliations
- King Abdulaziz University (SA)
- George Mason University (US)
- Corresponding author
- Almatrafi, Omaima
- Venue
- Computers and Education Open
- Publisher
- Elsevier BV
- Published
- 2024-06
- Volume
- 6
- Pages
- 100173
- ISSN
- 2666-5573 (ISSN-L)
- OpenAlex
- W4393359157
- Cited by
- 341
- References
- 20
- Citations by year
- 2026: 207 · 2025: 115 · 2024: 19
- FWCI
- 19.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Open (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- USDA NIFA
- National Science Foundation (1939105, 2319137, 1937950)
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Library and Information Sciences › Library Science and Information Literacy
- Keywords
- AI literacy, artificial intelligence in education, employee training
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