Draft:Critical AI Literacy

Critical AI literacy is a concept in education that extends artificial intelligence literacy to include the critical evaluation of artificial intelligence (AI) systems, their societal implications, and the power structures that shape their development and use.[1][2] It draws on traditions of Critical literacy, Critical pedagogy, and Media literacy, emphasizing not only how AI systems function but also their social and ethical implications.[3]

The concept emerged in the early 2020s alongside the increased accessibility of generative AI tools.[4] The term critical AI literacy has been used explicitly in academic literature since at least 2024.[1][2] International organizations such as UNESCO and the Organisation for Economic Co-operation and Development (OECD) have incorporated related competencies into AI frameworks, though not always using the term itself.[5][6]

Research on critical AI literacy spans both higher education and primary and secondary education, though the field remains emerging, with limited empirical evidence and variation in definitions.[4]

Origins and theoretical foundations

Critical AI literacy builds on several intellectual traditions.

Critical literacy, associated with the work of Paulo Freire, emphasizes analysis of power relations in texts and systems.[7] Media literacy extended this perspective to the interpretation of mass and digital media.

The concept of Datafication, discussed by scholars including José van Dijck, contributed to the development of literacies concerned with data practices.[8] Pangrazio and Selwyn (2023) describe critical data literacies as the competencies required to engage with data-driven systems.[9]

The broader field of AI literacy was formalized by Long and Magerko (2020), who defined it as a set of competencies enabling individuals to evaluate and use AI systems.[10] Ng et al. (2021) identified four dimensions: understanding, applying, evaluating, and addressing ethical issues in AI.[11]

Subsequent work has argued that these frameworks emphasize technical competence more than critical perspectives, contributing to the development of explicitly critical approaches.[3][2]

Definition and scope

There is no single agreed definition of critical AI literacy.

Veldhuis et al. (2024) identified five dimensions relevant to a critical perspective:[1]

  • understanding AI
  • using AI
  • evaluating AI
  • addressing ethical implications
  • critical engagement with AI in society

The authors suggest that the final two dimensions distinguish critical AI literacy from more functional approaches.[1]

Rapanta (2025) introduced the related concept of critical generative AI literacy, focusing on authorship, epistemic authority, and human–machine knowledge boundaries.[2]

A systematic review by Almatrafi et al. (2024) found that ethical and critical components vary significantly across frameworks.[4]

Frameworks

UNESCO AI competency frameworks

In 2024, UNESCO published AI competency frameworks for students and teachers, including competencies such as evaluating societal impacts and applying ethical reasoning.[5][12]

OECD framework

The Organisation for Economic Co-operation and Development (OECD) and European Commission’s 2025 report emphasizes critical evaluation, ethical reasoning, and awareness of AI limitations.[6]

In education

Higher education

The adoption of generative AI has prompted calls for critical engagement in higher education.[3]

The Modern Language Association and the Conference on College Composition and Communication have recommended integrating critical AI perspectives into writing instruction.

Illingworth (2025), writing in Nature, argues that AI should be studied as both a tool and a subject.[13]

Primary and secondary education

Veldhuis et al. (2024) found that most AI literacy studies for younger learners focus on technical skills, with fewer addressing critical engagement.[1]

Lintner (2024) found that most assessment tools measure knowledge rather than critical reasoning.[14]

Criticism and limitations

Limited empirical evidence

Most research is conceptual, with limited longitudinal studies.[4]

Definitional ambiguity

There is no single agreed definition, complicating implementation.[4]

Tension between critique and practice

Some researchers note a tension between critical reflection and practical engagement.[11]

Dependence on technical understanding

Critical evaluation may require foundational knowledge of AI systems.[11]

Measurement challenges

Assessment tools rarely capture critical reasoning or ethical judgment.[14]

Geographic concentration

Most studies originate from Europe and North America.[4]

See also

References

  1. ^ a b c d e Veldhuis, Annemiek (2024). "Critical Artificial Intelligence literacy: A scoping review and framework synthesis". International Journal of Child-Computer Interaction. 43 100708. doi:10.1016/j.ijcci.2024.100708.
  2. ^ a b c d Rapanta, Chrysi (2025). "Critical GenAI Literacy: Postdigital Configurations". Postdigital Science and Education. 7 (4): 1296–1333. doi:10.1007/s42438-025-00573-w.
  3. ^ a b c Selwyn, Neil (2022). "The future of AI and education: Some cautionary notes". European Journal of Education. 57 (1): 8–16. doi:10.1111/ejed.12532.
  4. ^ a b c d e f Almatrafi, O. (2024). "A systematic review of AI literacy conceptualization". Computers and Education Open. 5 100173. doi:10.1016/j.caeo.2024.100173.
  5. ^ a b UNESCO (2024). AI competency framework for students (Report). doi:10.54675/JKJB9835. ISBN 978-92-3-100709-5.
  6. ^ a b OECD; European Commission (2025). Empowering Learners for the Age of AI (Report).{{cite report}}: CS1 maint: multiple names: authors list (link)
  7. ^ Freire, Paulo (1970). Pedagogy of the Oppressed. ISBN 9780826412768.
  8. ^ van Dijck, José (2014). "Datafication, dataism and dataveillance". Surveillance & Society. 12 (2): 197–208. doi:10.24908/ss.v12i2.4776.
  9. ^ Pangrazio, Lucia; Selwyn, Neil (2023). Critical Data Literacies. MIT Press. ISBN 9780262547178.
  10. ^ Long, D.; Magerko, B. (2020). "What is AI Literacy?". CHI Conference Proceedings. doi:10.1145/3313831.3376727.
  11. ^ a b c Ng, D. T. K. (2021). "Conceptualizing AI literacy: An exploratory review". Computers and Education: Artificial Intelligence. 2 100041. doi:10.1016/j.caeai.2021.100041.
  12. ^ UNESCO (2024). AI competency framework for teachers (Report).
  13. ^ Illingworth, Sam (2025). "Take the time to ensure that AI is safe". Nature. 646 (8086): 804. doi:10.1038/d41586-025-03430-9. PMID 41120791.
  14. ^ a b Lintner, K. (2024). "A systematic review of AI literacy scales". npj Science of Learning. 9 (1) 50. Bibcode:2024npjSL...9...50L. doi:10.1038/s41539-024-00264-4. PMC 11303566. PMID 39107327.{{cite journal}}: CS1 maint: unflagged free DOI (link)

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