Every new literacy is built on the same six dimensions
Information, media, digital, data, and AI literacy each arrived as a response to something new. Set their frameworks side by side and the same six dimensions of literacy appear in each of them, described in different words and weighted differently. What changes from one literacy to the next is the context the dimensions are applied in.
Common architecture of literacy
One-sentence definition: The common architecture of literacy is the set of six dimensions that information, media, digital, data, and AI literacy frameworks share.
When information literacy was formalised in the late 1980s, it looked tailored to library research. Media literacy looked specific to constructed messages, and digital literacy to technology. The six dimensions below are my synthesis, from setting the main framework in each tradition beside the others: the Association of College & Research Libraries’ Framework for information literacy for higher education (2016), Livingstone’s (2004) definition of media literacy as the ability to access, analyse, evaluate, and create messages, the EU’s DigComp framework for digital competence (Vuorikari et al., 2022), Ridsdale et al.’s (2015) synthesis of data literacy competencies, and Ng et al.’s (2021) review of the AI literacy literature. Others have seen the overlap before, including Koltay (2015), who traces how data literacy relates to information literacy and the literacies before it, and Mackey and Jacobson (2011) went furthest, recasting information literacy as a metaliteracy that unifies the rest. There are other ways to divide the ground, but these six keep turning up.
The six dimensions of literacy
Access and recognition: knowing when something is present and relevant, and how to find or engage with it, whether that’s a library database, a news story, a digital platform, or an AI system.
Critical evaluation: assessing quality, reliability, limitations, and bias. Every tradition centres this, from questioning sources and reading how a media message was constructed to noticing algorithmic influence and catching an AI’s hallucinations.
Functional application: the practical ability to use something well for a purpose. It’s often mistaken for the whole of literacy, when it’s one dimension of six.
Creation and communication: producing new work and sharing it, such as a research paper, a media message, a digital artefact, or a piece of writing developed with AI.
Ethical awareness and responsibility: understanding the social, ethical, and civic consequences, including privacy, equity, and power. It has grown more prominent in recent frameworks, though it appears in early information literacy work too.
Contextual judgement and metacognition: knowing when and how to apply the other five, and being aware of your own practice as you do. It gets the least attention and may matter most, particularly for AI, where capabilities change quickly and context decides whether using them helps.
What it means for teaching AI literacy
If the architecture holds, AI literacy isn’t a new subject to be built from nothing, and health professions education has a head start. Evidence-based practice, taught in almost every medical, nursing, and allied health programme, is information literacy under another name: students learn to recognise when a clinical question needs evidence, find it, appraise it, apply it to a patient, and reflect on how that went. A programme that already teaches students to critically appraise a trial has taught the evaluation dimension, and asking them to appraise an AI-generated summary of the same trial with the same rigour extends that teaching to a new source.
The dimensions also work together. A student can’t be literate in functional application without critical evaluation and contextual judgement alongside it, and judgement develops through sustained practice and reflection more than through instruction. AI literacy works through what that looks like for one student, and the AI literacy development framework sets out how each dimension develops.
Sources
- Association of College & Research Libraries. (2016). Framework for information literacy for higher education. https://www.ala.org/sites/default/files/acrl/content/issues/infolit/framework1.pdf
- Koltay, T. (2015). Data literacy: In search of a name and identity. Journal of Documentation, 71(2), 401–415. https://doi.org/10.1108/JD-02-2014-0026
- Livingstone, S. (2004). Media literacy and the challenge of new information and communication technologies. The Communication Review, 7(1), 3–14. https://doi.org/10.1080/10714420490280152
- Mackey, T. P., & Jacobson, T. E. (2011). Reframing information literacy as a metaliteracy. College & Research Libraries, 72(1), 62–78. https://doi.org/10.5860/crl-76r1
- Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, Article 100041. https://doi.org/10.1016/j.caeai.2021.100041
- Ridsdale, C., Rothwell, J., Smit, M., Ali-Hassan, H., Bliemel, M., Irvine, D., Kelley, D., Matwin, S., & Wuetherick, B. (2015). Strategies and best practices for data literacy education: Knowledge synthesis report. Dalhousie University. https://dal.scholaris.ca/items/ab6d9110-4739-4a74-8b27-4e03b19601e9
- Vuorikari, R., Kluzer, S., & Punie, Y. (2022). DigComp 2.2: The digital competence framework for citizens. Publications Office of the European Union. https://publications.jrc.ec.europa.eu/repository/handle/JRC128415