AI literacy is not a standalone skill—it draws from and overlaps with several other critical literacies. To be truly literate in AI, individuals benefit from a broader foundation in areas like digital literacy, data literacy, and ethical reasoning. Below is an overview of the key literacies that contribute to a comprehensive understanding and responsible use of AI technologies.
“Digital literacy involves the ability to find, evaluate, utilize, share, and create content using information technologies and the Internet.”
— American Library Association (ALA)
AI tools are almost always accessed through digital platforms. Understanding how to navigate digital environments, protect privacy, and evaluate digital sources is foundational to AI literacy.
“The ability to read, work with, analyze and argue with data.”
— Data Literacy Project; coined by Jordan Morrow
AI systems rely on vast datasets to function. Users with strong data literacy can better understand how AI models are trained, what biases may be embedded, and how data influences AI outputs.
“The ability to access, analyze, evaluate, create, and act using all forms of communication.”
— National Association for Media Literacy Education (NAMLE)
As AI increasingly generates media (images, video, news, etc.), media literacy becomes essential for discerning between human-made and machine-generated content and evaluating credibility.
“The set of integrated abilities encompassing the reflective discovery of information, the understanding of how information is produced and valued, and the use of information in creating new knowledge.”
— Framework for Information Literacy for Higher Education, ACRL, 2016
Information literacy underpins AI use in academic and professional contexts. It enables users to critically assess AI outputs, trace sources, and cite responsibly when using AI-generated knowledge.
“A problem-solving process that includes (but is not limited to) characteristics such as logically ordering and analyzing data and creating solutions using a series of ordered steps (algorithms).”
— International Society for Technology in Education (ISTE)
Understanding how algorithms work, even at a basic level, helps users demystify AI processes and evaluate how outputs are generated, especially when dealing with black-box systems.
“The ability to recognize, evaluate, and act on ethical issues surrounding AI, such as bias, privacy, transparency, and accountability.”
— UNESCO’s “AI and Ethics in Higher Education” report, 2021
Ethical literacy in AI involves understanding the social and political implications of AI use, including who benefits, who is harmed, and how decisions are made and enforced by automated systems.