This is post 53 of 122 in the series “Artificial Intelligence”

Conceptualizing AI Literacy: A Critical Skill for the 21st Century

Authors: Yerin Seung and James Basham, PhD; info@ciddl.org

Artificial Intelligence (AI) is everywhere in our daily lives, significantly influencing how we work, learn, and communicate. However, we still need a more comprehensive understanding of how AI can be utilized in educational contexts. AI's increasing presence calls for educators and students to develop the necessary skills to leverage AI effectively. This is where AI literacy comes into play. AI literacy, the skills needed for people to interact safely and effectively with AI systems, is crucial to enhancing teachers’ and students’ understanding of AI, as highlighted across the state-level AI guidance. This blog post will explore the concept, its importance, and the components of AI literacy.

What is AI Literacy?

AI literacy is the essential knowledge and skills to understand, interact with, and evaluate AI technologies. Kandlofer and colleagues (2016), among the first researchers to introduce the concept of AI literacy, defined AI literacy as a set of competencies that enable individuals to know and understand AI and use AI technologies. Long and Magerko (2020) defined AI literacy as “a set of competencies that enables individuals to evaluate AI technologies critically; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace.” Several researchers developed the concept of AI literacy based on other literacies such as data literacy (i.e., “the ability to read, work with, analyze, and argue with data as part of a broader process of inquiry into the world”), information literacy (i.e., “an understanding and set of abilities enabling individuals to recognize when information is needed and have the capacity to locate, evaluate and use the needed information effectively”), digital literacy (i.e. competencies needed to use computational devices), and computational literacy (i.e. the ability to use code to express, explore, and communicate ideas). Some skills, such as digital literacy, can be considered prerequisites for AI literacy. However, unlike computational literacy, which views coding skills as prerequisites, AI literacy emphasizes a functional understanding of how AI works and how to use it responsibly.

Why AI Literacy Matters

AI literacy is necessary for everyone, not just computer scientists or tech enthusiasts. AI is integrated into many tools we use daily without us even realizing it. Some examples are search engines (e.g., Google), virtual assistants (e.g., Alexa, Siri), and personalized recommendations on streaming platforms (e.g., Youtube, Facebook). By developing AI literacy, students can better understand how these systems work, what data they collect, and how they influence behavior, thereby engaging critically with these technologies rather than passively consuming the content.

Furthermore, AI literacy is essential for promoting ethical AI use. Misusing AI systems can lead to academic misconduct, inaccurate or biased information, or privacy violations. Therefore, AI literacy should also involve critically evaluating AI systems, ethical considerations, and understanding AI’s impact on society.

The Components of AI Literacy

Long and Magerko (2020), among the first researchers to conceptualize AI literacy in detail, identified five overarching themes: What is AI? What can AI do? How does AI work? How should AI be used? And How do people perceive AI? Each theme involves AI competencies, with 17 competencies overall. First, “What is AI?” involves four competencies: Recognizing AI, Understanding intelligence, Interdisciplinarity, and General vs. Narrow AI. “What can AI do?” includes three competencies: AI’s Strengths & Weaknesses and Imagine Future AI. “How does AI work?” broadly encompasses understanding cognitive systems, machine learning, and robotics involving nine competencies: Representations, Decision-making, ML Steps, Human Role in AI, Data Literacy, Learning from Data, Critically Interpreting Data, Action & Reaction, and Sensors. “How should AI be used?” and “How do people perceive AI?” involve one competency, Ethics and Programmability.

Ng and colleagues (2021) framed AI literacy, connecting Bloom’s taxonomy with a more education-focused approach. They conceptualized AI literacy into four dimensions: know and understand AI, Use and apply AI, Evaluate and create AI, and AI ethics. In 2024, they further developed it as a multidimensional concept encompassing cognitive, ethical, affective, and behavioral domains. They emphasize that AI literacy is about understanding AI systems and fostering career interest and self-efficacy in AI-related fields.

Digital Promise modified Ng and colleagues' (2021) taxonomy of AI literacy and further developed the framework with four essential components of AI literacy: core values, modes of engagement, AI literacy practices, and types of use. First, they brought bringing human judgment and centering justice as core values. Surrounding the core values are the three modes of engagement: Understanding, Evaluating, and using AI. They set six AI literacy practices as actionable practices to understand and evaluate AI, such as algorithm thinking, Abstraction & decomposition, Data analysis & inference, Data privacy & security, Digital communication & expression, Ethics & impact, and Information & mis/disinformation. There are three types of use: interact, create, and problem-solve with AI.

Almatrafi and colleagues (2024) systematically reviewed and synthesized the existing literature and conceptualized AI literacy with six core constructs: Recognize (Be aware), Know & understand, Use & apply, Evaluate, Create, and Navigate ethically (Understand ethical and societal implications). While all six of them were addressed in either Long & Magerko (2020) and Ng and colleagues (2021) that were covered in this post, Almatrafi and colleagues pointed out that including “Create” in AI literacy is debatable because this requires the ability to design and code AI applications, thereby not aligning with the definition of AI literacy.

Conclusion: The Future of AI Literacy

As AI continues to shape the world, AI literacy will become a foundational skill for all students. In this AI-driven society, all students should have equitable access to AI. Schools should consider teaching AI literacy to all students, regardless of their background, to ensure they can benefit from AI’s opportunities. As AI systems benefit society and pose risks such as data privacy, security violations, and algorithmic biases, AI literacy should empower learners to understand and question these ethical dilemmas. Equipping educators and related personnel with the knowledge and skills to teach AI literacy is the first step to guiding students through such complex challenges. Prioritizing AI literacy in education will empower the next generation to navigate a technology-driven world responsibly with AI.

Resources on the Topic of AI Literacy

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