AI Literacy for All

As generative AI applications gain widespread adoption in society and education, a growing number of institutions are implementing AI usage policies and offering introductory AI courses and trainings. Historically, AI education has emphasized the technical underpinnings of AI—mastery of AI principles, mathematical foundations, and programming skills necessary to implement AI solutions. The socio-technical dimension of AI usually focuses on technology-oriented ethical implications, privacy, security, or the cognitive experience of interacting with AI.

However, to promote AI democratization and cultivate a culture of open-source AI literacy, these introductory AI courses should not replicate the purely technical focus found in introductory computer science courses like CS1 and CS2. In this context, we introduce a holistic approach to teaching and learning AI by defining key pillars and principles of AI literacy designed for broad access.

AI Literacy_Longer Canvas_FINAL.mp4

With the rapid evolution of artificial intelligence (AI), there is a need for AI literacy that goes beyond the traditional AI education curriculum and expands to consider social and global impact.

In this framework, AI Literacy for All, we emphasize a balanced curriculum that includes technical and non-technical learning outcomes to enable a conceptual understanding and critical evaluation of AI technologies in an interdisciplinary socio-technical context. We propose that the four pillars of AI literacy presented are essential for all students, whether they pursue advanced AI education or take alternative educational paths.

Our curriculum is organized into modules, each devoted to a different pillar, and emphasizes active engagement through careful attention to pedagogy, learning outcomes, and assessment.

Papers below showcase how AI literacy has been conceptualized and disseminated, including public literacy, competency building for designers, conceptual understanding of AI concepts, and domain-specific upskilling. They also present preliminary research in this area with future directions.

AI_Magazine___AI_Literacy_as_a_Core_Component_of_AI_Education.pdf

Tadimalla, S.Y., & Maher, M.L. (2025). AI Literacy as a Core Component of AI Education. AI Magazine

AAAI_Spring_2024__Expanding_Capacity_and_Diversity_in_Lifelong_AI_Education.pdf

Maher, M. L., & Tadimalla, S. Y. (2024, May). Increasing Diversity in Lifelong AI Education: Workshop Report. In Proceedings of the AAAI Symposium Series (Vol. 3, No. 1, pp. 493-500).

fie_conference__AI_Literacy_for_All__final_ (1).pdf

Tadimalla, S.Y. and Maher, M.L. (2024). AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculum, Proceedings of 2024 IEEE Frontiers in Education (FIE) Conference.

AI Education Context and Landscape

Broadly, the various ways in which AI literacy has evolved over the last 3 years can be categorized into the following

Technical AI Literacy/Education targets core skills in programming, machine learning, and data science (Ng et al. 2021b; Kreinsen and Schulz 2023) making them available to non-CS/AI professionals.
General AI Literacy equips non-experts to critically and responsibly engage with broader AI technologies they encounter daily (Long and Magerko 2020; Kong, Cheung, and Tsang 2024),
Gen-AI Literacy focuses specifically on the potential benefits and risks of generative models (Chen 2023).
Ethical and Social AI Literacy tackles fairness, accountability, and transparency issues (Zhang et al. 2023; Tadimalla and Maher 2024),
Cognitive or Meta-cognitive AI Literacy enhances problem-solving, decision-making, and self-reflective capabilities in an AI-driven work environment.
With emerging focus areas of AI literacy being funded in the Virtual and Augmented Reality (VR & AR) environments (Cao and Dede 2023; Sokołowska 2023; Herath, Mittal, and Kataria 2024).

There is also a jagged landscape of efforts in directions that are focused on preparing for a Artificial General Intelligence (Bikkasani 2024) and Agentic AI (Acharya, Kuppan, and Divya 2025).

More comprehensive AI literacy initiatives—grounded in socio-technical principles—are emerging to address broader societal implications (Servin et al. 2024; Touretzky et al. 2019; Lee et al. 2024).

(Top) AI literacy is the core course for various AI education pathways, concentrations, and specializations offered currently across the country; (Right) AI Career pathways and fields

The learning outcomes vary across the dimensions of technical proficiency and socio-technical awareness.

We illustrate how different roles—ranging from Naive AI Users to Responsible AI Creators—occupy distinct positions along these axes of AI knowledge, AI skills, AI attiudes and perceptions.

By mapping these roles onto the four pillars of AI learning (technical foundations, sociotechnical considerations, ethical perspectives, and user-focused competencies), we can better identify the pathways into AI as a field and the interventions needed to build comprehensive AI literacy to support those aspirations.

The 17 knowledge areas that compose the CS2023 curriculum. An instantiation of the curriculum at a college or university as a course of study would follow a sunfower model, including the full set of CS Core topics (in red) and selected other knowledge areas (the blue core plus additional elective topics in gray).

Across the various AI Literacy conceptualizations, course design is often informed according to whether the literacy measurement instruments are subjective or objective in nature.

Subjective assessments rely on interpretive, self-reported data, which can capture more nuanced or contextually rich information but may be influenced by biases and inaccuracies.
In contrast, objective assessments utilize stan- dardized tasks and criteria to yield less biased results, albeit within a narrower scope.

Building on this distinction, the paper maps emerging AI literacy scales from the past year onto four core competence areas—Knowledge/Cognition, Skills/Behavior, Values/AI Mental Model/Attitude, and Perception/Self-Efficacy—reflecting a continuum of technical and socio-technical competencies, as illustrated.

This Figure provides a visual summary of how these competencies align with our Four Pillars of AI literacy, offering a more comprehensive and inclusive framework for identifying which competencies are being assessed, by whom, and under what conditions.

AI Literacy Modules, Learning Objectives and Outcomes

The methodology for building this curriculum began with a literature review on AI literacy, focusing on identifying existing frameworks that outline key concepts taught by educators across the spectrum of technical and socio-technical approaches in AI literacy and AI education courses across the various types of emerging AI literacies mentioned above (see papers above).

The insights from the literature review (as discussed in the background section) were further supplemented by applying the Delphi method to presentations, observations, and discussions from the below efforts

An NSF-funded workshop ”Increasing Diversity in Lifelong AI Education” organized and facilitated by the authors
The 2024 AAAI Spring Symposium on ”Increasing Diversity in AI Education and Research” co-organized by the second co-author.

These 3-day workshops on AI education and literacy brought together researchers, policy experts, and educators to respond to the increasing concerns and opportunities raised by recent AI developments, and discuss directions for lifelong AI education.

Our curriculum was then refined based on experiences from teaching this content in various settings, including a 2-credit general education course, 3-credit CS- major undergraduate and graduate courses, and a summer camp for middle and high school students.

Below we show the concepts taught across the 16 modules across the 4 pillars of AI Literacy. Previous work and exploratory literature on some of the components of these modules for further exploration can be found in Tadimalla and Maher’s (2024) research on the AI ecosystem.

While this page shows each Module in terms of the topics and expected learning outcomes, the course materials (Linked here) integrate content from diverse resources such as textbooks, online courses, academic papers, and interactive tools. These materials include online videos (e.g., Code.org, IBM Technology Platform), plug- and-play applications, sample codes for LLM API integration, news and policy articles, and access to platforms like DeepLearning.AI, Coursera, and edX to adapt the course for different contexts. Hands-on tools such as Jupyter Notebooks and Google Colab can significantly enrich the learning experience.

AI Literacy for All poster.pdf

By weaving together social and technical perspectives, our approach democratizes AI education for students from diverse backgrounds, equipping them with the critical multidisciplinary mindset needed to navigate—and shape—the future ethical landscape of AI technology.

Author Details

Yash Tadimalla is a final year Ph.D. student in the College of Computing and Informatics at UNC Charlotte, where he is pursuing an interdisciplinary degree in Computer Science and Sociology. His research explores how an individual's identity influences their interaction with and learning of technology, particularly in the domains of Artificial Intelligence (AI) and Computer Science (CS) education.

At UNC Charlotte he is assisting various NSF research projects under the Center for Education Innovation (CEI) Lab and the Human-Centered Computing (HCC) Lab. As the Technology Focal Point for the UN Major Group for Children and Youth Science-Policy Interface and President-elect of the World Student Platform for Engineering Education and Development (SPEED), He advocates for the equitable advancement of STEM education, mental health, and tech advocacy on a global scale.

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