What is AI Literacy?
Artificial Intelligence (AI) Literacy refers to the ability to understand, use, and think critically about artificial intelligence. It includes knowing how AI tools work, using them responsibly and creatively, and recognizing their strengths, limits, and ethical and environmental impacts. AI literacy helps students make informed decisions about AI in academic, professional, and everyday settings.
Why is AI Literacy important?
AI Literacy is becoming a core skill in today's workplace and academic environments. Many employers are now expecting applicants to understand and use AI tools effectively. Across industries and disciplines, AI can support writing, research, data analysis, design, and decision-making.
Being AI literate means knowing how to use these tools responsibly by identifying errors or bias; understanding their ethical, social, and technical impacts; and recognizing their contribution to climate degradation. These skills help students succeed not only in their careers and education, but also in everyday life as AI becomes more widely used.
Core Competencies for AI Literacy
Ethical Aspects of AI
This area explores the social, legal, ethical, and environmental concerns raised by rapidly advancing AI technologies. Courses that meet this core competency examine how AI influences human decision-making, privacy, equity, labor, governance, and global well-being.
Social Aspects of AI
This area examines how AI shapes, and is shaped by, human societies. Courses explore AI’s influence on culture, communication, identity, work, politics, and social systems. Topics may include human–AI collaboration, digital labor, misinformation and media ecosystems, automation, and how AI can reinforce or challenge social inequalities.
Technical Aspects of AI
This area provides a foundational understanding of the computational methods behind modern AI. Courses introduce key principles, algorithms, and systems that support machine learning and intelligent technologies, balancing theory with hands-on practice. Students work with real data and AI tools to better understand how these systems are built and how they function.
Applications of AI
This area focuses on how AI is applied across industries and disciplines to address real-world challenges. Courses highlight practical use cases, tools, and case studies, giving students experience with current AI technologies. Emphasis is placed on understanding benefits, limitations, and the conditions needed for effective and responsible implementation.
L&S Courses That Incorporate AI Literacy
The following list are courses offered through the College of Letters and Science that incorporate AI Literacy in their curriculum. View the list below to see course codes, course names, descriptions, and the core competencies that the course satisfies. You can find more information about these courses through the General Catalog.
NOTE: The below list is under development; additional courses and their competencies will continue to be added. Please check back regularly for updates.
*For graduate level courses, please review program web sites directly. Note, they are often listed as "special topics" courses.
Chemistry (CHE)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
| CHE115 | Instrumental Analysis | Intermediate theory and laboratory techniques in analytical and physical chemistry. Advanced data analysis methods and goodness-of-fit criteria. Fourier-transform spectroscopic methods and instrumentation. Mass spectrometry. Electrochemistry. Liquid chromatography. |
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| CHE130B | Computational Drug Design | Continuation of CHE 130A with emphasis on case studies of various drugs and the use of computational methods in drug design. |
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| CHE155 | Scientific Programming for Chemistry | Chemical applications of computer programming with Python. Numpy, Scipy, Matplotlib libraries. Multidimensional arrays, data visualization, linear algebra routines. Force fields and molecular dynamics simulations. Numerical integration of differential equations with applications to chemical kinetics. Least squares fitting of experimental data. |
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Cognitive Science (CGS)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
| CGS010/PHI010 | Introduction to Cognitive Science | Introduction to the interdisciplinary cognitive scientific approach to the study of mind, drawing concepts and methods from psychology, philosophy, linguistics, artificial intelligence, and other disciplines. |
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Communication (CMN)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
CMN110
| Communication Networks | Theoretical approaches to communication networks, practical applications of network studies, and network analysis tools. Friendship, political discussion, social support, organizational, social media, and disease transmission networks are examined. Impact of emerging technologies on network creation, maintenance, and expansion. |
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| CMN131/131V | Strategic Communication for Public Relations | Principles, evolution, and professional practice of public relations. Planning and execution of effective, ethical communication strategies and campaigns. Distribution of messages through traditional and new media, including social media. Cultivation of relationships between organizations and their publics. Crisis communication management. |
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| CMN132 | Social Media for Public Relations | Course Description: Uses of social media technologies in contemporary public relations practice. Social and behavioral theories of social media processes and effects. Strategies and tools for authoring content that builds relationships and creates conversations with key publics. |
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| CMN136/136V | Organizational Communication | Organizational communication theory and practice is examined with an emphasis on the use of effective communication strategies for achieving organizational goals. |
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| CMN147 | Children, Adolescents, & the Media | Research on the adaptive and maladaptive effects of media (e.g., television, movies, video games, social media) on the social, emotional, cognitive, and physical development of youth, considering the protective role of parents, teachers, ethics, and policy. |
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| CMN150V | Computational Social Science | Nontechnical survey of modern computational research methods. Web scraping, artificial intelligence, visualizing social networks, and computer simulations. Hands-on use of diverse software applications. Professors from all ten UC campuses contribute. |
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| CMN151 | Simulating Communication Processes | Simulations of communication and sociality using agent-based models. Focus on strategic behavior, cooperation, coordination, self-organization, information diffusion, and other communication phenomena. No programming skills assumed. |
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| CMN152V | Social Science with Online Data | Survey of web-driven social science and its methods. Focus on web scraping and social media API’s. Covers wrangling and analysis of data from social networks, online experiments, and other digital traces. Python programming skills helpful, but not assumed. |
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| CMN170/170Y | Digital Technology & Social Change | Conceptual understanding of how digital communication technologies transform our lives through social media, mobile connectivity, globalization, and big data. Contexts include education, health, entrepreneurship, democracy, and poverty. |
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| CMN172 | Interpersonal Technologies | Theories and research findings on how people use technologies for interpersonal and relational purposes, including impression formation, self-presentation, deception, anonymity, friendship maintenance, online dating, and emotional expression. |
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| CMN174 | Social Media | Application of communication theories to the study and design of social media. Examination of social media in contexts such as political activism and collaboration. Topics include online credibility, participatory culture, viral media and privacy |
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| CMN178 | Persuasive Technologies | Designing and testing ethical, technology-based communication interventions in the domains of health, marketing, education, and environment. Social media, mobile apps, wearable devices, recommendation systems, serious games, and augmented reality. |
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Linguistics (LIN)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
| LIN002 | Language, the Mind, & Computers | Analysis of human language and language processing by humans and machines. How language is represented and processed in the human mind, how computers process language, and similarities and differences between human and computer processing of language. Human-media interaction for language and society. |
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| LIN105 | Topics in Language & Linguistics: Language Technology and Society (Spring 2026) | Language technologies - large language models, chatbots, AI, whatever you want to call them - are becoming ever-more ubiquitous. In this course we will examine the bi-directional relationship between language technology and society. On one hand, these technologies are a product of society. They are mirrors that reflect the humans on whose language they are trained, for good and for ill. They can at times perform surprising feats of apparent creativity or insight, but can also display critical limitations and perpetuate troubling social biases. On the other hand, the reverse is also true - society is increasingly influenced by language technology. Human languages, behavior, and experience are changing as these technologies become more pervasive. Real potential has been demonstrated for impactful applications of language technologies that can address social issues and improve people's lives; at the same time, real concerns have been raised about impacts on privacy, jobs, mental health, and the environment. |
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| LIN127 | Text Processing & Corpus Linguistics | Investigation of the lexical organization of human languages through corpus linguistics. Application of principles of linguistic analysis, automatic text processing, and statistical research to solving problems of textual evaluation and classification, as well as information retrieval and extraction. |
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| LIN177 | Computational Linguistics | Understanding the nature of language through computer modeling of linguistic abilities. Relationships between human cognition and computer representations of cognitive processing. |
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Philosophy (PHI)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
| PHI010/CGS010 | Introduction to Cognitive Science | Introduction to the interdisciplinary cognitive scientific approach to the study of mind, drawing concepts and methods from psychology, philosophy, linguistics, artificial intelligence, and other disciplines. |
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| PHI013 | Minds, Brains, and Computers | Computational theories of the nature of the mind. Mind as a computer process. Possibility of machine intelligence, consciousness, and mentality. |
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| PHI133 | Logic, Probability, & Artificial Intelligence | Introduction to theoretical artificial intelligence with a focus on nonmonotonic logic, Bayesian networks, and learning theory. |
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Mathematics (MAT)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
| MAT165 | Mathematics and Computers | Introduction to computational mathematics, symbolic computation, and computer generated/verified proofs in algebra, analysis and geometry. Investigation of rigorous new mathematics developed in conjunction with modern computational questions and the role that computers play in mathematical conjecture and experimentation. |
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| MAT170 | Mathematics for Data Analytics and Decision Making | Relational model; relational algebra, relational calculus, normal forms, functional and multivalued dependencies, separability. Cost benefit analysis of physical database design and reorganization. Performance via analytical modeling, simulation, and queueing theory. Block accesses; buffering; operating system contention; CPU intensive operations. |
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Science & Technology Studies (STS)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
| STS101 | Data & Society | Basic concepts in data science from a socio-cultural perspective. Identifying data stakeholders and their biases, reading and evaluating data documentation, exploring data through analysis and visualization, identifying knowledge gaps, and assessing data ethics. |
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| STS102 | Artificial Intelligence in Society | Artificial intelligence and machine learning in social context. Implications of AI for scientific, legal, educational, economic, and political systems. History of AI research and development. Debates about privacy, security, authenticity, and public policy in the era of AI, with focus on diversity, social justice, and ethical decision-making. Hands-on exercises and projects using AI that generates text and images. |
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| STS110 | Computing, Data, and Law in the United States | Introduction to the problems in American law and policy borne out of the creation and use of information technologies. Topics include intellectual property, corporate law, privacy, and emerging problems surrounding big data. |
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| STS 114 | The Global Information Age | Introduction to the global spread of information technologies like computers and smartphones. Special focus on their social, cultural, and commercial impact. |
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| STS115 | Data Sense & Exploration: Critical Storytelling with Analysis | Data science and the communication of data insights through critical storytelling. Attention to the historical and social contexts of data analysis, emphasizing narrative, visualization, and exploration. Introduction to the R computing environment for data analysis. |
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| STS176/SOC176 | Sociology of Science | Social, cultural, and historical dimensions of science. Problems, methods, and theories in the sociology of scientific knowledge. Laboratories and field sites as social spaces. Scientific and technical knowledge in institutional and organizational contexts. |
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| STS195 | Research in Data Studies | Analysis of real-world data in the form of case studies engaging current issues. Emphasizes teamwork in the identification of problems and sources of relevant data; data cleaning, exploration, analysis, and visualization using R; and interpretation and presentation of results to a variety of stakeholders in oral, visual, and textual formats. |
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Sociology (SOC)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
| SOC135 | Social Relationships | Social and cultural factors influencing friendships and intimate relationships. Topics include relationship development, relationship maintenance, and relationship loss. |
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| SOC159 | Work, Employment, & Careers in the 21st Century | Historical and contemporary overview of employment, work, and occupations in American society. Study of authority and power relations, labor markets, control systems, stratification, and corporate structures, and how these factors shape work in diverse or organizational and employment setting. | |
| SOC176/STS176 | Sociology of Science | Social, cultural, and historical dimensions of knowledge, especially scientific knowledge. Problems, methods, and theory in sociology of scientific knowledge. Laboratory and historical case studies. Scientific and technical knowledge in institutional and organizational contexts. |
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| SOC195 | Special Topics in Sociological Analysis | In-depth examination of topics in sociology. Emphasis on student research and writing. |
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University Writing Program (UWP)
| Course Code | Course Name | Course Description | AI Literacy Core Competency |
|---|---|---|---|
| UWP011 | Popular Science & Technology Writing | Positioning of science and technology in society as reflected and constructed in popular texts. Topics include genre theory, demarcation, rhetorical figures, forms of qualitative and quantitative reasoning, and the epistemic role of popularization in science. |
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| UWP012 | Writing & Visual Rhetoric | Introduction to writing needs, conventions, and genres in design contexts. Emphasis on applying critical reading, analysis, and writing skills to designed products, such as graphics, visual communications, and clothes, and designed spaces, such as exhibitions and interior architecture. |
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| UWP016 | Writing with AI Coming Soon! | Composing with artificial intelligence (AI) tools; prompting, editing, and refining AI-generated content. Critical evaluation and integration of AI tools into writing processes. Development of professional and academic writing through hands-on projects; emphasis on voice, integrity, and effective AI use in research and drafting.
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| UWP112A | Introduction to Professional Editing | Introduction to general editing practices and principles, with an emphasis on professional editing in organizational contexts, including academia and the workplace. Extensive practice in copy, comprehensive, and collaborative editing. |
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| UWP120 | Rhetorical Approaches to Scientific & Technological Issues Coming Soon! | Application of rhetorical theories to scientific issues. Topics include: Rhetorical dimensions of scientific knowledge-making; scientific voice; rhetorical figures in science; incommensurability and demarcation; epistemology, definition, and classification; science wars; models of scientific literacy and accommodation, and implications for risk communication.
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| UWP150 | Digital Rhetorics Coming Soon! | Rhetorical concepts and processes applied to digital environments with an emphasis on user experience, universal design, and writing for networked publics. Application of rhetorical theory as both an analytic method and as a heuristic for the production of digital texts and performances.
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UC AI Primer: Core Concepts and Fundamentals
Explore the core concepts and gain a foundational knowledge of AI literacy with this free, interactive online course developed by the UC system (UCOP).
Move through the module at your own pace or dive straight into the sections that interest you most.
AI Resources for Students
Explore a curated resource guide designed for UC Davis students to access reputable sources of information and strengthen their AI literacy.
Highlights include:
- AI Guidance (UC Davis)
- Student Guide to Generative AI (UC Davis)
- Advancing Responsible AI at the University of California (UCOP)
- Ethics of AI (UNESCO)
**Note: This is a living document
