AI Literacy for Students

Module 0: Introduction and Goal of this Systemwide Initiative

This foundational module introduces the UNC System AI Foundational Skills Initiative. Learners explore the overarching goals, framework, and structure of the course, along with essential course mechanics including formatting, activities, assessments, disclosures regarding AI use, and information on the course development team and supported AI tools.

Module 1: AI Fundamentals and the College Toolkit (Goal 1 – Understand)

Learners cover the technical underpinnings of artificial intelligence, practical prompt engineering strategies, and methods for integrating AI into academic workflows.

• 1.1 What is AI and Generative AI? Deconstructs the core concepts, underlying technologies, and mechanisms behind modern AI and generative AI models, ending with a knowledge check.
• 1.2 Explaining How AI Tools Work: Explores predictive modeling, output topologies, and prompt engineering frameworks. Includes hands-on exercises for creating study aids, critiquing AI outputs, and performing policy audits.
• 1.3 AI in Your Academic Life: Focuses on leveraging generative AI as a personalized learning partner for research, critical thinking, productivity, and time management while establishing core guardrails.
• Module 1 Check-Out: Evaluates overall foundational AI literacy through instructor-graded assessments (offered with AI-enabled and non-AI options) and a comprehensive module quiz.

Module 2: Benefits, Challenges, and Pitfalls (Goal 2 – Evaluate)

Learners analyze both the transformative opportunities and operational risks of using generative AI across academic, professional, and personal environments.

• 2.1 The Assess-Plan-Learn Framework: Introduces a structured framework designed to help students evaluate tasks, plan strategic AI usage, and optimize their learning outcomes.
• 2.2 The Promise: Benefits of Strategic Use: Examines how deliberate AI integration enhances writing, productivity, feedback loops, professional workflows, and creative collaboration.
• 2.3 The Perils: Challenges and Pitfalls: Highlights the limitations, risks, and potential drawbacks of over-relying on AI across academic, workplace, personal, and environmental settings, paired with reflective exercises.
• Module 2 Check-Out: Synthesizes module concepts through an applied evaluation project and a final knowledge assessment quiz.

Module 3: Responsible Application and Workflow (Goals 3 & 4 – Use)

This module establishes clear ethical boundaries, policy guidelines, and practical workflows for responsible AI deployment.

• 3.1 Foundational Knowledge for Responsible Use: Covers the essential principles required to maintain ethical boundaries when working with AI tools.
  • 3.1.1 Academic Integrity Principles: Defines core academic integrity standards in the context of emerging technologies.
  • 3.1.2 Distinguishing Between Using AI for Assistance and Completion: Clarifies the boundary between AI-assisted learning and non-academic task completion.
  • 3.1.3 Understanding Institutional Policies Regarding AI Usage: Teaches students how to locate, interpret, and adhere to specific institutional and course-level AI policies.
  • 3.1.4 Core Ethical Risks: Details key societal and technical risks, including data privacy, algorithmic bias, copyright concerns, and intellectual property.
• 3.2 Applied Ethical and Responsible Practices: Focuses on real-world execution, covering proper citation methods, transparency practices, ethical decision-making frameworks, and case study analysis.
• Module 3 Check-Out: Tests mastery of AI transparency, citation standards, and institutional policies through an instructor-graded audit lab and a module quiz.

Module 4: Discipline-Specific Uses (Optional)

An exploratory module detailing how generative AI tools apply to specific academic and professional fields.

• 4.1 AI in Art & Design: Examines generative image models across media domains—still images, motion graphics, 3D spatial design, and audio—alongside mixed-modality workflows, best practices, and industry terminology.
• 4.2 AI in MathematicsAnalyzes the intersection of mathematics and artificial intelligence.
  • 4.2.1 The Mathematics of AI (and AI for Math): Covers the underlying mathematical principles that power AI systems and how AI aids discovery and education.
  • 4.2.2 Generative AI for Learning Mathematics: Explores AI as a math learning partner, addressing common technical pitfalls, avoiding over-reliance, and encouraging productive struggle.
• 4.3 AI in English Studies: Explores how AI transforms reading, research, and writing processes while emphasizing authorial voice, authenticity, and human critique.
• 4.4 AI in Business: Demonstrates practical business applications of AI, offering six concrete enterprise use cases and hands-on exercises.
• 4.5 AI in Health Sciences: Surveys clinical decision support, administrative operations, public health regulation, and ethical considerations surrounding health equity and human-AI collaboration.
• 4.6 AI in Library and Information Science: Examines how AI tools impact archival preservation, information organization, public library services, and collection management systems.

Module 5: Industry Perspectives (Optional)

Provides access to external, industry-aligned learning pathways, featuring specialized AWS Skill Builder courses to build market-ready technical skills.

Course Completion & Certificate

Outlines the criteria for earning the official UNC System Co-branded Certificate, granted upon successfully completing Modules 1, 2, and 3.