AI in Practice: Preparing for AI Course

Lesson 1 • How AI Models Learn

Explains supervised, unsupervised, and reinforcement learning at a conceptual level. Helps learners understand why AI behaves the way it does.

Lesson 2 • The Current AI Landscape

Maps the major categories of AI tools available to professionals today. Provides context for evaluating new tools as the landscape evolves.

Lesson 3 • Types of AI Systems

Surveys narrow AI, generative AI, and autonomous agents by function. Connects each type to real workplace scenarios professionals encounter.

Lesson 1 • Known Failure Modes

Examines hallucination, bias, and brittleness as structural AI weaknesses. Prepares learners to anticipate errors before they cause harm.

Lesson 2 • Evaluating AI Output Quality

Teaches practical methods for verifying, fact-checking, and grading AI responses. Directly supports responsible use in professional workflows.

Lesson 3 • Matching Tasks to AI Capabilities

Provides a decision framework for choosing when AI adds value vs. when it introduces risk. Builds judgment that applies across all future chapters.

Lesson 1 • Avoiding Common Prompting Mistakes

Identifies the most frequent errors that produce poor AI outputs. Prevents wasted effort and builds disciplined prompting habits.

Lesson 2 • Prompt Patterns for Common Tasks

Introduces reusable prompt templates for writing, analysis, summarization, and brainstorming. Accelerates productivity by reducing trial-and-error.

Lesson 3 • Prompting for Accuracy and Safety

Covers techniques that reduce hallucination and steer AI away from harmful outputs. Connects prompting skill to responsible professional use.

Lesson 4 • Iterative Prompting Techniques

Teaches refinement strategies when initial outputs miss the mark. Builds confidence in steering AI toward the intended result.

Lesson 1 • AI for Planning and Decision Support

Leverages AI to generate options, structure plans, and stress-test decisions. Positions AI as a thinking partner rather than an answer machine.

Lesson 2 • AI for Research and Synthesis

Applies AI to gather, summarize, and organize information from multiple sources. Teaches verification habits to maintain research integrity.

Lesson 3 • AI for Written Communication

Uses AI to draft, edit, and adapt professional documents and messages. Demonstrates measurable time savings without sacrificing quality.

Lesson 1 • Transparency and Disclosure

Establishes norms for disclosing AI involvement in professional work products. Builds trust with colleagues, clients, and stakeholders.

Lesson 2 • Privacy and Data Responsibility

Covers what data should never be shared with AI tools and why. Directly protects organizations and individuals from data exposure risks.

Lesson 3 • Core Ethical Principles for AI

Introduces fairness, transparency, accountability, and privacy as foundational AI ethics pillars. Provides a principled lens for every subsequent decision.

Lesson 4 • Ethical Decision-Making in Practice

Applies a structured ethical reasoning process to realistic workplace dilemmas. Converts abstract principles into actionable professional judgment.

Lesson 1 • Organizational AI Policies

Teaches how to read, interpret, and apply internal AI use policies. Connects governance theory to day-to-day professional behavior.

Lesson 2 • Building a Compliance Mindset

Develops habits for staying current with evolving AI rules and organizational policies. Positions learners as proactive compliance contributors.

Lesson 3 • Regulatory Landscape for AI

Surveys global regulatory trends around AI transparency, safety, and accountability. Prepares learners to recognize compliance obligations without jurisdiction-specific detail.

Lesson 4 • Intellectual Property and AI

Addresses ownership questions around AI-generated content and training data. Protects learners and organizations from unintentional IP exposure.

Lesson 1 • Measuring AI Impact

Defines metrics for productivity, quality, cost, and risk outcomes from AI use. Enables evidence-based decisions about scaling or discontinuing AI tools.

Lesson 2 • Identifying High-Value AI Use Cases

Applies a prioritization framework to select AI projects with the highest return. Prevents wasted investment on low-impact or high-risk initiatives.

Lesson 3 • Change Management for AI Adoption

Addresses resistance, communication, and training needs during AI rollouts. Ensures technology adoption translates into sustained behavior change.

Lesson 4 • Assessing AI Readiness

Evaluates data quality, talent, infrastructure, and culture as readiness dimensions. Gives teams a realistic baseline before committing to AI projects.

Lesson 1 • Career Positioning in an AI-Augmented World

Explores how AI reshapes roles, skills demand, and career trajectories. Helps learners position themselves as AI-capable professionals.

Lesson 2 • Tracking the AI Landscape

Identifies reliable sources for staying informed about AI developments and trends. Prevents information overload while ensuring learners stay current.

Lesson 3 • Building an AI Learning Habit

Designs a sustainable routine for experimenting with and reflecting on AI tools. Converts one-time training into ongoing professional development.

Lesson 4 • Identifying Emerging AI Capabilities

Teaches a framework for evaluating new AI capabilities before adopting them. Builds disciplined curiosity rather than reactive tool-chasing.