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Course Overview
In a world where artificial intelligence accelerates information, analysis, communication, and decision-making, the ability to think critically is more essential than ever. AI can help leaders generate ideas, summarize information, and explore possibilities quickly, but it cannot determine what is accurate, ethical, relevant, or wise. This workshop helps participants use critical insight to validate AI-assisted output, challenge assumptions, reduce bias, and apply sound human judgment to leadership decisions.
In this workshop, participants learn to use AI as a thinking partner while keeping critical thinking at the center of the process. Using the same four-section flow as Thinking with Critical Insight, participants explore how open-mindedness, analysis, COACHES characteristics, LAB, fallacies, evaluation, and SWOT can be applied to AI-supported work. The course emphasizes practical leadership habits: ask better questions, organize information, test outputs, reduce bias where possible, and decide responsibly.
Looking for a half-day or open enrollment option for yourself or your team? Find our public course schedule here.
This course will earn you 7 PDUs
Who Should Attend
This workshop is designed for anyone who does knowledge work and wants to get more done with less manual effort. No technical background is required. No prior AI experience is needed. If you have a recurring process that takes more time than it should, this session is for you.
Roles that benefit most include project coordinators, operations professionals, analysts, administrative professionals, team leads, and anyone who regularly produces written work, manages information, or coordinates across teams.
Course Objectives
- Apply the four major steps of critical thinking to AI-supported decisions and workflows
- Recognize the COACHES characteristics of successful critical thinkers and apply them to responsible AI use
- Explain sources of human and AI-enabled bias and how to minimize them through objectivity, validation, and open-mindedness
- Evaluate, identify, and distinguish between relevant, irrelevant, unsupported, and AI-generated information
- Use curiosity and stronger questions to improve AI prompts, human reasoning, and final judgment
- Understand how to apply critical thinking best practices in daily leadership work when using AI tools and outputs
Course Outline
An Introduction to Modern Critical Thinking in the Age of AI
- The Challenges of Modern Thinking When AI Accelerates Information
- Why Critical Thinking Becomes More Important, Not Less Important
- Becoming Rational Thinkers in an AI-Supported Workplace
- Distinguishing Thinking, Critical Thinking, and AI-Assisted Thinking
- Open-Mindedness, Multiple Perspectives, and Prompted Perspectives
- Non-Linear Thinking with AI as a Thinking Partner
- Activities: Prompt, Perception, and Perspective; AI-Assisted Problem Exploration
Utilizing Analysis and Logic with AI as the Resource
- Four Steps to Utilizing Analysis and Logic
- Prompting as Questioning: Defining the Problem Before Using AI
- Asking the Right Questions of the Situation and the Output
- Organizing Human, Verified, and AI-Generated Information
- Evaluating Accuracy, Relevance, Completeness, Logic, and Risk
- Drawing Conclusions Based on Facts, Not Fluency
- Building Better Processes Instead of Fire-Fighting
- Activities: AI-Supported Recommendations by Thinking Style; AI-Specific Critical Thinking Challenges
COACHES: Characteristics of Critical Thinking for AI-Ready Leaders
- COACHES as a Leadership Model for Responsible AI Use
- Curiosity: Asking Better Prompts, Follow-Ups, and Challenges
- Objectivity: Separating Confidence from Evidence
- Active Listening: Using AI to Prepare, Not Replace, Human Understanding
- Contextualization: Providing Business, Stakeholder, and Risk Context
- Humility: Treating AI Output as Draft Until Validated
- Emotional Control: Slowing Down Under Pressure, Conflict, or Urgency
- Self-Awareness: Spotting Assumptions and Preferred Conclusions in Prompts
- Activity: Ranking Your AI-Ready COACHES Characteristics
Performing Better Evaluations and Analyses of AI-Supported Work
- Applying LAB: Limitations, Assumptions, and Bias to AI Outputs
- Identifying What AI Does Not Know, Cannot See, or May Distort
- Recognizing AI-Era Fallacies and Strengthening Reasoning
- Separating Truth, Validity, and Persuasive Language
- Using Diagnostic, Formative, and Summative Evaluation for AI Initiatives
- Reducing Bias and Validating Outputs Before Trusting Them
- Conducting an AI-Informed SWOT Analysis
- Activities: AI Fallacies in Action; AI-Enabled Strategic Planning Implementation SWOT