This course provides a concise overview of critical thinking and professional skepticism in AI-augmented CPA work. As artificial intelligence increasingly performs drafting, analysis, calculation, research, and other preparation tasks, CPAs are shifting toward higher-value roles involving review, verification, challenge, and professional judgment. Participants will examine professional standards, human cognitive biases, AI failure modes, evidence requirements, questioning techniques, vendor claims, documentation practices, and firm-level controls. The course emphasizes a central principle: AI output should be treated as an unverified claim requiring appropriate human

Prerequisite Knowledge: A general understanding of accounting, auditing, tax, or professional CPA practice is recommended; prior technical knowledge of artificial intelligence is not required.

Topics Covered: 

• Professional skepticism and existing CPA standards. 
• Six traits of the skeptical professional mind. 
• Automation bias and other AI-related cognitive biases. 
• Evidence and verification rules for AI outputs.
• Practical questioning and challenge techniques.
• Vendor claims, benchmarks, and AI due diligence.
• Review, documentation, accountability, and skeptical firm culture.

Upon completion of this course, participants will be able to:

• Explain how professional skepticism applies to AI-augmented CPA work.
• Identify cognitive biases and AI failure modes that can impair professional judgment.
• Analyze AI-generated outputs using appropriate evidence and reliability principles.
• Apply questioning techniques to challenge AI conclusions, assumptions, citations, and calculations.
• Evaluate vendor claims, benchmarks, and AI system performance with appropriate skepticism.
• Design risk-based verification and documentation procedures for AI-assisted work.
• Develop practices that strengthen skepticism, accountability, and professional judgment within CPA firms.