Lecture: 2 hours/week
and
Seminar: 2 hours/week
This course integrates theoretical learning with applied experience using the following methods:
- Case Studies & Industry Engagement: Discussions and analyses of real-world AI applications.
- Experiential Learning: AI simulations and accounting software applications.
- Project-Based Learning: Development of AI-driven strategies for real or simulated businesses.
- AI Fundamentals, Human Biases, and Ethical Foundations
- AI Tools, Frameworks, and Techniques
- AI for Core Operational Efficiency
- AI in Auditing, Tax, Compliance, and Risk
- Generative AI in Accounting
- Strategic Application and Emerging Trends
Upon successful completion of this course, students will be able to:
- Explain core AI concepts, generative AI, and how AI is used in accounting while analyzing human and AI biases and applying the CPA AI Credo, professional skepticism, and ethical principles.
- Demonstrate proficiency with leading AI tools, professional frameworks (e.g., ANALYZE, NARRATE, CONNECT), and prompt engineering techniques.
- Apply AI to automate routine accounting workflows, integrate with accounting software, and utilize advanced prompting.
- Apply AI techniques in auditing and taxation while analyzing data security, cybersecurity risks, quantum computing threats, and ways to reduce bias.
- Create professional client-ready reports, financial narratives, visualizations, and communications using generative AI and professional frameworks (e.g. the NARRATE and CONNECT frameworks).
- Develop a personal AI adoption strategy and analyze the impact of automation, agentic AI, and new career roles on the future of accounting.
This is a letter-graded course.
Assessment will be based on course objectives and will be in accordance with the Douglas College Evaluation Policy. An evaluation schedule is presented at the beginning of the course. Instructors may use a student’s record of attendance and/or level of active participation in the course as part of the student’s graded performance. Where this occurs, expectations and grade calculations regarding class attendance and participation will be clearly defined in the Instructor's Course Outline.
| Assessment Component | Weight |
|---|---|
| Participation &/or Weekly Reflections | 10–15% |
| AI Tools Exercises &/or Case Analysis (minimum 5) | 55–60% |
| Final Exam or Capstone Project & Presentation | 25–30% |
| Total | 100% |
STUDENTS MUST COMPLETE ALL COMPONENTS OF THE COURSE AND ACHIEVE A MINIMUM AVERAGE GRADE OF AT LEAST 50 PERCENT ON THEIR TOTAL NON-GROUP EVALUATIONS TO OBTAIN CREDIT FOR THE COURSE.
Note: No single assignment to be worth more than 40%.
Students may conduct research as part of their coursework in this class. Instructors for the course are responsible for ensuring that student research projects comply with College policies on ethical conduct for research involving humans, which can require obtaining Informed Consent from participants and getting the approval of the Douglas College Research Ethics Board prior to conducting the research.
Text books and materials are to be purchased by students. A list of required text books and materials is provided for students at the beginning of the semester. Examples include:
- Free AI tools (ChatGPT, Grok, Claude, Gemini, Microsoft Copilot, Perplexity, DALL·E, HeyGen, NotebookLM, etc.)
- Accounting software (Quickbooks or Sage - student access)
- Selected open educational resources and case studies on AI in accounting
None
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