Lecture: 3 hours/week
and
Seminar: 1 hour/week
This course integrates theoretical learning with applied experience using the following methods:
- Case Studies & Industry Engagement: Discussions and analyses of real-world AI marketing challenges.
- Experiential Learning: AI simulations and marketing tool applications.
- Project-Based Learning: Development of AI-driven marketing strategies for real or simulated businesses.
- Work-Integrated Learning (WIL): Engagement with industry professionals through guest lectures, workshops, and networking events.
This course will cover the principles, tools, and strategic applications of Artificial Intelligence (AI) in modern marketing practices.
-
Overview of Artificial Intelligence and Machine Learning in marketing
-
AI’s role in digital transformation and marketing innovation
-
Introduction to key AI marketing tools and platforms
-
AI applications in market research and audience segmentation
-
Predictive analytics and consumer behavior modeling using AI
-
Generative AI for content development across digital channels
-
AI-enhanced SEO and digital advertising techniques
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Personalization and recommendation systems powered by AI
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Consumer behaviour and psychological impacts of AI marketing
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AI strategies in social media marketing and trend analysis
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Ethical considerations, data privacy, and bias in AI marketing
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Current and emerging trends in AI for marketing strategy development
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Development and refinement of AI-driven marketing strategies
By the end of this course, students will be able to:
- Explain AI’s role in marketing, including applications in machine learning, generative AI, and predictive analytics.
- Utilize AI tools for research, content creation, and customer engagement.
- Optimize digital marketing campaigns using AI-driven SEO, PPC, and automation.
- Analyze AI-generated marketing strategies and evaluate their effectiveness compared to human-created content.
- Assess ethical, legal, and strategic considerations in AI marketing applications.
- Develop and present an AI-powered marketing strategy for a business or simulated client.
Assessment will be based on course objectives and will be carried out in accordance with the 51Ç鱨վ 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 Course Outline.
|
Assessment Component |
Weight |
|
Participation |
5-10% |
|
AI Simulations and Applied Exercises |
20-30% |
|
Assignments and Cases |
15-20% |
|
Term Project and Presentation |
25-30% |
|
Final Exam or Capstone Report |
15-25% |
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 51Ç鱨վ Research Ethics Board prior to conducting the research.
Required learning materials may include:
- DMI Advanced AI for Digital Marketing (Core content replacing traditional textbook)
- AI marketing tools: Free versions such as ChatGPT, Midjourney, Jasper AI, Google Gemini, SEMrush, Canva AI, and others.
- Podcast case studies for real-world applications. Example - Marketing Corner Talks Podcast
- Novela Generative AI Skills Simulation (if applicable for hands-on AI skill-building).
Latest edition or equivalent as approved by the Department.
None
None