Teaching
Teaching Philosophy & Experience
I approach teaching as an evidence-based, student-centered practice grounded in the power of education, human connection, and applied learning. My classrooms are designed to foster intellectual rigor, collaboration, and long-term professional value.
I intentionally learn my students’ names, actively engage them in discussion, and cultivate peer connection as a resource—both for navigating the course and as a source of lifelong career support.
My goal is to prepare students—whether full-time learners or working professionals—for the realities of today’s data-driven, ethically complex marketing landscape.
My teaching spans:
- Designing new courses from scratch
- Redesigning and coordinating existing programs
- Delivering lectures and interactive tutorials
- Supervising Master’s theses and mentoring career transitions
Courses taught:
- Applied AI for Marketing (Master’s Elective)
- Digital Marketing & Analytics (DMA) – Core course for both full-time and Executive Master’s students
- Quantitative Data Analysis 2 (Bachelor’s, Economics)
Applied AI for Marketing (Master’s Elective)
This course trains students to critically assess and deploy AI in marketing contexts. It is structured around three core literacies:
- Functional AI literacy: How AI works, conceptually and practically
- Ethical AI literacy: How to navigate the ethical issues related to AI (e.g., Fairness, bias, transparency, and responsible use)
- Rhetorical AI literacy: How natural language and AI-generated content persuades and performs
Key Features
- Python-based tutorials in Google Colab
- Deep learning models (e.g., BERT, transformers for text and image classification)
- Business data from YouTube, websites, and social media
- Predictive and causal modeling (e.g., DiD, logistic/linear regression)
- Tutorials on single, multi-agent, and agentic AI
- Live debates on political polarization, fairness, and explainability in AI
- Collaborations with businesses to solve real challenges (e.g., Danone, OHRA, and Lucid Motors)
Sample Student Exercises
- Design custom AI agents for marketing use cases
- Compare zero-shot vs fine-tuned sentiment models
- Use AI to classify emotions, values, or persuasive styles in brand messaging
Outcome
Students complete a strategic portfolio demonstrating fluency in AI tools, data analysis, and critical application to real-world challenges.
Digital Marketing & Analytics (DMA)
DMA is a foundational course for both traditional MSc students and Executive Master’s students with industry experience. It covers performance metrics, customer journeys, and omnichannel campaign design.
AI & Analytics Focus
- Campaign strategy using AI-powered segmentation and persuasion
- Analysis of structured and unstructured data (CRM, clickstream, scraped content)
- Regression and classification modeling with real business datasets
- Personalization, emotion-rich content, and multi-touch attribution frameworks
Innovations
- AI applied to multimodal data and predictive analytics modules
- Use of AI for language and paralanguage classification (topic modeling, sentiment, emotion, stance, etc.)
- Ethical debates on AI surveillance, bias, and political ad targeting
- Applied projects simulating real digital marketing pipelines
Outcome
Students leave the course with a portfolio-ready analytics toolkit and confidence in applying AI to omnichannel strategy and ROI modeling.
Quantitative Data Analysis 2 (Bachelor, Economics)
This statistics course introduces students to foundational quantitative methods using SPSS.
Sample of Topics Covered
- Hypothesis testing and statistical inference
- Linear and logistic regression
- ANOVA and model diagnostics
Teaching Approach
I led lab sessions and offered individualized support, ensuring students built confidence in applying statistical concepts independently.
“Kedma was clear, structured, and incredibly supportive.”
“A true gem—the best tutorial teacher I had.”
🌟🌟🌟🌟🌟 Recognition & Student Feedback
- Elected Best Lecturer by Executive students in the Digital Business Track
- Consistently rated 8+/10 for clarity, engagement, and real-world relevance
- Known for combining academic rigor with warmth, humor, and real-world orientation
- Recognized for mentoring, personalized support, and helping students build lasting professional networks
“Thanks to your teaching style, I had fun while learning AI.”
“She creates a community—her feedback goes beyond the classroom.”
“Challenging, funny, and deeply committed to our growth.”