AI-Enabled Digital Marketing Course

Session Description

This opening module starts with a quick refresher on the main areas of digital marketing (website, search, email, social media, content, advertising, and analytics) and how they connect, then turns to the main focus: how to work with AI well. Students will learn, in plain terms, how AI tools come up with their answers, why they can sound confident and still be wrong, and when AI is (and is not) the right tool for a task. Students will practice a simple three-step process (Start, Talk, Check) that begins with a clear goal, treats AI as a conversation rather than a one-time question, and ends with checking the result before using it. The module also covers what stays the student's responsibility, including getting facts right, protecting privacy and data, respecting other people's work and cultural knowledge, watching for bias, and making the final call. Students will leave with a one-page reference they will use throughout the rest of the course.

Instructional Objectives

• Revisit the main areas of digital marketing and how they connect, seeing where AI fits across them.

• Understand, in plain terms, how AI tools produce answers, why they can sound confident and still be wrong, and when AI is (and is not) the right tool for a marketing task.

• Learn a simple, repeatable process for working with AI (Start, Talk, Check) that goes beyond one-off prompts by giving context, asking follow-up questions, correcting mistakes, and refining the result.

• Understand the limits and responsibilities that come with using AI in marketing, including accuracy, privacy and data use, respecting others' work (intellectual property), bias, and cultural respect.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Name the main areas of digital marketing and, for a given marketing task, decide whether AI is a good fit and explain why.

• Write a first message to an AI tool that includes a clear goal, the target audience, relevant background, and any limits or source material needed for the task.

• Review an AI response, identify at least two problems (such as a made-up fact, a wrong assumption, missing context, or a biased description of people), and use follow-up messages to improve it.

• Use a short checklist to decide whether an AI result needs fact-checking or human review, and name at least one concern related to accuracy, privacy, intellectual property, bias, or data use.

Session Description

AI-powered content creation extends well beyond generating captions or written copy. AI can support the entire content workflow, from research and ideation to writing, visual and video development, editing, transcription, adaptation, and repurposing. This module looks at how students can integrate AI into that process strategically, using it to accelerate and expand creative work without handing over the decisions that require audience understanding, brand knowledge, and human judgment. Students will also examine where AI falls short, including generic or inaccurate outputs, and the considerations around privacy, copyright, ownership, bias, and responsible use.

Instructional Objectives

• Explore where AI adds value across the content creation workflow and where human expertise, creativity, and judgment remain essential.

• Learn how to give AI the context, source material, audience insight, and creative direction needed to produce stronger, more relevant work.

• Develop a process for directing, evaluating, and refining AI-assisted content rather than treating the first output as the finished product.

• Build an understanding of the practical and responsible use of AI in content creation, including accuracy, privacy, copyright, ownership, bias, and brand considerations.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Identify where AI can meaningfully support a content workflow and where student input and decision-making are required.

• Use audience insight, source material, brand context, and creative direction to guide AI toward a specific content objective.

• Critically evaluate AI-assisted content for accuracy, relevance, originality, brand fit, and platform suitability, and refine it accordingly.

• Apply a responsible review process to AI-assisted content before publishing, including consideration of privacy, copyright, ownership, bias, and factual accuracy.

Session Description

This module explores how generative and predictive AI are changing email marketing and lifecycle automation, from smarter, self-updating list segmentation to AI-assisted copywriting, personalization, and send-time optimization. Students will learn specifically where AI tools add speed and insight, and where strategy, brand judgment, and consent still have to lead. A portion of the session is dedicated to the ethical guardrails students need before feeding subscriber data or brand voice into an AI tool, including accuracy, bias, privacy, and authenticity. Students will leave with a repeatable AI prompting framework and a responsible-use checklist they can apply to their next email campaign.

Instructional Objectives

• Identify where AI can automate parts of the email lifecycle, like send-time optimization, audience segmentation, and flow-triggered content, and where human oversight still needs to lead.

• Apply AI-assisted automation concepts (predictive segmentation, send-time optimization, automated flows, and triggers) to strengthen an existing email program.

• Use a structured prompting framework to draft and refine on-brand email copy (subject lines, body copy, personalization variants) with AI tools.

• Evaluate AI-generated email content and targeting against a responsible-use checklist covering accuracy, bias, data privacy/consent, and brand authenticity before sending.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Map at least three specific points in an automated email flow (e.g., welcome series, cart abandonment, re-engagement) where AI can add automation, personalization, or timing improvements, and name one concrete action to take at each point.

• Draft at least one AI-assisted subject line set (three variants) and one AI-assisted body copy draft for an email, using the module's prompting framework.

• Identify at least two ethical risks of using AI in email marketing (e.g., hallucinated claims, biased segmentation, subscriber data privacy) and describe one mitigation step for each.

• Complete a quality assurance pass on an AI-generated email draft using the responsible-use checklist before it would be considered ready to send.

Session Description

Search is rapidly evolving beyond traditional blue links into an AI-driven discovery layer where platforms like ChatGPT, Gemini, and Perplexity deliver direct, synthesized answers to prospective customers. This module demystifies Generative Engine Optimization (GEO) for students looking to adapt organic growth strategies as marketers, founders, or local business owners. Students will learn high-impact tactics to structure and format digital content so AI assistants can easily understand, summarize, and cite their brand. By combining practical technical SEO fundamentals with ethical, human-first AI practices, students will gain actionable tactics to future-proof organic reach across all discovery channels.

Instructional Objectives

• Understand how customer discovery is shifting from standard search engine results pages to AI-generated direct answers.

• Identify practical content formats (such as FAQs, glossaries, comparison tables, and how-to guide) that AI engines can easily see, evaluate, and reference in their answers.

• Learn how core technical SEO foundations and external authority signals work together to place a brand on the trusted shelf of generative engines.

• Apply ethical, human-first content practices to create unique, clear, and concise pages and page sections that AI systems reference in their answers.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Differentiate between traditional keyword-focused tactics and the semantic clarity required to be included inside AI-generated direct responses.

• Reformat an existing piece of digital marketing content (such as a local service page or product description) into a structured, AI-extractable asset.

• Evaluate organic presence across generative search platforms using accessible tools like Google Search Console (to track AI Overview impressions), Otterly AI, and SEMrush One to measure unified SEO and AI visibility.

• Evaluate traffic from AI tools to a website using Google Analytics (GA4).

Session Description

This module shows students how AI can support everyday marketing analytics, from automating calculations like Cost Per Lead (CPL) and Return on Investment (ROI), to spotting patterns in the customer journey (funnel analysis and attribution), to catching bad or unusual data before it skews a report. Students will see how AI turns messy spreadsheets and multiple data sources into clear, plain-language answers, so they spend less time crunching numbers and more time deciding what to do next. The module also covers simple, common-sense rules for using AI responsibly with marketing and customer data. Students leave with practical prompts they can reuse right away, and a short checklist for checking any AI-assisted analysis before trusting it.

Instructional Objectives

• Explain how AI can help automate common marketing calculations, like CPL and ROI, and model simple "what-if" scenarios.

• Use AI to help read a marketing funnel and understand which stage or channel is contributing most to results (attribution).

• Use AI to spot possible errors or unusual patterns in marketing data before trusting it for a decision.

• Apply a short responsible-use checklist any time AI is used to analyze marketing or customer data.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Use AI to calculate CPL and ROI for a sample campaign, and ask AI to model at least one "what-if" scenario (e.g., a change in conversion rate).

• Use AI to summarize which stage of a sample funnel looks weakest and state one possible reason why.

• Spot at least one likely data error or anomaly in a sample dataset with AI’s help (e.g., a spike that looks like bot traffic) and describe one step to verify the number before trusting it.

• Complete a responsible-use checklist on a piece of AI-assisted analysis before treating it as final.

Session Description

This final module shows students how AI can be a genuine partner in building and running a marketing strategy, not just for writing content, but for thinking through the plan itself: figuring out a goal, an audience, a message, and the right channels, and then creating and improving content across social media, email, paid ads, and search (including AI-powered search, or GEO). It also covers simple, common-sense rules for using AI responsibly, including the fact that AI tools and industry guidelines are still changing fast, so students feel confident rather than overwhelmed. Students leave understanding that AI is not just a content tool; it is a resource they can turn to at every stage of building an effective marketing strategy.

Instructional Objectives

• Explain what a marketing strategy needs (a goal, an audience, a message, and the right channels) and describe how AI can help think through each one.

• Use AI to help draft content across the core digital channels: social media, email, paid ads, and search/GEO.

• Use AI to help understand what is working with an audience and decide what to try next.

• Use a short checklist to make sure AI-assisted strategy work and content stay accurate, honest, and true to the brand voice, while staying aware that AI tools and rules around them can change.

Measurable Learning Outcomes

By the end of this module, students will be able to:

• Describe how AI can help with each of the four basic building blocks of a strategy (goal, audience, message, channels) and give one example of each.

• Use AI to draft content for at least one channel (social, email, paid ad, or search/GEO) using a simple prompt.

• Use AI to summarize a set of results and suggest one next step.

• Complete the responsible-use checklist on a piece of AI-assisted work before considering it complete, including a quick check for anything that may have changed regarding the tool or relevant rules.