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Welcome to the Friday edition of our newsletter. We spend Fridays going deeper into tools and trends related to generative AI (and Tuesdays sharing news updates).

This year, the Innovation Profs teamed up for the first time to offer a six-week class called Applied Generative AI for Professionals in Drake University's Master of Arts in Communication program.

Not only did the students create high-quality projects for themselves and build AI tools for others, many of them noted they had little idea Gen AI could do the things we were assigning them to do. We’ll call that a win.

For those of you considering grad school, or those of you just curious what we teach on the graduate level of gen AI, here's a rundown of the class.

The first three weeks of the class were focused on some fundamentals that we felt each student needed before jumping in to building with AI.

Week 1: This was an overview of generative AI and large language models (the models - like GPT 5.5 and Claude Opus 4.8 - that power our AI text tools). Students explored four different text cases for LLMs, including writing structured prompts using our BRIEF method and making LLMs face off in the Arena.

Week 2: The second week also focused on large language models. Students built libraries of reusable prompts in their line of work and tested advanced tools including Deep Research and simple AI agents (Gemini Gems, Copilot Agents and ChatGPT GPTs).

Week 3: The third week was focused on multimodal AI, or what Gen AI tools can do for images, audio and video. This is an area where AI has moved forward rapidly in the past 12 months. Students were surprised to see tools like ChatGPT Images 2.0 could make publishable images that match a company’s colors and fonts. The assignments here were mostly to explore the tools - things like build a synthetic voice using Elevenlabs, clean up some low-quality audio with Adobe Enhance Speech and use Descript to turn a short script or blog post into a video.

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Now that the students had a baseline of the tools, how to use them and what they can do, we could move onto the fun part of the class - putting ideas together to build with AI.

Week 4: The fourth week of the class focused on vibe coding and AI productivity tools. Students leaned how to write code using Claude Artifacts, ChatGPT Canvas, and Lovable. We also discussed what AI can do in tools we use everyday such as Microsoft 365 and Google Workspace. The homework this week included brainstorming (with AI) and ultimately creating a game, app, or tool.

Week 5: Week 5 was focused on AI ethics, policy and governance. Students were tasked with analyzing and improving an existing AI policy or to taking the first steps to draft a potential policy for their organization.

Week 6: The final week was a fun one (maybe more for us that the students). Just as we did in our undergrad class, students had to use the knowledge from this class to build an AI tool that solves a real-world problem for someone else. They met with a client, researched solutions, built a prototype, got feedback and put together a finished project.

In six weeks, we took a group of working professionals in our grad program from thinking that Gen AI was mostly text tools to vibe coding tools that solve problems for them and for others. Interested in doing the same? Everything we taught in this class (minus us grading the assignments) is available in our Gen AI Fundamentals course. Sign up today.