Your students don't need another semester of AI theory. They need one week of building it.
Most AI courses teach about AI. Students leave knowing the vocabulary, the history, the theory — and nothing they can actually use on day one of a job.
The fastest path to AI literacy is doing. One week of building real things with real AI tools changes how students think about work — permanently. Not what AI is. What AI does. What they can do with it.
The gap isn't knowledge. It's capability. And capability only comes from doing.
The course "Emerging Technology" is built around one principle: no slides without proof. Every concept is immediately applied. Students don't watch demonstrations — they build working AI workflows themselves.
"Studierende berichten, dass sie mehr gelernt haben als in einem ganzen Semester. Nicht wegen des Inhalts — wegen der Art, wie sie gearbeitet haben." — Program Director, IMC Krems
Slides are the wrong medium for teaching tools. You cannot learn to ride a bike from a slide about bikes. AI is the same.
Students who watch demonstrations learn observation. Students who build learn capability. The entire course is structured around doing: each concept introduction is 5–10 minutes, followed immediately by student application.
The constraint of "no slides" forces the instructor to teach through demonstration and forces students to learn through action. That's the point.
A course that generates real student outcomes. Documented experiments, working prototypes, and a cohort that leaves knowing how to operate AI systems — not just discuss them.
The tools covered shift as the landscape evolves — that's intentional. The meta-skill is learning how to evaluate and extend any AI tool, not memorize this year's stack.
Current coverage: large language model interfaces, workflow automation tools, agent frameworks, and output evaluation methods. Students leave knowing how to stay current, not just what's current.
If your program has specific tools or domains (healthcare AI, legal AI, marketing automation), the course adapts. The framework is tool-agnostic; the application is field-specific.
The lightest path: replace one existing module or course week with a build-sprint format. Students bring their domain knowledge; the course adds AI capability. No new department, no new hire. One visiting instructor, one week, measurable outcomes.
Nothing specific to AI. Basic digital literacy (can use a laptop, familiar with the web) is sufficient. The course starts from zero and builds fast. Domain knowledge in any field is an asset — students apply AI to what they already know.
Assessment is outcome-based: does the student's prototype work? Can they explain how it works? Can they extend it? No traditional exams. No essay submissions. Works across different grading frameworks — contact to discuss integration with your institution's requirements.
Yes. The format runs in-person and remotely. Remote cohorts use shared collaborative AI environments and async check-ins between live sessions. The build-first principle applies regardless of format.
Tell me about your program. I'll respond personally.