Heidelberg AI Curriculum

5books in this track

L01 · User

Who is affectedOnly you

Make One Image or Narrated Clip, and Label It

Make one finished image or 60-second narrated clip for your own work. Let the generator supply the look, keep exact words and structure in your own editor, check the rights for your use, and label what AI made before anyone sees it.

Outcome: One finished image or clip with its AI label, its alt text or captions, and the prompt that produced it; it passes when a colleague finds no wrong word, no missing label and no input you lack the rights to use.

User15 minLast edited: Sep 23, 2026

L02 · Power user

Who is affectedYour team sees a bad document or a wrong figure

T09-L02

Keep One Look Across Your Team's Images

Turn one good image into a style kit your whole team can use: a one-page style sheet, three references you have the rights to, a prompt frame and a template. Then test it the only way that counts: a colleague makes a matching image from the kit without asking you.

Outcome: A shared style-kit folder (style sheet, three references with their sources, prompt frame, template, label line) plus five finished images from it, one made by a colleague; it passes when a third person can't pick out the colleague's image and finds no reference without a recorded source.

Power user17 minLast edited: Sep 23, 2026

L03 · Builder

Who is affectedColleagues depend on something you built

T09-L03

Build a Caption Pipeline Your Team Can Rerun Every Week

Turn weekly recordings into reviewed captions without one person clicking through them: a small script that drafts captions with a pinned model and your glossary, a test set with known transcripts, a named reviewer for every item, and a README a deputy can follow. It passes when the deputy runs a week without you.

Outcome: A pipeline folder (script, settings, glossary, manifest, test recordings with known transcripts, check script, README) plus one week of reviewed captions; it passes when the deputy runs the check and one week's recordings on their own computer from the README alone, the check passes, and every published caption file has a named reviewer.

Builder20 minLast edited: Oct 12, 2026

L04 · Integrator

Who is affectedCompany or lab systems and real data are in the loop

T09-L04

Transcribe Recordings That Must Stay in the Building

Run Whisper on a workstation your organisation controls when recordings may not leave it: check that the model fits and is fast enough, get the data protection officer and IT to sign the data route, give the job the smallest access that works, and keep a tested way to stop, delete and undo.

Outcome: A signed one-page data route plus a working local transcription setup; it passes when the test clips pass the check with the local model, fit and speed are measured on your own hardware, the job account is shown to be unable to read outside its folder, one approved real recording has a reviewed transcript, and a colleague has run the stop-and-delete procedure from the run sheet.

Integrator23 minLast edited: Oct 12, 2026

L05 · Operator

Who is affectedWork stops when it breaks, and it must survive you leaving

T09-L05

Keep Every Published AI Image, Voice and Video Labelled, Cleared and Traceable

Run AI media for your whole institute or company over months: one register of every asset with its tool, rights, consent, label and approver, label rules that meet the EU AI Act, a monthly check, a tested way to pull an asset back, and a deputy who can answer an auditor without you.

Outcome: A media register and a one-page runbook in a place the organisation owns; it passes when every AI asset published in the last quarter has a complete register row, one monthly check has been run and logged, a takedown drill has been done on one asset, and the deputy answers an auditor's question about a randomly chosen asset from the register alone within five minutes.

Operator18 minLast edited: Sep 23, 2026