Training course · Prompt Engineering · Education & Teaching

Prompt Engineering for Educators

Vendor-neutral prompt engineering: roles, context, few-shot examples, prompt chains, prompt libraries and systematic quality checks.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Prompt Engineering for Educators” is a hands-on course for roles such as Teachers in general and vocational schools, University lecturers and Trainers in adult and continuing education. Rather than learning Prompt Engineering in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Education & Teaching.

Whether you use ChatGPT, Claude, Copilot, Gemini or Le Chat, the quality of the output depends largely on how the task is framed. Prompt engineering is the skill of conveying goal, context, constraints and desired format to a language model so that it reliably produces usable results. In this vendor-neutral practical day you learn proven techniques that work across all mainstream AI assistants: defining roles and audiences, selecting relevant context, working with examples and breaking complex tasks down into traceable steps. You try out every technique directly on tasks from your own working day.

The second part focuses on repeatability and quality across the team. You develop prompt templates with placeholders, build multi-step prompt chains for analysis, writing and review processes, and set up a shared prompt library. You learn to evaluate output systematically – with review criteria, test cases and targeted self-checks by the model – and to avoid typical pitfalls such as hallucinations, ambiguity and missing context. By the end you have a structured approach that works regardless of the tool you use and continues to pay off as new models arrive.

Schools, universities, adult education providers and corporate academies are undergoing a fundamental shift with generative AI. Educators use AI assistants to differentiate teaching materials, create tasks at different levels, prepare feedback and write course descriptions. At the same time, AI challenges established assessment formats, coursework and the very notion of independent work. Educational institutions therefore have two jobs: to make their own work lighter in sensible ways and to equip learners to use AI competently and critically. A pedagogically sound approach frees up time for individual support, while careless use risks lower quality, data protection issues and unclear grading criteria.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Teachers in general and vocational schools
  • University lecturers
  • Trainers in adult and continuing education
  • L&D and corporate academy managers
  • Heads of school and programme directors

Typical challenges we address

  • Mixed-ability groups that need differentiated materials
  • Heavy workload for marking, feedback and documentation
  • Uncertainty about assessment and independent work in the age of AI
  • Unclear rules on which tools may be used with which data

What you will be able to do

  • You understand how language models process prompts and derive effective wording from this.
  • You structure prompts with role, context, task, constraints and output format.
  • You use few-shot examples and prompt chains for complex, multi-step tasks.
  • You create reusable templates and build a shared prompt library.
  • Create differentiated teaching and learning materials with AI while safeguarding subject accuracy.
  • Develop assessment and task formats that deal sensibly with AI use.
  • Draft practical rules for privacy-compliant AI use in your institution.

Course outline

Module 1 How language models process prompts
  • Core principles of language models: probabilities, context window, limitations
  • Why wording, order and examples change the result
  • What causes hallucinations and how prompts reduce them
  • Differences and common ground between mainstream AI assistants
Module 2 Building blocks of an effective prompt
  • Defining role, audience, task, context and output format
  • Stating constraints, exclusions and quality criteria
  • Structured input with sections, delimiters and tags
  • Asking the model to raise questions instead of making assumptions
Module 3 Advanced techniques
  • Zero-shot, one-shot and few-shot: setting style and structure with examples
  • Requesting step-by-step reasoning for complex tasks
  • Prompt chains: separating research, drafting, review and revision
  • Using change of perspective, counter-arguments and model self-critique
Module 4 Templates and prompt libraries
  • Creating reusable templates with placeholders and variables
  • Storing persistent instructions in projects, custom assistants and agents
  • Building, naming, maintaining and sharing a team prompt library
  • Documenting templates: purpose, inputs, expected output, owner
Module 5 Quality assurance and responsible use
  • Defining review criteria and scoring rubrics for AI output
  • Setting up test cases and systematically comparing and improving prompts
  • Verifying facts, figures and sources; establishing the four-eyes principle
  • Data protection, confidential content and transparency under GDPR and the EU AI Act
Module 6 Practice lab: Education & Teaching
  • Differentiating materials
  • Generating tasks and exercises
  • Preparing feedback
  • Course and module planning
  • Building learners' AI literacy
  • Communication and administration
Module 7 Data protection, quality & transfer to daily work
  • Pupil and learner data, grades and special needs information are highly sensitive and may only be processed in approved systems, ideally anonymised; national and regional education and data protection rules apply. Grading and examination decisions remain with educators, and AI use in assessed work must be governed by transparent rules.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Differentiating materials

From a source text, AI creates versions for different ability levels, language proficiency or support needs – with glossaries, guiding questions and extension tasks that the teacher checks and adapts.

Generating tasks and exercises

Practice questions, case studies, quizzes and model answers are created to match learning objectives and competence levels and can be imported into learning platforms. Variants for resits are easy to produce too.

Preparing feedback

Based on a marking rubric, AI drafts constructive, criteria-based feedback on anonymised work, which the educator adds to, weighs and takes responsibility for. Learners receive detailed feedback sooner.

Course and module planning

AI supports outline and lesson planning for teaching units, seminars or trainings, suggests changes of method and drafts module descriptions with clear learning outcomes. Timings and material lists are included.

Building learners' AI literacy

Educators design tasks in which learners use AI deliberately, verify its output and reflect on their process, rather than simply banning or ignoring AI use.

Communication and administration

Letters to parents, participant information, meeting minutes and FAQ answers are drafted faster, prepared in several languages and brought into a consistent tone. Key dates and deadlines are highlighted.

Prerequisites

Some initial experience with at least one AI assistant such as ChatGPT, Claude, Copilot or Gemini is helpful; no technical background is required. Experience in teaching, training or education management; no technical background required.

Technical requirements

A laptop with an up-to-date browser and access to at least one AI assistant, ideally the tool approved within your organisation.

Data protection & compliance

Pupil and learner data, grades and special needs information are highly sensitive and may only be processed in approved systems, ideally anonymised; national and regional education and data protection rules apply. Grading and examination decisions remain with educators, and AI use in assessed work must be governed by transparent rules.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Fri, 27 November 2026 Online Request
  • Tue, 1 December 2026 Augsburg Request
  • Mon, 14 December 2026 Online Request
  • Fri, 15 January 2027 Kaiserslautern Request
  • Tue, 19 January 2027 Online Request
  • Wed, 3 February 2027 Bremen Request

Frequently asked questions

Do these techniques work with every AI assistant?

Yes, the course is deliberately vendor-neutral. The principles – a clear task, relevant context, examples, structured output and review steps – work equally well in ChatGPT, Claude, Copilot, Gemini, Le Chat and other tools. Where assistants differ in detail, for example with projects or custom assistants, we show the corresponding features in each.

Won't better models soon make prompt engineering obsolete?

Models are getting better at interpreting vague requests, but they still cannot know what you do not tell them: audience, quality standards, internal requirements or relevant background. Good prompts are therefore mainly good task descriptions. Templates, prompt chains and quality checks also keep their value for teams as new models arrive.

What is a prompt library and how do we maintain it as a team?

A prompt library is an organised collection of proven templates for recurring tasks, each with its purpose, required inputs, a sample result and an owner. It can live in a shared document, a wiki or directly in the AI tool's projects. In the course you set up a first library and agree simple rules for maintenance and approval.

Can I let AI mark my students' work?

AI can prepare feedback drafts on anonymised texts, but grading remains your responsibility. Names, marks and support information do not belong in unapproved tools. In the course we practise anonymising work, applying rubrics and critically weighing AI suggestions. Responsibility stays clearly with you.

How should I deal with AI-written coursework?

Bans alone fall short, and AI detectors are unreliable. More effective are formats that make process and reflection visible, such as oral components, learning journals or disclosed AI use. The course presents concrete task formats and policies that allow independent work to be assessed fairly.

How much does “Prompt Engineering for Educators” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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