Training course · Generative AI · Education & Teaching

Generative AI for Educators

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

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

“Generative AI 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 Generative AI in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Education & Teaching.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

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 can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • 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 large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
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

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. Experience in teaching, training or education management; no technical background required.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

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.

  • Tue, 24 November 2026 Online Request
  • Tue, 1 December 2026 Munich Request
  • Tue, 8 December 2026 Online Request
  • Thu, 7 January 2027 Online Request
  • Tue, 12 January 2027 Hanover Request
  • Wed, 27 January 2027 Online Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

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 “Generative AI 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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