Interactive learning content · M5-T1-MLC1

Automating Repetitive Administrative Tasks with AI

Less repetitive paperwork, more time for learners

Author: Zsolt LengyeliTStudy Hungary Training and Research Centre
Reviewer: Mária HartyányiiTStudy Hungary Training and Research Centre

Reducing paperwork allows the teacher to spend more time with learners. — Time saved on paperwork is time for learners.

Learning objective

A significant part of teachers’ working time is taken up by routine, repetitive tasks that are not closely related to teaching itself. Every day, they have to write messages, reminders and reports, and fill in different forms. This learning content demonstrates how some of these administrative tasks can be automated with the help of AI.

By the end of this learning content, you will be able to

Decide which repetitive tasks can safely be supported by AI.
Work with placeholders to keep personal data out of AI tools.
Check the accuracy of drafts and maintain professional responsibility.
Build reusable templates and measure the time saved.

Introduction

Too much administration!

In the OECD TALIS 2024 survey, around half of lower secondary teachers – an average of 52% – named administrative work as a source of stress.

Besides registers, reports and parent communication, VET teachers coordinate work-based learning with training companies, keep records of occupational safety briefings, prepare exam documentation and manage workshop materials and equipment.

Source: OECD (2025): Results from TALIS 2024.

A teacher at her desk surrounded by piles of paperwork. — Too much administration! Illustration: ChatGPT.

Humans remain in control

Large language models can be compared to an “excavator” for intellectual work: the machine speeds up the work while the teacher remains in control. If we use AI only for tasks we could also do ourselves, we keep oversight and control in our own hands throughout.

The purpose of using AI is to increase efficiency; professional responsibility remains with us.

Where AI can help

Five task types that can be prepared safely

AI mainly helps with drafting, organising and preparing recurring documents. Suitable input and teacher checking are what make these uses safe.

A letter, table, chart, document and checklist around AI represent the five task types. — Drafting messages, organising information, summarising routine data, preparing templates and supporting documentation.

01Drafting messages
02Organising information
03Summarising routine data
04Templates and checklists
05Supporting documentation

Task types · Communication and organisation

Make the purpose and output clear

Drafting messages

Parent letters about a company visit, reminders about work clothing and personal protective equipment, e-mails to training companies and invitations to an open day are everyday examples.

Give the AI the purpose, audience, key facts and tone, and ask for a short and a longer version. The same message can be rewritten for parents, for learners and for the company mentor.

Organising information

Turn scattered notes into a table, sort tasks by deadline, build a checklist from a long regulation or internal procedure, or compile a material order list from a project plan.

The most reliable results come from structured input and a clearly requested output format – such as a table, checklist or CSV. For internal material, also check institutional approval and confidentiality.

Task types · Calculations, templates and documentation

Check the result and make it reusable

Summarising routine data

Summarise anonymised survey results, count categories in an exported table, or compare this term’s aggregated results with last term’s.

For calculations, ask the AI to use its data analysis function and program code. Always spot-check a few inputs and results yourself.

Templates and checklists

A safety briefing record, a report on a visit to a training company, a field trip plan or a meeting agenda are ideal for AI-prepared templates.

Make the template once and check it thoroughly. After that it can be reused many times, even without AI.

Supporting documentation

AI note-taking in online meetings can produce a structured summary of decisions, tasks and deadlines.

Inform participants before recording, and ask for consent where required. The minutes must be checked before they are sent.

Safe workflow

Work with placeholders, add personal details locally

The safest workflow keeps personal data out of the AI tool altogether. Write the prompt with placeholders such as [LEARNER], [DATE] or [COMPANY].

A prompt with placeholders produces an AI draft; the checked text is completed in the school’s own system. — Personal details are added only in the school’s own system.

1. Prompt with placeholders

Replace real names and details with labels in square brackets. Ask the AI to keep these placeholders unchanged.

2. Checked draft

The AI drafts the text. We check the facts, tone and suitability for the audience.

3. Add details locally

Insert the real details only after checking, in the school’s own system: e-mail, e-register or document.

Safe workflow · Example prompt

One prompt, two message versions

Arranging a visit to a training company

“You are the assistant of a vocational teacher. Write a short, polite e-mail to [COMPANY MENTOR] about the work-based training of [LEARNER], a second-year electrician apprentice. Purpose: to arrange a visit on [DATE] and to ask for a brief assessment of punctuality, safety awareness and teamwork. Tone: professional and friendly. Maximum 120 words. Keep the placeholders in square brackets unchanged. Then write a two-sentence version for a text message.”

What makes it reusable?

A clear purpose and audience.
Specific assessment criteria.
A defined tone and length.
Placeholders kept unchanged.
E-mail and text-message versions.

A shared prompt library

Good prompts are worth collecting in a shared prompt library of the department. If your AI tool allows it, recurring instructions can be saved in a project or a custom assistant.

Never store learner data there. Related learning content: M1-T2-MLC1.

Boundaries · Learner data and confidentiality

School rules apply to every tool

Learner data

Names, grades, absences, health information, special educational needs, family circumstances and disciplinary cases are personal data when linked to an identifiable learner. Some belong to special categories that require particular protection.

Never copy electronic school register data into public AI chatbots. Use only tools approved by your school. If in doubt, ask the school’s data protection officer.

Confidentiality

Exam tasks before the exam, internal staff matters, contracts with training companies and complaints are confidential even if they contain no personal data.

Using placeholders alone therefore does not make every internal document suitable for uploading. Its confidentiality must also be considered.

A school account does not replace local rules

Institutional versions – for example Microsoft 365 Copilot Chat with a school account, or Gemini and NotebookLM in Google Workspace for Education – come with contractual data protection commitments. The protection available also depends on the account, service and institutional settings used.

These commitments do not replace the school’s own data protection rules and approval.

Legal background: GDPR, particularly Articles 5 and 9. Provider information is available in the Sources section.

Boundaries · Accuracy and accountability

We are responsible for what we send and sign

Accuracy

AI can invent dates, figures or references to regulations. Check every fact, deadline and number against the original source.

For calculations, also check a few inputs and results independently. On hallucination, see M1-T1-MLC3.

Accountability

The teacher who sends or signs a document is responsible for it. Grades, justification of absences, disciplinary measures and placements remain human decisions.

Browser agents that can act in administrative interfaces should work in a mode where they ask for approval, and they should not be given access to the school e-register.

Boundaries · Traffic light

What may go into an AI tool?

The colour indicates what kinds of data can be used and under what conditions. Always consider the content, whether individuals can be identified and the school’s rules.

Pseudonymisation is not the same as full anonymisation. Learners may still be recognised from small groups or indirect details.

Green for generic content, amber for data requiring care, and red for identifiable learner data. — Generic content, information requiring care, and information that must stay out of public AI tools.

Green · Generally fine

Generic texts and templates.
Public information.
Your own notes without personal data.
Anonymised and aggregated figures, for example: “12 of 24 learners completed the task”.

Amber · Only with care

Only with an approved tool and under the school’s rules:

Pseudonymised case descriptions.
Data of small groups in which learners could be recognised.
Internal documents.
Meeting recordings, after informing participants in advance.

Red · Never in public AI tools

Names with grades or absences.
E-register exports.
Health and special educational needs information.
Family or disciplinary matters.
Photos of learners.
Passwords and login data.

Start small

Measure the time saved too

Choose two or three recurring tasks, measure how long they take now, build and test an AI-supported template for them, and measure again a few weeks later.

In an English trial, teachers who used ChatGPT together with a short guide spent about 31% less time on lesson planning, with no loss of quality in the materials reviewed (EEF, 2024).

This research result concerns lesson planning. For our administrative tasks, we can establish the actual savings through our own measurements, including the time spent checking.

EEF: ChatGPT in lesson preparation

Time saved on paperwork means more attention for learners. — Time saved is time for learners.

List your recurring tasks for one week and mark each of them green, amber or red.
Start with green tasks; agree on rules with colleagues and the school leadership for amber tasks.
Keep the reusable result – template, checklist or prompt – not only the one-off answer.
Share what works: a common prompt library saves time for everyone. Link to DigCompEdu 1.1, Organisational communication.

Putting it into practice

Three templates and shared rules

Individual exercise · 40 minutes

My admin audit and three safe templates

List one week’s administrative tasks and how long they took. Classify them with the traffic light, then choose three green tasks.

Write a prompt with placeholders for each one.
Test it, check every fact and adjust the tone.
Save the final templates and the prompts.
Use them for a week, then compare the time spent with your earlier estimate.

Add personal details only in the school’s own system.

Group exercise · 45 minutes

Staff-room traffic light

Work in a department or subject team of 4–8 teachers.

Each colleague lists three recurring administrative tasks.
Sort the cards into green, amber or red categories and discuss borderline cases.
In pairs, test prompts with placeholders for two green tasks and save them in a shared folder with notes on what must be checked.
Formulate three to five common rules and list open questions for the school leadership and the data protection officer.

Conditions for the exercises

Use a school-approved tool and invented examples. Never bring real learner documents to the exercise. If possible, involve the school’s data protection officer or IT coordinator.

Detailed instructions, tool lists and lessons learnt are included in the practical exercises PDF available on the final page.

Takeaways for teachers

Five reminders for the coming working week

Start with recurring tasks!
AI helps most with recurring drafting, organising, summarising and template tasks.
Use placeholders instead of personal data!
Add the real details only after checking, in the school’s own system.
Protect learner data!
Never paste e-register data, grades, health or family information into public AI tools.
Check and remain accountable!
Check every fact, figure and deadline. You remain responsible for what you send and sign.
Measure and share!
Start with two or three tasks, measure the time saved including checking time, and share good templates with colleagues.

Self-check quiz

Check your knowledge!

Select one answer for each question, then check your answers. Your self-check result is shown only on this page.

1Which task is a good starting point for AI-supported administration?Analysing a named e-register export in a public chatbot.A generic parent-letter template with placeholders.Automatically deciding a learner’s disciplinary case.

2When should real learner details be added to the document?In the prompt, so the AI can write more accurately.When saving it in the AI tool’s shared prompt library.After checking the draft, in the school’s own system.

3What should we do for calculation tasks?Ask for code-based analysis and independently check a few results.Accept the figures if the explanation sounds convincing.Check only the table formatting.

4Which statement is true about an internal document?It automatically becomes public if it contains no names.It may be confidential even without personal data, so confidentiality must also be checked.It can be uploaded to any AI tool if it is short.

5How should a nameless case description of a recognisable small group be classified?Always green, because the names have been removed.Public information that can be used without restrictions.Amber: only with care and an approved tool.

6What should be included when measuring time spent on an AI-supported task?Prompt writing, checking, corrections and finalising the document.Only the time the AI takes to produce its answer.Only sending the finished document.

Sources

References and further reading

Sources for this learning content

OECD (2025): Results from TALIS 2024: The State of Teaching. Paris: OECD Publishing.
Regulation (EU) 2016/679 of the European Parliament and of the Council (GDPR), particularly Articles 5 and 9.
Education Endowment Foundation (2024): ChatGPT in lesson preparation – A Teacher Choices trial. Evaluation report.
Redecker, C. (2017): European Framework for the Digital Competence of Educators: DigCompEdu. Luxembourg: Publications Office of the European Union. Framework website.
UNESCO (2023): Guidance for generative AI in education and research. Paris: UNESCO.
Lengyel, Zs. (2026): Concrete applications for content generation. VITA training presentation.

Data protection and institutional tools

Credits and copyright

Author: Zsolt LengyeliTStudy Hungary Training and Research Centre
Reviewer: Mária HartyányiiTStudy Hungary Training and Research Centre
Project ID: 2025-1-LT01-KA220-VET-000362456
Learning content: M5-T1-MLC1

Attachments

Download learning contentM5-T1-MLC1-TEXT-EN.pdf Download practical exercisesM5-T1-MLC1-EXC-EN.pdf

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Funded by the European Union. Views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the National Agency.