aiMOOC

ABI - Artificial intelligence and work

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ABI - Artificial intelligence and work

Mini-description: This aiMOOC helps you analyse how artificial intelligence may change tasks, occupations and skills. You work with current, serious evidence, distinguish observed developments from estimates and FORECASTS, and practise English for the Baden-Württemberg upper secondary level. The course is designed for a shared Basisfach / Leistungsfach setting: BF uses a smaller evidence set and more scaffolding; LF connects more complex materials and places greater emphasis on analysis, evaluation and transfer.

Level: B2, with selected C1-oriented language and analysis. Difficulty comes from thinking and source work, not from artificially difficult English.

Last source and Abi check: 3 October 2026.

Media entry: What is changing — the job or the tasks?

Look first. Do not search yet. In 60 seconds, note three activities a human worker in this setting might still perform and three activities technology might support. Then compare: Did you describe whole professions, or individual tasks?

Listening starter — BBC Learning English: Watch without a transcript first. Catch the main idea and three useful expressions about the future of work. On a second viewing, check how cautiously the speakers talk about future developments. Do not copy a full transcript.

I-can goals

After this course, I can ...

World of work: distinguish a task, a job, an occupation and a skill.
AI: explain why AI exposure is not the same as automation or job loss.
Source literacy: identify whether a claim is observed evidence, a model-based estimate, a survey expectation or a forecast.
English: summarise, analyse and evaluate non-fiction material at B2 and, where appropriate, C1-oriented level.
Listening: extract claims, evidence and uncertainty markers from authentic English media.
Speaking: argue about AI and work with evidence rather than slogans.
Writing: write a structured article, speech manuscript or personal response using verified source material.
Mediation: select relevant information from German material and communicate it appropriately in English.
Abi skills: apply reading, analysis, visual-material and communication skills in an exam-oriented way.

Prior knowledge: quick diagnostic

Write T = task, O = occupation, S = skill next to each item: checking invoices, accountant, negotiating with a client, critical reading, writing a first draft, nurse. Then explain one borderline case to a partner.

Self-check: An occupation is usually a bundle of many tasks. A skill is an ability used to perform tasks. AI may affect some tasks inside an occupation without removing the whole occupation.

Input: AI, tasks, professions and skills

Core idea: use a task lens

A headline such as “AI will replace accountants” is too broad for serious analysis. A profession contains many different tasks: collecting information, checking results, communicating with people, making decisions, documenting work and taking responsibility. AI systems may be able to support some of these activities while being unsuitable for others.

Key distinction: exposure means that AI could technically affect part of the work. It does not prove that the task is automated, that firms adopt the technology, that productivity rises, or that a job disappears.

Visual task: Name what is automated in the picture. Then list what still has to happen around the machine: planning, maintenance, safety, quality control, coordination or exception handling. This is the difference between analysing a machine and analysing a job.

How AI works — only as much as you need here

Modern AI systems can recognise patterns and generate outputs from data. For this course, you do not need to explain the mathematics of neural networks. You do need to understand that an AI output is not automatically correct, neutral or suitable for a workplace decision. Human checking, context and responsibility remain relevant.

Evidence snapshot 2025–2026

Source	What the evidence says	What kind of claim is it?
ILO, 2025	About one quarter of global employment is in occupations with some potential exposure to generative AI; 3.3% of global employment falls in the highest exposure category. Clerical occupations remain especially exposed.	Exposure estimate. It measures technical potential across tasks. It is not an observed job-loss rate.
OECD, 2026	AI can affect work through automation of existing tasks, creation of new tasks and occupations, and productivity changes. Most workers are unlikely to need advanced AI-development skills; digital, data, managerial and human skills remain important.	Synthesis of current evidence. Effects differ by occupation, sector and context.
Cedefop, 2025	Among European workers already using AI, 30% reported that some tasks had disappeared, 41% reported new tasks, and 68% said AI mainly helped them complete tasks faster. Only 15% of adult workers had taken AI-related training in the previous year.	Worker survey / self-report. Useful evidence about experience, but not proof that AI alone caused every change.
Eurofound, 2026	EU27 evidence through 2025 does not support the claim that generative AI has caused a youth-specific employment shock. Hiring slowed for both younger and prime-age workers after the unusually strong 2022 labour market.	Observed labour-market analysis. This does not prove that no future effect will occur.
World Economic Forum, 2025	Employers surveyed expected major job and skill changes by 2030. The report projected 170 million jobs created and 92 million displaced across several macrotrends, not AI alone, and expected close to 40% of core skills to change.	FORECAST / Zukunftsannahme. Employer expectations for 2025–2030, not observed future facts.

Rule for this course: Whenever a source speaks about 2030 or another future date, write FORECAST or use cautious language such as is projected to, employers expect, may, could or under the report's assumptions.

Visual evidence: job postings are not the whole labour market

Read the map carefully: It shows the share of job postings that mention at least one AI-related skill in 2025. It does not show the share of all workers using AI, nor the probability that a worker will lose a job.

Germany focus: Describe the trend without explaining it first. Then propose two possible explanations. Finally, state what extra evidence you would need before claiming that AI has changed total employment.

Listening and viewing: three levels of evidence

World Economic Forum, 2025 — Jobs and Tasks. Before watching, predict three terms you expect to hear. While watching, separate statements about the present from statements about 2030. After watching, label every future claim FORECAST.

ILO background video, 2023. Use this as historical research context, then compare its message with the updated ILO 2025 evidence in this course. Which ideas remained stable? Which numbers or methods were updated?

BF route: Use the BBC video plus either the WEF or ILO video. Record five keywords and one cautious conclusion.

LF route: Use all three media. Compare purpose, evidence base, date, language of certainty and one methodological limitation. Link at least two media to a current written source.

Reading: five short source cards

Card A — ILO: The 2025 index scores nearly 30,000 occupational tasks and groups occupations by potential GenAI exposure. The ILO stresses that most occupations still contain tasks requiring human input. Its conclusion about transformation being more likely than full replacement is a research-based expectation, not an observed future outcome.

Card B — OECD: A 2026 synthesis distinguishes exposure from automation risk. High-skilled occupations can be highly exposed because they contain many information-processing tasks, yet their non-routine cognitive and social tasks may make full automation less likely.

Card C — Cedefop: Survey evidence shows simultaneous task removal and task creation among AI users. This supports a transformation perspective, but survey answers describe workers' experiences and perceptions; they do not by themselves establish causality.

Card D — Eurofound: EU labour-market data through 2025 do not show a youth-specific AI employment shock. This matters because some early US studies suggested weaker outcomes for young workers in highly AI-exposed occupations. Different countries, periods and measures can lead to different findings.

Card E — WEF: The Future of Jobs Report asks employers what they expect by 2030. Its job-creation and displacement figures combine AI with other macrotrends such as demographics, the green transition and economic change. Treat these figures as scenarios based on employer expectations, not as guaranteed outcomes.

Source-check routine

Before using a number, ask four questions: Who collected it? What exactly was measured? Which population and time period does it cover? Is it observation, exposure estimate, expectation or forecast?

Weak statement	Better academic statement
AI will destroy 92 million jobs.	The WEF's 2025 employer survey projects 92 million displaced jobs by 2030 across several macrotrends; this is a forecast, not an observed AI-only outcome.
One in four jobs will be automated.	The ILO estimates that about one quarter of global employment is in occupations with some GenAI exposure; exposure is not the same as automation.
Young people are losing jobs because of AI.	Eurofound's EU27 analysis through 2025 does not find a youth-specific AI employment shock; causal effects remain an open research question.

Context vocabulary and word formation

Word family	English explanation	Useful collocation
automate – automation – automated	make a process run partly or fully by technology	automate a routine task
augment – augmentation	support or extend human capability rather than fully replace it	human-AI augmentation
employ – employee – employer – employment	words connected with paid work and the people or organisations involved	employment rate
expose – exposure	be open to possible influence or effect	occupational exposure to AI
displace – displacement	move work or workers out of an existing role or task	job displacement
skill – skilled – reskill – upskill	develop abilities for current or different work	reskill workers
predict – prediction – predictive	state what is expected to happen	predictive model
transform – transformation – transformative	change the form or structure of something	task transformation

Help ladder: English first

Use help in this order.

Context: infer the meaning from the sentence, chart or image.
Word family: identify prefix, root and suffix, for example re + skill + ing.
Visual clue: use the chart, task map or picture.
English definition: explain the word in simpler English.
Monolingual dictionary: check meaning, collocation and pronunciation.
Bilingual dictionary: use only if the English explanation is still insufficient.
Deutsch als letzte Hilfe: exposure = Ausgesetztsein bzw. potenzielle Betroffenheit; augmentation = Unterstützung/Erweiterung menschlicher Arbeit; displacement = Verdrängung. Do not translate whole source texts.

Analysis: from data to argument

Use the three-step method describe – analyse – evaluate.

Describe: What can you directly observe in the material?

Analyse: How does the material frame human control, automation or responsibility?

Evaluate: What can the material support as evidence, and what cannot it prove?

BF: Use one visual plus two source cards. Build one paragraph with a topic sentence, two pieces of evidence and one limitation.

LF: Connect two visuals and at least three sources. Compare their methods and populations before drawing a qualified conclusion.

Speaking

BF task: Prepare a two-minute statement on: AI is more likely to change tasks than erase whole professions. Use one current source and one example occupation. Then answer two partner questions.

LF task: Give a four-minute evidence-based mini-presentation comparing two occupations with different task profiles. Include one counterargument, one methodological limitation and one forecast clearly marked as a future assumption.

Useful language: The evidence suggests ...; This source measures ... rather than ...; A limitation is ...; By contrast ...; This forecast assumes ...; The available data do not prove that ...

Writing

Practice prompt: Write an article for a school careers magazine: “AI will change tasks faster than professions.” Discuss.

BF scaffold: introduction – task/occupation distinction – evidence from two sources – one counterpoint – conclusion. Suggested practice length: 180–250 words. This is a course practice range, not an official Abi word limit.

LF extension: connect at least three sources, distinguish observed evidence from forecasts, compare one method, and evaluate a counterclaim. Suggested practice length: 300–400 words. This is a course practice range, not an official Abi word limit.

No replacement essays: Do not submit an AI-generated full essay as your own. If your teacher allows AI-assisted planning or language checking, keep your own reasoning visible and verify every factual claim against the original source.

Mediation

German source note — fictional data: „Ein fiktiver mittelständischer Betrieb testet ein Assistenzsystem für Kundenanfragen. Beschäftigte berichten, dass Standardanfragen schneller bearbeitet werden, während schwierige Fälle mehr Abstimmung benötigen. Die Personalabteilung plant Schulungen zu Datenschutz, Qualitätskontrolle und Gesprächsführung. Es liegen noch keine belastbaren Daten dazu vor, ob sich die Zahl der Stellen verändert.“

Task: A British partner school asks what the trial shows. Mediate the relevant information into concise English. Do not translate sentence by sentence. Make clear that the company data are fictional and that no employment effect has yet been established.

Responsible learning and privacy

This course does not require an external AI account. Do not upload personal data, school work, voice recordings or images to an external service without informed permission and a clear educational reason. Speaking recordings are voluntary and may stay private on your own device. Use fictional workplace data for simulations unless your teacher provides cleared material. Verify AI-generated information and sources independently.

Feedback and error analysis

Common problem	Repair strategy
Treating exposure as job loss	Name the measured variable precisely: exposure, adoption, use, productivity or employment.
Presenting a forecast as fact	Add the source, date, horizon and an uncertainty marker such as projects, expects or may.
Confusing correlation with causation	Say is associated with unless the study design supports a causal claim.
Using one statistic without population	Add place, group and time period.
Writing “AI says”	Name the actual institution, dataset or study.
Replacing analysis with summary	Explain how evidence supports, qualifies or challenges the claim.

Peer feedback code: E = evidence precise; M = method named; F = forecast marked; L = limitation included; R = reasoning clear; G = language issue. Give one strength and one next step.

Abi alignment Baden-Württemberg: checked for 2027

For the 2027 general-education Gymnasium Abitur in Baden-Württemberg, the official English focus topic is On the Move: Migration and Cross-Cultural Encounters, with the specified compulsory works and the USA/UK thematic fields. Therefore, Artificial intelligence and work is not presented here as an official 2027 Schwerpunktthema. It is an exam-oriented transfer course for functional communication, non-fiction analysis, visual material, speaking, writing and source evaluation.

The 2027 Facherlass states that the Leistungsfach written exam includes listening and writing, with reading comprehension, analysis and a personal or creative writing task; visual materials such as charts can be included. Basisfach and Leistungsfach lead to the same CEFR target level, while the LF requires greater complexity, breadth and depth; in the oral exam guidance, LF places more emphasis on AFB II/III and BF on AFB I/II.

Official check: Ministerium für Kultus Baden-Württemberg – Abitur information and Facherlass Abitur 2027, Stand 09.09.2025.

Abi check: can you do this without a text wall?

Reading: Can you state what a source measures before quoting its result?
Listening: Can you separate present evidence from future expectations?
Analysis: Can you explain structure, wording, statistics and communicative strategy?
Visual material: Can you describe a chart before interpreting it?
Speaking: Can you respond flexibly and support a claim with evidence?
Writing: Can you use cautious language where evidence is uncertain?
Transfer: Can you apply the task lens to a profession not discussed in the course?

Follow-up course

Continue with ABI - Artificial intelligence and society, ABI - Data, algorithms and bias, ABI - Future of work or ABI - Media literacy and AI. Reuse the same source-check routine: measure, population, date, evidence type, limitation.

Interaktive Aufgaben

Quiz: Teste Dein Wissen

What is a task in labour-market analysis? (A specific activity carried out within work) (!A complete economic sector) (!A guaranteed future profession) (!A type of employment contract)

Which statement best matches the ILO 2025 finding? (About one in four workers is in an occupation with some GenAI exposure) (!One in four workers has already lost a job to GenAI) (!One in four occupations is fully automated) (!One in four workers develops AI systems)

What does AI exposure mean? (AI has the technical potential to affect some tasks) (!A job will certainly disappear) (!A company has already adopted AI) (!A worker has already been replaced)

What did 68 percent of AI-using workers in the cited Cedefop evidence mainly report? (AI helped them complete tasks faster) (!AI removed every task in their job) (!AI guaranteed a pay rise) (!AI ended the need for training)

What does the cited Eurofound 2026 analysis conclude about the EU through 2025? (It finds no youth-specific AI employment shock) (!It proves AI can never affect youth employment) (!It shows all hiring increased after 2022) (!It measures only United States workers)

According to the cited OECD 2026 synthesis, which statement is most accurate? (Most workers are unlikely to need advanced AI-development skills) (!Every worker will need to become a programmer) (!Human skills are becoming irrelevant) (!Only manual jobs are exposed to AI)

How should the WEF 2030 job figures be labelled? (As a forecast based on employer expectations and several macrotrends) (!As observed employment data from 2030) (!As an AI-only job-loss count) (!As a legal requirement for employers)

Which topic is the official English Schwerpunktthema for the Baden-Württemberg Abitur 2027? (On the Move Migration and Cross Cultural Encounters) (!Artificial intelligence and work) (!Digital labour markets only) (!Robotics in manufacturing)

What is a central difference between BF and LF in the official 2027 oral-exam guidance? (LF requires greater complexity breadth depth and more AFB II and III) (!BF and LF have different CEFR target levels) (!BF uses only German source texts) (!LF does not analyse non-fiction)

Which sentence shows appropriate academic caution? (The report projects that some skills may change by 2030) (!The future data prove what will happen) (!Exposure always causes unemployment) (!Every forecast is a fact)

Memory

Task	Single activity within work
Occupation	Bundle of work activities
Exposure	Potential technological effect
Augmentation	Human work supported by technology
Reskilling	Learning for changed or different work
Forecast	Evidence-based statement about a possible future

Drag and Drop

Ordne die richtigen Begriffe zu.	Thema
Observed evidence	Labour-market data already measured
Exposure estimate	Technical potential across tasks
Worker survey	Self-reported workplace experience
Forecast	Future assumption based on a model or expectations
Causal claim	Statement that one factor produces an effect

Kreuzworträtsel

Exposure	What term describes potential technological impact without proving automation?
Automation	What term means that technology performs a process or task automatically?
Augmentation	What term describes technology supporting human capability?
Reskilling	What term means learning skills for changed or different work?
Occupation	What term means a bundle of work tasks associated with a profession?
Forecast	What term describes a statement about a possible future development?

LearningApps

Lückentext

A profession contains many individual

. AI

does not automatically mean that a job will disappear. The ILO 2025 index estimates potential effects at the level of occupational

. OECD evidence stresses the difference between automation and human-machine

. Cedefop workers reported both disappearing and newly created

. Eurofound found no youth-specific AI employment

in EU27 data through 2025. WEF statements about 2030 must be marked as a

. Serious analysis names the population, period, method and main

. In academic English, future uncertainty should be expressed with verbs such as

.

Offene Aufgaben

Leicht

Task map: Choose one familiar profession and draw six tasks around it. Mark which could be supported by AI and explain why.
Vocabulary: Build eight word-family cards from the course and add one useful collocation to each.
Media analysis: Choose one Commons image from the course and write three observations and one interpretation.
Listening log: Watch the BBC video and note five keywords, two uncertainty markers and one question you still have.

Standard

Source comparison: Compare ILO and Cedefop. Explain how an exposure index differs from a worker survey and why that matters.
Data commentary: Analyse the Germany AI-job-postings chart using describe, analyse and evaluate.
Speaking: Hold a three-minute pair discussion on whether schools should teach AI literacy as part of career preparation. Use two verified sources.
Mediation: Turn the fictional German workplace note into a concise English message for an international school partner.

Schwer

Evidence audit: Find a current public claim about AI and jobs. Trace it to the original source and classify it as observation, estimate, expectation or forecast.
Occupation comparison: Compare two occupations with different task structures and explain why equal AI exposure would not necessarily mean equal automation risk.
Mini debate: Defend and challenge the statement that AI mainly transforms jobs rather than removes them. Use at least three sources and identify what remains uncertain.
Research design: Design a small ethical classroom study to investigate how AI changes a task. Use only fictional or anonymous data, no compulsory recordings and no external uploads.

Lernkontrolle

Transferanalyse: You receive a new chart claiming that AI-intensive sectors grew faster. Explain what additional evidence you need before making a causal claim.
Quellenkritik: Two reports disagree about young workers. Develop three methodological questions that could explain the difference without assuming that one source is dishonest.
Task versus profession: Apply the task lens to a nurse, electrician or teacher and show why whole-job predictions can be misleading.
Forecast language: Rewrite an overconfident paragraph about 2030 so that every future statement accurately reflects its evidence status.
Materialverknüpfung: Build one argument that combines an image, a survey result and a labour-market analysis. Explain the different evidential value of each.
Mediation und Adressatenbezug: Explain the central findings to a German careers-information audience without translating the English sources sentence by sentence.

Lernnachweis

For a successful learning record, you should be able to:

distinguish task, job, occupation, profession and skill;
explain exposure, automation, augmentation, upskilling and reskilling;
separate observed evidence, estimates, surveys and forecasts;
identify population, period, method and limitation of a source;
analyse a non-fiction text or visual material with evidence;
use cautious and precise English when outcomes are uncertain;
compare BF and LF expectations without confusing difficulty with language obscurity;
complete a short speaking or writing task independently;
document corrections after feedback and explain what you improved;
apply the source-check routine to a new AI-and-work claim.

OERs zum Thema

Seriöse Quellen und freie Medien

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