IELTS Task 2 · Work
IELTS Task 2 Work Essay: Band 6.5, 7.5 and 9.0 Samples
Three complete responses to an automation and employment discussion question, with examiner band scores across all four IELTS Writing criteria and annotated commentary.
What you will see on this page
- ✓ Three complete essays — Band 6.5, 7.5 and 9.0 — on the same question
- ✓ Criterion-by-criterion scores (TR · CC · LR · GRA) for each response
- ✓ Examiner commentary explaining exactly why each band was awarded
The Question
Task 2 · Discussion + Opinion
Some people believe that the introduction of artificial intelligence and automation will lead to widespread unemployment and social problems, while others argue that technology has always created more jobs than it has destroyed.
Discuss both views and give your own opinion.
Write at least 250 words. Spend approximately 40 minutes on this task.
Band 6.5 Response
Band 6.5Nowadays, many people are worried about the effects of artificial intelligence and automation on jobs. Some people think that these new technologies will cause many people to lose their jobs and create social problems. Others believe that technology always creates more jobs than it destroys. This essay will discuss both views and give my opinion.
On the one hand, people who are worried about automation say that machines and computer programs can now do many jobs that humans used to do. For example, in factories, robots can replace large numbers of workers, so companies do not need to employ as many people. Also, AI programs can do some office jobs such as data entry and customer service. This means that many people could lose their jobs and face serious financial difficulties in the future.
On the other hand, people who are more optimistic believe that technology has always created new jobs in the past. When the industrial revolution happened, many people lost their traditional jobs, but new industries created even more employment. The same thing happened with computers and the internet, which created millions of jobs in the technology sector. So some people argue that AI and automation will also create new industries and new types of work over time.
In my opinion, automation will cause some problems in the short term, but new jobs will appear over time. However, the government needs to help workers to learn new skills so that they can find work in new industries. Without retraining programmes, many workers will struggle to find employment and this will cause social problems.
In conclusion, although automation may destroy some jobs, it is likely to create new opportunities if governments provide enough support for affected workers.
Band 7.5 Response
Band 7.5The rapid advance of artificial intelligence and automation has prompted heated debate about its consequences for employment. Pessimists predict widespread job losses and social dislocation, while optimists draw on historical precedent to argue that technological progress consistently generates more opportunities than it eliminates. My view is that the transition will be disruptive for specific sectors, but ultimately productive, provided that governments invest in workforce retraining.
Those who foresee widespread unemployment highlight the unprecedented scope of modern automation. Unlike previous technologies, AI systems can perform cognitive as well as physical tasks, threatening white-collar professions — legal research, accounting, and medical diagnostics — that were once considered immune to mechanisation. This breadth of potential disruption, they argue, means displacement could occur faster than new roles emerge, leaving large segments of the workforce in prolonged difficulty.
However, historical experience offers a more optimistic perspective. The mechanisation of agriculture displaced millions of rural workers in the nineteenth century, yet the subsequent growth of manufacturing absorbed them and raised living standards considerably. Similarly, personal computers eliminated typing pools and bookkeeping roles while creating software development, IT support, and digital marketing as entirely new fields. Each technological wave has ultimately expanded employment, even when the transition proved difficult for affected workers.
Balancing these arguments, I believe the key variable is not the technology itself but the policy response. Economies that invest proactively in adult education, digital literacy, and income support during transitions can capture the productivity gains of automation while limiting harm to displaced workers. Without such investment, those gains will accrue narrowly to capital owners, deepening rather than resolving inequality.
Band 9.0 Response
Band 9.0The prospect of large-scale automation displacing human labour has generated intense commentary across economics, politics, and popular culture. Sceptics contend that artificial intelligence poses a structural threat to employment, while historical optimists point to centuries of evidence that technological revolutions ultimately expand the range and quantity of available work. I will argue that both positions contain genuine insight, but that the decisive factor determining outcomes will be the quality and speed of governmental policy intervention during the transition.
The pessimist's case draws its force from the qualitative novelty of AI. Where earlier mechanisation substituted for physical effort, advanced machine learning systems can now replicate analytical and communicative functions once considered the exclusive domain of educated professionals. This compression of the skill premium — the wage advantage that previously accrued to cognitive workers — has led labour economists to project structural, rather than merely cyclical, unemployment across sectors from legal services to medical imaging.
Conversely, the historical record provides compelling grounds for scepticism about such predictions. The anxieties surrounding early industrialisation, computing in the 1980s, and offshoring in the early 2000s all preceded net increases in employment. Each displacement created adjustment costs, yet the productivity gains from new technology consistently generated demand for novel roles that earlier forecasters had failed to anticipate. The emergence of social media management, cloud architecture, and data science as major employment categories within a single generation illustrates this generative capacity.
The weight of evidence therefore suggests cautious optimism, provided the precondition holds: that fiscal and educational institutions adapt sufficiently quickly to equip workers for roles that AI cannot readily replicate. Absent this, the gains of automation will accumulate to capital holders alone, sharpening rather than resolving economic inequality.
Criterion-by-criterion comparison
| Criterion | Band 6.5 | Band 7.5 | Band 9.0 |
|---|---|---|---|
| Task Response | Both views identified; examples uncritical; opinion stated but not argued | AI's cognitive reach identified as qualitatively different; clear position with policy condition | Distinguishes structural from cyclical unemployment; three-way argument; fully developed throughout |
| Coherence & Cohesion | Mechanical markers; ideas listed not developed; comma splices reduce clarity | Referencing alongside markers; logical paragraph structure; topic-to-implication progression | Entirely organic; no mechanical markers; 'conversely', 'yet', 'absent this' carry the argument |
| Lexical Resource | Accurate but simple; 'financial difficulties', 'struggle to find employment' — Band 6 range | 'Dislocation', 'mechanisation', 'accrue' used correctly; range appropriate for Band 7 | 'Compression of skill premium', 'generative capacity', 'precondition' — precise formal register |
| Grammatical Range | Mostly accurate simple sentences; comma splices and sentence-initial 'So' reduce range | Range of complex structures; minor hedging imprecision; errors do not impede meaning | Embedding, passives, nominalisations — full range, all accurate and purposeful; virtually error-free |
What pushes your band up
6.5 → 7.5 Four targeted changes
- 1.
Move beyond generic examples. The industrial revolution and the internet appear in almost every candidate's essay. Name specific jobs at risk — radiology, legal research, data entry — or cite specific emerging roles. Concrete specificity lifts Task Response above the predictable.
- 2.
Identify AI's qualitative difference. Earlier mechanisation replaced physical effort; AI can replicate cognitive tasks. Flagging this distinction — even in one sentence — shows you understand why this debate is different from previous technology waves and earns the analytical credit that lifts TR to 7.
- 3.
Reduce 'also', 'and', 'so' between ideas. Replace coordinating conjunctions with subordinating ones: 'whereas', 'given that', 'provided that'. This signals logical relationships rather than just listing points, and immediately improves both CC and GRA scores.
- 4.
State your position in the introduction. Examiners need to know your stance from the opening paragraph to assess whether the whole essay is coherent. A position stated only in the conclusion cannot receive full credit for Task Response or Coherence and Cohesion.
7.5 → 9.0 Four targeted changes
- 1.
Distinguish structural from cyclical unemployment. Displacement that resolves as new industries grow is cyclical; permanent skill obsolescence is structural. Naming this distinction demonstrates the analytical precision that separates Band 9 Task Response from Band 7 development.
- 2.
Frame perspectives analytically, not formulaically. Replace 'proponents argue that' with 'the pessimist's case draws its force from...' This signals meta-commentary — you are analysing the logic of each position, not simply reporting it — which is what Band 9 Task Response requires.
- 3.
Nominalise to raise register and compress ideas. 'The compression of the skill premium' carries more analytical weight than 'skilled workers earning less'. 'Labour market absorption' replaces 'people finding new jobs'. Nominalisation signals command of academic English at the Band 9 level.
- 4.
Earn your conclusion with a conditional structure. 'The weight of evidence suggests cautious optimism, provided the precondition holds' is more analytically precise than 'in conclusion, I believe automation will be positive'. Conditional reasoning in the conclusion signals you have genuinely integrated both perspectives.
Common questions about Task 2 work and technology essays
How do I write about automation without specialist knowledge of AI?+
Focus on effects, not mechanisms. You do not need to explain how machine learning works — write about what automation does: it displaces certain job categories, changes skill requirements, and redistributes economic gains. Terms like 'technological advancement', 'AI systems', and 'machine processes' are sufficient. IELTS rewards analysis of social and economic impact, not technical knowledge.
Is it acceptable to argue that automation is entirely positive for employment?+
Yes, if you support the position with evidence and reasoning. Historical examples — industrialisation creating net manufacturing jobs, computers creating software and IT support roles — are legitimate and widely accepted. IELTS rewards a consistent, well-supported position, not a particular view. Fully committing to one side is often stronger than hedging unless the question specifically asks you to discuss both views.
How do I avoid repeating the word 'automation' in every sentence?+
Rotate naturally between synonyms and related noun phrases: 'technological advancement', 'AI systems', 'machine processes', 'new technologies', 'these developments', 'such innovations'. Referencing with 'this', 'these changes', or 'the technology' also reduces repetition. Vary at the paragraph level too — open a paragraph with 'The rapid advance of AI...' and continue with 'these systems' rather than 'automation' again.
What does 'discuss both views and give your own opinion' mean exactly?+
Structure: one body paragraph fully develops View A with evidence and implication; a second body paragraph fully develops View B in the same way. Your opinion should emerge from this analysis — often integrating both views with a qualifying condition ('both are partly correct, but the key variable is...') — rather than sitting in a separate third section. State your position in the introduction and return to it in the conclusion.
What grammar errors most commonly lower scores in Task 2 essays about work?+
The most frequent: article errors with countable nouns ('the job market', 'the workforce' need 'the' because they refer to specific systems); wrong prepositions in collocations ('impact on employment', 'result in job losses' — not 'impact to' or 'result to'); and verb-noun collocations ('make a profit', 'create jobs', 'gain skills' — not 'do a profit' or 'win a job'). These are Band 6-level errors that prevent a GRA score of 7 or above.
See exactly what band your essay would receive
A qualified IELTS examiner marks your Task 2 essay against all four official criteria and returns detailed written feedback within 48 hours.