IELTS Writing Task 2: The impact of artificial intelligence on jobs – Sample Essays Band 6-9 with Analysis

Introduction

Artificial intelligence now shapes how companies hire, automate, and retrain people, so it is no surprise that “The impact of artificial intelligence on jobs” appears frequently in IELTS Writing Task 2. Examiners love this topic because it tests your ability to balance opportunities (productivity, new roles) against risks (displacement, inequality), and to propose practical solutions. In this guide, you will learn how to handle this topic with confidence through three complete sample essays (Bands 6, 7, and 9), detailed scoring analysis, topic-specific vocabulary, and high-scoring sentence structures.

Verified past exam questions on this theme have included:

• “Many jobs, both at home and at work, are being done by robots. Is this a positive or negative development?” (reported by IELTS Liz and IELTS-Blog)
• “Some people think the development of artificial intelligence will cause unemployment; others believe it will create more jobs. Discuss both views and give your opinion.” (reported by IELTS-Blog test-takers)
• “Advances in automation are changing the way we work. Do the advantages of this trend outweigh the disadvantages?” (reported by IELTS preparation sites and official-style practice materials)

For a broader context on mechanization and work, you can explore an adjacent discussion in The impact of automation on jobs, which helps you compare AI with earlier waves of technology change.

1. Question & Analysis

Some people argue that the growth of artificial intelligence will lead to widespread job losses, while others believe it will create more employment. Discuss both views and give your own opinion.

• Question type and requirements:

  • Discuss both views + give your opinion
  • Balanced coverage: explain why AI may cause job losses and why it may create jobs; then give a clear position and support it
  • Minimum of two well-developed body paragraphs, with specific examples and a direct opinion

• Key terms:

  • “Artificial intelligence” includes machine learning, automation, and decision-support systems across sectors (manufacturing, services, healthcare).
  • “Widespread job losses” suggests displacement at scale, not just isolated cases.
  • “Create more employment” implies net job creation via new industries, roles, and complementary tasks.

• Common pitfalls:

  • Overgeneralizing: claiming “all” jobs will vanish or “no” jobs will be affected.
  • Vague examples: failing to name sectors or real tasks affected.
  • Hidden opinion: forgetting to state a clear stance in the introduction and conclusion.

• Strategic approach:

  • Map your ideas: identify 2-3 vulnerable job categories (routine manual or routine cognitive) and 2-3 growth areas (AI maintenance, data ethics, human-centered services).
  • Use balanced topic sentences and concrete examples (e.g., Asian manufacturing, fintech, customer support).
  • Offer solutions or conditions under which benefits outweigh costs (reskilling, lifelong learning, regulation).

When you consider sector-by-sector impacts, it helps to compare public and private domains. For instance, service delivery in cities is changing—see how similar dynamics play out in The impact of AI on public services.

Students writing IELTS essays about AI job impacts

2. Band 8-9 Sample Essay

A Band 8-9 essay presents a nuanced position, precise examples, wide-ranging vocabulary, and consistently accurate complex grammar.

Essay (about 300 words):
In debates about artificial intelligence, the real question is not whether jobs will change, but how societies will manage the transition. While AI will undoubtedly eliminate some roles, especially repetitive ones, it is more likely to reconfigure work than to eradicate it. In my view, AI’s net impact on employment can be positive provided that governments and firms invest seriously in reskilling.

Those who fear mass unemployment point to manufacturing lines that have become largely automated and to call centres where chatbots handle routine queries. Such concerns are valid: tasks that are rule-based and predictable are particularly vulnerable. In parts of East and Southeast Asia, for instance, low-cost assembly jobs have already been squeezed by smart robotics. Yet this does not constitute a terminal decline in human work; rather, it signals a shift towards roles that require judgment, empathy, and complex problem-solving.

On the other hand, AI also catalyses job creation. New positions are emerging in data engineering, algorithm auditing, and human-AI collaboration design. In healthcare, AI triage systems accelerate diagnosis, but they increase the demand for clinicians who can interpret results, communicate uncertainty, and co-create treatment plans with patients. Similarly, small businesses can use AI to analyse customer data, which in turn expands opportunities in digital marketing and compliance.

Crucially, the outcome depends on policy. If countries embed lifelong learning into their education systems and subsidise mid-career training, displaced workers can transition into adjacent occupations. Conversely, if reskilling is neglected, AI will deepen inequality. Therefore, I believe AI will be a net creator of employment in the medium term, but only in economies that pair innovation with inclusion.

In sum, AI will both displace and generate jobs; whether the balance is favourable rests on human choices about skills, regulation, and social protection.

Scoring analysis:

• Task Response: 9
• Coherence & Cohesion: 8.5
• Lexical Resource: 9
• Grammatical Range & Accuracy: 8.5
  Overall: 8.5–9

Why this essay excels:

1. Clear stance stated early and revisited in the conclusion.
2. Balanced development with sector-specific examples (manufacturing, call centres, healthcare).
3. Cohesive devices used naturally (“provided that,” “on the other hand,” “crucially”).
4. Topic vocabulary deployed precisely (“algorithm auditing,” “human-AI collaboration,” “rule-based tasks”).
5. Advanced structures used accurately (conditionals, participle phrases, non-defining relatives).
6. Critical insight into policy as the decisive variable.
7. No overgeneralization; acknowledges conditions and limits.

For deeper sector detail, note how clinical roles evolve alongside AI decision-support, as discussed in The impact of AI on healthcare delivery.

3. Band 6.5-7 Sample Essay

A Band 6.5-7 essay is clear and relevant, with a logical structure and mostly accurate grammar, though it may feature occasional repetition or less precise word choice.

Essay (about 265 words):
People often worry that artificial intelligence will replace human workers. This concern is understandable because many routine tasks have already been automated. However, I believe AI will also open new types of work, and the final result depends on how well people can update their skills.

On the one hand, some jobs are at high risk. Factory work that follows the same steps every day can be done faster and more safely by machines. Customer service is another example: chatbots now answer common questions instantly. When companies try to cut costs, they may replace human staff with AI systems, and this can cause unemployment in the short term.

On the other hand, AI also supports job creation. New roles appear in data analysis, AI maintenance, and training models. In services like education and healthcare, AI can take over repetitive paperwork so that professionals have more time to deal with complex, human-centered tasks. Small firms can use AI tools for marketing and logistics, which can help them grow and hire more people. Therefore, although some positions will disappear, others will be added.

In my opinion, the key factor is reskilling. If governments and companies provide practical training and workers are willing to learn, the workforce can move into better-paid and more creative roles. In short, AI is not simply a threat; it is a powerful tool that should be guided carefully.

Scoring analysis:

• Task Response: 7
• Coherence & Cohesion: 7
• Lexical Resource: 7
• Grammatical Range & Accuracy: 6.5
  Overall: 7.0

Direct comparison with Band 8-9:

• Depth of analysis: Band 9 frames policy and inequality explicitly; Band 7 mentions training but less critically.
• Vocabulary: Band 9 uses “algorithm auditing,” “human-AI collaboration”; Band 7 uses broader terms like “AI maintenance.”
• Examples: Band 9 cites sector mechanics (triage, compliance); Band 7 keeps to general cases (factory, customer service).
• Grammar sophistication: Band 9 uses advanced conditionals and non-defining relatives; Band 7 relies on standard complex sentences.
• Cohesion: Band 9 varies connectors naturally; Band 7 repeats simpler signals like “on the one hand” and “on the other hand.”

For those curious about AI’s effects on creative work, a useful parallel is outlined in The impact of AI on creative industries, which can inspire examples beyond manufacturing and services.

4. Band 5-6 Sample Essay

A Band 5-6 essay addresses the topic but has limited development, noticeable grammar issues, and some unclear or repetitive ideas.

Essay (about 260 words) with error highlighting:
Some people say AI will remove many jobs, but others think it will make new ones. I think both sides are true, but the problem are how we manage the change. In factories, robots can do a dangerous works and repeat the same movement. This is good for safety, but it cause workers to lose jobs quickly. Also in customer service, AI are answering questions day and night, so companies might cut off many staffs.

However, AI also helps to create jobs in IT, like making trainings of models and fixing systems. In hospitals, machines read medical images and doctors can work faster. But some peoples worry about mistakes and privacy, so we still need a human to check. If government and schools give enough informations, workers can change to new positions.

In conclusion, AI will remove some jobs and give new jobs. The main thing is how to support the workers for move. If we focus on training and not only profit, the result will be more positive for the most of people.

Scoring analysis:

• Task Response: 6
• Coherence & Cohesion: 5.5
• Lexical Resource: 5.5
• Grammatical Range & Accuracy: 5
  Overall: 5.5–6.0

Error analysis and corrections:
| Mistake | Correction | Why |
|—|—|—|
| the problem are | the problem is | Subject-verb agreement (singular). |
| a dangerous works | dangerous work | Uncountable noun “work”; no article; adjective form. |
| it cause | it causes | Third-person singular -s. |
| AI are | AI is | Treat AI as a singular system/uncountable. |
| cut off many staffs | cut many staff / cut many jobs | “Staff” is uncountable; collocation error. |
| making trainings of models | training models | “Training” as a gerund/uncountable; correct collocation. |
| some peoples | some people | “People” is plural; no -s. |
| a human to check | humans to check / a human expert to check | Article choice and clarity. |
| enough informations | enough information | “Information” is uncountable. |
| support the workers for move | support workers to move | Infinitive after “support” for purpose. |
| the most of people | most people | Remove article “the” and “of”. |

How to improve from Band 6 to 7:

• Replace vague nouns with precise ones (e.g., “profit” → “short-term cost-cutting”).
• Fix uncountable nouns and SVA consistently (“information,” “staff,” “AI is”).
• Add specific, relevant examples and causes-effects rather than repeating claims.
• Upgrade connectors: instead of “but,” use “however,” “nevertheless,” “as a result.”

5. Essential Vocabulary

Word/Phrase	Type	Pronunciation	Definition	Example	Collocations
displacement	noun	/dɪsˈpleɪsmənt/	replacement of workers by technology	AI may cause short-term displacement in retail.	worker displacement; displacement effects
routine tasks	noun	/ruːˈtiːn/	repetitive, predictable work	AI excels at routine tasks in data entry.	automate routine tasks
upskilling	noun/gerund	/ˈʌpskɪlɪŋ/	learning new skills for new roles	Government-funded upskilling reduces unemployment.	upskilling programs; continuous upskilling
reskilling	noun/gerund	/ˌriːˈskɪlɪŋ/	retraining for different jobs	Reskilling helps factory workers move into logistics.	rapid reskilling; reskilling pathways
human-AI collaboration	noun	/ˌeɪˈaɪ/	joint work between people and AI systems	Firms that design human-AI collaboration outperform competitors.	collaboration design; human-AI teams
algorithm auditing	noun	/ˈælɡərɪðəm/	checking AI systems for bias/errors	Algorithm auditing creates compliance jobs.	conduct auditing; independent auditing
labour market	noun	/ˈleɪbə ˈmɑːkɪt/	supply and demand for workers	AI reshapes the labour market for graduates.	tight labour market; labour market mismatch
net impact	noun	/net ˈɪmpækt/	overall effect after positives/negatives	The net impact of AI depends on policy.	net positive/negative impact
productivity gains	noun	/ˌprɒdʌkˈtɪvɪti ɡeɪnz/	efficiency improvements	Productivity gains can finance training.	achieve gains; reinvest gains
non-routine work	noun	/nɒn ruːˈtiːn/	tasks needing judgment/creativity	Non-routine work grows with automation.	shift to non-routine
equitable	adj	/ˈekwɪtəbl/	fair and impartial	Equitable access to training is essential.	equitable policies; equitable outcomes
catalyse	verb	/ˈkætəlaɪz/	cause or accelerate change	Startups can catalyse job creation.	catalyse innovation; catalyse growth
nevertheless	linker	/ˌnevəðəˈles/	despite the previous point	AI reduces some roles; nevertheless, it creates others.	transitional phrase
provided that	linker	/prəˈvaɪdɪd ðæt/	only if	AI helps workers provided that training is available.	conditional connector
disproportionately	adv	/ˌdɪsprəˈpɔːʃənətli/	to an excessive degree	Low-skilled workers are disproportionately affected.	disproportionately impact/affect

6. High-Scoring Sentence Structures

1. Complex subordination

• Formula: Main clause + subordinating conjunction + dependent clause
• Example: “AI’s net impact on employment can be positive provided that governments and firms invest seriously in reskilling.”
• Why it scores well: Shows conditional reasoning and control over clause hierarchy.
• Additional examples:
  • Jobs will be created provided that firms expand training budgets.
  • Inequality may rise unless access to education is widened.
• Common mistake: Using “if” when “unless” or “provided that” is required for nuance.

2. Non-defining relative clauses

• Formula: Noun, which/who + extra information, main clause
• Example: “New positions are emerging in data engineering, which were scarce a decade ago.”
• Why: Adds precise, non-essential detail smoothly.
• Additional examples:
  • Reskilling, which is often underfunded, must be prioritised.
  • Call centres, which handle predictable queries, face rapid automation.
• Mistake: Omitting commas; using “that” instead of “which” in non-defining clauses.

3. Participle phrases

• Formula: -ing or -ed phrase + main clause
• Example: “Accelerating diagnosis, AI triage systems increase demand for clinicians.”
• Why: Concise cause-effect, stylistic variety.
• Additional examples:
  • Reducing paperwork, AI frees teachers’ time.
  • Built on biased data, models can harm applicants.
• Mistake: Dangling modifiers that do not clearly refer to the subject.

4. Cleft sentences

• Formula: It + be + focus + that/who + clause
• Example: “It is policy that ultimately determines whether AI creates or destroys jobs.”
• Why: Emphasises key idea; improves rhetorical impact.
• Additional examples:
  • It is reskilling that can turn disruption into opportunity.
  • It is low-skilled roles that are first to be automated.
• Mistake: Overusing clefts, making prose heavy.

5. Advanced conditionals

• Formula: If + present, will + base verb; If + past, would + base verb; If + had + V3, would have + V3
• Example: “If reskilling is neglected, AI will deepen inequality.”
• Why: Precise hypothetical reasoning.
• Additional examples:
  • If governments subsidised courses, more workers would transition.
  • If firms had invested earlier, fewer employees would have been displaced.
• Mistake: Tense mismatch across conditional parts.

6. Inversion for emphasis

• Formula: Negative adverbial + auxiliary + subject + main verb
• Example: “Only then will AI deliver inclusive growth.”
• Why: High-level control and stylistic sophistication.
• Additional examples:
  • Rarely has the labour market changed so quickly.
  • Not until training is accessible will outcomes improve.
• Mistake: Forgetting subject-auxiliary inversion after the fronted adverbial.

7. Self-Assessment Checklist

Before writing:

• Identify question type (opinion, discuss both, advantages/disadvantages).
• Brainstorm 2-3 ideas for each side plus 1-2 solutions.
• Choose a clear stance and plan paragraph topics.

While writing:

• Use clear topic sentences and logical connectors.
• Support claims with specific sector examples (manufacturing, services, healthcare).
• Keep sentences varied; avoid overlong, unfocused lines.

After writing:

• Check that your opinion is explicit in both introduction and conclusion.
• Proofread for SVA, article use, and uncountable nouns.
• Replace vague words with precise collocations (e.g., “job displacement,” “reskilling pathways”).

Time management tips:

• Planning: 6–8 minutes
• Writing: 25–28 minutes
• Review: 4–6 minutes
• If stuck, write a simpler sentence accurately rather than a complex one with errors.

To widen your perspective beyond government and corporate roles, see how city agencies deploy AI similar to private firms in The impact of AI on public services.

Conclusion

The impact of artificial intelligence on jobs is neither purely destructive nor automatically beneficial; it displaces routine tasks but can expand non-routine, human-centered roles. Your path to a higher band score is to show balanced reasoning, provide concrete sector examples, and use accurate advanced structures. Practice regularly, compare your work to models, and build a personal bank of topic vocabulary and flexible sentence patterns. Share your essay under timed conditions, then refine it using the checklist above; with focused practice, many learners can see a noticeable improvement over 6–8 weeks.

If you want to explore how entertainment roles are changing, a helpful companion read is The impact of AI on entertainment industries. For adjacent labour-market shifts unrelated to algorithms, you might also revisit The impact of automation on jobs. Keep writing, keep revising, and aim for clarity, specificity, and control—these are the qualities examiners reward.