14. IELTS Writing Task 2 Agree/Disagree Tutorial with Band 6–8 Sample Answers, Vocabulary, and Interactive Quiz

Many people argue that artificial intelligence is spreading too quickly in classrooms and will damage learning. In my view, I largely disagree with this statement because AI can support personal learning and reduce teacher workload when it is used carefully.

The first reason is that AI tools can make study more personalised. Traditional lessons give the same materials to everyone, but students have different speeds and gaps. An AI tutor can check answers instantly, show simple explanations, and give extra practice where a learner is weak. For example, if a student struggles with conditionals in English, the system can provide short hints and more tasks at the right level. This helps the learner improve step by step. In this way, the main value of AI is educational support rather than entertainment or shortcuts.

A second reason is that AI can take over routine tasks so teachers can focus on real teaching. Marking basic quizzes, sorting homework, and tracking attendance often cost much time. When software does these jobs quickly, teachers can plan better lessons and speak to students who need help. It is true that AI may also bring risks, such as plagiarism, unfair access, or weak privacy rules. However, these problems can be reduced by clear school policies, simple honour codes, and checks that compare student style across tasks. If schools train learners to use AI as a tool, the harms are less likely.

In conclusion, I do not believe AI will do more harm than good in education. With guidance and limits, it can personalise practice and free teachers for high-quality support. Therefore, the benefits are stronger than the drawbacks.

Why this Band 6 answer works (step-by-step)

1) The introduction paraphrases the prompt without copying it.

2) The thesis states a clear position (“largely disagree”) and previews two reasons.

3) Body 1 starts with a topic sentence that directly answers the task.

4) The paragraph explains the mechanism of benefit (personalised practice).

5) A compact, realistic example illustrates how the tool would work in class.

6) The final sentence of Body 1 links back to the main claim (education over shortcuts).

7) Body 2 adds a distinct reason (reducing routine workload).

8) It develops the reason by naming common teacher tasks AI can automate.

9) The paragraph includes a concession about risks (plagiarism, access, privacy).

10) It proposes straightforward solutions (policies, honour codes, style checks).

11) The stance remains consistent: AI is useful if guided.

12) Vocabulary is mostly accurate and topic-appropriate, though simple.

13) Sentences are generally clear with some variety in length.

14) Cohesion is supported by logical linkers (“first reason”, “second reason”, “however”).

15) The conclusion restates the position and synthesises reasons without new ideas.

16) Overall development is sufficient for Task Response, meeting the 250-word minimum.

Whether artificial intelligence will ultimately harm education is a contentious issue. I partly disagree with this claim: while uncritical adoption can create serious problems, AI is more likely to improve learning when it is aligned with clear goals and responsible classroom practice.

To begin with, well-designed systems provide timely feedback and adaptive tasks that are difficult for a single teacher to deliver to a large class. Automated hints, worked examples, and spaced review can target specific misconceptions and strengthen long-term memory. For instance, an AI writing assistant can highlight recurring grammar errors in a learner’s own sentences, while also modelling more natural phrasing. Because such feedback is immediate and personalised, it increases students’ opportunity to correct mistakes and build confidence.

Nevertheless, the harms are real if AI use is poorly controlled. Plagiarism becomes easier, weak prompts can spread biased or inaccurate content, and data may be collected without transparent consent. Even so, these risks are not inevitable. They can be reduced through assessment design that requires planning notes and drafts, oral follow-ups to verify authorship, model comparisons to detect stylistic shifts, and clear data-protection rules. More importantly, teachers should frame AI as a thinking partner that supports analysis rather than replaces it.

In summary, I do not accept that AI will do more harm than good by default. With explicit learning outcomes, robust assessment, and ethical safeguards, AI can extend teachers’ reach and give learners higher-quality feedback than they would otherwise receive.

Why this Band 7 answer works (step-by-step)

1) The introduction paraphrases and adopts a nuanced stance (“partly disagree”).

2) The thesis signals conditions for success (alignment, responsibility), previewing the line of argument.

3) Body 1 gives a clear topic sentence focused on learning value (feedback + adaptivity).

4) It explains mechanisms (hints, worked examples, spaced review).

5) The example is specific (grammar feedback in the learner’s own writing).

6) Cause-and-effect logic is explicit (immediate feedback → confidence and accuracy).

7) Body 2 acknowledges concrete risks (plagiarism, bias, privacy).

8) It then reframes risk as manageable, offering multiple, practical mitigations.

9) Assessment design ideas are varied (drafts, orals, style checks).

10) The paragraph keeps a consistent line: guide the tool; do not ban it.

11) Lexis is more precise (adaptive, misconceptions, consent, safeguards).

12) Sentence structures are varied with accurate subordination and modifiers.

13) Cohesion uses referencing (“these risks”, “such feedback”) rather than overusing linkers.

14) The conclusion synthesises the stance and conditions without adding new claims.

15) Overall, ideas are well developed with relevant, plausible examples.

16) Tone is formal and academic, suitable for Task 2.

The claim that artificial intelligence will inflict more damage than benefit on education assumes that new tools inevitably displace genuine learning. I largely disagree. While unregulated deployment can amplify plagiarism, bias, and surveillance, AI is more likely to enhance outcomes when it is constrained by pedagogy, assessment design, and governance.

Primarily, AI extends high-quality formative feedback to every student. Systems can diagnose error patterns, stage explanations from simple to sophisticated, and schedule practice according to how well concepts are retained. Consider an academic-writing assistant that traces a learner’s argument, flags weak claims, and proposes alternatives while citing model paragraphs. Because feedback arrives at the moment of need, students iterate more, teachers can target misconceptions they see in dashboards, and class time shifts from marking to coaching. In short, the technology multiplies the teacher’s impact rather than replacing professional judgement.

However, the harms are real if AI is bolted onto assessment or procurement without standards. To minimise cheating, teachers can require planning artefacts, in-class micro-tasks, and oral defences that verify authorship. To reduce bias and misinformation, schools should mandate transparent sources and expose model outputs to critique as part of lessons. Privacy risks demand strict data minimisation, local processing where feasible, and contracts that ban secondary use. These guardrails convert a blunt tool into a reliable instrument of learning.

Ultimately, whether AI helps or harms depends on professional choices. When systems are designed around clear learning outcomes and assessed with authentic tasks, they raise the floor of support without lowering the ceiling of ambition. Therefore, with principled implementation, AI will do substantially more good than harm in education.

Why this Band 8+ answer works (step-by-step)

1) The introduction challenges the assumption behind the claim and presents a decisive yet qualified thesis.

2) The stance is consistent and nuanced (“largely disagree” with conditions), signalling control of argument.

3) Body 1 opens with a strong topic sentence that frames AI as scalable formative feedback.

4) Mechanisms are unpacked (diagnosis, staged explanations, spaced practice) showing analytical depth.

5) The example integrates process detail (tracing arguments, proposing alternatives, model citations).

6) Clear causal chain links feedback timing to iteration, teacher targeting, and lesson redesign.

7) Lexis is precise and field-appropriate (formative, dashboards, data minimisation, procurement).

8) Sentences vary in length and structure with accurate punctuation and control of modifiers.

9) Body 2 concedes risks and then specifies concrete guardrails mapped to each risk.

10) Mitigations are realistic (planning artefacts, oral defences, transparent sources, local processing).

11) The paragraph maintains cohesion through parallelism and reference chains (“these guardrails”).

12) Tone remains academic and objective while keeping a clear viewpoint.

13) Examples are plausible and concise; no speculative statistics are used.

14) The conclusion synthesises the argument and re-answers the question directly.

15) Overall development is thorough, supporting a high score for Task Response and Coherence.

16) Accuracy is strong with few, if any, grammatical slips, supporting Band 8+ for GRA.