Band 6 → Band 9, side by side

Many people say technology makes students lazy. I agree with this idea because students use a lot of tools now. For example, they use calculators and the internet for homework. This means they don't think a lot. However, technology also helps students learn new things. In my country, many kids watch educational videos. So, technology is both good and bad for students.

It is often argued that modern technology has reduced students' willingness to think independently. I partly agree with this view, as over-reliance on digital tools can weaken critical thinking, though technology itself is not the root cause. On the one hand, students who use search engines or AI assistants may accept the first result they find without questioning it. On the other hand, when used carefully, technology gives learners access to a vast range of sources and encourages deeper analysis. In conclusion, the problem lies in how technology is used rather than in the tools themselves.

It is frequently claimed that the pervasive use of digital tools has eroded students' capacity for independent thought. I largely agree with this proposition, although I would argue that the issue reflects pedagogical habits more than technology itself. To begin with, an over-reliance on search engines and AI assistants can undermine analytical reasoning: learners now expect instant, packaged answers rather than engaging in sustained reflection. Nevertheless, technology also facilitates deeper inquiry when it is embedded within a rigorous curriculum. In conclusion, whether technology fosters or hinders thinking depends less on the tool and more on how educators frame its use.

The contention that digital technology has dulled students' intellectual autonomy is one I find largely persuasive, though the causal picture is more nuanced than critics allow. Undeniably, the frictionless availability of search engines and generative AI has cultivated a habit of intellectual outsourcing: rather than wrestling with a problem, learners increasingly retrieve pre-formed conclusions. Yet to lay the blame at technology's door is to overlook the curricular and cultural context in which it is deployed. Where teachers scaffold enquiry — insisting on source triangulation, transparent reasoning and iterative revision — the same tools become catalysts for deeper thinking rather than substitutes for it. The pathology, in short, lies not in the technology but in an education system that has not yet learned to teach alongside it.

Nowadays many companies use AI tools like ChatGPT for work. This happens because AI is fast and cheap. Bosses want to save money and time. In my opinion, this is both good and bad. It is good because workers can finish jobs faster. It is bad because some people can lose their jobs. So companies should train workers to use AI, not replace them.

In recent years, generative AI has been widely adopted at work because it dramatically cuts the time and cost of routine tasks such as drafting emails, summarising reports and writing code. Employers naturally want the productivity gains that follow. The impact on the workforce, however, is mixed. On the positive side, employees can offload repetitive work and focus on higher-value thinking. On the negative side, entry-level roles in fields like copywriting and customer support are already shrinking. Overall, I believe the effect is positive only where companies retrain staff rather than simply cutting them.

The rapid uptake of generative AI at work is driven primarily by economics: models such as GPT-class assistants can perform, in seconds, drafting and analytical tasks that once absorbed hours of skilled labour. Firms facing tight margins have every incentive to adopt them. The net effect on the workforce, however, is far from uniform. For senior professionals, AI acts as a force multiplier, offloading routine cognitive work and freeing capacity for judgement-heavy tasks. For junior and administrative staff, the picture is bleaker: the very tasks that once served as career on-ramps are now the first to be automated. On balance, the outcome is positive only where organisations pair adoption with genuine reskilling; without it, gains in productivity will come at the cost of narrower entry routes into skilled work.

Generative AI has moved into the workplace at a pace few technologies have matched, and the reasons are almost entirely economic: a general-purpose model can now discharge, in seconds and at negligible marginal cost, the drafting, summarising and coding tasks that once justified whole teams. Whether this constitutes progress depends less on the technology than on how firms choose to metabolise it. Used as a complement, AI amplifies experienced judgement and liberates senior staff from clerical drag; used as a substitute, it quietly erodes the junior roles through which that judgement was traditionally acquired. The genuine risk, then, is not mass unemployment but a hollowing-out of the professional ladder — a workforce in which there are seats at the top and at the entry point of the algorithm, but progressively fewer rungs in between.