Làm thử Đề thi thử tốt nghiệp THPT · Tiếng Anh · Đề 117

Question 16. a. As a result, many young corals fail to survive their first year.
b. Coral reefs support around a quarter of all marine species, yet they are extremely sensitive to changes in water temperature.
c. For this reason, scientists are now growing heat-resistant coral in nurseries before transplanting them onto damaged reefs.
d. When the ocean warms even slightly, corals expel the tiny algae that give them both colour and food.
e. This process, known as bleaching, leaves the coral white and weakened.

A new study conducted by researchers at a European sustainability institute has examined how quickly clothes bought from fast-fashion retailers end up in landfill. The team tracked more than two thousand garments purchased online over an eighteen-month period, recording how often each item was worn before being thrown away or donated. The findings were striking: on average, a fast-fashion item was worn only seven times before its owner stopped using it, compared with roughly forty times for clothes bought from traditional retailers.

According to the report, the main reason customers gave up on these garments so quickly was poor quality rather than a simple change in taste. Thin fabric, loose stitching and dye that faded after a few washes were the most common complaints. Many shoppers admitted that they had expected the clothes to last only a short time, so they were not surprised when seams came apart or colours ran in the wash. Researchers argue that this expectation of short-term use is exactly what keeps the fast-fashion business model profitable, since customers simply buy replacements rather than demand better quality.

The study also looked at what happens to discarded fast-fashion items. Only a small fraction were recycled into new textiles, mainly because the cheap blended fabrics used in fast fashion are difficult to separate and reprocess. Most of the garments were either sent to landfill or shipped abroad, where local markets are already struggling to absorb the volume of used clothing arriving from wealthier countries. Environmental groups quoted in the report warn that this pattern places a heavy burden on communities that had no part in producing the waste.

A new study conducted by researchers at a European sustainability institute has examined how quickly clothes bought from fast-fashion retailers end up in landfill. The team tracked more than two thousand garments purchased online over an eighteen-month period, recording how often each item was worn before being thrown away or donated. The findings were striking: on average, a fast-fashion item was worn only seven times before its owner stopped using it, compared with roughly forty times for clothes bought from traditional retailers.

According to the report, the main reason customers gave up on these garments so quickly was poor quality rather than a simple change in taste. Thin fabric, loose stitching and dye that faded after a few washes were the most common complaints. Many shoppers admitted that they had expected the clothes to last only a short time, so they were not surprised when seams came apart or colours ran in the wash. Researchers argue that this expectation of short-term use is exactly what keeps the fast-fashion business model profitable, since customers simply buy replacements rather than demand better quality.

The study also looked at what happens to discarded fast-fashion items. Only a small fraction were recycled into new textiles, mainly because the cheap blended fabrics used in fast fashion are difficult to separate and reprocess. Most of the garments were either sent to landfill or shipped abroad, where local markets are already struggling to absorb the volume of used clothing arriving from wealthier countries. Environmental groups quoted in the report warn that this pattern places a heavy burden on communities that had no part in producing the waste.

A new study conducted by researchers at a European sustainability institute has examined how quickly clothes bought from fast-fashion retailers end up in landfill. The team tracked more than two thousand garments purchased online over an eighteen-month period, recording how often each item was worn before being thrown away or donated. The findings were striking: on average, a fast-fashion item was worn only seven times before its owner stopped using it, compared with roughly forty times for clothes bought from traditional retailers.

According to the report, the main reason customers gave up on these garments so quickly was poor quality rather than a simple change in taste. Thin fabric, loose stitching and dye that faded after a few washes were the most common complaints. Many shoppers admitted that they had expected the clothes to last only a short time, so they were not surprised when seams came apart or colours ran in the wash. Researchers argue that this expectation of short-term use is exactly what keeps the fast-fashion business model profitable, since customers simply buy replacements rather than demand better quality.

The study also looked at what happens to discarded fast-fashion items. Only a small fraction were recycled into new textiles, mainly because the cheap blended fabrics used in fast fashion are difficult to separate and reprocess. Most of the garments were either sent to landfill or shipped abroad, where local markets are already struggling to absorb the volume of used clothing arriving from wealthier countries. Environmental groups quoted in the report warn that this pattern places a heavy burden on communities that had no part in producing the waste.

A new study conducted by researchers at a European sustainability institute has examined how quickly clothes bought from fast-fashion retailers end up in landfill. The team tracked more than two thousand garments purchased online over an eighteen-month period, recording how often each item was worn before being thrown away or donated. The findings were striking: on average, a fast-fashion item was worn only seven times before its owner stopped using it, compared with roughly forty times for clothes bought from traditional retailers.

According to the report, the main reason customers gave up on these garments so quickly was poor quality rather than a simple change in taste. Thin fabric, loose stitching and dye that faded after a few washes were the most common complaints. Many shoppers admitted that they had expected the clothes to last only a short time, so they were not surprised when seams came apart or colours ran in the wash. Researchers argue that this expectation of short-term use is exactly what keeps the fast-fashion business model profitable, since customers simply buy replacements rather than demand better quality.

The study also looked at what happens to discarded fast-fashion items. Only a small fraction were recycled into new textiles, mainly because the cheap blended fabrics used in fast fashion are difficult to separate and reprocess. Most of the garments were either sent to landfill or shipped abroad, where local markets are already struggling to absorb the volume of used clothing arriving from wealthier countries. Environmental groups quoted in the report warn that this pattern places a heavy burden on communities that had no part in producing the waste.

A new study conducted by researchers at a European sustainability institute has examined how quickly clothes bought from fast-fashion retailers end up in landfill. The team tracked more than two thousand garments purchased online over an eighteen-month period, recording how often each item was worn before being thrown away or donated. The findings were striking: on average, a fast-fashion item was worn only seven times before its owner stopped using it, compared with roughly forty times for clothes bought from traditional retailers.

According to the report, the main reason customers gave up on these garments so quickly was poor quality rather than a simple change in taste. Thin fabric, loose stitching and dye that faded after a few washes were the most common complaints. Many shoppers admitted that they had expected the clothes to last only a short time, so they were not surprised when seams came apart or colours ran in the wash. Researchers argue that this expectation of short-term use is exactly what keeps the fast-fashion business model profitable, since customers simply buy replacements rather than demand better quality.

The study also looked at what happens to discarded fast-fashion items. Only a small fraction were recycled into new textiles, mainly because the cheap blended fabrics used in fast fashion are difficult to separate and reprocess. Most of the garments were either sent to landfill or shipped abroad, where local markets are already struggling to absorb the volume of used clothing arriving from wealthier countries. Environmental groups quoted in the report warn that this pattern places a heavy burden on communities that had no part in producing the waste.

A new study conducted by researchers at a European sustainability institute has examined how quickly clothes bought from fast-fashion retailers end up in landfill. The team tracked more than two thousand garments purchased online over an eighteen-month period, recording how often each item was worn before being thrown away or donated. The findings were striking: on average, a fast-fashion item was worn only seven times before its owner stopped using it, compared with roughly forty times for clothes bought from traditional retailers.

According to the report, the main reason customers gave up on these garments so quickly was poor quality rather than a simple change in taste. Thin fabric, loose stitching and dye that faded after a few washes were the most common complaints. Many shoppers admitted that they had expected the clothes to last only a short time, so they were not surprised when seams came apart or colours ran in the wash. Researchers argue that this expectation of short-term use is exactly what keeps the fast-fashion business model profitable, since customers simply buy replacements rather than demand better quality.

The study also looked at what happens to discarded fast-fashion items. Only a small fraction were recycled into new textiles, mainly because the cheap blended fabrics used in fast fashion are difficult to separate and reprocess. Most of the garments were either sent to landfill or shipped abroad, where local markets are already struggling to absorb the volume of used clothing arriving from wealthier countries. Environmental groups quoted in the report warn that this pattern places a heavy burden on communities that had no part in producing the waste.

A new study conducted by researchers at a European sustainability institute has examined how quickly clothes bought from fast-fashion retailers end up in landfill. The team tracked more than two thousand garments purchased online over an eighteen-month period, recording how often each item was worn before being thrown away or donated. The findings were striking: on average, a fast-fashion item was worn only seven times before its owner stopped using it, compared with roughly forty times for clothes bought from traditional retailers.

According to the report, the main reason customers gave up on these garments so quickly was poor quality rather than a simple change in taste. Thin fabric, loose stitching and dye that faded after a few washes were the most common complaints. Many shoppers admitted that they had expected the clothes to last only a short time, so they were not surprised when seams came apart or colours ran in the wash. Researchers argue that this expectation of short-term use is exactly what keeps the fast-fashion business model profitable, since customers simply buy replacements rather than demand better quality.

The study also looked at what happens to discarded fast-fashion items. Only a small fraction were recycled into new textiles, mainly because the cheap blended fabrics used in fast fashion are difficult to separate and reprocess. Most of the garments were either sent to landfill or shipped abroad, where local markets are already struggling to absorb the volume of used clothing arriving from wealthier countries. Environmental groups quoted in the report warn that this pattern places a heavy burden on communities that had no part in producing the waste.

A new study conducted by researchers at a European sustainability institute has examined how quickly clothes bought from fast-fashion retailers end up in landfill. The team tracked more than two thousand garments purchased online over an eighteen-month period, recording how often each item was worn before being thrown away or donated. The findings were striking: on average, a fast-fashion item was worn only seven times before its owner stopped using it, compared with roughly forty times for clothes bought from traditional retailers.

According to the report, the main reason customers gave up on these garments so quickly was poor quality rather than a simple change in taste. Thin fabric, loose stitching and dye that faded after a few washes were the most common complaints. Many shoppers admitted that they had expected the clothes to last only a short time, so they were not surprised when seams came apart or colours ran in the wash. Researchers argue that this expectation of short-term use is exactly what keeps the fast-fashion business model profitable, since customers simply buy replacements rather than demand better quality.

The study also looked at what happens to discarded fast-fashion items. Only a small fraction were recycled into new textiles, mainly because the cheap blended fabrics used in fast fashion are difficult to separate and reprocess. Most of the garments were either sent to landfill or shipped abroad, where local markets are already struggling to absorb the volume of used clothing arriving from wealthier countries. Environmental groups quoted in the report warn that this pattern places a heavy burden on communities that had no part in producing the waste.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.

Artificial intelligence has moved out of research laboratories and into the ordinary routines of daily life faster than most experts predicted a decade ago. Voice assistants schedule appointments, algorithms recommend what to watch or buy, and software increasingly drafts emails and summarises documents on our behalf. What once seemed like a distant possibility now shapes decisions that affect employment, education and even personal relationships, and the pace of change shows no sign of slowing.

Nowhere is this shift more visible than in the workplace. Routine tasks such as data entry, basic translation and simple customer-service queries are increasingly handled by AI systems rather than by junior staff. Economists disagree sharply about whether this trend will destroy more jobs than it creates, since new roles in AI oversight and system maintenance are also emerging. Some analysts point to previous waves of automation, arguing that workers eventually moved into new kinds of work, while others warn that the speed of the current change leaves far less time for retraining.

[I] Classrooms are changing too, as AI tutoring programs adjust the difficulty of exercises to match each student's pace. [II] Teachers in several countries report that struggling students respond well to this kind of immediate, judgement-free feedback. [III] However, some educators worry that constant reliance on AI-generated hints could weaken students' ability to work through difficult problems on their own. [IV] For this reason, a growing number of schools now limit AI tools to homework support rather than allowing them during exams.

Beyond the classroom and the office, AI systems now influence decisions that once rested entirely with human judgement, from loan approvals to medical screening. Supporters argue that these systems can process far more data than a person ever could, making decisions more consistent and less swayed by personal bias. Critics counter that the data used to train such systems often reflects historical inequalities, meaning that an algorithm can quietly repeat old patterns of unfair treatment under the guise of objectivity. A 2024 review of loan-approval software in several countries found that applicants from lower-income neighbourhoods were rejected at notably higher rates, even when their financial details were otherwise similar to approved applicants.

None of this means that artificial intelligence should be treated with either blind trust or outright fear. Most researchers now call for clear rules on how AI systems are tested, who is responsible when they make mistakes, and how their decisions can be explained to the people affected by them. Whether such rules keep pace with the technology, however, remains an open question, and the answer will shape how much power society ultimately hands over to machines.