Mở bài
Chủ đề về tác động của trí tuệ nhân tạo (AI) đến việc làm truyền thống đang trở thành một trong những đề tài nóng hổi và xuất hiện ngày càng thường xuyên trong các kỳ thi IELTS Reading gần đây. Với sự phát triển vũ bão của công nghệ AI, chủ đề này không chỉ phản ánh xu hướng toàn cầu mà còn đánh giá khả năng hiểu biết của thí sinh về những thay đổi quan trọng trong xã hội hiện đại.
Trong bài viết này, bạn sẽ được trải nghiệm một đề thi IELTS Reading hoàn chỉnh với 3 passages có độ khó tăng dần từ Easy đến Hard, bao gồm đầy đủ 40 câu hỏi với 7 dạng câu hỏi khác nhau thường gặp trong thi thật. Mỗi passage được thiết kế tỉ mỉ để phản ánh chính xác cấu trúc và độ khó của đề thi Cambridge IELTS chính thức.
Bạn sẽ nhận được đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin, kỹ thuật paraphrase, và chiến lược làm bài hiệu quả. Ngoài ra, bộ từ vựng chuyên ngành về AI và việc làm được tổng hợp kỹ lưỡng sẽ giúp bạn không chỉ chinh phục bài thi mà còn nâng cao vốn từ học thuật.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên và là tài liệu luyện tập chất lượng cao cho những ai đang chuẩn bị cho kỳ thi IELTS.
1. Hướng dẫn làm bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test là bài thi kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng tương ứng với 1 điểm, và không bị trừ điểm khi trả lời sai.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (độ khó thấp nhất)
- Passage 2: 18-20 phút (độ khó trung bình)
- Passage 3: 23-25 phút (độ khó cao nhất)
Lưu ý quan trọng: Không có thời gian riêng để chuyển đáp án sang answer sheet, vì vậy bạn cần quản lý thời gian cẩn thận và ghi đáp án trực tiếp trong quá trình làm bài.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Chọn đáp án đúng từ 3-4 lựa chọn
- True/False/Not Given – Xác định thông tin đúng, sai hay không được đề cập
- Matching Information – Nối thông tin với đoạn văn tương ứng
- Sentence Completion – Hoàn thành câu với từ trong bài
- Matching Headings – Chọn tiêu đề phù hợp cho các đoạn văn
- Summary Completion – Điền từ vào đoạn tóm tắt
- Short-answer Questions – Trả lời ngắn gọn dựa trên thông tin bài đọc
2. IELTS Reading Practice Test
PASSAGE 1 – The Rise of Automation in Modern Workplaces
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
The world of work is experiencing a profound transformation driven by artificial intelligence (AI) and automation technologies. Over the past decade, machines have become increasingly capable of performing tasks that were once the exclusive domain of human workers. From self-checkout counters in supermarkets to automated customer service chatbots, AI systems are now ubiquitous in our daily lives and workplaces.
The manufacturing sector has been at the forefront of this change. Industrial robots have been used in factories since the 1960s, but today’s robots are far more sophisticated. They can assemble products, inspect quality, and even work alongside human employees in what experts call “collaborative robotics“. A car manufacturing plant that once required hundreds of workers can now operate with a fraction of that workforce, with robots handling most of the assembly line tasks. This has led to significant increases in productivity and consistency in product quality.
However, it is not just manual labour that is being affected. White-collar jobs are also experiencing disruption. AI systems can now analyze legal documents, process insurance claims, and even write basic news reports. For instance, many financial institutions use AI algorithms to detect fraud, a task that previously required teams of human analysts working long hours. These systems can scan thousands of transactions per second, identifying suspicious patterns that might take humans days or weeks to discover.
Despite these advances, experts emphasize that AI is not simply replacing workers but transforming how work is done. Many jobs are being redesigned to incorporate AI tools, which means workers need to adapt and learn new skills. A radiologist, for example, now works with AI systems that can detect abnormalities in medical scans, but the final diagnosis still requires human expertise and clinical judgment. The AI serves as a powerful assistant, improving accuracy and efficiency, rather than replacing the professional entirely.
The retail sector provides another clear example of this transformation. While e-commerce platforms and automated warehouses have reduced the need for traditional shop assistants and warehouse workers, new roles have emerged. Companies now need data analysts to understand customer behaviour, user experience designers to create better online shopping experiences, and logistics coordinators to manage complex supply chains. These new positions often require higher levels of education and technical skills than the jobs they replace.
Education systems worldwide are responding to these changes. Many schools and universities are introducing courses in coding, data science, and artificial intelligence to prepare students for the job market of the future. Vocational training programs are also evolving, teaching workers how to operate and maintain sophisticated machinery rather than simply performing repetitive tasks. The concept of lifelong learning has become crucial, as workers may need to retrain multiple times throughout their careers to remain employable.
Some economists argue that concerns about mass unemployment due to AI are overstated. They point to historical precedents: when mechanization transformed agriculture in the 19th and 20th centuries, it displaced millions of farm workers, but the economy created new industries and jobs that ultimately absorbed this workforce. Similarly, they believe that AI will create opportunities in sectors we cannot yet fully imagine. Emerging fields such as AI ethics, robot maintenance, and human-AI interaction design are already creating new employment opportunities.
Nevertheless, the transition period may be challenging for many workers, particularly those in middle-skilled jobs that are most vulnerable to automation. Policymakers and business leaders must work together to ensure that the benefits of AI are distributed fairly across society, and that adequate support systems are in place for workers who need to transition into new careers.
Questions 1-13
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C or D.
1. According to the passage, today’s industrial robots differ from earlier versions because they:
A. work faster than human employees
B. can perform more complex operations
C. are cheaper to maintain
D. require less space in factories
2. The passage suggests that AI systems in financial institutions:
A. have completely replaced human fraud analysts
B. work more slowly than human teams
C. can process large amounts of data quickly
D. are less accurate than human workers
3. The example of radiologists in the passage demonstrates that:
A. AI will eventually replace medical professionals
B. doctors are resistant to using new technology
C. AI tools can enhance human expertise
D. medical diagnosis is now fully automated
4. According to the passage, new jobs in the retail sector typically require:
A. less training than traditional retail positions
B. more advanced qualifications and skills
C. only physical strength
D. experience in customer service
5. The comparison with agricultural mechanization suggests that:
A. AI will cause permanent unemployment
B. economic adaptation takes many generations
C. new technologies eventually create new job opportunities
D. farming jobs were better than factory jobs
Questions 6-9: True/False/Not Given
Do the following statements agree with the information given in the passage?
Write:
- TRUE if the statement agrees with the information
- FALSE if the statement contradicts the information
- NOT GIVEN if there is no information on this
6. Self-checkout counters are an example of AI technology in everyday use.
7. Car manufacturing now requires more workers than it did in the past.
8. AI systems can write complex investigative journalism.
9. All countries have successfully reformed their education systems to prepare for AI.
Questions 10-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
10. Modern robots can work together with humans in what is known as _____.
11. Many financial organizations use AI to identify _____ in transactions.
12. The passage states that _____ has become essential because workers may need to change careers several times.
13. Workers in _____ jobs face the highest risk of being replaced by automation.
Robot công nghiệp làm việc cùng công nhân trong nhà máy hiện đại thể hiện xu hướng cộng tác giữa AI và con người
PASSAGE 2 – Redefining Employment in the Age of Intelligent Machines
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
A. The advent of artificial intelligence has sparked intense debate about the future of employment, with perspectives ranging from utopian visions of a work-free society to dystopian warnings of widespread joblessness. While these extremes capture public imagination, the reality is likely to be more nuanced and complex. Research by leading economists suggests that rather than causing a simple substitution of machines for humans, AI is precipitating a fundamental restructuring of the labour market, with some occupations disappearing, others emerging, and many more being radically transformed.
B. A comprehensive study by McKinsey Global Institute examined more than 2,000 work activities across 800 occupations and concluded that while fewer than 5% of jobs can be fully automated with current technology, about 60% of occupations have at least 30% of their constituent activities that could be automated. This distinction between jobs and tasks is crucial. Automation typically affects specific tasks within a job rather than eliminating the entire occupation. For instance, accountants now spend less time on data entry and calculation—tasks easily handled by software—and more time on strategic financial planning and client consultation, activities that require human judgment and interpersonal skills.
C. The occupational categories most susceptible to automation share certain characteristics. Jobs involving routine, predictable tasks in structured environments are particularly vulnerable. This includes not only manual labour such as assembly line work but also cognitive tasks like data processing, basic legal research, and preliminary medical diagnostics. Conversely, occupations requiring creativity, complex problem-solving, emotional intelligence, and dexterous physical manipulation in unpredictable settings remain largely resistant to automation. Nurses, for example, must navigate complex social interactions, make nuanced judgments about patient care, and perform varied physical tasks—a combination that current AI systems cannot replicate.
D. However, the geographical distribution of AI’s impact reveals significant disparities. Developed economies with aging populations and labour shortages may view automation as a solution to demographic challenges, potentially using AI to maintain productivity without expanding their workforce. In contrast, developing nations with young, growing populations may face more severe disruption if automation reduces demand for the low-skilled manufacturing jobs that have traditionally provided a pathway to economic development. This asymmetry could exacerbate global economic inequality unless proactive measures are taken.
E. The wage polarization observed in many developed countries over recent decades offers instructive insights. Middle-income jobs, particularly those involving routine cognitive and manual tasks, have been hollowed out, while employment has grown at both the high end (professional, technical, and managerial positions) and the low end (personal services, care work, and hospitality). AI and automation may accelerate this trend, creating an hourglass economy where middle-skilled workers face the most precarious prospects. This has profound implications for social mobility and income distribution, as the traditional pathway from middle-class manufacturing jobs to economic security becomes increasingly obstructed.
F. Forward-thinking organizations are already reimagining their workforce strategies. Rather than viewing AI as purely a cost-cutting tool for reducing headcount, progressive companies see it as an opportunity to augment human capabilities and redeploy workers to higher-value activities. Some firms have established “reskilling academies” to help employees transition from roles being automated to emerging positions. For example, a major telecommunications company successfully retrained thousands of network technicians as data scientists and cybersecurity specialists, roles critical to the company’s digital transformation but suffering from talent shortages.
G. The policy response to these changes remains inadequate in most countries. While some nations have begun experimenting with universal basic income or expanded social safety nets, comprehensive strategies that combine education reform, labour market policies, and social protection are rare. Finland and Singapore have implemented notable initiatives: Finland has pioneered programs guaranteeing workers the right to continuous education throughout their careers, while Singapore’s SkillsFuture program provides citizens with individual learning accounts to pursue training in emerging fields. These models demonstrate that proactive government intervention can help smooth the transition and ensure that technological progress benefits society broadly rather than concentrating gains among a narrow elite.
H. Ultimately, the impact of AI on employment will be determined not by technology alone but by the policy choices, business practices, and social adaptations that accompany it. History suggests that technological revolutions create turbulent transition periods but eventually lead to higher living standards and new forms of meaningful work. The challenge for contemporary society is to minimize the disruption and maximize the benefits through thoughtful governance and inclusive growth strategies.
Questions 14-26
Questions 14-18: Matching Headings
Choose the correct heading for paragraphs B-F from the list of headings below.
List of Headings:
i. The geographical variations in automation’s effects
ii. Historical patterns of employment change
iii. The difference between task automation and job elimination
iv. Government programs addressing workforce transitions
v. Corporate strategies for managing technological change
vi. The growing divide in wage levels
vii. Characteristics of jobs at risk from automation
viii. Predictions about future unemployment rates
14. Paragraph B
15. Paragraph C
16. Paragraph D
17. Paragraph E
18. Paragraph F
Questions 19-23: Yes/No/Not Given
Do the following statements agree with the views of the writer in the passage?
Write:
- YES if the statement agrees with the views of the writer
- NO if the statement contradicts the views of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
19. The most extreme predictions about AI’s impact on employment are likely to be accurate.
20. Developing countries may face greater challenges from automation than developed nations.
21. The disappearance of middle-income jobs is a recent phenomenon that started with AI.
22. Companies that view AI only as a way to reduce costs are missing opportunities.
23. Most governments have created effective policies to address AI’s impact on workers.
Questions 24-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Research shows that AI is causing a fundamental change to the labour market rather than simple 24. of workers. While complete automation of jobs is rare, many occupations have tasks that can be automated. Jobs most at risk involve 25. tasks in structured settings, while those requiring creativity and emotional intelligence are safer. Some countries like Finland and Singapore have created successful 26. _____ to help workers adapt to these changes.
Biểu đồ minh họa sự phân bố việc làm theo trình độ kỹ năng trong thời đại AI
PASSAGE 3 – The Socioeconomic Ramifications of Artificial Intelligence Integration in Labor Markets
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The inexorable advance of artificial intelligence and machine learning technologies has engendered a paradigmatic shift in the conceptualization of labour, productivity, and economic value creation. While technological unemployment—the displacement of workers by machinery—has been a recurring phenomenon since the Industrial Revolution, the contemporary wave of AI-driven automation differs fundamentally in its scope, velocity, and cognitive penetration. Unlike previous technological transitions that primarily affected physical labour, AI systems now demonstrate capabilities in pattern recognition, natural language processing, and even creative tasks that were long considered quintessentially human domains. This unprecedented development necessitates a rigorous re-examination of established economic theories regarding technological change and labour market dynamics.
The orthodox economic perspective, rooted in neoclassical growth theory, posits that technological progress ultimately generates compensating mechanisms that mitigate job losses through increased productivity, lower prices, and expanded demand across the economy. According to this view, while specific occupations may become obsolete, the overall employment level remains stable or even increases as new industries emerge and real incomes rise. Historical evidence provides some vindication for this optimistic stance: the mechanization of agriculture, which once employed the vast majority of the workforce in industrialized nations, did not result in permanent mass unemployment but rather facilitated a transition to manufacturing and, subsequently, service economies. However, several contemporary economists and labour market scholars challenge the applicability of these historical precedents to the current technological juncture.
Scholars such as Erik Brynjolfsson and Andrew McAfee argue that we have entered a “second machine age” characterized by exponential improvements in computing power and the emergence of general-purpose technologies with pervasive applications across economic sectors. They contend that the pace of technological advancement now exceeds the adaptive capacity of educational institutions and labour market mechanisms, creating a temporal mismatch between skill obsolescence and skill acquisition. This disequilibrium manifests in the concurrent existence of high unemployment in certain sectors and persistent skills shortages in others—a phenomenon known as structural unemployment. Moreover, the winner-take-all dynamics of digital markets, where network effects and economies of scale concentrate market power among a handful of technology firms, may exacerbate income inequality by disproportionately rewarding capital owners and highly skilled workers while diminishing the bargaining power of labour more broadly.
The distributional consequences of AI integration extend beyond simple employment metrics to encompass deeper questions of economic justice and social cohesion. Empirical studies by economists such as Daron Acemoglu and Pascual Restrepo have documented a negative correlation between industrial robot density and regional employment rates in the United States, with effects particularly pronounced among workers without college degrees. Their research suggests that automation has contributed to the declining labour share of national income—the proportion of GDP accruing to workers rather than capital owners—a trend observed across most advanced economies since the 1980s. This secular shift in factor income distribution has profound implications for consumer demand, social mobility, and political stability, as an increasing share of the population finds itself economically marginalized despite aggregate economic growth.
Furthermore, the cognitive characteristics of AI systems introduce novel considerations regarding occupational vulnerability. Moravec’s paradox—the observation that tasks humans find intellectually challenging are often computationally straightforward, while routine tasks requiring sensorimotor skills prove remarkably difficult for machines—no longer holds as consistently as it once did. Recent advances in computer vision, robotic manipulation, and reinforcement learning have enabled machines to perform increasingly sophisticated physical tasks, while large language models demonstrate impressive capabilities in text generation and semantic understanding. Consequently, the occupational taxonomy is being reconfigured along new dimensions: rather than the traditional manual-cognitive or routine-nonroutine dichotomies, vulnerability to automation may increasingly correlate with the degree of standardization in task environments and the feasibility of learning from large datasets.
The policy implications of these transformations are multifaceted and contested. Proponents of universal basic income (UBI) argue that it represents an essential adaptation to an economy where traditional employment may no longer suffice as the primary mechanism for income distribution. Pilot programs in regions such as Kenya and Finland have yielded preliminary data on UBI’s effects on work incentives, well-being, and entrepreneurship, though results remain ambiguous and context-dependent. Alternative proposals include negative income taxes, guaranteed employment programs, and sovereign wealth funds that distribute dividends from capital gains—each with distinct advantages and implementation challenges.
Educational reform constitutes another critical policy lever. The traditional model of front-loaded education—where individuals acquire skills primarily during youth—appears increasingly inadequate in an era of rapid technological change. Many education theorists advocate for lifelong learning ecosystems that enable continuous skill updating throughout individuals’ careers. Denmark’s “flexicurity” model, which combines flexible labour markets with generous unemployment benefits and extensive retraining opportunities, has been cited as a potential template, though its transferability to different institutional contexts remains uncertain. Additionally, the curriculum content itself requires fundamental revision: rather than discipline-specific knowledge that may quickly become outdated, education should prioritize metacognitive skills, adaptability, and distinctively human capabilities such as ethical reasoning, creative synthesis, and interpersonal communication.
Ultimately, the trajectory of AI’s impact on employment hinges on sociotechnical choices rather than technological determinism. The design of AI systems, the governance frameworks regulating their deployment, and the economic institutions mediating their effects all represent domains of human agency and collective decision-making. Whether AI engenders a dystopian concentration of wealth and power or a more equitable distribution of prosperity and leisure depends substantially on policy interventions, corporate practices, and social movements that shape the terms of technological integration. The imperative for policymakers, business leaders, and civil society is to foster an inclusive dialogue that ensures technological progress serves broadly shared human flourishing rather than narrow interests.
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C or D.
27. According to the passage, current AI-driven automation differs from previous technological changes primarily because it:
A. affects only manufacturing jobs
B. develops at a slower pace
C. impacts cognitive as well as physical work
D. creates more jobs than it eliminates
28. The neoclassical economic view suggests that technological progress:
A. always causes permanent unemployment
B. eventually creates compensating benefits for employment
C. should be restricted by governments
D. only affects agricultural workers
29. The “second machine age” concept emphasizes:
A. the slow development of technology
B. the decline of the computer industry
C. that educational systems are adapting quickly
D. a mismatch between technological change and human adaptation
30. Research by Acemoglu and Restrepo found that industrial robots:
A. increased employment in all regions
B. had no effect on workers with degrees
C. negatively affected regional employment rates
D. only impacted highly educated workers
31. Moravec’s paradox originally suggested that:
A. all human tasks are easy for computers
B. physically simple tasks can be computationally difficult
C. machines cannot learn at all
D. cognitive work is impossible to automate
Questions 32-36: Matching Features
Match each policy approach (A-G) with the correct description (Questions 32-36).
Policy Approaches:
A. Universal Basic Income
B. Negative income taxes
C. Guaranteed employment programs
D. Sovereign wealth funds
E. Denmark’s flexicurity model
F. Front-loaded education
G. Lifelong learning ecosystems
32. A system where people receive unconditional regular payments
33. An approach that combines flexible job markets with strong social support
34. A traditional educational model that is becoming inadequate
35. Funds that share profits from capital investments with citizens
36. Continuous skill development throughout working life
Questions 37-40: Short-answer Questions
Answer the questions below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
37. What type of unemployment occurs when there is a mismatch between available skills and job requirements?
38. What proportion of GDP going to workers has been declining since the 1980s?
39. What type of models can now generate text and understand meaning?
40. According to the passage, what should education prioritize instead of knowledge that becomes obsolete quickly?
Chuyên gia chính sách và nhà kinh tế thảo luận về chiến lược ứng phó với tác động của AI lên thị trường lao động
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- C
- B
- C
- TRUE
- FALSE
- NOT GIVEN
- NOT GIVEN
- collaborative robotics
- fraud / suspicious patterns
- lifelong learning
- middle-skilled
PASSAGE 2: Questions 14-26
- iii
- vii
- i
- vi
- v
- NO
- YES
- NO
- YES
- NO
- substitution
- routine / predictable
- initiatives / models
PASSAGE 3: Questions 27-40
- C
- B
- D
- C
- B
- A
- E
- F
- D
- G
- structural unemployment
- labour share
- large language models
- metacognitive skills
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: today’s industrial robots, differ from earlier versions
- Vị trí trong bài: Đoạn 2, dòng 2-3
- Giải thích: Bài đọc nói rõ “today’s robots are far more sophisticated” (robots ngày nay tinh vi hơn nhiều), tương đương với “can perform more complex operations”. Đáp án A không được đề cập, C và D không có thông tin trong bài.
Câu 2: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI systems, financial institutions
- Vị trí trong bài: Đoạn 3, dòng 3-5
- Giải thích: Đoạn văn chỉ rõ “These systems can scan thousands of transactions per second” – có thể xử lý hàng ngàn giao dịch mỗi giây, thể hiện khả năng xử lý dữ liệu lớn nhanh chóng. Đáp án A sai vì AI không thay thế hoàn toàn, B và D mâu thuẫn với thông tin bài đưa ra.
Câu 3: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: radiologists, demonstrates
- Vị trí trong bài: Đoạn 4, dòng 3-5
- Giải thích: Ví dụ về bác sĩ X-quang cho thấy “The AI serves as a powerful assistant, improving accuracy and efficiency” – AI là trợ lý tăng cường năng lực con người, không thay thế hoàn toàn. Đây là paraphrase của “enhance human expertise”.
Câu 6: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: self-checkout counters, AI technology, everyday use
- Vị trí trong bài: Đoạn 1, dòng 3-4
- Giải thích: Bài đọc đề cập “From self-checkout counters in supermarkets to automated customer service chatbots, AI systems are now ubiquitous in our daily lives” – rõ ràng khẳng định thông tin này đúng.
Câu 7: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: car manufacturing, more workers, past
- Vị trí trong bài: Đoạn 2, dòng cuối
- Giải thích: Bài viết nói “A car manufacturing plant that once required hundreds of workers can now operate with a fraction of that workforce” – chỉ cần một phần nhỏ lực lượng lao động trước đây, nghĩa là ít hơn, mâu thuẫn với câu phát biểu.
Câu 10: collaborative robotics
- Dạng câu hỏi: Sentence Completion
- Từ khóa: work together with humans
- Vị trí trong bài: Đoạn 2, dòng 4-5
- Giải thích: Cụm từ chính xác trong bài là “collaborative robotics”, được đề cập khi nói về robots làm việc cùng với công nhân.
Câu 12: lifelong learning
- Dạng câu hỏi: Sentence Completion
- Từ khóa: essential, workers, change careers several times
- Vị trí trong bài: Đoạn 6, dòng 5-7
- Giải thích: Bài đọc nói “The concept of lifelong learning has become crucial, as workers may need to retrain multiple times throughout their careers” – học tập suốt đời trở nên thiết yếu vì công nhân cần đào tạo lại nhiều lần.
Passage 2 – Giải Thích
Câu 14: iii
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn B
- Giải thích: Đoạn B tập trung vào sự khác biệt giữa việc tự động hóa các nhiệm vụ cụ thể (tasks) và việc loại bỏ hoàn toàn công việc (jobs). McKinsey study được trích dẫn cho thấy “fewer than 5% of jobs can be fully automated” nhưng “60% of occupations have at least 30% of their constituent activities that could be automated” – đây chính là điểm phân biệt task automation và job elimination.
Câu 15: vii
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn C
- Giải thích: Đoạn C mô tả đặc điểm của các công việc dễ bị tự động hóa: “routine, predictable tasks in structured environments” và những công việc khó tự động hóa: “creativity, complex problem-solving, emotional intelligence”. Đây chính là nội dung về “characteristics of jobs at risk”.
Câu 16: i
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn D
- Giải thích: Đoạn D thảo luận về “geographical distribution of AI’s impact” và sự khác biệt giữa các nền kinh tế phát triển và đang phát triển trong cách họ đối mặt với tự động hóa – rõ ràng về “geographical variations”.
Câu 19: NO
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí: Đoạn A, dòng 1-3
- Giải thích: Tác giả nói “While these extremes capture public imagination, the reality is likely to be more nuanced and complex” – thực tế có khả năng phức tạp hơn những dự đoán cực đoan, cho thấy tác giả không đồng ý với việc những dự đoán cực đoan là chính xác.
Câu 20: YES
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí: Đoạn D, giữa đoạn
- Giải thích: Tác giả viết “developing nations with young, growing populations may face more severe disruption” – các nước đang phát triển có thể đối mặt với sự gián đoạn nghiêm trọng hơn, khẳng định quan điểm trong câu hỏi.
Câu 24: substitution
- Dạng câu hỏi: Summary Completion
- Từ khóa: fundamental change, labour market, simple
- Vị trí: Đoạn A, dòng cuối
- Giải thích: Bài đọc đề cập “rather than causing a simple substitution of machines for humans, AI is precipitating a fundamental restructuring” – thay vì thay thế đơn giản, AI tạo ra tái cấu trúc cơ bản.
Câu 25: routine / predictable
- Dạng câu hỏi: Summary Completion
- Từ khóa: jobs most at risk, tasks, structured settings
- Vị trí: Đoạn C, đầu đoạn
- Giải thích: Đoạn C nói rõ “Jobs involving routine, predictable tasks in structured environments are particularly vulnerable” – có thể chọn routine hoặc predictable đều đúng.
Passage 3 – Giải Thích
Câu 27: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: current AI-driven automation, differs, previous technological changes
- Vị trí: Đoạn 1, giữa đoạn
- Giải thích: Bài viết chỉ rõ “Unlike previous technological transitions that primarily affected physical labour, AI systems now demonstrate capabilities in pattern recognition, natural language processing, and even creative tasks” – khác với các thay đổi trước đây chỉ ảnh hưởng lao động thể chất, AI giờ ảnh hưởng cả công việc nhận thức.
Câu 29: D
- Dạng câu hỏi: Multiple Choice
- Từ khóa: second machine age concept
- Vị trí: Đoạn 3, đầu đoạn
- Giải thích: Khái niệm này nhấn mạnh rằng “the pace of technological advancement now exceeds the adaptive capacity of educational institutions and labour market mechanisms, creating a temporal mismatch” – tốc độ công nghệ vượt khả năng thích ứng của con người, tạo ra sự không khớp.
Câu 30: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Acemoglu and Restrepo, industrial robots
- Vị trí: Đoạn 4, đầu đoạn
- Giải thích: Nghiên cứu của họ “documented a negative correlation between industrial robot density and regional employment rates” – mối tương quan âm giữa mật độ robot và tỷ lệ việc làm khu vực, nghĩa là robot làm giảm việc làm.
Câu 31: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Moravec’s paradox, originally suggested
- Vị trí: Đoạn 5, đầu đoạn
- Giải thích: Nghịch lý này quan sát rằng “tasks humans find intellectually challenging are often computationally straightforward, while routine tasks requiring sensorimotor skills prove remarkably difficult for machines” – các nhiệm vụ đơn giản về thể chất có thể khó về mặt tính toán.
Câu 37: structural unemployment
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: unemployment, mismatch, available skills, job requirements
- Vị trí: Đoạn 3, giữa đoạn
- Giải thích: Bài viết định nghĩa “a phenomenon known as structural unemployment” khi nói về sự không khớp giữa kỹ năng có sẵn và yêu cầu công việc.
Câu 38: labour share
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: proportion of GDP, workers, declining since 1980s
- Vị trí: Đoạn 4, giữa đoạn
- Giải thích: “The declining labour share of national income—the proportion of GDP accruing to workers rather than capital owners” – tỷ lệ thu nhập lao động trong GDP đang giảm.
Câu 39: large language models
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: models, generate text, understand meaning
- Vị trí: Đoạn 5, cuối đoạn
- Giải thích: Bài viết đề cập “large language models demonstrate impressive capabilities in text generation and semantic understanding” – các mô hình ngôn ngữ lớn có khả năng tạo văn bản và hiểu nghĩa.
Câu 40: metacognitive skills
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: education, prioritize, instead of knowledge, becomes obsolete
- Vị trí: Đoạn 7, cuối đoạn
- Giải thích: “Education should prioritize metacognitive skills, adaptability, and distinctively human capabilities” – giáo dục nên ưu tiên kỹ năng siêu nhận thức thay vì kiến thức có thể lỗi thời.
5. Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| profound transformation | noun phrase | /prəˈfaʊnd trænsˈfɔːmeɪʃən/ | sự chuyển đổi sâu sắc | The world of work is experiencing a profound transformation | undergo/experience a profound transformation |
| ubiquitous | adj | /juːˈbɪkwɪtəs/ | có mặt khắp nơi | AI systems are now ubiquitous in our daily lives | become ubiquitous, ubiquitous presence |
| sophisticated | adj | /səˈfɪstɪkeɪtɪd/ | tinh vi, phức tạp | Today’s robots are far more sophisticated | highly sophisticated, increasingly sophisticated |
| collaborative robotics | noun phrase | /kəˈlæbərətɪv rəʊˈbɒtɪks/ | robot cộng tác | Work alongside human employees in collaborative robotics | field of collaborative robotics |
| disruption | noun | /dɪsˈrʌpʃən/ | sự gián đoạn, phá vỡ | White-collar jobs are experiencing disruption | cause/face disruption, major disruption |
| clinical judgment | noun phrase | /ˈklɪnɪkəl ˈdʒʌdʒmənt/ | phán đoán lâm sàng | The final diagnosis requires human clinical judgment | exercise clinical judgment, sound clinical judgment |
| emerging fields | noun phrase | /ɪˈmɜːdʒɪŋ fiːldz/ | các lĩnh vực mới nổi | Emerging fields such as AI ethics | work in emerging fields, explore emerging fields |
| vulnerable | adj | /ˈvʌlnərəbl/ | dễ bị tổn thương, yếu thế | Middle-skilled jobs are most vulnerable to automation | particularly vulnerable, highly vulnerable |
| transition period | noun phrase | /trænˈzɪʃən ˈpɪəriəd/ | giai đoạn chuyển tiếp | The transition period may be challenging | during the transition period, smooth transition period |
| absorb | verb | /əbˈzɔːb/ | hấp thụ, tiếp nhận | The economy absorbed this workforce | absorb workers, absorb into the workforce |
| retrain | verb | /riːˈtreɪn/ | đào tạo lại | Workers need to retrain multiple times | retrain workers, retrain for new roles |
| employable | adj | /ɪmˈplɔɪəbl/ | có khả năng làm việc | To remain employable throughout careers | stay employable, highly employable |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| advent | noun | /ˈædvent/ | sự ra đời, xuất hiện | The advent of artificial intelligence | the advent of technology, mark the advent |
| nuanced | adj | /ˈnjuːɑːnst/ | tinh tế, phức tạp | The reality is likely to be more nuanced | nuanced understanding, nuanced approach |
| precipitating | verb | /prɪˈsɪpɪteɪtɪŋ/ | gây ra, thúc đẩy nhanh | AI is precipitating a fundamental restructuring | precipitate change, precipitate a crisis |
| constituent activities | noun phrase | /kənˈstɪtjuənt ækˈtɪvɪtiz/ | các hoạt động cấu thành | At least 30% of their constituent activities | analyze constituent activities |
| susceptible | adj | /səˈseptɪbl/ | dễ bị ảnh hưởng | Occupational categories most susceptible to automation | highly susceptible, susceptible to change |
| cognitive tasks | noun phrase | /ˈkɒɡnɪtɪv tɑːsks/ | nhiệm vụ nhận thức | Automation affects cognitive tasks | perform cognitive tasks, complex cognitive tasks |
| asymmetry | noun | /eɪˈsɪmətri/ | sự bất cân xứng | This asymmetry could exacerbate inequality | economic asymmetry, address asymmetry |
| wage polarization | noun phrase | /weɪdʒ pəʊləraɪˈzeɪʃən/ | sự phân cực tiền lương | Wage polarization observed in many countries | growing wage polarization, address wage polarization |
| hollowed out | phrasal verb | /ˈhɒləʊd aʊt/ | bị rỗng ruột, bị xói mòn | Middle-income jobs have been hollowed out | hollowed out the middle class |
| precarious | adj | /prɪˈkeəriəs/ | bấp bênh, không chắc chắn | Middle-skilled workers face precarious prospects | precarious employment, precarious situation |
| augment | verb | /ɔːɡˈment/ | tăng cường, bổ sung | Use AI to augment human capabilities | augment capabilities, augment workforce |
| reskilling | noun | /riːˈskɪlɪŋ/ | đào tạo lại kỹ năng | Established reskilling academies | reskilling programs, invest in reskilling |
| social safety nets | noun phrase | /ˈsəʊʃəl ˈseɪfti nets/ | mạng lưới an sinh xã hội | Expanded social safety nets | strengthen social safety nets, comprehensive social safety nets |
| inclusive growth | noun phrase | /ɪnˈkluːsɪv ɡrəʊθ/ | tăng trưởng bao trùm | Ensure inclusive growth strategies | promote inclusive growth, achieve inclusive growth |
| smooth | verb | /smuːð/ | làm êm ả, thuận lợi | Help smooth the transition | smooth the transition, smooth the process |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| inexorable | adj | /ɪnˈeksərəbl/ | không thể ngăn cản | The inexorable advance of AI | inexorable progress, inexorable trend |
| engendered | verb | /ɪnˈdʒendəd/ | tạo ra, gây nên | Has engendered a paradigmatic shift | engender change, engender debate |
| paradigmatic shift | noun phrase | /pærədɪɡˈmætɪk ʃɪft/ | sự chuyển dịch mô hình | A paradigmatic shift in conceptualization | represent a paradigmatic shift |
| cognitive penetration | noun phrase | /ˈkɒɡnɪtɪv penɪˈtreɪʃən/ | sự thâm nhập nhận thức | Differs in its cognitive penetration | deep cognitive penetration |
| quintessentially | adv | /kwɪntɪˈsenʃəli/ | một cách tinh túy nhất | Tasks considered quintessentially human | quintessentially human, quintessentially British |
| compensating mechanisms | noun phrase | /ˈkɒmpenseɪtɪŋ ˈmekənɪzəmz/ | cơ chế bù đắp | Generate compensating mechanisms | develop compensating mechanisms |
| vindication | noun | /vɪndɪˈkeɪʃən/ | sự xác nhận, chứng minh | Provides some vindication for this stance | seek vindication, provide vindication |
| temporal mismatch | noun phrase | /ˈtempərəl ˈmɪsmætʃ/ | sự không khớp về thời gian | Creating a temporal mismatch | address temporal mismatch |
| disequilibrium | noun | /dɪsiːkwɪˈlɪbriəm/ | sự mất cân bằng | This disequilibrium manifests | economic disequilibrium, market disequilibrium |
| exacerbate | verb | /ɪɡˈzæsəbeɪt/ | làm trầm trọng thêm | May exacerbate income inequality | exacerbate problems, exacerbate tensions |
| distributional consequences | noun phrase | /dɪstrɪˈbjuːʃənəl ˈkɒnsɪkwənsɪz/ | hậu quả về phân phối | The distributional consequences of AI | analyze distributional consequences |
| secular shift | noun phrase | /ˈsekjələ ʃɪft/ | sự thay đổi lâu dài | This secular shift in income distribution | long-term secular shift |
| marginalized | adj | /ˈmɑːdʒɪnəlaɪzd/ | bị gạt ra lề | Finds itself economically marginalized | increasingly marginalized, marginalized communities |
| Moravec’s paradox | proper noun | /məˈrɑːveks ˈpærədɒks/ | nghịch lý Moravec | Moravec’s paradox no longer holds | explain Moravec’s paradox |
| occupational taxonomy | noun phrase | /ɒkjʊˈpeɪʃənəl tækˈsɒnəmi/ | phân loại nghề nghiệp | The occupational taxonomy is being reconfigured | develop occupational taxonomy |
| multifaceted | adj | /mʌltɪˈfæsɪtɪd/ | nhiều mặt, đa diện | The policy implications are multifaceted | multifaceted approach, multifaceted problem |
| lifelong learning ecosystems | noun phrase | /ˈlaɪflɒŋ ˈlɜːnɪŋ ˈiːkəʊsɪstəmz/ | hệ sinh thái học tập suốt đời | Advocate for lifelong learning ecosystems | build lifelong learning ecosystems |
| technological determinism | noun phrase | /teknəˈlɒdʒɪkəl dɪˈtɜːmɪnɪzəm/ | chủ nghĩa quyết định công nghệ | Rather than technological determinism | reject technological determinism |
| sociotechnical choices | noun phrase | /səʊsiəʊˈteknɪkəl ˈtʃɔɪsɪz/ | các lựa chọn xã hội-kỹ thuật | Hinges on sociotechnical choices | make sociotechnical choices |
Học viên IELTS luyện tập đọc hiểu với đề thi chủ đề AI và tác động việc làm trên laptop
Kết bài
Chủ đề về tác động của AI đến việc làm truyền thống không chỉ là một đề tài thời sự mà còn là chủ đề quan trọng mà bạn có thể gặp trong kỳ thi IELTS Reading thực tế. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm một bài thi hoàn chỉnh với 3 passages có độ khó tăng dần từ Easy đến Hard, phản ánh chính xác cấu trúc của đề thi Cambridge IELTS chính thức.
Passage 1 giới thiệu những khái niệm cơ bản về tự động hóa trong môi trường làm việc hiện đại, phù hợp với band 5.0-6.5. Passage 2 đi sâu vào phân tích tác động đa chiều của AI lên các ngành nghề khác nhau, yêu cầu kỹ năng đọc hiểu ở mức band 6.0-7.5. Passage 3 khám phá những hệ quả kinh tế-xã hội phức tạp với từ vựng học thuật và cấu trúc câu tinh vi, thử thách thí sinh ở band 7.0-9.0.
Đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin, kỹ thuật paraphrase và chiến lược làm bài sẽ giúp bạn không chỉ kiểm tra kết quả mà còn hiểu rõ phương pháp tiếp cận đúng đắn cho từng dạng câu hỏi. Đặc biệt, bộ từ vựng chuyên ngành được tổng hợp kỹ lưỡng theo từng passage sẽ là nguồn tài nguyên quý giá cho việc mở rộng vốn từ học thuật của bạn.
Hãy luyện tập đề thi này trong điều kiện giống thi thật – 60 phút liên tục không tra từ điển. Sau đó, dành thời gian phân tích kỹ những câu sai để rút ra bài học. Với sự luyện tập đều đặn và phương pháp đúng đắn, bạn hoàn toàn có thể đạt được band điểm mục tiêu trong phần IELTS Reading.
Chúc bạn ôn tập hiệu quả và đạt kết quả cao trong kỳ thi IELTS sắp tới!
