Mở bài
Chủ đề tự động hóa và tác động của nó đến việc đào tạo lại lực lượng lao động đang ngày càng trở nên phổ biến trong các kỳ thi IELTS Reading gần đây. Với sự phát triển vượt bậc của công nghệ và trí tuệ nhân tạo, vấn đề này không chỉ là xu hướng toàn cầu mà còn là nội dung được các nhà ra đề IELTS ưa chuộng bởi tính thời sự và tầm quan trọng của nó.
Trong bài viết này, bạn sẽ được thực hành với một đề thi IELTS Reading hoàn chỉnh bao gồm 3 passages với độ khó tăng dần từ Easy đến Hard. Mỗi passage được thiết kế cẩn thận để phản ánh đúng cấu trúc và yêu cầu của bài thi thật, cung cấp đầy đủ 40 câu hỏi với nhiều dạng bài đa dạng như Multiple Choice, True/False/Not Given, Matching Headings, và Summary Completion.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với chủ đề về công nghệ và thị trường lao động, đồng thời rà soát lại các kỹ năng làm bài quan trọng. Bên cạnh đáp án chính xác, bạn sẽ nhận được giải thích chi tiết về cách tìm thông tin, kỹ thuật paraphrase, và bộ từ vựng quan trọng được sắp xếp theo từng passage để tối ưu hóa việc học.
Hướng dẫn làm bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test là một trong bốn kỹ năng được đánh giá trong kỳ thi IELTS, yêu cầu thí sinh hoàn thành 40 câu hỏi trong vòng 60 phút. Bài thi bao gồm 3 passages với độ dài và độ khó tăng dần, từ khoảng 700 từ đến hơn 900 từ mỗi passage.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (độ khó Easy, phù hợp band 5.0-6.5)
- Passage 2: 18-20 phút (độ khó Medium, phù hợp band 6.0-7.5)
- Passage 3: 23-25 phút (độ khó Hard, phù hợp band 7.0-9.0)
Lưu ý rằng không có thời gian bổ sung để chép đáp án sang phiếu trả lời, vì vậy bạn cần quản lý thời gian một cách khoa học để vừa đọc hiểu, vừa trả lời câu hỏi và ghi chép cẩn thận ngay từ đầu.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến trong IELTS Reading:
- Multiple Choice – Câu hỏi trắc nghiệm nhiều lựa chọn
- True/False/Not Given – Xác định thông tin đúng, sai hoặc không được nhắc đến
- Matching Information – Ghép thông tin với đoạn văn tương ứng
- Matching Headings – Ghép tiêu đề với đoạn văn
- Summary Completion – Hoàn thành đoạn tóm tắt
- Sentence Completion – Hoàn thành câu
- Short-answer Questions – Trả lời câu hỏi ngắn
Mỗi dạng câu hỏi yêu cầu kỹ năng đọc hiểu khác nhau, từ scanning để tìm thông tin cụ thể đến skimming để nắm ý chính của đoạn văn.
IELTS Reading Practice Test
PASSAGE 1 – The Changing Landscape of Work in the Digital Age
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
The world of work is undergoing a fundamental transformation as automation technologies continue to advance at an unprecedented pace. From manufacturing plants to office buildings, machines and artificial intelligence systems are increasingly taking over tasks that were once performed exclusively by human workers. This shift is not merely a temporary trend but represents a profound change in how societies organize production and employment. While some view this development with concern, others see it as an opportunity to reshape the workforce and create new possibilities for human potential.
Automation refers to the use of technology to perform tasks with minimal human intervention. In factories, robotic systems can now assemble products faster and more accurately than human workers. In offices, software programs handle data entry, scheduling, and even customer service through chatbots. The retail sector has witnessed the introduction of self-checkout machines and automated warehouses, while the transportation industry is experimenting with self-driving vehicles. These technological advances have led to significant improvements in productivity and efficiency, allowing companies to reduce costs and increase output.
However, the rise of automation has created a pressing challenge: what happens to workers whose jobs are replaced by machines? This question has become central to discussions about the future of employment. Economic studies suggest that millions of jobs in routine manual tasks and basic cognitive work are at risk of automation in the coming decades. Workers in industries such as manufacturing, data processing, and transportation face particular vulnerability to job displacement. The concern is not just about unemployment but about the creation of a workforce that lacks the skills needed for the jobs of tomorrow.
In response to these challenges, governments, educational institutions, and private companies have begun to focus on workforce retraining programs. These initiatives aim to help workers develop new skills that are relevant to the changing job market. Retraining typically involves teaching workers how to use new technologies, think critically, solve complex problems, and adapt to different roles. For example, a factory worker who once operated machinery might be retrained to program and maintain automated systems. Similarly, an administrative assistant could learn data analytics or digital marketing to transition into a more technology-focused role.
The success of retraining programs depends on several factors. First, they must be accessible to workers from all backgrounds, including those with limited education or financial resources. Many programs are now offered online, making them more convenient for people who need to balance work and family responsibilities. Second, the training must be practical and industry-relevant, focusing on skills that employers actually need. Partnerships between training providers and businesses help ensure that curricula align with real-world demands. Third, there must be adequate support systems in place, including career counseling, job placement services, and financial assistance to help workers during the transition period.
Different countries have adopted various approaches to workforce retraining. In Scandinavia, governments have invested heavily in lifelong learning programs that allow workers to continuously update their skills throughout their careers. Germany’s dual education system combines classroom instruction with hands-on apprenticeships, preparing workers for specific industries while allowing them to earn income. Singapore has implemented a national skills framework that provides citizens with credits for training courses, encouraging continuous professional development. The United States relies more on a combination of community colleges, private training providers, and employer-sponsored programs, though critics argue that this fragmented approach leaves many workers without adequate support.
Despite these efforts, significant challenges remain. Many workers, particularly older employees or those in rural areas, may struggle to access training opportunities or adapt to new technologies. There is also the question of whether retraining programs can keep pace with the speed of technological change. Some experts worry that by the time workers complete a training program, the skills they have learned may already be becoming obsolete. Furthermore, not all displaced workers will successfully transition to new careers, and some may face long periods of unemployment or underemployment in jobs that do not fully utilize their abilities.
The social implications of automation and workforce retraining extend beyond individual workers to entire communities. Regions that depend heavily on industries facing automation may experience economic decline as jobs disappear and workers relocate in search of opportunities. This can lead to decreased tax revenues, reduced public services, and a loss of social cohesion. Addressing these broader impacts requires not just retraining individual workers but also regional economic development strategies that attract new industries and create diverse employment opportunities.
Looking ahead, the relationship between automation and workforce retraining will likely become even more important. As technology continues to evolve, the need for workers to adapt and learn new skills will be a constant feature of modern working life. Rather than viewing automation solely as a threat, many experts encourage a perspective that sees it as a catalyst for change – one that can free humans from dangerous or monotonous work and allow them to focus on more creative, strategic, and interpersonally demanding tasks. The key to realizing this positive vision lies in ensuring that adequate resources are devoted to helping workers navigate these transitions successfully.
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. According to the passage, automation in the workplace is best described as:
A. A temporary phenomenon that will soon pass
B. A significant and lasting change in work organization
C. A minor adjustment to existing work practices
D. A development limited to manufacturing industries
2. The passage suggests that automation has primarily benefited companies by:
A. Eliminating the need for human workers entirely
B. Reducing costs while increasing production
C. Creating more jobs than it eliminates
D. Making products more expensive
3. Workers most at risk from automation are those in jobs involving:
A. Creative and strategic thinking
B. Complex interpersonal relationships
C. Routine manual and basic cognitive tasks
D. Advanced technical problem-solving
4. According to the passage, successful retraining programs must:
A. Be expensive and exclusive
B. Focus only on theoretical knowledge
C. Be accessible and practical
D. Replace all traditional education
5. The passage indicates that the approach to workforce retraining in different countries is:
A. Completely uniform across all nations
B. Varied, with different models and strategies
C. Non-existent in most developed countries
D. Only focused on young workers
Questions 6-10: 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. Chatbots are examples of automation technology used in customer service.
7. All displaced workers will eventually find better jobs through retraining programs.
8. Scandinavian countries have invested significantly in lifelong learning initiatives.
9. Online training programs are less effective than traditional classroom-based training.
10. Automation could allow workers to focus on more creative and strategic tasks.
Questions 11-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
11. Economic studies indicate that millions of jobs involving __ are at risk in the coming decades.
12. Germany’s approach to workforce training combines classroom learning with __.
13. Communities that rely heavily on automating industries may experience __ as employment opportunities disappear.
PASSAGE 2 – The Economics and Social Dynamics of Workforce Adaptation
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The economic ramifications of automation-driven workforce displacement represent one of the most contentious issues in contemporary labor economics. While technological optimists argue that history demonstrates technology’s capacity to create more jobs than it destroys, skeptics point to qualitative differences in the current wave of automation that may fundamentally alter this historical pattern. Unlike previous industrial revolutions, which primarily affected physical labor, modern automation increasingly targets cognitive tasks previously considered the exclusive domain of educated professionals. This expansion into white-collar work has profound implications for workforce retraining strategies and raises questions about the scalability and effectiveness of traditional educational approaches.
Empirical evidence regarding automation’s impact on employment presents a nuanced picture. Research by economists such as David Autor has revealed that while automation does eliminate certain jobs, it simultaneously creates demand for workers in complementary roles. For instance, the introduction of ATMs (Automated Teller Machines) reduced the need for bank tellers to dispense cash but increased demand for customer service roles that required more complex problem-solving and interpersonal skills. Similarly, computer-aided design software eliminated many drafting positions but created opportunities for designers who could leverage these tools to produce more sophisticated work. This pattern suggests that the relationship between automation and employment is characterized by substitution in some areas and complementation in others.
However, the transition from displaced jobs to new opportunities is neither automatic nor painless. Workers face what economists call structural unemployment – a mismatch between the skills they possess and those required by available jobs. This problem is exacerbated by geographic immobility, as new opportunities may emerge in different locations from where jobs are lost, and workers often face significant barriers to relocation, including family ties, housing costs, and regional wage differences. Additionally, there exists an age-related dimension to retraining challenges; older workers who have invested decades developing industry-specific expertise may find it particularly difficult to acquire entirely new skill sets, especially those involving digital literacy or advanced technical knowledge.
The design and implementation of effective workforce retraining programs must address these multifaceted challenges. Contemporary research emphasizes the importance of competency-based training that focuses on transferable skills rather than job-specific tasks. This approach recognizes that in a rapidly changing economy, the specific technical skills workers learn today may become obsolete relatively quickly, whereas broader competencies such as critical thinking, adaptability, communication, and continuous learning remain valuable across various contexts. Furthermore, effective programs incorporate experiential learning components, including internships, apprenticeships, and project-based work, which allow participants to apply new knowledge in realistic settings and build professional networks that facilitate job placement.
Funding mechanisms for workforce retraining present both practical and philosophical challenges. Traditional models that place responsibility primarily on individuals or employers face criticism for being inadequate to the scale of the automation challenge. Individual workers, particularly those from disadvantaged backgrounds, may lack the financial resources to invest in retraining while managing daily expenses. Employers, meanwhile, face collective action problems – companies that invest in training workers may see those workers poached by competitors who avoid training costs. This dynamic creates a market failure that many argue justifies government intervention through publicly funded training programs, tax incentives, or training subsidies.
Different jurisdictions have experimented with innovative financing approaches. Singapore’s SkillsFuture initiative provides citizens with individual training credits that can be used throughout their lives for approved courses, creating a portable benefit that moves with workers between jobs. France’s personal training account system accumulates training rights based on hours worked, ensuring that even workers who change employers retain access to educational opportunities. Income share agreements, where training providers receive payment as a percentage of graduates’ future earnings, represent a market-based solution that aligns incentives between providers and participants. Meanwhile, proposals for automation taxes – levying charges on companies that replace workers with machines – remain controversial but represent one approach to generating revenue specifically targeted at supporting displaced workers.
The psychological and social dimensions of workforce retraining deserve equal attention to economic considerations. Research in occupational psychology demonstrates that job loss and career transitions, particularly those involuntary in nature, can have significant impacts on mental health, self-esteem, and social identity. Workers who have defined themselves through their profession may experience identity disruption when forced to retrain for entirely different roles. Effective retraining programs therefore incorporate counseling services, peer support groups, and career coaching to address these psychological challenges alongside skill development.
Moreover, the social context in which retraining occurs significantly influences outcomes. Communities with strong social capital – characterized by trust, reciprocity, and active civic engagement – tend to facilitate more successful workforce transitions. In such environments, informal knowledge networks allow workers to learn about training opportunities and job openings, while community organizations provide practical support. Conversely, communities experiencing economic distress may face a vicious cycle where declining opportunities lead to out-migration of younger, more educated residents, further eroding the social infrastructure needed to support those who remain.
The role of educational institutions in workforce retraining has evolved considerably. Traditional universities and colleges, historically focused on preparing young people for initial entry into the workforce, increasingly recognize the need to serve mid-career learners with different needs and constraints. This has led to the proliferation of executive education programs, professional certificates, and micro-credentials designed for working adults. Meanwhile, community colleges have emerged as crucial providers of vocational retraining, offering flexible scheduling, practical curricula, and lower costs than four-year institutions. The rise of online learning platforms and Massive Open Online Courses (MOOCs) has further expanded access, though research suggests that completion rates and learning outcomes vary widely, with disadvantaged learners often facing particular challenges in self-directed online environments.
Sectoral partnerships represent another promising approach to workforce retraining. These collaborative arrangements bring together employers, training providers, labor unions, and government agencies to develop training programs aligned with industry needs. By involving employers directly in curriculum design and providing pathways to employment upon completion, sectoral programs address the criticism that traditional education often produces graduates whose skills do not match labor market demands. Evaluation studies of sectoral programs have shown promising results, including higher employment rates and wages compared to participants in more generic training programs.
Questions 14-19: Yes/No/Not Given
Do the following statements agree with the claims of the writer in the passage?
Write:
- YES if the statement agrees with the claims of the writer
- NO if the statement contradicts the claims of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
14. The current wave of automation is fundamentally different from previous industrial revolutions because it targets cognitive as well as physical work.
15. The introduction of ATMs completely eliminated the need for bank tellers.
16. Older workers find it easier to learn new digital skills than younger workers.
17. Training programs that focus on transferable skills are more effective than those teaching specific tasks.
18. Companies always benefit when they invest in training their workers.
19. Communities with strong social capital tend to have better workforce transition outcomes.
Questions 20-23: Matching Information
Match each statement with the correct approach or system (A-F).
You may use any letter more than once.
A. Singapore’s SkillsFuture initiative
B. France’s personal training account system
C. Income share agreements
D. Automation taxes
E. Sectoral partnerships
F. Germany’s dual education system
20. Provides citizens with credits usable for approved training courses
21. Training providers receive payment based on graduates’ future income
22. Accumulates training rights based on hours worked
23. Brings together employers, training providers, and government agencies
Questions 24-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Workers who lose their jobs due to automation often face 24. __, which occurs when their existing skills do not match available positions. This problem is made worse by 25. __, as workers may be unable to move to areas where new jobs exist. Additionally, job loss can cause 26. __ disruption**, affecting workers’ sense of self and mental wellbeing.
PASSAGE 3 – Theoretical Frameworks and Future Trajectories in Labor Market Transformation
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The theoretical apparatus employed to analyze automation’s impact on workforce retraining draws from multiple disciplinary traditions, each offering distinct analytical lenses through which to examine this multifaceted phenomenon. Classical economic theory, grounded in assumptions of perfect information and rational actors, predicts that labor markets will achieve equilibrium as displaced workers retrain and relocate in response to wage signals. However, this neoclassical framework has faced substantial criticism for its failure to account for information asymmetries, transaction costs, institutional rigidities, and the bounded rationality that characterizes actual human decision-making. Institutional economists emphasize how labor market outcomes are shaped by formal regulations, informal norms, power relationships, and historical path dependencies that cannot be reduced to simple supply-demand dynamics.
Technological determinism – the view that technological change follows an autonomous logic that drives social and economic adaptation – represents one conceptual extreme in debates about automation and retraining. This perspective, often implicit in discussions of inevitable technological progress, suggests that workforce adaptation is fundamentally a matter of humans adjusting to exogenous technological change. Critics of this view, drawing on social construction of technology (SCOT) theory and actor-network theory (ANT), argue that technological development is itself shaped by social choices, power structures, and cultural values. From this perspective, the speed, direction, and implementation of automation reflect deliberate decisions by corporate leaders, policymakers, and other stakeholders, rather than inevitable technological imperatives. This distinction has profound implications for workforce retraining policy: if automation is socially constructed rather than technologically determined, then societies possess greater agency in shaping both the nature of technological change and the distributional consequences that follow.
Human capital theory provides another influential framework for understanding workforce retraining. Originating in the work of economists such as Gary Becker and Theodore Schultz, this approach conceptualizes education and training as investments that enhance workers’ productivity and consequently their market value. From this perspective, retraining represents a rational investment in which individuals, employers, or governments expend resources in the present to generate future returns through enhanced earning capacity. However, the application of human capital theory to automation-driven retraining faces several theoretical complications. First, the depreciation rate of human capital may accelerate in rapidly changing technological environments, potentially rendering training investments obsolete before their costs can be recovered. Second, credit market imperfections may prevent individuals from financing optimal levels of training investment, particularly when returns are uncertain or distant. Third, the theory’s focus on pecuniary returns neglects the non-monetary benefits and costs of retraining, including psychological impacts, effects on work-life balance, and changes in job satisfaction and social status.
Skill-biased technological change (SBTC) theory offers a more nuanced account of how automation affects different categories of workers. This framework distinguishes between routine tasks – whether manual or cognitive – that can be codified and automated, and non-routine tasks requiring abstract reasoning, complex communication, or adaptability, which remain complementary to technology rather than substitutable by it. SBTC theory predicts a polarization of the labor market, with growing demand for high-skill workers who can perform non-routine cognitive tasks and for some low-skill workers performing non-routine manual tasks that are difficult to automate (such as personal care services), while middle-skill workers performing routine tasks face displacement. This framework has significant implications for retraining strategy, suggesting that effective programs must move workers not merely to different occupations but specifically to roles characterized by non-routine task content.
Recent refinements to SBTC theory incorporate concepts of task routinization and occupational adaptation. Research by Autor and colleagues demonstrates that occupations are not uniformly automated but rather experience task-level changes as certain components are automated while others expand or transform. This suggests that workforce retraining might profitably focus not only on occupational transitions but also on helping workers adapt within evolving occupational roles. For instance, paralegals might shift from routine document review (increasingly performed by artificial intelligence) toward client interaction and strategic case analysis, requiring retraining in interpersonal skills and legal strategy rather than complete occupational change.
Credentialism and signaling theories highlight another dimension of workforce retraining that purely skill-based analyses overlook. Educational credentials serve not only to certify competence but also to signal worker quality to potential employers facing uncertainty about candidates’ abilities. In contexts where information asymmetries are substantial, the signaling function of credentials may equal or exceed their human capital function. This has ambiguous implications for retraining programs: credentials from prestigious institutions or well-recognized certification bodies may generate substantial labor market returns even if the actual skills conveyed are modest, while effective training lacking recognized credentials may fail to yield employment outcomes commensurate with skills acquired. This suggests the importance of credential portability, standardization, and employer recognition in retraining program design.
Life course theory and age-stratification perspectives from sociology provide crucial insights into how the timing and context of workforce retraining influence outcomes. These frameworks emphasize that educational interventions occur within biographical trajectories shaped by age-related transitions, family obligations, health status, and cumulative advantage or disadvantage from earlier life experiences. Research demonstrates that retraining effectiveness varies significantly across the life course: younger workers possess greater cognitive flexibility and longer time horizons over which to recoup training investments, while older workers may have more financial resources and work experience but face age discrimination and cognitive changes that complicate learning. Furthermore, the gendered nature of work and caregiving responsibilities creates differential constraints on retraining participation, with women often facing greater barriers due to family obligations and occupational segregation that concentrates them in sectors offering limited advancement opportunities.
Power resource theory and political economy approaches direct attention to how class relations, organized labor, and political mobilization shape workforce retraining systems. Comparing across countries, scholars observe substantial variation in retraining support that correlates with union density, labor organization, and the political strength of working-class parties. Nations with strong labor movements, such as the Nordic countries, have developed comprehensive active labor market policies including extensive retraining support, while countries with weaker labor movements tend toward more market-oriented approaches that place greater responsibility on individuals. This suggests that the adequacy of retraining systems depends not merely on technical program design but on the political capacity of workers and their allies to demand public investment in workforce development.
The ecological systems theory developed by Urie Bronfenbrenner offers a holistic framework for understanding how multiple contextual levels – from immediate training environments to broad policy structures – interact to influence retraining outcomes. At the microsystem level, the immediate environment of training programs, including instructor quality, peer interactions, and pedagogical approaches, directly affects learning. The mesosystem encompasses interactions between training programs and other life domains, such as how workplace flexibility enables or constrains participation. The exosystem includes broader institutional structures like labor market regulations and social welfare systems that indirectly influence retraining. Finally, the macrosystem encompasses cultural values, ideological beliefs, and societal norms about work, education, and technological change. This multi-level perspective highlights how successful workforce retraining requires coordinated interventions across multiple systemic levels rather than isolated programmatic changes.
Looking toward future trajectories, several emergent developments warrant attention. Artificial general intelligence (AGI), should it be realized, could automate even high-level cognitive tasks currently considered beyond machine capability, potentially requiring unprecedented scale and speed of workforce retraining. The rise of the platform economy and algorithmic management creates new forms of work that challenge traditional employment relationships and the institutional frameworks through which retraining has historically been delivered. Climate change and the energy transition will necessitate massive workforce reallocation from carbon-intensive industries to sustainable sectors, overlapping with automation-driven displacement to create compounded challenges. Demographic shifts, including population aging in developed nations and youth bulges in developing regions, will create divergent retraining needs and differential capacities to respond.
The normative dimension of workforce retraining – questions about what ought to be done rather than merely what can be done – deserves explicit attention. Should retraining primarily serve economic efficiency, maximizing aggregate productivity even if some individuals or communities are left behind? Or should it prioritize equity and social inclusion, ensuring all displaced workers receive support regardless of cost-effectiveness? These questions connect to broader debates about distributive justice, social rights, and the obligations of governments and corporations toward workers affected by profit-driven technological decisions. Different theoretical traditions – libertarian, egalitarian, communitarian – offer competing answers that ultimately rest on philosophical commitments rather than empirical evidence alone.
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, classical economic theory has been criticized for failing to account for:
A. The existence of wage differences between regions
B. Information asymmetries and bounded rationality
C. The benefits of workforce retraining
D. Technological progress
28. The social construction of technology theory suggests that:
A. Technology develops according to its own autonomous logic
B. Technological change is inevitable and cannot be controlled
C. Automation reflects deliberate social choices and power structures
D. Workers cannot adapt to new technologies
29. According to skill-biased technological change theory, which workers face the greatest displacement risk?
A. High-skill workers performing non-routine cognitive tasks
B. Low-skill workers in personal care services
C. Middle-skill workers performing routine tasks
D. All workers equally regardless of skill level
30. Credentialism and signaling theories suggest that educational credentials:
A. Only serve to certify worker competence
B. Function both to certify skills and signal worker quality
C. Are unnecessary in modern labor markets
D. Reduce information asymmetries completely
31. Power resource theory explains differences in retraining systems across countries based on:
A. Geographic and climatic factors
B. The strength of labor movements and political organization
C. The level of technological development
D. Population size and density
Questions 32-36: Matching Features
Match each theoretical framework (A-H) with the correct statement (32-36).
A. Human capital theory
B. Skill-biased technological change theory
C. Life course theory
D. Ecological systems theory
E. Actor-network theory
F. Power resource theory
G. Neoclassical economics
H. Technological determinism
32. Emphasizes that retraining effectiveness varies depending on age and biographical context
33. Views education and training as investments that enhance future productivity
34. Predicts labor markets will reach equilibrium through wage signals
35. Suggests multiple contextual levels interact to influence retraining outcomes
36. Predicts a polarization of the labor market into high-skill and low-skill jobs
Questions 37-40: Short-answer Questions
Answer the questions below.
Choose NO MORE THAN THREE WORDS AND/OR A NUMBER from the passage for each answer.
37. What term describes the rate at which human capital becomes outdated in changing technological environments?
38. What type of tasks remain complementary to technology rather than being easily replaced by it?
39. According to the passage, what might require unprecedented scale and speed of workforce retraining in the future?
40. What overlaps with automation-driven displacement to create compounded workforce challenges?
Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- B
- C
- C
- B
- TRUE
- FALSE
- TRUE
- NOT GIVEN
- TRUE
- routine manual tasks
- hands-on apprenticeships
- economic decline
PASSAGE 2: Questions 14-26
- YES
- NO
- NO
- YES
- NOT GIVEN
- YES
- A
- C
- B
- E
- structural unemployment
- geographic immobility
- identity
PASSAGE 3: Questions 27-40
- B
- C
- C
- B
- B
- C
- A
- G
- D
- B
- depreciation rate
- non-routine tasks
- artificial general intelligence
- climate change
Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: automation, workplace, described as
- Vị trí trong bài: Đoạn 1, dòng 1-4
- Giải thích: Đoạn đầu tiên nói rằng “This shift is not merely a temporary trend but represents a profound change in how societies organize production and employment.” Điều này được paraphrase thành đáp án B – “A significant and lasting change in work organization”. Các đáp án khác không phù hợp vì bài viết không mô tả tự động hóa là tạm thời (A), nhỏ (C), hay giới hạn ở sản xuất (D).
Câu 2: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: automation, benefited companies
- Vị trí trong bài: Đoạn 2, dòng cuối
- Giải thích: Bài viết nêu rõ “These technological advances have led to significant improvements in productivity and efficiency, allowing companies to reduce costs and increase output.” Đây chính là ý của đáp án B. Đáp án A sai vì vẫn cần con người, C không được nhắc đến, D ngược lại với nội dung.
Câu 3: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: workers most at risk
- Vị trí trong bài: Đoạn 3, dòng 3-5
- Giải thích: Đoạn 3 chỉ ra “millions of jobs in routine manual tasks and basic cognitive work are at risk of automation”. Đây là đáp án C. Các công việc sáng tạo, phức tạp (A, B, D) không bị đe dọa như được ngụ ý trong bài.
Câu 6: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: chatbots, automation, customer service
- Vị trí trong bài: Đoạn 2, dòng 3-4
- Giải thích: Bài viết nêu rõ “software programs handle data entry, scheduling, and even customer service through chatbots”. Điều này khớp hoàn toàn với câu phát biểu.
Câu 7: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: all displaced workers, better jobs
- Vị trí trong bài: Đoạn 7, dòng cuối
- Giải thích: Bài viết nói rõ “not all displaced workers will successfully transition to new careers, and some may face long periods of unemployment”. Điều này mâu thuẫn với câu phát biểu rằng tất cả đều tìm được việc tốt hơn.
Câu 8: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Scandinavian countries, invested, lifelong learning
- Vị trí trong bài: Đoạn 6, dòng 1-2
- Giải thích: Đoạn 6 nêu “In Scandinavia, governments have invested heavily in lifelong learning programs”. Đây là thông tin trực tiếp khớp với câu phát biểu.
Câu 11: routine manual tasks
- Dạng câu hỏi: Sentence Completion
- Từ khóa: Economic studies, millions of jobs, at risk
- Vị trí trong bài: Đoạn 3, dòng 3-4
- Giải thích: Cụm từ “routine manual tasks” xuất hiện trong câu “Economic studies suggest that millions of jobs in routine manual tasks and basic cognitive work are at risk”. Đây chính là đáp án cần điền.
Tác động của tự động hóa đến việc đào tạo lại lực lượng lao động trong thời đại số
Passage 2 – Giải Thích
Câu 14: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: current wave of automation, fundamentally different, targets cognitive work
- Vị trí trong bài: Đoạn 1, dòng 3-6
- Giải thích: Bài viết nhấn mạnh “Unlike previous industrial revolutions, which primarily affected physical labor, modern automation increasingly targets cognitive tasks”. Đây chính là quan điểm của tác giả về sự khác biệt cơ bản.
Câu 15: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: ATMs, completely eliminated, bank tellers
- Vị trí trong bài: Đoạn 2, dòng 3-5
- Giải thích: Bài viết nói “the introduction of ATMs reduced the need for bank tellers to dispense cash but increased demand for customer service roles”. Từ “reduced” cho thấy không phải “completely eliminated”, và còn tạo ra vai trò mới, nên câu phát biểu là sai.
Câu 16: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: older workers, easier, learn digital skills
- Vị trí trong bài: Đoạn 3, dòng cuối
- Giải thích: Đoạn 3 nêu rõ “older workers… may find it particularly difficult to acquire entirely new skill sets, especially those involving digital literacy”. Điều này mâu thuẫn trực tiếp với câu phát biểu.
Câu 20: A (Singapore’s SkillsFuture initiative)
- Dạng câu hỏi: Matching Information
- Từ khóa: citizens, credits, approved training courses
- Vị trí trong bài: Đoạn 6, dòng 2-3
- Giải thích: Bài viết nêu “Singapore’s SkillsFuture initiative provides citizens with individual training credits that can be used throughout their lives for approved courses”. Đây khớp chính xác với mô tả trong câu hỏi.
Câu 24: structural unemployment
- Dạng câu hỏi: Summary Completion
- Từ khóa: workers, lose jobs, skills do not match
- Vị trí trong bài: Đoạn 3, dòng 1-2
- Giải thích: Đoạn văn định nghĩa “Workers face what economists call structural unemployment – a mismatch between the skills they possess and those required by available jobs”. Cụm từ này chính là đáp án cần điền.
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: classical economic theory, criticized, failing to account
- Vị trí trong bài: Đoạn 1, dòng 3-6
- Giải thích: Bài viết chỉ ra “this neoclassical framework has faced substantial criticism for its failure to account for information asymmetries, transaction costs, institutional rigidities, and the bounded rationality”. Đây chính là đáp án B, các đáp án khác không được nhắc đến như những điểm bị chỉ trích.
Câu 28: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: social construction of technology, suggests
- Vị trí trong bài: Đoạn 2, dòng 4-8
- Giải thích: Đoạn 2 nêu rõ lý thuyết này “argue that technological development is itself shaped by social choices, power structures, and cultural values” và “the speed, direction, and implementation of automation reflect deliberate decisions”. Đây là ý của đáp án C.
Câu 29: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: skill-biased technological change, greatest displacement risk
- Vị trí trong bài: Đoạn 4, dòng cuối
- Giải thích: Lý thuyết SBTC dự đoán “middle-skill workers performing routine tasks face displacement”. Điều này trực tiếp chỉ ra đáp án C, trong khi các nhóm lao động khác được mô tả là có nhu cầu tăng.
Câu 32: C (Life course theory)
- Dạng câu hỏi: Matching Features
- Từ khóa: retraining effectiveness varies, age, biographical context
- Vị trí trong bài: Đoạn 7, dòng 1-3
- Giải thích: Bài viết nêu “Life course theory… provide crucial insights into how the timing and context of workforce retraining influence outcomes. These frameworks emphasize that educational interventions occur within biographical trajectories”. Đây khớp với mô tả trong câu 32.
Câu 37: depreciation rate
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: term, human capital becomes outdated, technological environments
- Vị trí trong bài: Đoạn 3, dòng 5-6
- Giải thích: Bài viết sử dụng thuật ngữ “the depreciation rate of human capital may accelerate in rapidly changing technological environments”. Cụm “depreciation rate” chính là thuật ngữ được hỏi.
Câu 39: artificial general intelligence
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: require unprecedented scale and speed, workforce retraining, future
- Vị trí trong bài: Đoạn 10, dòng 2-3
- Giải thích: Đoạn 10 nêu “Artificial general intelligence (AGI), should it be realized, could automate even high-level cognitive tasks… potentially requiring unprecedented scale and speed of workforce retraining”. Đây là đáp án chính xác.
Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| automation | n | /ˌɔːtəˈmeɪʃn/ | tự động hóa | Automation technologies continue to advance | automation technology, industrial automation |
| fundamental | adj | /ˌfʌndəˈmentl/ | cơ bản, căn bản | a fundamental transformation | fundamental change, fundamental principle |
| workforce | n | /ˈwɜːkfɔːs/ | lực lượng lao động | reshape the workforce | workforce retraining, skilled workforce |
| displacement | n | /dɪsˈpleɪsmənt/ | sự thay thế, dời chỗ | job displacement | workforce displacement, employment displacement |
| retraining | n | /riːˈtreɪnɪŋ/ | đào tạo lại | workforce retraining programs | retraining programs, vocational retraining |
| vulnerable | adj | /ˈvʌlnərəbl/ | dễ bị tổn thương | particular vulnerability | vulnerable workers, vulnerable to automation |
| accessible | adj | /əkˈsesəbl/ | dễ tiếp cận | must be accessible | accessible programs, easily accessible |
| relevant | adj | /ˈreləvənt/ | phù hợp, liên quan | industry-relevant | relevant skills, relevant experience |
| adapt | v | /əˈdæpt/ | thích nghi | adapt to new technologies | adapt to change, quickly adapt |
| transition | n | /trænˈzɪʃn/ | chuyển đổi | transition period | career transition, smooth transition |
| obsolete | adj | /ˈɒbsəliːt/ | lỗi thời | becoming obsolete | become obsolete, technologically obsolete |
| catalyst | n | /ˈkætəlɪst/ | chất xúc tác | catalyst for change | catalyst for innovation, serve as a catalyst |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| ramifications | n | /ˌræmɪfɪˈkeɪʃnz/ | hậu quả, tác động | economic ramifications | serious ramifications, far-reaching ramifications |
| contentious | adj | /kənˈtenʃəs/ | gây tranh cãi | contentious issues | contentious debate, contentious topic |
| empirical | adj | /ɪmˈpɪrɪkl/ | thực nghiệm | empirical evidence | empirical research, empirical data |
| nuanced | adj | /ˈnjuːɑːnst/ | tinh tế, nhiều sắc thái | nuanced picture | nuanced understanding, nuanced approach |
| complementary | adj | /ˌkɒmplɪˈmentri/ | bổ sung | complementary roles | complementary skills, complementary relationship |
| substitution | n | /ˌsʌbstɪˈtjuːʃn/ | sự thay thế | characterized by substitution | substitution effect, direct substitution |
| structural unemployment | n phrase | /ˈstrʌktʃərəl ˌʌnɪmˈplɔɪmənt/ | thất nghiệp cơ cấu | face structural unemployment | reduce structural unemployment, high structural unemployment |
| competency-based | adj | /ˈkɒmpɪtənsi beɪst/ | dựa trên năng lực | competency-based training | competency-based approach, competency-based education |
| transferable skills | n phrase | /trænsˈfɜːrəbl skɪlz/ | kỹ năng chuyển đổi được | focuses on transferable skills | develop transferable skills, valuable transferable skills |
| collective action | n phrase | /kəˈlektɪv ˈækʃn/ | hành động tập thể | collective action problems | collective action failure, require collective action |
| market failure | n phrase | /ˈmɑːkɪt ˈfeɪljə/ | thất bại thị trường | creates a market failure | address market failure, correct market failure |
| poached | v | /pəʊtʃt/ | săn mồi, lôi kéo | workers poached by competitors | poached by rivals, poached employees |
| occupational psychology | n phrase | /ˌɒkjuˈpeɪʃənl saɪˈkɒlədʒi/ | tâm lý học nghề nghiệp | research in occupational psychology | occupational psychology research, field of occupational psychology |
| social capital | n phrase | /ˈsəʊʃl ˈkæpɪtl/ | vốn xã hội | strong social capital | build social capital, high social capital |
| vicious cycle | n phrase | /ˈvɪʃəs ˈsaɪkl/ | vòng xoáy luẩn quẩn | face a vicious cycle | break the vicious cycle, trapped in a vicious cycle |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| theoretical apparatus | n phrase | /ˌθɪəˈretɪkl ˌæpəˈreɪtəs/ | bộ máy lý thuyết | theoretical apparatus employed | develop theoretical apparatus, complex theoretical apparatus |
| disciplinary traditions | n phrase | /ˈdɪsɪplɪnəri trəˈdɪʃnz/ | truyền thống kỷ luật | multiple disciplinary traditions | draw from disciplinary traditions, cross disciplinary traditions |
| neoclassical framework | n phrase | /ˌniːəʊˈklæsɪkl ˈfreɪmwɜːk/ | khung lý thuyết tân cổ điển | neoclassical framework | within neoclassical framework, challenge neoclassical framework |
| bounded rationality | n phrase | /ˈbaʊndɪd ˌræʃəˈnæləti/ | lý trí hữu hạn | bounded rationality | concept of bounded rationality, characterized by bounded rationality |
| technological determinism | n phrase | /ˌteknəˈlɒdʒɪkl dɪˈtɜːmɪnɪzəm/ | chủ nghĩa quyết định công nghệ | technological determinism | critique technological determinism, reject technological determinism |
| exogenous | adj | /ekˈsɒdʒənəs/ | ngoại sinh, bên ngoài | exogenous technological change | exogenous factors, exogenous variables |
| agency | n | /ˈeɪdʒənsi/ | quyền chủ động | possess greater agency | human agency, exercise agency |
| distributional consequences | n phrase | /ˌdɪstrɪˈbjuːʃənl ˈkɒnsɪkwənsɪz/ | hậu quả phân phối | distributional consequences | examine distributional consequences, unequal distributional consequences |
| human capital theory | n phrase | /ˈhjuːmən ˈkæpɪtl ˈθɪəri/ | lý thuyết vốn nhân lực | human capital theory | according to human capital theory, human capital theory framework |
| depreciation rate | n phrase | /dɪˌpriːʃiˈeɪʃn reɪt/ | tỷ lệ khấu hao | depreciation rate of human capital | high depreciation rate, accelerated depreciation rate |
| skill-biased technological change | n phrase | /skɪl baɪəst ˌteknəˈlɒdʒɪkl tʃeɪndʒ/ | thay đổi công nghệ thiên về kỹ năng | SBTC theory | skill-biased technological change theory, evidence of skill-biased technological change |
| routine tasks | n phrase | /ruːˈtiːn tɑːsks/ | công việc thường ngày | routine tasks can be automated | perform routine tasks, automate routine tasks |
| non-routine tasks | n phrase | /nɒn ruːˈtiːn tɑːsks/ | công việc không theo lối mòn | non-routine cognitive tasks | require non-routine tasks, non-routine manual tasks |
| credentialism | n | /krɪˈdenʃəlɪzəm/ | chủ nghĩa bằng cấp | credentialism and signaling theories | academic credentialism, impact of credentialism |
| information asymmetries | n phrase | /ˌɪnfəˈmeɪʃn əˈsɪmətriz/ | bất cân xứng thông tin | information asymmetries are substantial | reduce information asymmetries, information asymmetries problem |
| life course theory | n phrase | /laɪf kɔːs ˈθɪəri/ | lý thuyết quá trình đời | life course theory provides insights | life course theory perspective, according to life course theory |
| power resource theory | n phrase | /ˈpaʊə rɪˈsɔːs ˈθɪəri/ | lý thuyết nguồn lực quyền lực | power resource theory | power resource theory approach, power resource theory explains |
| ecological systems theory | n phrase | /ˌiːkəˈlɒdʒɪkl ˈsɪstəmz ˈθɪəri/ | lý thuyết hệ thống sinh thái | ecological systems theory developed | ecological systems theory framework, apply ecological systems theory |
Chương trình đào tạo lại lực lượng lao động trong bối cảnh tự động hóa toàn cầu
Kết bài
Chủ đề về tác động của tự động hóa đến việc đào tạo lại lực lượng lao động không chỉ là một vấn đề quan trọng trong thế giới hiện đại mà còn là nội dung xuất hiện thường xuyên trong các đề thi IELTS Reading gần đây. Qua ba passages với độ khó tăng dần từ Easy đến Hard, bạn đã được trải nghiệm một bài thi hoàn chỉnh với 40 câu hỏi đa dạng, phản ánh chính xác cấu trúc và yêu cầu của bài thi thật.
Passage 1 giới thiệu những khái niệm cơ bản về tự động hóa và sự cần thiết của việc đào tạo lại, với ngôn ngữ dễ hiểu và cấu trúc câu đơn giản. Passage 2 đi sâu vào các khía cạnh kinh tế và xã hội của vấn đề, yêu cầu khả năng phân tích và suy luận cao hơn. Passage 3 mang tính học thuật với các lý thuyết phức tạp, thách thức kỹ năng đọc hiểu ở mức độ cao nhất.
Các đáp án chi tiết kèm theo giải thích cụ thể về vị trí thông tin và cách paraphrase sẽ giúp bạn tự đánh giá chính xác khả năng của mình. Đặc biệt, bộ từ vựng được tổng hợp theo từng passage với phiên âm, nghĩa và cách sử dụng sẽ là tài liệu quý giá để bạn mở rộng vốn từ vựng học thuật.
Hãy luyện tập thường xuyên với các đề thi mẫu chất lượng cao như thế này và áp dụng các kỹ thuật làm bài đã học để nâng cao band điểm Reading của bạn. Việc tương tác với Impact of automation on public transportation systems cũng sẽ giúp bạn hiểu rõ hơn về phạm vi ảnh hưởng của tự động hóa trong nhiều lĩnh vực khác nhau. Tương tự, việc nghiên cứu về The role of artificial intelligence in healthcare sẽ cung cấp góc nhìn bổ sung về ứng dụng công nghệ trong các ngành nghề cụ thể. Ngoài ra, để nắm bắt bức tranh toàn diện về Economic impacts of automation on service industries, bạn có thể tìm hiểu thêm về những thay đổi cơ cấu kinh tế đang diễn ra.
Chúc bạn học tốt và đạt được band điểm như mong muốn trong kỳ thi IELTS sắp tới.
