Mở bài
Trí tuệ nhân tạo (AI) đang ngày càng trở thành chủ đề được quan tâm hàng đầu trong các kỳ thi IELTS, đặc biệt là trong phần Reading. Chủ đề “What Are The Implications Of AI In Workforce Management?” xuất hiện với tần suất ngày càng cao, phản ánh xu hướng toàn cầu về chuyển đổi số trong quản lý nguồn nhân lực.
Bài viết này cung cấp một bộ đề thi IELTS Reading hoàn chỉnh gồm 3 passages với độ khó tăng dần từ Easy đến Hard, giúp bạn làm quen với nhiều góc độ khác nhau về ảnh hưởng của AI đến quản lý lực lượng lao động. Bạn sẽ được luyện tập với các dạng câu hỏi đa dạng như trong bài thi thật, kèm theo đáp án chi tiết và giải thích cụ thể.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, với vocabulary được phân loại rõ ràng theo từng cấp độ. Ngoài ra, bạn còn được trang bị bảng từ vựng chuyên ngành và các kỹ thuật làm bài thực chiến để tối ưu hóa kết quả trong kỳ thi IELTS Reading của mình.
1. Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
Phần thi IELTS Reading kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Đây là thời gian cố định không có thêm phút nào để chép đáp án vào answer sheet, do đó bạn cần quản lý thời gian một cách khoa học.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (câu hỏi 1-13)
- Passage 2: 18-20 phút (câu hỏi 14-26)
- Passage 3: 23-25 phút (câu hỏi 27-40)
Lưu ý rằng độ khó tăng dần từ Passage 1 đến Passage 3, do đó bạn nên dành nhiều thời gian hơn cho những phần sau.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm đầy đủ các dạng câu hỏi phổ biến trong IELTS Reading:
- Multiple Choice: Chọn đáp án đúng từ các lựa chọn cho sẵn
- True/False/Not Given: Xác định thông tin đúng, sai hay không được đề cập
- Yes/No/Not Given: Xác định ý kiến của tác giả
- Matching Headings: Nối tiêu đề phù hợp với đoạn văn
- Sentence Completion: Hoàn thành câu với từ trong bài đọc
- Summary Completion: Hoàn thành đoạn tóm tắt
- Matching Features: Nối thông tin với đặc điểm tương ứng
- Short-answer Questions: Trả lời câu hỏi ngắn
2. IELTS Reading Practice Test
PASSAGE 1 – The Rise of AI in Modern Workplaces
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
Artificial Intelligence (AI) is rapidly transforming the way businesses manage their workforce. From recruitment processes to performance evaluation, AI technologies are being integrated into various aspects of human resource management. This revolutionary change is not just about replacing human workers but rather about enhancing efficiency and making better data-driven decisions.
In the recruitment phase, companies are increasingly using AI-powered tools to screen candidates. These systems can analyze thousands of resumes in minutes, identifying the most qualified applicants based on predetermined criteria. For instance, some sophisticated algorithms can detect patterns in successful employees’ backgrounds and use this information to predict which candidates are most likely to succeed in specific roles. This automated screening process saves HR departments countless hours that would otherwise be spent on manual review.
Performance monitoring has also been revolutionized by AI technology. Traditional methods of evaluating employee performance often relied on annual reviews and subjective assessments by managers. However, AI systems can now continuously track various performance metrics in real-time. These might include productivity levels, quality of work, collaboration patterns, and even employee engagement indicators. By analyzing this data, managers can identify trends and address issues before they become serious problems.
One of the most significant advantages of AI in workforce management is its ability to eliminate human bias. Research has shown that human decision-making is often influenced by unconscious prejudices related to gender, race, age, or other factors. AI systems, when properly designed, can make decisions based purely on relevant qualifications and performance data. This objectivity can lead to fairer hiring practices and more equitable workplace environments.
Employee scheduling is another area where AI has made substantial contributions. In industries like retail and hospitality, creating optimal work schedules can be extremely complex. Managers must balance employee preferences, labor laws, peak business hours, and budget constraints. AI-powered scheduling software can process all these variables simultaneously and generate schedules that maximize efficiency while respecting workers’ needs. Some advanced systems can even predict busy periods based on historical data and adjust staffing levels accordingly.
Training and development have also been enhanced by AI technologies. Personalized learning platforms use AI to assess each employee’s strengths and weaknesses and recommend specific training programs. These systems can adapt the learning pace and content to match individual needs, making professional development more effective and engaging. Additionally, AI can identify skill gaps across the organization and suggest strategic training initiatives to address these deficiencies.
However, the integration of AI into workforce management is not without challenges. Many employees express concerns about job security and the impersonal nature of AI-driven management. There are also questions about data privacy and the ethical implications of constant monitoring. Companies must carefully navigate these issues to ensure that AI implementation benefits both the organization and its workers.
Communication between management and employees becomes crucial during AI adoption. Organizations that successfully integrate AI into their workforce management strategies typically invest in transparent communication about how these systems work and how they will affect employees. They also provide adequate training to help workers adapt to new technologies and understand how to work alongside AI systems.
Questions 1-13
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. According to the passage, the main purpose of AI in workforce management is to:
A. Replace all human workers with machines
B. Improve efficiency and decision-making
C. Reduce company expenses significantly
D. Monitor employee activities constantly
2. AI-powered recruitment tools can:
A. Conduct face-to-face interviews
B. Only process a limited number of resumes
C. Analyze thousands of applications quickly
D. Guarantee perfect hiring decisions
3. Traditional performance evaluation methods were characterized by:
A. Real-time data analysis
B. Annual reviews and subjective judgments
C. Continuous monitoring systems
D. Automated reporting processes
4. AI systems can help eliminate bias by:
A. Completely removing human involvement
B. Making decisions based on relevant data
C. Ignoring all personal characteristics
D. Following manager preferences
5. AI scheduling software is particularly useful in:
A. Technology companies only
B. Small family businesses
C. Retail and hospitality sectors
D. Manufacturing plants exclusively
Questions 6-10: True/False/Not Given
Do the following statements agree with the information 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. AI recruitment systems can predict which candidates will be successful in particular positions.
7. All AI systems in workforce management are completely free from bias.
8. AI-powered scheduling considers employee preferences and legal requirements.
9. Companies using AI in workforce management always achieve higher profits.
10. Personalized learning platforms adjust training content based on individual needs.
Questions 11-13: Sentence Completion
Complete the sentences below. Choose NO MORE THAN THREE WORDS from the passage for each answer.
11. Many employees worry about ___ when AI is introduced into workplace management.
12. Successful AI integration requires ___ about how the systems function.
13. AI can identify ___ across an organization and suggest appropriate training.
PASSAGE 2 – The Economic and Social Impact of AI-Driven Workforce Management
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The proliferation of artificial intelligence in workforce management represents a paradigm shift that extends far beyond the operational efficiency of individual companies. This technological evolution is reshaping entire industries, redefining employment relationships, and raising fundamental questions about the future of work itself. While proponents argue that AI will create new opportunities and liberate workers from mundane tasks, critics warn of potential disruptions to traditional employment structures and widening inequality.
From an economic perspective, the deployment of AI in workforce management offers substantial benefits. Companies implementing these technologies report significant reductions in administrative costs, with some organizations achieving savings of up to 40% in HR-related expenses. The automation of routine tasks such as payroll processing, benefits administration, and compliance monitoring allows human resource professionals to focus on strategic initiatives that add greater value to the organization. This reallocation of human capital towards more complex, creative endeavors can enhance overall productivity and innovation.
However, the economic implications are not uniformly positive across all segments of the workforce. Entry-level positions and roles involving repetitive tasks are disproportionately vulnerable to automation. A recent study by the McKinsey Global Institute estimated that up to 375 million workers worldwide may need to transition to new occupational categories by 2030 due to AI and automation. This displacement could exacerbate existing social inequalities, particularly affecting workers with limited education or specialized skills in declining industries.
The transformation of workforce dynamics through AI also influences organizational culture and employee relations. AI systems that monitor performance metrics can create an environment of perpetual surveillance, potentially undermining trust between employers and employees. Some workers report feeling dehumanized by algorithm-driven management, where decisions about schedules, assignments, and even terminations are made by impersonal systems. This erosion of the human element in management can negatively impact employee morale, engagement, and loyalty.
Conversely, when implemented thoughtfully, AI can enhance the employee experience in meaningful ways. Predictive analytics can identify employees at risk of burnout or disengagement, enabling proactive interventions. AI-powered chatbots can provide instant responses to employee inquiries about policies or benefits, improving satisfaction with HR services. Sophisticated workforce planning tools can forecast future skill requirements, allowing organizations to invest in upskilling programs that prepare employees for evolving roles rather than rendering them obsolete.
The regulatory landscape surrounding AI in workforce management remains underdeveloped in most jurisdictions. Questions about data ownership, algorithmic transparency, and accountability for AI-driven decisions are only beginning to be addressed by policymakers. The European Union has taken preliminary steps with its proposed AI Act, which would classify AI systems used in employment as “high-risk” and subject them to stringent requirements. However, comprehensive frameworks that balance innovation with worker protection are still largely absent.
Labor unions and worker advocacy groups have expressed concerns about the power imbalance created when employers deploy AI systems without meaningful worker input. Some organizations are calling for “algorithmic bargaining rights,” which would grant employees and their representatives a voice in how AI systems are designed and implemented. This collaborative approach could help ensure that AI serves to augment human capabilities rather than diminish worker autonomy.
The skills required for workforce management professionals are themselves undergoing transformation. Traditional HR competencies in interpersonal communication and conflict resolution remain essential, but they must now be complemented by data literacy, technological proficiency, and an understanding of AI ethics. HR professionals increasingly need to interpret complex analytics, evaluate algorithmic fairness, and navigate the intersection of technology and human needs. This shift necessitates significant investment in professional development and may alter the recruitment profile for HR roles.
Looking forward, the trajectory of AI in workforce management will likely depend on how stakeholders – including employers, employees, policymakers, and technology developers – negotiate the tensions between efficiency and equity, innovation and stability, optimization and humanity. The optimal outcome would harness AI’s capabilities to create workplaces that are not only more productive but also more fulfilling, inclusive, and respectful of human dignity.
Ứng dụng trí tuệ nhân tạo trong quản lý nhân sự và phân tích dữ liệu nhân viên hiện đại
Questions 14-26
Questions 14-18: 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
14. The introduction of AI in workforce management is solely about improving company profits.
15. The economic benefits of AI in workforce management are equally distributed across all worker categories.
16. Algorithm-driven management can potentially damage trust in the workplace.
17. The European Union has already implemented comprehensive AI regulations for employment.
18. Future HR professionals will need both traditional interpersonal skills and technical knowledge.
Questions 19-23: Matching Headings
The passage has eight paragraphs (1-8). Choose the correct heading for paragraphs 2, 4, 5, 7, and 8 from the list of headings below.
List of Headings:
i. The need for regulatory frameworks
ii. Economic advantages for organizations
iii. Negative effects on employee psychology
iv. The role of worker representatives
v. Positive applications of AI for employees
vi. Required skills for HR professionals
vii. Global employment transitions
viii. Future considerations and balance
ix. Traditional management approaches
19. Paragraph 2
20. Paragraph 4
21. Paragraph 5
22. Paragraph 7
23. Paragraph 8
Questions 24-26: Summary Completion
Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.
AI implementation in workforce management creates both opportunities and challenges. While companies can reduce (24) ___ significantly, many workers in entry-level roles face potential job displacement. The use of AI monitoring systems may create an atmosphere of (25) ___, which can harm employee trust. However, organizations can use (26) ___ to identify at-risk employees and provide support before problems escalate.
PASSAGE 3 – Algorithmic Governance and the Future of Human Capital Management
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The ascendance of artificial intelligence as a pivotal instrument in workforce management constitutes not merely a technological upgrade but a fundamental reconceptualization of how organizational hierarchies mediate the relationship between labor and capital. This emergent paradigm, often termed “algorithmic governance,” represents a qualitative departure from traditional managerial structures, wherein discretionary human judgment is progressively supplanted by data-driven computational processes. The ramifications of this transition extend into epistemological, ethical, and socioeconomic domains, necessitating rigorous examination of both its transformative potential and its inherent limitations.
Algorithmic governance in workforce management operates through the systematic collection and analysis of granular data pertaining to employee behaviors, performance outputs, and interpersonal dynamics. Contemporary AI systems leverage machine learning algorithms to discern patterns imperceptible to human observers, thereby facilitating what proponents characterize as “evidence-based management.” These systems can assess multidimensional performance indicators, integrate contextual variables, and generate predictive models that ostensibly transcend the cognitive biases and informational constraints that encumber human decision-makers. The appeal of such systems lies in their promise of meritocratic objectivity – the notion that employment decisions can be rendered on the basis of demonstrable competence rather than subjective preferences or structural prejudices.
However, critical scholars have interrogated the presumption of algorithmic neutrality, arguing that AI systems inevitably encode the biases present in their training data and design parameters. When historical employment data reflects discriminatory practices – whether in hiring patterns, promotion trajectories, or performance evaluations – algorithms trained on such data will perpetuate and potentially amplify these inequities. The opacity of many sophisticated machine learning models, often described as “black boxes,” compounds this problem by rendering algorithmic decision-making processes inscrutable even to their designers. This lack of transparency poses significant challenges for accountability mechanisms and undermines the capacity for meaningful redress when erroneous or unjust decisions occur.
The deployment of AI in workforce management also engenders novel forms of workplace surveillance that raise profound privacy concerns. Sophisticated monitoring systems can track not only tangible outputs but also behavioral minutiae: keystroke patterns, email sentiment, collaboration networks, and even physiological indicators such as voice stress levels or facial expressions. While advocates contend that such comprehensive monitoring enables proactive identification of organizational dysfunctions and individual distress, critics characterize it as “workplace panopticism” – a reference to philosopher Jeremy Bentham’s architectural design for perpetual surveillance. The psychological ramifications of constant monitoring may include heightened stress, diminished autonomy, and the cultivation of performative behaviors that prioritize measurable metrics over substantive contributions.
Economic analyses of AI-driven workforce management reveal complex distributional effects. While aggregate productivity gains appear substantial, the distribution of these benefits exhibits marked asymmetry. Capital owners and highly skilled workers capable of complementing AI systems accrue disproportionate advantages, whereas those performing routinized tasks face displacement or wage suppression. This bifurcation of the labor market into “AI-complementary” and “AI-substitutable” roles threatens to exacerbate existing inequalities, potentially eroding the middle-skill employment that has historically constituted the foundation of economic mobility in developed economies. The concentration of economic returns from AI-enhanced productivity in progressively fewer hands raises questions about the sustainability of consumption-driven economic models and the adequacy of existing social safety nets.
From an organizational theory perspective, algorithmic governance challenges conventional notions of managerial legitimacy and organizational culture. Traditional management derives authority from a combination of formal position, expertise, and interpersonal influence – elements that cultivate what organizational scholars term “identification” between employees and employers. When managerial functions are delegated to algorithmic systems, this relational foundation may be attenuated, potentially diminishing organizational commitment and facilitating a more transactional employment relationship. Moreover, the reduction of complex human performance to quantifiable metrics may inadvertently incentivize “gaming the algorithm” – behaviors that optimize measured outputs while potentially compromising unmeasured dimensions of organizational effectiveness, such as knowledge sharing, mentorship, or innovative risk-taking.
Regulatory responses to algorithmic workforce management have been fragmented and largely reactive. Existing labor law frameworks, predicated on assumptions of human managerial discretion, struggle to address algorithmic decision-making. Emerging regulatory proposals – including algorithmic impact assessments, explainability requirements, and rights to human review of automated decisions – represent initial attempts to establish guardrails, yet consensus on appropriate standards remains elusive. The transnational nature of digital platforms and the rapid pace of technological development further complicate jurisdictional governance, necessitating international coordination that has proven difficult to achieve.
Philosophical considerations surrounding algorithmic workforce management touch upon fundamental questions of human agency, dignity, and the nature of work itself. If meaningful employment is understood not merely as instrumental activity but as a source of identity, purpose, and social connection, then the reduction of workers to data points in optimization algorithms may represent a form of existential diminishment. Conversely, if AI can genuinely liberate humans from tedious, hazardous, or degrading tasks, enabling greater allocation of human effort toward creative, empathetic, and intellectually fulfilling endeavors, it might facilitate a more humanistic conception of work. The actualization of either scenario depends critically on deliberate choices regarding technology design, organizational implementation, and societal governance – choices that must be informed by robust ethical frameworks and inclusive stakeholder participation.
Hệ thống phân tích dữ liệu nhân viên bằng trí tuệ nhân tạo và các chỉ số đánh giá hiệu suất
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, algorithmic governance primarily represents:
A. A simple technological improvement
B. A fundamental change in management structures
C. A temporary trend in business
D. A cost-reduction strategy
28. The concept of “workplace panopticism” refers to:
A. Improved employee productivity
B. Better organizational communication
C. Constant surveillance of workers
D. Democratic workplace practices
29. The passage suggests that middle-skill employment is threatened because:
A. Workers lack motivation
B. AI creates a division between complementary and substitutable roles
C. Companies prefer highly educated employees
D. Technology is too expensive for most organizations
30. “Gaming the algorithm” describes behavior where employees:
A. Use computer games during work hours
B. Improve their overall job performance
C. Optimize measured outputs while neglecting unmeasured aspects
D. Collaborate with AI systems effectively
31. Current labor law frameworks are inadequate because they:
A. Are too expensive to implement
B. Assume human rather than algorithmic decision-making
C. Favor employers over employees
D. Cannot be enforced internationally
Questions 32-36: Matching Features
Match each challenge (Questions 32-36) with the correct domain (A-F) from the list below. You may use any letter more than once.
Domains:
A. Technical limitations
B. Ethical concerns
C. Economic effects
D. Legal inadequacy
E. Psychological impact
F. Organizational culture
32. Algorithms trained on biased historical data perpetuate discrimination
33. Concentration of productivity benefits among capital owners and skilled workers
34. Constant monitoring causes stress and reduces employee autonomy
35. Existing regulations cannot adequately address automated decision-making
36. Algorithmic management may weaken employee identification with organizations
Questions 37-40: Short-answer Questions
Answer the questions below. Choose NO MORE THAN THREE WORDS from the passage for each answer.
37. What term describes AI systems whose decision-making processes are difficult to understand?
38. What historical architectural concept is used to describe perpetual workplace surveillance?
39. What two categories divide the modern labor market according to AI impact?
40. What type of participation is needed to inform choices about technology implementation?
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- B
- B
- C
- TRUE
- FALSE
- TRUE
- NOT GIVEN
- TRUE
- job security
- transparent communication
- skill gaps
PASSAGE 2: Questions 14-26
- NO
- NO
- YES
- NO
- YES
- ii
- iii
- v
- iv
- viii
- administrative costs
- perpetual surveillance
- predictive analytics
PASSAGE 3: Questions 27-40
- B
- C
- B
- C
- B
- A
- C
- E
- D
- F
- black boxes
- Bentham’s (architectural) design / workplace panopticism
- AI-complementary and AI-substitutable
- stakeholder participation
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main purpose, AI, workforce management
- Vị trí trong bài: Đoạn 1, dòng 2-4
- Giải thích: Bài đọc nói rõ “This revolutionary change is not just about replacing human workers but rather about enhancing efficiency and making better data-driven decisions.” Đáp án A sai vì AI không thay thế hoàn toàn, C và D không phải mục đích chính được nhấn mạnh.
Câu 2: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI-powered recruitment tools
- Vị trí trong bài: Đoạn 2, dòng 2-3
- Giải thích: “These systems can analyze thousands of resumes in minutes” – paraphrase của “applications” thành “resumes” và “quickly” thành “in minutes”.
Câu 6: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: predict, candidates, successful, particular positions
- Vị trí trong bài: Đoạn 2, dòng 4-6
- Giải thích: “sophisticated algorithms can detect patterns… and use this information to predict which candidates are most likely to succeed in specific roles” – khớp hoàn toàn với câu hỏi.
Câu 7: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: all AI systems, completely free, bias
- Vị trí trong bài: Đoạn 4, dòng 3-4
- Giải thích: Bài viết nói “AI systems, when properly designed, can make decisions…” – từ “when properly designed” chỉ ra rằng không phải TẤT CẢ hệ thống AI đều tự động không có bias.
Câu 11: job security
- Dạng câu hỏi: Sentence Completion
- Từ khóa: employees worry, AI introduced
- Vị trí trong bài: Đoạn 7, dòng 2
- Giải thích: “Many employees express concerns about job security” – chính xác từ trong bài.
Câu 13: skill gaps
- Dạng câu hỏi: Sentence Completion
- Từ khóa: AI, identify, organization, training
- Vị trí trong bài: Đoạn 6, dòng 6-7
- Giải thích: “Additionally, AI can identify skill gaps across the organization and suggest strategic training initiatives” – khớp với cấu trúc câu hỏi.
Passage 2 – Giải Thích
Câu 14: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: solely, company profits
- Vị trí trong bài: Đoạn 1, toàn đoạn
- Giải thích: Tác giả đề cập đến nhiều khía cạnh khác nhau “reshaping entire industries, redefining employment relationships” – không chỉ về lợi nhuận, do đó “solely” làm câu này sai với quan điểm tác giả.
Câu 15: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: economic benefits, equally distributed
- Vị trí trong bài: Đoạn 3, dòng 1-2
- Giải thích: “the economic implications are not uniformly positive across all segments” và “Entry-level positions… are disproportionately vulnerable” – rõ ràng lợi ích KHÔNG được phân bổ đều.
Câu 16: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: algorithm-driven management, damage trust
- Vị trí trong bài: Đoạn 4, dòng 2-3
- Giải thích: “potentially undermining trust between employers and employees” – tác giả đồng ý với quan điểm này.
Câu 19: ii (Economic advantages for organizations)
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn 2
- Giải thích: Cả đoạn nói về “substantial benefits”, “significant reductions in administrative costs”, “achieving savings of up to 40%” – rõ ràng về lợi ích kinh tế.
Câu 24: administrative costs
- Dạng câu hỏi: Summary Completion
- Từ khóa: companies, reduce, significantly
- Vị trí trong bài: Đoạn 2, dòng 2
- Giải thích: “significant reductions in administrative costs” – khớp với ngữ cảnh summary.
Công nghệ trí tuệ nhân tạo hỗ trợ quy trình tuyển dụng và sàng lọc ứng viên tự động
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: algorithmic governance, primarily represents
- Vị trí trong bài: Đoạn 1, dòng 2-5
- Giải thích: “constitutes not merely a technological upgrade but a fundamental reconceptualization” và “represents a qualitative departure from traditional managerial structures” – rõ ràng là thay đổi căn bản cấu trúc quản lý.
Câu 28: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: workplace panopticism
- Vị trí trong bài: Đoạn 4, dòng 4-6
- Giải thích: “critics characterize it as ‘workplace panopticism’ – a reference to philosopher Jeremy Bentham’s architectural design for perpetual surveillance” – khái niệm về giám sát liên tục.
Câu 29: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: middle-skill employment, threatened
- Vị trí trong bài: Đoạn 5, dòng 4-7
- Giải thích: “This bifurcation of the labor market into ‘AI-complementary’ and ‘AI-substitutable’ roles threatens to exacerbate existing inequalities, potentially eroding the middle-skill employment” – sự phân chia này đe dọa việc làm tầm trung.
Câu 32: A (Technical limitations)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 3, dòng 1-5
- Giải thích: Vấn đề về thuật toán được huấn luyện trên dữ liệu có thiên kiến là hạn chế kỹ thuật của AI.
Câu 37: black boxes
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: AI systems, decision-making processes, difficult to understand
- Vị trí trong bài: Đoạn 3, dòng 6-7
- Giải thích: “The opacity of many sophisticated machine learning models, often described as ‘black boxes'” – thuật ngữ chính xác trong bài.
Câu 40: stakeholder participation
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: participation, inform choices, technology implementation
- Vị trí trong bài: Đoạn 8, dòng cuối
- Giải thích: “choices that must be informed by robust ethical frameworks and inclusive stakeholder participation” – chính xác từ trong bài.
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 |
|---|---|---|---|---|---|
| rapidly transforming | adv + v | /ˈræpɪdli trænsˈfɔːmɪŋ/ | chuyển đổi nhanh chóng | AI is rapidly transforming the way businesses manage their workforce | rapidly change/evolve/develop |
| recruitment processes | n | /rɪˈkruːtmənt ˈprəʊsesɪz/ | quy trình tuyển dụng | AI technologies are being integrated into recruitment processes | recruitment strategy/campaign/drive |
| data-driven decisions | adj + n | /ˈdeɪtə ˈdrɪvən dɪˈsɪʒənz/ | quyết định dựa trên dữ liệu | Making better data-driven decisions in HR | data-driven approach/analysis/insights |
| screen candidates | v + n | /skriːn ˈkændɪdeɪts/ | sàng lọc ứng viên | AI-powered tools screen candidates efficiently | screen applications/resumes/applicants |
| predetermined criteria | adj + n | /ˌpriːdɪˈtɜːmɪnd kraɪˈtɪəriə/ | tiêu chí định trước | Identifying qualified applicants based on predetermined criteria | set/establish/meet criteria |
| sophisticated algorithms | adj + n | /səˈfɪstɪkeɪtɪd ˈælɡərɪðəmz/ | thuật toán tinh vi | Some sophisticated algorithms can detect patterns | develop/design/use algorithms |
| performance metrics | n | /pəˈfɔːməns ˈmetrɪks/ | các chỉ số hiệu suất | AI systems continuously track performance metrics | measure/analyze/monitor metrics |
| eliminate bias | v + n | /ɪˈlɪmɪneɪt ˈbaɪəs/ | loại bỏ thiên kiến | AI’s ability to eliminate human bias | reduce/remove/minimize bias |
| unconscious prejudices | adj + n | /ʌnˈkɒnʃəs ˈpredʒədɪsɪz/ | định kiến vô thức | Decision-making influenced by unconscious prejudices | overcome/address/challenge prejudices |
| optimal work schedules | adj + n | /ˈɒptɪməl wɜːk ˈʃedjuːlz/ | lịch làm việc tối ưu | Creating optimal work schedules can be complex | create/design/maintain schedules |
| personalized learning | adj + n | /ˈpɜːsənəlaɪzd ˈlɜːnɪŋ/ | học tập cá nhân hóa | Personalized learning platforms use AI effectively | personalized approach/experience/service |
| skill gaps | n | /skɪl ɡæps/ | khoảng trống kỹ năng | AI can identify skill gaps across organizations | identify/address/close gaps |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| proliferation | n | /prəˌlɪfəˈreɪʃən/ | sự gia tăng nhanh | The proliferation of AI in workforce management | proliferation of technology/weapons/information |
| paradigm shift | n | /ˈpærədaɪm ʃɪft/ | sự thay đổi mô hình | Represents a paradigm shift in employment | undergo/experience/represent a paradigm shift |
| proponents | n | /prəˈpəʊnənts/ | người ủng hộ | Proponents argue that AI will create opportunities | strong/leading/vocal proponents |
| deployment | n | /dɪˈplɔɪmənt/ | sự triển khai | The deployment of AI offers substantial benefits | rapid/strategic/successful deployment |
| disproportionately vulnerable | adv + adj | /ˌdɪsprəˈpɔːʃənətli ˈvʌlnərəbəl/ | dễ bị tổn thương không cân đối | Entry-level positions are disproportionately vulnerable | disproportionately affected/impacted |
| exacerbate | v | /ɪɡˈzæsəbeɪt/ | làm trầm trọng thêm | Could exacerbate existing social inequalities | exacerbate problems/tensions/conditions |
| perpetual surveillance | adj + n | /pəˈpetʃuəl səˈveɪləns/ | giám sát liên tục | Create an environment of perpetual surveillance | under surveillance, constant/continuous surveillance |
| dehumanized | adj | /diːˈhjuːmənaɪzd/ | mất tính người | Workers report feeling dehumanized | feel/become dehumanized |
| predictive analytics | adj + n | /prɪˈdɪktɪv ˌænəˈlɪtɪks/ | phân tích dự đoán | Predictive analytics can identify at-risk employees | use/apply/leverage predictive analytics |
| burnout | n | /ˈbɜːnaʊt/ | kiệt sức | Identify employees at risk of burnout | prevent/avoid/experience burnout |
| upskilling | n | /ˈʌpskɪlɪŋ/ | nâng cao kỹ năng | Invest in upskilling programs for employees | upskilling initiatives/opportunities/programs |
| regulatory landscape | adj + n | /ˈreɡjələtəri ˈlændskeɪp/ | bối cảnh quy định | The regulatory landscape remains underdeveloped | navigate/shape/influence the regulatory landscape |
| algorithmic transparency | adj + n | /ˌælɡəˈrɪðmɪk trænsˈpærənsi/ | tính minh bạch thuật toán | Questions about algorithmic transparency | ensure/demand/improve transparency |
| power imbalance | n | /ˈpaʊər ɪmˈbæləns/ | mất cân bằng quyền lực | Concerns about the power imbalance created | address/correct/redress power imbalance |
| augment | v | /ɔːɡˈment/ | tăng cường, bổ sung | AI serves to augment human capabilities | augment skills/abilities/resources |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| ascendance | n | /əˈsendəns/ | sự lên ngôi, thống trị | The ascendance of artificial intelligence | rise to ascendance, gain ascendance |
| pivotal instrument | adj + n | /ˈpɪvətl ˈɪnstrəmənt/ | công cụ then chốt | AI as a pivotal instrument in management | pivotal role/moment/factor |
| reconceptualization | n | /ˌriːkənˌseptʃuəlaɪˈzeɪʃən/ | tái khái niệm hóa | A fundamental reconceptualization of hierarchies | require/undergo reconceptualization |
| discretionary | adj | /dɪˈskreʃənəri/ | thuộc về quyền quyết định | Discretionary human judgment in management | discretionary power/authority/spending |
| supplanted | v | /səˈplɑːntɪd/ | thay thế, chiếm chỗ | Human judgment supplanted by computational processes | supplant traditional methods/systems |
| ramifications | n | /ˌræmɪfɪˈkeɪʃənz/ | hậu quả, tác động | The ramifications of this transition | serious/wide-ranging/potential ramifications |
| granular data | adj + n | /ˈɡrænjələr ˈdeɪtə/ | dữ liệu chi tiết | Systematic collection of granular data | collect/analyze/process granular data |
| imperceptible | adj | /ˌɪmpəˈseptəbəl/ | không thể nhận thấy | Patterns imperceptible to human observers | almost/virtually/nearly imperceptible |
| meritocratic objectivity | adj + n | /ˌmerɪtəˈkrætɪk ˌɒbdʒekˈtɪvəti/ | tính khách quan dựa trên thành tích | The promise of meritocratic objectivity | pursue/achieve/maintain objectivity |
| encode | v | /ɪnˈkəʊd/ | mã hóa, chứa đựng | AI systems inevitably encode biases | encode information/data/values |
| opacity | n | /əʊˈpæsəti/ | tính mờ đục, khó hiểu | The opacity of machine learning models | increase/reduce/eliminate opacity |
| inscrutable | adj | /ɪnˈskruːtəbəl/ | khó hiểu, bí ẩn | Rendering processes inscrutable to designers | remain/become inscrutable |
| engenders | v | /ɪnˈdʒendəz/ | gây ra, tạo ra | AI deployment engenders novel forms of surveillance | engender trust/conflict/debate |
| panopticism | n | /pænˈɒptɪsɪzəm/ | chủ nghĩa toàn giám | Workplace panopticism and constant monitoring | digital/workplace panopticism |
| bifurcation | n | /ˌbaɪfəˈkeɪʃən/ | sự chia đôi | Bifurcation of the labor market | lead to/result in bifurcation |
| attenuated | adj | /əˈtenjueɪtɪd/ | suy yếu, giảm đi | This relational foundation may be attenuated | become/remain attenuated |
| elusive | adj | /ɪˈluːsɪv/ | khó nắm bắt | Consensus on standards remains elusive | prove/remain elusive |
| existential diminishment | adj + n | /ˌeɡzɪˈstenʃəl dɪˈmɪnɪʃmənt/ | sự suy giảm hiện hữu | Represent a form of existential diminishment | experience/face diminishment |
Kết bài
Chủ đề “What are the implications of AI in workforce management?” không chỉ là một topic phổ biến trong IELTS Reading mà còn phản ánh những vấn đề thời sự quan trọng của thế giới hiện đại. Qua bộ đề thi mẫu với 3 passages từ dễ đến khó, bạn đã được luyện tập toàn diện các kỹ năng cần thiết để chinh phục phần thi Reading.
Passage 1 giúp bạn làm quen với vocabulary và ý tưởng cơ bản về AI trong quản lý nhân sự. Passage 2 đào sâu vào các tác động kinh tế và xã hội với độ phức tạp cao hơn. Passage 3 mang tính học thuật, yêu cầu khả năng phân tích và hiểu sâu về các khái niệm triết học, đạo đức liên quan đến chủ đề.
Đáp án chi tiết kèm giải thích cụ thể đã chỉ ra cách xác định thông tin trong bài, nhận diện paraphrase và áp dụng chiến lược làm bài cho từng dạng câu hỏi. Hãy xem lại những câu bạn làm sai, phân tích lý do và rút ra bài học để cải thiện kỹ năng.
Bảng từ vựng phân loại theo 3 cấp độ sẽ giúp bạn mở rộng vốn từ học thuật, đặc biệt hữu ích cho các chủ đề liên quan đến công nghệ, kinh tế và xã hội. Hãy học các collocations và thực hành sử dụng chúng trong writing và speaking.
Chúc bạn ôn tập hiệu quả và đạt band điểm cao trong kỳ thi IELTS sắp tới. Hãy truy cập VN.IELTS.NET thường xuyên để cập nhật thêm nhiều đề thi mẫu chất lượng và tài liệu luyện thi bổ ích khác!
