IELTS Reading: Trí Tuệ Nhân Tạo Cải Thiện Cứu Trợ Thiên Tai – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở bài

Chủ đề về công nghệ và ứng dụng của trí tuệ nhân tạo (AI) trong các lĩnh vực khác nhau, đặc biệt là trong công tác cứu trợ thiên tai, đã xuất hiện với tần suất ngày càng cao trong các bài thi IELTS Reading thực tế. Đây là một trong những chủ đề thuộc nhóm “Technology and Innovation” – một trong ba nhóm chủ đề phổ biến nhất trong kỳ thi IELTS, cùng với “Environment” và “Society”.

Bài viết này cung cấp cho bạn một đề thi IELTS Reading hoàn chỉnh với ba passages tăng dần độ khó từ Easy (Band 5.0-6.5), Medium (Band 6.0-7.5) đến Hard (Band 7.0-9.0), giúp bạn làm quen với cấu trúc đề thi thực tế. Bạn sẽ được luyện tập với 40 câu hỏi đa dạng các dạng bài phổ biến trong IELTS Reading, kèm theo đáp án chi tiết và giải thích từng bước để hiểu rõ phương pháp làm bài.

Ngoài ra, bạn sẽ học được hơn 40 từ vựng quan trọng liên quan đến công nghệ AI, thiên tai và cứu trợ nhân đạo – những từ vựng không chỉ hữu ích cho phần Reading mà còn có thể áp dụng vào Writing và Speaking. Đề thi này phù hợp cho học viên từ band 5.0 trở lên, đặc biệt hữu ích cho những bạn đang mục tiêu đạt band 6.5-7.5.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test bao gồm 3 passages với tổng cộng 40 câu hỏi cần hoàn thành trong 60 phút. Mỗi câu trả lời đúng được tính là 1 điểm, không trừ điểm cho câu sai.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (dễ nhất, nên làm nhanh để dành thời gian cho phần sau)
• Passage 2: 18-20 phút (độ khó trung bình, cần đọc kỹ hơn)
• Passage 3: 23-25 phút (khó nhất, cần thời gian suy luận và phân tích)

Lưu ý dành 2-3 phút cuối để chuyển đáp án vào answer sheet và kiểm tra lại.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm các dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Câu hỏi trắc nghiệm nhiều lựa chọn
2. True/False/Not Given – Xác định thông tin đúng, sai hoặc không được đề cập
3. Matching Headings – Nối tiêu đề với đoạn văn
4. Sentence Completion – Hoàn thành câu
5. Summary Completion – Hoàn thành đoạn tóm tắt
6. Matching Features – Nối thông tin với các đặc điểm
7. Short-answer Questions – Câu hỏi trả lời ngắn

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2. IELTS Reading Practice Test

PASSAGE 1 – Early Warning Systems: AI’s First Line of Defense

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Natural disasters strike with devastating force, often leaving communities with little time to prepare. Earthquakes, tsunamis, hurricanes, and floods claim thousands of lives annually and cause billions of dollars in damage. However, recent advances in artificial intelligence are transforming how we predict, prepare for, and respond to these catastrophic events. By analyzing vast amounts of data far more quickly than humans can, AI systems are becoming an invaluable tool in disaster management.

One of the most promising applications of AI in disaster relief is in early warning systems. Traditional methods of predicting natural disasters relied on human experts analyzing historical data and current conditions. This process was time-consuming and often failed to detect subtle patterns that could indicate an impending disaster. Modern AI systems, particularly those using machine learning algorithms, can process enormous datasets from multiple sources simultaneously. These sources include satellite imagery, seismic sensors, weather stations, and social media feeds. By identifying patterns that humans might miss, AI can provide earlier and more accurate warnings.

For example, in earthquake prediction, AI systems analyze data from thousands of sensors placed along fault lines. The technology examines tiny changes in ground movement, temperature, and even electromagnetic signals that may precede a major earthquake. While scientists cannot yet predict earthquakes with complete accuracy, AI has improved the reliability of forecasts by approximately 30% in some regions. This advancement gives communities precious extra minutes or even hours to evacuate or take protective measures.

Hurricane tracking has also benefited significantly from AI technology. Traditional forecasting models could predict a hurricane’s path, but often with considerable uncertainty, especially more than 48 hours in advance. Deep learning models trained on decades of hurricane data can now analyze atmospheric conditions, ocean temperatures, and wind patterns to produce more accurate forecasts up to five days ahead. The European Centre for Medium-Range Weather Forecasts reported that AI-enhanced models have reduced prediction errors by 15-20% compared to conventional methods.

Flood prediction represents another area where AI is making substantial progress. By analyzing rainfall patterns, river levels, soil moisture, and topographical data, AI systems can predict which areas are most at risk of flooding. In countries like Bangladesh and India, where monsoon-related floods affect millions of people annually, these systems have proved particularly valuable. Authorities can now issue warnings to specific communities days in advance, allowing for organized evacuations and the distribution of emergency supplies.

The technology also helps in predicting wildfires, which have become increasingly common due to climate change. AI analyzes data including weather conditions, vegetation density, historical fire patterns, and even human activity in forested areas. In California, AI systems can now identify areas at high risk of wildfires weeks before they start, enabling preventive measures such as controlled burns or increased monitoring of vulnerable regions.

What makes AI particularly effective is its ability to learn and improve over time. Each disaster provides new data that helps refine the algorithms, making future predictions more accurate. This continuous improvement cycle means that AI systems become more reliable with each passing year. Moreover, AI can process information in real-time, constantly updating its predictions as new data becomes available. This dynamic approach is far superior to traditional static models that required manual updates.

Despite these advances, experts emphasize that AI is a tool to assist human decision-making, not replace it. The technology works best when combined with human expertise and local knowledge. Emergency response teams still need to interpret AI predictions within the context of their specific situations and make final decisions about evacuations and resource allocation. However, by providing faster, more accurate information, AI gives these professionals a significant advantage in their race against time.

Questions 1-13

Questions 1-5: Multiple Choice

Choose the correct letter, A, B, C, or D.

1. According to the passage, what is the main advantage of AI over traditional disaster prediction methods?
A. It is cheaper to implement
B. It can analyze more data more quickly
C. It requires fewer human experts
D. It works without any human intervention

2. In earthquake prediction, AI systems have improved forecast reliability by approximately:
A. 15%
B. 20%
C. 30%
D. 50%

3. AI-enhanced hurricane forecasting models can now predict storms accurately up to:
A. 24 hours in advance
B. 48 hours in advance
C. 3 days in advance
D. 5 days in advance

4. Which of the following does AI NOT analyze when predicting wildfires?
A. Weather conditions
B. Vegetation density
C. Ocean temperatures
D. Human activity in forests

5. According to the passage, AI systems become more reliable because they:
A. replace human experts entirely
B. use only the most recent data
C. learn from each disaster event
D. ignore historical information

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. Natural disasters cause billions of dollars in damage every year.

7. AI can now predict earthquakes with complete accuracy.

8. Bangladesh and India both experience significant monsoon-related flooding.

9. California has completely eliminated wildfires using AI technology.

Questions 10-13: Sentence Completion

Complete the sentences below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

10. Traditional disaster prediction methods often failed to identify __ that could indicate an upcoming disaster.

11. Modern AI systems can examine information from various sources including satellite imagery and __.

12. In flood prediction, AI analyzes factors such as rainfall patterns and __ to determine risk areas.

13. Experts state that AI should be used to support __, not replace it entirely.

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PASSAGE 2 – AI-Powered Response Coordination: Managing Chaos in Crisis

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

When disaster strikes, the immediate aftermath is characterized by confusion, disrupted communications, and urgent competing priorities. Emergency responders, government agencies, non-governmental organizations (NGOs), and volunteers must coordinate their efforts quickly and efficiently to save lives. This coordination challenge has historically been one of the most difficult aspects of disaster response. However, artificial intelligence is revolutionizing how relief operations are managed, bringing order to chaos and significantly improving outcomes for survivors.

One of the most critical applications of AI in disaster response is resource allocation. Following a major disaster, there is typically an overwhelming need for food, water, medical supplies, shelter, and personnel across a wide geographic area. The challenge lies in determining which areas need what resources most urgently, and how to distribute supplies efficiently given damaged infrastructure and limited transportation. AI systems address this problem by integrating data from multiple sources: damage assessments, population density maps, survivor reports, and real-time updates on road conditions and supply availability.

Machine learning algorithms can analyze this complex information and generate optimal distribution plans that would take human planners days to develop. During the 2017 Hurricane Maria in Puerto Rico, an AI system developed by researchers at Carnegie Mellon University helped coordinate relief efforts by analyzing social media posts, news reports, and satellite imagery to identify which communities were in most urgent need. The system could process and categorize thousands of messages per hour, flagging critical situations for human responders. This capability proved invaluable in a situation where traditional communication infrastructure had been destroyed.

AI-powered chatbots and virtual assistants are also playing an increasingly important role in disaster response. These systems can handle thousands of inquiries simultaneously, providing survivors with information about evacuation routes, shelter locations, and available services. During disasters, emergency hotlines are typically overwhelmed with calls, leaving many people unable to get through. AI assistants can answer routine questions, freeing up human operators to focus on critical emergencies. Moreover, these systems can communicate in multiple languages, ensuring that information reaches diverse populations. The International Federation of Red Cross and Red Crescent Societies has implemented AI chatbots in several disaster zones, reporting that they successfully handled 70% of routine inquiries, allowing human staff to concentrate on complex cases requiring personal judgment and empathy.

Drone technology, enhanced by AI, has become another crucial component of disaster response. Equipped with cameras and sensors, drones can quickly survey large disaster areas, creating detailed maps that show the extent of damage, identify survivors needing rescue, and locate hazards such as downed power lines or unstable structures. AI algorithms process the imagery in real-time, automatically identifying people, vehicles, and structural damage, and prioritizing areas for search and rescue teams. This technology proved particularly valuable during the 2015 Nepal earthquake, where drones surveyed remote mountain villages that were inaccessible by road, helping responders understand the full scope of the disaster and plan their operations accordingly.

AI is also transforming how medical resources are deployed in disaster zones. Triage – the process of determining which patients need treatment most urgently – becomes extremely challenging when hospitals are overwhelmed with casualties. AI systems can assist medical personnel by analyzing patient symptoms, vital signs, and injury patterns to recommend treatment priorities. While final medical decisions remain with human doctors, these systems provide valuable decision support, particularly for less experienced practitioners working under extreme pressure. Some hospitals in earthquake-prone regions of Japan have implemented AI triage systems that have reduced critical decision-making time by an average of 40%.

Furthermore, AI contributes to improved coordination among the many organizations involved in disaster response. Different agencies often use incompatible communication systems and databases, making information sharing difficult. AI-powered data integration platforms can collect information from diverse sources, standardize it, and make it accessible to all authorized responders through a unified interface. This interoperability ensures that all teams are working with the same information, reducing duplication of effort and preventing critical needs from being overlooked.

Predictive analytics represents another frontier in AI-assisted disaster response. By analyzing patterns from previous disasters, AI can anticipate how situations are likely to develop and what problems responders should prepare for. For instance, AI models can predict when and where disease outbreaks are likely to occur in disaster-affected areas, based on factors such as water quality, sanitation conditions, population density, and climate. This foresight allows health authorities to pre-position medical supplies and deploy vaccination teams before outbreaks occur, rather than reacting after people have already fallen ill.

However, implementing AI in disaster response is not without challenges. The technology requires substantial initial investment in equipment, software, and training. Many disaster-prone regions lack the financial resources or technical infrastructure to deploy sophisticated AI systems. Additionally, there are concerns about data privacy and security, particularly when systems collect personal information from survivors. There is also the risk of algorithmic bias – if AI systems are trained primarily on data from developed countries, they may not perform well in different cultural or environmental contexts. Experts emphasize the need for inclusive development of AI systems that consider the diverse situations where they will be deployed.

Despite these challenges, the integration of AI into disaster response operations represents a significant advancement in humanitarian assistance. As the technology continues to evolve and become more accessible, it promises to save more lives and reduce suffering in the critical hours and days following disasters.

Hệ thống trí tuệ nhân tạo điều phối hoạt động cứu trợ thiên tai với máy bay không người lái và phân tích dữ liệu

Questions 14-26

Questions 14-18: Yes/No/Not Given

Do the following statements agree with the claims of the writer?

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. Coordinating disaster response has traditionally been straightforward for emergency services.

15. AI systems can develop resource distribution plans faster than human planners.

16. During Hurricane Maria, AI systems replaced human responders entirely.

17. AI chatbots can communicate with disaster survivors in multiple languages.

18. All disaster-prone regions have adequate financial resources to implement AI systems.

Questions 19-22: Matching Headings

The passage has eight paragraphs. Choose the correct heading for paragraphs B-E from the list of headings below.

List of Headings:
i. Medical applications of AI in emergency situations
ii. The role of drones in disaster assessment
iii. Financial and ethical challenges of AI implementation
iv. Optimizing the distribution of essential supplies
v. Future developments in AI technology
vi. AI communication tools for disaster survivors
vii. Training emergency responders to use AI
viii. Integrating data across multiple organizations

19. Paragraph B (starting with “One of the most critical applications…”)

20. Paragraph D (starting with “AI-powered chatbots…”)

21. Paragraph E (starting with “Drone technology…”)

22. Paragraph F (starting with “AI is also transforming…”)

Questions 23-26: Summary Completion

Complete the summary below.

Choose NO MORE THAN TWO WORDS from the passage for each answer.

AI is improving disaster response coordination in several ways. During Hurricane Maria, an AI system helped identify communities in urgent need by analyzing (23) __, news reports, and satellite imagery. AI-powered platforms help different organizations share information by creating a (24) __ that all responders can access. Additionally, (25) __ can help authorities anticipate future problems, such as disease outbreaks, allowing them to prepare in advance. However, implementation faces challenges including concerns about (26) __ when collecting survivor information.

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PASSAGE 3 – The Future of AI in Disaster Management: Ethical Considerations and Technological Frontiers

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The exponential growth in artificial intelligence capabilities has precipitated a fundamental transformation in how humanity conceives of and responds to natural disasters. While the preceding decades witnessed incremental improvements in disaster management through conventional technological means, the integration of sophisticated AI systems represents a paradigm shift of unprecedented magnitude. This revolution, however, brings not merely technical challenges but profound ethical dilemmas and societal implications that demand rigorous examination by policymakers, technologists, and humanitarian practitioners alike.

At the forefront of emerging AI applications in disaster management is the development of autonomous decision-making systems capable of initiating response actions without direct human authorization. These systems, predicated on advanced neural networks and reinforcement learning algorithms, can theoretically respond to rapidly evolving crisis situations with superhuman speed and analytical precision. Proponents argue that in scenarios where minutes determine survival outcomes, the latency inherent in human decision-making processes constitutes an unacceptable liability. Prototype systems deployed in experimental settings have demonstrated the capacity to autonomously redirect emergency vehicles, activate warning sirens, and even trigger infrastructure shutdowns based on real-time threat assessments. Nevertheless, the delegation of life-or-death decisions to algorithmic processes raises fundamental questions about accountability, moral agency, and the appropriate locus of authority in crisis management.

The epistemological challenges associated with AI disaster management systems warrant particular attention. Machine learning models derive their predictive capabilities from patterns identified in historical data, yet disasters by definition represent aberrant events that deviate from normal conditions. This creates an inherent tension: the more catastrophic and unprecedented a disaster, the less historical precedent exists to train AI systems effectively. The 2011 Fukushima disaster exemplified this limitation; the confluence of earthquake, tsunami, and nuclear emergency created conditions outside the parameters of existing predictive models, rendering many automated systems ineffective or counterproductive. Scholars in the field of disaster informatics have termed this the “black swan paradox” – the most critical events are precisely those for which AI systems are least prepared.

Algorithmic bias represents another critical concern that has garnered increasing scholarly attention. AI systems trained predominantly on data from affluent, technologically advanced societies may encode assumptions and priorities that prove inappropriate or even harmful when applied in different socioeconomic contexts. For instance, evacuation route optimization algorithms developed for urban areas with robust transportation infrastructure may generate untenable recommendations in rural regions of developing nations where such infrastructure is absent or rudimentary. More insidiously, if training data underrepresents certain demographic groups, AI resource allocation systems may systematically deprioritize the needs of marginalized communities, thereby exacerbating existing inequalities. Research conducted by the International Institute for Applied Systems Analysis revealed that several widely deployed disaster response AI systems exhibited significant performance disparities across different demographic groups, with accuracy rates varying by as much as 35% between the best and worst-performing population segments.

The geopolitical dimensions of AI-driven disaster management merit substantive consideration. Nations and organizations possessing advanced AI capabilities may accrue significant strategic advantages in both predicting and responding to disasters, potentially creating a new form of technological dependency whereby less developed regions become reliant on AI systems designed, controlled, and operated by foreign entities. This dynamic raises questions of data sovereignty – who owns and controls the vast quantities of information that AI systems require to function effectively? When international organizations deploy AI disaster management systems in vulnerable countries, do they acquire unprecedented access to sensitive data about critical infrastructure, population movements, and institutional capabilities? Such information could conceivably be leveraged for purposes beyond humanitarian assistance, including economic or political advantage.

Privacy considerations intersect uncomfortably with the data-intensive nature of effective AI disaster management. Optimal system performance requires integrating information from myriad sources, including social media activity, mobile phone location data, financial transactions, and medical records. While such comprehensive data integration undoubtedly enhances predictive accuracy and response effectiveness, it simultaneously enables surveillance capabilities of a scope and sophistication previously unattainable. The normalization of extensive data collection during emergencies may establish precedents and infrastructure that persist long after the immediate crisis has passed, potentially eroding privacy protections in the name of disaster preparedness. Civil liberties advocates have expressed concern about the creation of “disaster surveillance states” where exceptional powers granted during emergencies become entrenched features of governance.

The economic implications of AI integration in disaster management reveal stark disparities in global capacity. Development and deployment of sophisticated AI systems require substantial investment in computational infrastructure, specialized personnel, and continuous system maintenance. A World Bank analysis estimated that implementing comprehensive AI disaster management systems comparable to those used in technologically advanced nations would cost developing countries an average of 2-4% of annual GDP – an insurmountable expenditure for nations already struggling with fiscal constraints. This creates a troubling scenario wherein those populations most vulnerable to disasters – often in low-income regions experiencing rapid urbanization and environmental degradation – are least able to access the protective benefits of advanced AI systems. Some scholars have proposed internationalized AI infrastructure as a global public good, analogous to weather forecasting networks, but questions remain about governance mechanisms, funding structures, and equitable access protocols.

Looking toward the technological frontier, researchers are exploring quantum computing applications that could exponentially enhance AI disaster management capabilities. Quantum algorithms could potentially process incomprehensibly vast datasets and model catastrophically complex scenarios beyond the reach of classical computing architectures. However, such systems remain largely theoretical, and their eventual deployment may further concentrate disaster management capabilities among a small number of technologically elite nations and institutions. Alternatively, advances in distributed computing and open-source AI platforms could democratize access to sophisticated disaster management tools, enabling even resource-constrained organizations to deploy effective systems. The trajectory of development remains contested, with competing visions of either concentrated technological power or democratized capability distribution.

The philosophical questions surrounding AI in disaster management ultimately concern the nature of human agency and responsibility in an age of increasingly autonomous systems. If we construct AI capable of managing disasters more effectively than humans can, do we have a moral obligation to defer to algorithmic recommendations even when they contradict human judgment or values? Conversely, if we maintain human authority over AI systems, how do we justify decisions that result in worse outcomes than the AI would have produced? These questions, while abstract, have concrete implications for legal frameworks, professional standards, and public accountability mechanisms governing disaster management. As AI capabilities continue their relentless advancement, societies must grapple with fundamental questions about the proper relationship between human judgment and machine intelligence in matters of collective survival.

Công nghệ trí tuệ nhân tạo tương lai trong quản lý thiên tai với máy tính lượng tử và mạng lưới toàn cầu

Questions 27-40

Questions 27-31: Multiple Choice

Choose the correct letter, A, B, C, or D.

27. According to the passage, autonomous AI decision-making systems are controversial because:
A. they are too expensive to implement
B. they raise questions about who is responsible for decisions
C. they are less accurate than human decision-making
D. they require too much time to process information

28. The “black swan paradox” refers to the problem that:
A. AI systems work best for common, predictable disasters
B. unprecedented catastrophic events are the hardest for AI to handle
C. black swans cause more disasters than other birds
D. AI cannot analyze historical data effectively

29. Research by the International Institute for Applied Systems Analysis found that:
A. AI systems performed equally well for all demographic groups
B. AI accuracy varied by up to 35% between different population segments
C. AI could not be used effectively in disaster management
D. all AI systems exhibited the same performance patterns

30. The passage suggests that comprehensive AI disaster management systems would cost developing countries approximately:
A. 1% of annual GDP
B. 2-4% of annual GDP
C. 5-10% of annual GDP
D. more than 10% of annual GDP

31. The author’s attitude toward quantum computing in disaster management can best be described as:
A. enthusiastically optimistic
B. completely skeptical
C. cautiously uncertain
D. strongly opposed

Questions 32-36: Matching Features

Match each concern (32-36) with the correct category (A-F) from the list below.

Write the correct letter, A-F.

List of Categories:
A. Privacy issues
B. Economic inequality
C. Algorithmic bias
D. Geopolitical power
E. Technological limitations
F. Environmental impact

32. AI systems may systematically provide lower quality service to marginalized communities.

33. Foreign entities may gain access to sensitive national infrastructure information.

34. Extensive data collection during emergencies could lead to permanent surveillance systems.

35. Poor nations cannot afford the same AI protection as wealthy nations.

36. AI trained on past events may fail during unprecedented catastrophes.

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 learning algorithms do autonomous AI decision-making systems use?

38. What term do disaster informatics scholars use to describe AI’s difficulty with unprecedented events?

39. What has been proposed as a global public good analogous to weather forecasting networks?

40. What two competing visions exist for the future development of AI disaster management technology?

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. D
4. C
5. C
6. TRUE
7. FALSE
8. TRUE
9. FALSE
10. subtle patterns
11. social media feeds
12. topographical data
13. human decision-making

PASSAGE 2: Questions 14-26

14. NO
15. YES
16. NO
17. YES
18. NO
19. iv
20. vi
21. ii
22. i
23. social media posts
24. unified interface
25. Predictive analytics
26. data privacy

PASSAGE 3: Questions 27-40

27. B
28. B
29. B
30. B
31. C
32. C
33. D
34. A
35. B
36. E
37. reinforcement learning algorithms
38. black swan paradox
39. internationalized AI infrastructure
40. concentrated technological power / democratized capability distribution

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main advantage, AI, traditional disaster prediction methods
• Vị trí trong bài: Đoạn 1, dòng cuối: “By analyzing vast amounts of data far more quickly than humans can, AI systems are becoming an invaluable tool”
• Giải thích: Đáp án B đúng vì passage nói rõ lợi thế chính của AI là khả năng phân tích lượng dữ liệu khổng lồ nhanh hơn nhiều so với con người. Các đáp án khác không được đề cập như là lợi thế chính.

Câu 2: C

• Dạng câu hỏi: Multiple Choice – số liệu cụ thể
• Từ khóa: earthquake prediction, improved forecast reliability, approximately
• Vị trí trong bài: Đoạn 3: “AI has improved the reliability of forecasts by approximately 30% in some regions”
• Giải thích: Con số 30% được nêu rõ ràng trong bài.

Câu 3: D

• Dạng câu hỏi: Multiple Choice – thông tin thời gian
• Từ khóa: AI-enhanced hurricane forecasting, predict, accurately
• Vị trí trong bài: Đoạn 4: “can now analyze atmospheric conditions… to produce more accurate forecasts up to five days ahead”
• Giải thích: 5 days = D là đáp án chính xác.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: natural disasters, billions of dollars, damage, every year
• Vị trí trong bài: Đoạn 1: “cause billions of dollars in damage”
• Giải thích: Thông tin khớp chính xác với câu trong bài.

Câu 7: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: predict earthquakes, complete accuracy
• Vị trí trong bài: Đoạn 3: “While scientists cannot yet predict earthquakes with complete accuracy”
• Giải thích: Bài văn nói rõ không thể dự đoán hoàn toàn chính xác, trái ngược với câu hỏi.

Câu 10: subtle patterns

• Dạng câu hỏi: Sentence Completion
• Từ khóa: traditional methods, failed to identify
• Vị trí trong bài: Đoạn 2: “often failed to detect subtle patterns that could indicate an impending disaster”
• Giải thích: Paraphrase: “failed to identify” = “failed to detect”

Câu 13: human decision-making

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI, support, not replace
• Vị trí trong bài: Đoạn cuối: “AI is a tool to assist human decision-making, not replace it”
• Giải thích: Paraphrase: “support” = “assist”

Passage 2 – Giải Thích

Câu 14: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: coordinating disaster response, traditionally, straightforward
• Vị trí trong bài: Đoạn 1: “This coordination challenge has historically been one of the most difficult aspects”
• Giải thích: Bài văn nói coordination là “most difficult” (khó nhất), trái ngược với “straightforward” (đơn giản) trong câu hỏi.

Câu 15: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI systems, develop resource distribution plans, faster, human planners
• Vị trí trong bài: Đoạn 2: “generate optimal distribution plans that would take human planners days to develop”
• Giải thích: AI có thể tạo kế hoạch mà con người cần nhiều ngày, ngụ ý AI nhanh hơn.

Câu 19: iv (Optimizing the distribution of essential supplies)

• Dạng câu hỏi: Matching Headings
• Vị trí: Paragraph B bắt đầu với “One of the most critical applications of AI in disaster response is resource allocation”
• Giải thích: Đoạn văn tập trung vào phân bổ tài nguyên (resource allocation) và phân phối nguồn cung (distribution of supplies).

Câu 20: vi (AI communication tools for disaster survivors)

• Dạng câu hỏi: Matching Headings
• Vị trí: Paragraph D về AI chatbots và virtual assistants
• Giải thích: Đoạn này nói về các công cụ giao tiếp AI (chatbots, virtual assistants) giúp người sống sót.

Câu 23: social media posts

• Dạng câu hỏi: Summary Completion
• Vị trí trong bài: Đoạn 3: “by analyzing social media posts, news reports, and satellite imagery”
• Giải thích: Câu tóm tắt paraphrase lại thông tin về Hurricane Maria.

Câu 26: data privacy

• Dạng câu hỏi: Summary Completion
• Vị trí trong bài: Đoạn 9: “there are concerns about data privacy and security, particularly when systems collect personal information”
• Giải thích: Đây là thách thức được nêu rõ về thu thập thông tin người sống sót.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: autonomous AI decision-making systems, controversial
• Vị trí trong bài: Đoạn 2: “the delegation of life-or-death decisions to algorithmic processes raises fundamental questions about accountability, moral agency”
• Giải thích: Hệ thống tự động gây tranh cãi vì vấn đề trách nhiệm (accountability) – ai chịu trách nhiệm cho quyết định.

Câu 28: B

• Dạng câu hỏi: Multiple Choice – thuật ngữ chuyên môn
• Từ khóa: black swan paradox
• Vị trí trong bài: Đoạn 3: “the most critical events are precisely those for which AI systems are least prepared”
• Giải thích: Nghịch lý là các sự kiện thảm khốc chưa từng có (unprecedented catastrophic events) lại là những sự kiện mà AI ít được chuẩn bị nhất.

Câu 29: B

• Dạng câu hỏi: Multiple Choice – dữ liệu nghiên cứu
• Từ khóa: International Institute for Applied Systems Analysis, research found
• Vị trí trong bài: Đoạn 4: “accuracy rates varying by as much as 35% between the best and worst-performing population segments”
• Giải thích: Nghiên cứu cho thấy độ chính xác dao động đến 35% giữa các nhóm dân số khác nhau.

Câu 32: C (Algorithmic bias)

• Dạng câu hỏi: Matching Features
• Từ khóa: systematically provide lower quality service, marginalized communities
• Vị trí trong bài: Đoạn 4: “AI resource allocation systems may systematically deprioritize the needs of marginalized communities”
• Giải thích: Đây là ví dụ điển hình của algorithmic bias (thiên kiến thuật toán).

Câu 34: A (Privacy issues)

• Dạng câu hỏi: Matching Features
• Từ khóa: extensive data collection, emergencies, permanent surveillance
• Vị trí trong bài: Đoạn 6: “The normalization of extensive data collection during emergencies may establish precedents and infrastructure that persist long after the immediate crisis has passed, potentially eroding privacy protections”
• Giải thích: Đây rõ ràng là vấn đề riêng tư (privacy issues).

Câu 37: reinforcement learning algorithms

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: autonomous AI decision-making systems, type of learning algorithms
• Vị trí trong bài: Đoạn 2: “predicated on advanced neural networks and reinforcement learning algorithms”
• Giải thích: Câu hỏi yêu cầu loại thuật toán học, đáp án nằm trong cụm từ mô tả hệ thống.

Câu 38: black swan paradox

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: term, disaster informatics scholars, unprecedented events
• Vị trí trong bài: Đoạn 3: “Scholars in the field of disaster informatics have termed this the ‘black swan paradox'”
• Giải thích: Thuật ngữ được đặt trong ngoặc kép, dễ nhận biết.

Câu 40: concentrated technological power / democratized capability distribution

• Dạng câu hỏi: Short-answer Questions (cần 2 đáp án đối lập)
• Từ khóa: two competing visions, future development
• Vị trí trong bài: Đoạn 8: “competing visions of either concentrated technological power or democratized capability distribution”
• Giải thích: Hai tầm nhìn đối lập được liệt kê rõ ràng trong câu.

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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
devastating	adj	/ˈdevəsteɪtɪŋ/	tàn phá, hủy diệt	strike with devastating force	devastating effect/impact/consequences
artificial intelligence	n	/ˌɑːtɪˈfɪʃl ɪnˈtelɪdʒəns/	trí tuệ nhân tạo	advances in artificial intelligence	artificial intelligence system/technology
invaluable	adj	/ɪnˈvæljuəbl/	vô giá, cực kỳ quý báu	an invaluable tool	invaluable resource/asset/contribution
early warning system	n	/ˈɜːli ˈwɔːnɪŋ ˈsɪstəm/	hệ thống cảnh báo sớm	early warning systems for disasters	early warning system for earthquakes
machine learning	n	/məˈʃiːn ˈlɜːnɪŋ/	học máy	machine learning algorithms	machine learning model/technique
seismic	adj	/ˈsaɪzmɪk/	thuộc địa chấn	seismic sensors	seismic activity/wave/data
fault line	n	/fɔːlt laɪn/	đường đứt gãy địa chất	sensors along fault lines	major/active fault line
protective measures	n	/prəˈtektɪv ˈmeʒəz/	biện pháp bảo vệ	take protective measures	implement/adopt protective measures
deep learning	n	/diːp ˈlɜːnɪŋ/	học sâu	deep learning models	deep learning algorithm/network
prediction error	n	/prɪˈdɪkʃn ˈerə/	sai số dự đoán	reduced prediction errors	minimize/reduce prediction errors
topographical	adj	/ˌtɒpəˈɡræfɪkl/	thuộc địa hình	topographical data	topographical map/survey/feature
continuous improvement	n	/kənˈtɪnjuəs ɪmˈpruːvmənt/	cải tiến liên tục	continuous improvement cycle	continuous improvement process/strategy

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
disrupted	adj	/dɪsˈrʌptɪd/	bị gián đoạn, bị phá vỡ	disrupted communications	disrupted service/supply/infrastructure
competing priorities	n	/kəmˈpiːtɪŋ praɪˈɒrətiz/	các ưu tiên cạnh tranh	urgent competing priorities	manage/balance competing priorities
relief operations	n	/rɪˈliːf ˌɒpəˈreɪʃnz/	hoạt động cứu trợ	coordinate relief operations	humanitarian relief operations
resource allocation	n	/rɪˈsɔːs ˌæləˈkeɪʃn/	phân bổ nguồn lực	resource allocation challenge	optimal/efficient resource allocation
damage assessment	n	/ˈdæmɪdʒ əˈsesmənt/	đánh giá thiệt hại	based on damage assessments	conduct/carry out damage assessment
virtual assistant	n	/ˈvɜːtʃuəl əˈsɪstənt/	trợ lý ảo	AI-powered virtual assistants	virtual assistant technology
empathy	n	/ˈempəθi/	sự đồng cảm	requiring personal judgment and empathy	show/express empathy
downed power lines	n	/daʊnd ˈpaʊə laɪnz/	đường dây điện bị đứt	locate hazards such as downed power lines	avoid downed power lines
search and rescue	n	/sɜːtʃ ənd ˈreskjuː/	tìm kiếm và cứu hộ	search and rescue teams	search and rescue operation/mission
triage	n	/ˈtriːɑːʒ/	phân loại cấp cứu	medical triage process	triage system/procedure
interoperability	n	/ˌɪntərˌɒpərəˈbɪləti/	khả năng tương tác	ensures interoperability	improve/enhance interoperability
predictive analytics	n	/prɪˈdɪktɪv ˌænəˈlɪtɪks/	phân tích dự đoán	predictive analytics represents	predictive analytics tool/model
disease outbreak	n	/dɪˈziːz ˈaʊtbreɪk/	dịch bệnh bùng phát	predict disease outbreaks	prevent/contain disease outbreak
foresight	n	/ˈfɔːsaɪt/	tầm nhìn xa, sự nhìn trước	this foresight allows	strategic foresight/planning
algorithmic bias	n	/ˌælɡəˈrɪðmɪk ˈbaɪəs/	thiên kiến thuật toán	risk of algorithmic bias	address/mitigate algorithmic bias

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
paradigm shift	n	/ˈpærədaɪm ʃɪft/	sự thay đổi mô hình	represents a paradigm shift	undergo/experience paradigm shift
ethical dilemma	n	/ˈeθɪkl dɪˈlemə/	tình huống khó xử về đạo đức	profound ethical dilemmas	face/resolve ethical dilemma
humanitarian practitioner	n	/hjuːˌmænɪˈteəriən prækˈtɪʃənə/	người thực hành nhân đạo	policymakers and humanitarian practitioners	experienced humanitarian practitioner
autonomous	adj	/ɔːˈtɒnəməs/	tự động, tự trị	autonomous decision-making systems	autonomous vehicle/system
reinforcement learning	n	/ˌriːɪnˈfɔːsmənt ˈlɜːnɪŋ/	học tăng cường	reinforcement learning algorithms	reinforcement learning technique
latency	n	/ˈleɪtənsi/	độ trễ	the latency inherent in	reduce/minimize latency
accountability	n	/əˌkaʊntəˈbɪləti/	trách nhiệm giải trình	questions about accountability	ensure/maintain accountability
epistemological	adj	/ɪˌpɪstəməˈlɒdʒɪkl/	thuộc nhận thức luận	epistemological challenges	epistemological question/issue
aberrant	adj	/æˈberənt/	lệch lạc, bất thường	represent aberrant events	aberrant behavior/pattern
confluence	n	/ˈkɒnfluəns/	sự hợp lưu, giao thoa	the confluence of disasters	confluence of factors/events
black swan paradox	n	/blæk swɒn ˈpærədɒks/	nghịch lý thiên nga đen	termed this the black swan paradox	black swan event/theory
algorithmic bias	n	/ˌælɡəˈrɪðmɪk ˈbaɪəs/	thiên kiến thuật toán	algorithmic bias represents	address algorithmic bias
socioeconomic	adj	/ˌsəʊsiəʊˌiːkəˈnɒmɪk/	thuộc kinh tế xã hội	different socioeconomic contexts	socioeconomic status/factor/condition
underrepresent	v	/ˌʌndəˌreprɪˈzent/	thiếu đại diện	if training data underrepresents	underrepresented group/minority
marginalized	adj	/ˈmɑːdʒɪnəlaɪzd/	bị gạt ra ngoài lề	needs of marginalized communities	marginalized population/group
geopolitical	adj	/ˌdʒiːəʊpəˈlɪtɪkl/	thuộc địa chính trị	geopolitical dimensions	geopolitical tension/risk/consideration
data sovereignty	n	/ˈdeɪtə ˈsɒvrənti/	chủ quyền dữ liệu	questions of data sovereignty	protect/ensure data sovereignty
surveillance	n	/sɜːˈveɪləns/	sự giám sát	surveillance capabilities	surveillance system/technology
fiscal constraint	n	/ˈfɪskl kənˈstreɪnt/	hạn chế tài chính	struggling with fiscal constraints	face/overcome fiscal constraints
quantum computing	n	/ˈkwɒntəm kəmˈpjuːtɪŋ/	điện toán lượng tử	quantum computing applications	quantum computing technology/power
democratize	v	/dɪˈmɒkrətaɪz/	dân chủ hóa	democratize access	democratize technology/education

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Kết bài

Chủ đề “How Artificial Intelligence Is Improving Disaster Relief Efforts” không chỉ là một trong những chủ đề đương đại quan trọng nhất trong IELTS Reading mà còn phản ánh những tiến bộ công nghệ đang thay đổi cách chúng ta ứng phó với thảm họa tự nhiên. Qua bộ đề thi mẫu hoàn chỉnh này, bạn đã được trải nghiệm với ba passages tăng dần độ khó, từ giới thiệu cơ bản về hệ thống cảnh báo sớm (Passage 1), đến phối hợp phản ứng phức tạp (Passage 2), và cuối cùng là những cân nhắc đạo đức sâu sắc (Passage 3).

Ba passages này đã cung cấp tổng cộng 40 câu hỏi với đầy đủ các dạng bài phổ biến nhất trong IELTS Reading: Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Headings, Sentence Completion, Summary Completion, Matching Features, và Short-answer Questions. Sự đa dạng này giúp bạn làm quen với mọi thử thách có thể gặp trong kỳ thi thực tế.

Phần đáp án chi tiết không chỉ cung cấp câu trả lời đúng mà còn giải thích rõ ràng vị trí thông tin trong bài, cách paraphrase được sử dụng, và lý do tại sao các đáp án khác không đúng. Đây là chìa khóa để bạn tự đánh giá và cải thiện kỹ năng làm bài của mình.

Hơn 40 từ vựng quan trọng được tổng hợp theo từng passage sẽ là nền tảng vững chắc cho vốn từ học thuật của bạn, không chỉ trong Reading mà còn có thể áp dụng vào Writing Task 2 và Speaking Part 3 khi thảo luận về các chủ đề công nghệ và xã hội.

Hãy nhớ rằng, thành công trong IELTS Reading không chỉ đến từ việc làm nhiều đề mà từ việc phân tích kỹ lưỡng từng bài làm, hiểu rõ lý do đúng sai, và rút ra bài học cho những lần sau. Chúc bạn ôn tập hiệu quả và đạt band điểm mục tiêu trong kỳ thi IELTS sắp tới!