Mở bài
Trong bối cảnh biến đổi khí hậu ngày càng gay gắt, thảm họa thiên nhiên đang trở thành mối đe dọa lớn đối với nhân loại. Chủ đề về vai trò của trí tuệ nhân tạo (AI) trong việc quản lý các thảm họa thiên nhiên đã và đang xuất hiện với tần suất ngày càng cao trong các đề thi IELTS Reading, đặc biệt là từ năm 2020 đến nay. Chủ đề này thường xuất hiện ở dạng passage có độ khó Medium hoặc Hard, đòi hỏi khả năng đọc hiểu học thuật tốt.
Bài viết này cung cấp cho bạn một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages có độ khó tăng dần từ Easy đến Hard, bao gồm 40 câu hỏi đa dạng như trong đề thi thật. Bạn sẽ học được cách xử lý các dạng câu hỏi khó như Matching Headings, True/False/Not Given, và Multiple Choice. Đi kèm là đáp án chi tiết với giải thích cụ thể về vị trí thông tin và kỹ thuật paraphrase, cùng với bảng từ vựng quan trọng giúp bạn mở rộng vốn từ học thuật.
Đề thi này phù hợp cho học viên có mục tiêu từ band 5.0 trở lên, đặc biệt hữu ích cho những ai đang luyện tập để đạt band 7.0-8.0.
1. Hướng dẫn làm bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính 1 điểm, không có điểm âm nếu trả lời sai. Độ khó của các passages tăng dần, với Passage 1 thường dễ nhất và Passage 3 khó nhất.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (13 câu hỏi)
- Passage 2: 18-20 phút (13 câu hỏi)
- Passage 3: 23-25 phút (14 câu hỏi)
Lưu ý dành 2-3 phút cuối để chuyển đáp án vào answer sheet, vì không có thời gian phụ sau khi hết giờ.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Chọn đáp án đúng từ 3-4 phương án
- True/False/Not Given – Xác định thông tin đúng, sai, hay không được đề cập
- Matching Information – Nối thông tin với đoạn văn tương ứng
- Matching Headings – Chọn tiêu đề phù hợp cho mỗi đoạn
- Sentence Completion – Hoàn thành câu với từ trong bài
- Summary Completion – Điền từ vào bản tóm tắt
- Short-answer Questions – Trả lời câu hỏi ngắn
2. IELTS Reading Practice Test
PASSAGE 1 – Early Warning Systems: How Technology Predicts Disasters
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
Natural disasters have claimed millions of lives throughout human history. Earthquakes, tsunamis, hurricanes, and floods strike with devastating force, often leaving communities with little time to prepare. However, the landscape of disaster management has transformed dramatically in recent years, thanks to advances in technology and particularly artificial intelligence (AI). Today, scientists and emergency response teams are using AI-powered systems to predict disasters more accurately than ever before, potentially saving countless lives.
Early warning systems represent one of the most crucial applications of AI in disaster management. These systems use vast amounts of data collected from various sources including satellites, weather stations, seismic sensors, and ocean buoys. Traditional methods of disaster prediction relied heavily on human analysts examining this data manually, a process that was both time-consuming and prone to error. Modern AI systems, however, can process information from thousands of sources simultaneously, identifying patterns that might indicate an approaching disaster within seconds.
The technology behind these systems is remarkably sophisticated yet increasingly accessible. Machine learning algorithms are trained using historical data from past disasters, learning to recognize the warning signs that precede catastrophic events. For example, before a major earthquake occurs, there are often subtle changes in seismic activity that may go unnoticed by human observers. AI systems can detect these minute variations and alert authorities to the possibility of a larger event. Similarly, when monitoring weather patterns, AI can identify the specific combination of atmospheric conditions that typically lead to severe storms or hurricanes.
Japan has emerged as a global leader in implementing AI-powered disaster warning systems, particularly for earthquakes and tsunamis. The country’s sophisticated network of sensors covers the entire nation, constantly monitoring ground movements and ocean conditions. When the AI system detects unusual patterns, it can issue warnings to millions of people through their mobile phones within seconds. During the 2011 Tōhoku earthquake, although the disaster was devastating, the early warning system gave many people precious minutes to seek safety, undoubtedly saving numerous lives.
Flood prediction has also benefited enormously from AI technology. Rivers and coastal areas around the world are now monitored by AI systems that can predict flooding with remarkable accuracy. These systems consider multiple factors including rainfall patterns, snow melt rates, river levels, and even soil saturation. In the United Kingdom, the Environment Agency uses an AI-powered flood warning system that has reduced false alarms by 30% while improving the accuracy of genuine warnings. This improvement is crucial for maintaining public trust in warning systems, as repeated false alarms can lead to complacency.
The integration of social media data has added another dimension to disaster prediction. During emergencies, people naturally share information about what they are experiencing on platforms like Twitter and Facebook. AI systems can analyze these posts in real-time, identifying emerging patterns that might indicate a developing disaster. This crowdsourced information can provide authorities with valuable insights into the scope and impact of an event as it unfolds, allowing for more effective resource allocation and emergency response.
Despite these advances, challenges remain. One significant issue is the need for extensive data to train AI systems effectively. Regions that lack historical disaster data or adequate monitoring infrastructure struggle to implement these technologies. Additionally, while AI can identify patterns and make predictions, it cannot account for every variable in complex natural systems. Unexpected factors can still lead to inaccurate predictions or missed warnings.
The cost of implementing comprehensive AI warning systems is another barrier, particularly for developing nations where disasters often have the most severe impact. However, international cooperation and technology sharing initiatives are beginning to address this disparity. Organizations like the United Nations and various non-governmental organizations are working to make AI disaster prediction technology more accessible to vulnerable communities worldwide.
Looking to the future, experts believe AI will play an even more central role in disaster management. As systems become more sophisticated and data collection improves, predictions will become increasingly accurate and timely. The goal is not just to warn people of impending disasters but to provide specific, actionable information about what steps they should take to stay safe. This shift from general warnings to personalized guidance represents the next frontier in AI-powered disaster management.
Questions 1-6
Do the following statements agree with the information given in Passage 1?
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
- Traditional disaster prediction methods were faster than modern AI systems.
- Machine learning algorithms learn from data about previous disasters.
- Japan’s warning system during the 2011 earthquake prevented all casualties.
- AI flood prediction systems in the UK have completely eliminated false alarms.
- Social media information can help authorities understand the scale of a disaster.
- All countries now have equal access to AI disaster warning technology.
Questions 7-10
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
- AI systems can examine data from __ of sources at the same time.
- Before major earthquakes, AI can detect small __ in seismic activity.
- Japan’s network of sensors monitors both ground movements and __.
- One problem with false alarms is that they can cause __ among the public.
Questions 11-13
Choose the correct letter, A, B, C or D.
-
According to the passage, what is the main advantage of AI over human analysts in disaster prediction?
- A) AI is more expensive
- B) AI can process data from many sources quickly
- C) AI doesn’t need training
- D) AI can prevent all disasters
-
The passage suggests that developing nations face difficulties because:
- A) They experience fewer disasters
- B) Their populations are too small
- C) The technology is too expensive to implement
- D) They refuse to use AI systems
-
What does the passage say about the future of AI in disaster management?
- A) It will replace all human decision-making
- B) It will focus only on earthquakes
- C) It will provide personalized safety guidance
- D) It will become less important
PASSAGE 2 – AI-Powered Response: Coordinating Emergency Services
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
When disaster strikes, the first few hours are absolutely critical. The effectiveness of emergency response during this golden window can mean the difference between life and death for thousands of people. Artificial intelligence has revolutionized not only how we predict disasters but also how we respond to them once they occur. By optimizing resource allocation, improving communication, and providing real-time situational awareness, AI systems are transforming emergency management into a more coordinated and efficient operation.
A. Resource Optimization
One of the most challenging aspects of disaster response is determining where to deploy limited resources. Emergency services must make rapid decisions about where to send ambulances, fire trucks, rescue teams, and medical supplies, often with incomplete information about the situation on the ground. AI-powered logistics systems address this challenge by analyzing multiple data streams simultaneously. These systems consider factors such as population density, infrastructure damage reports, traffic conditions, and the location of available resources to create optimal deployment strategies.
The city of Barcelona has implemented an AI system called “Emergency Response Optimizer” that has reduced average response times by 25%. The system uses predictive analytics to anticipate where resources will be needed based on the type and scale of the disaster. For instance, during a major flood, the AI can predict which neighborhoods are likely to require evacuation assistance based on topographical data, historical flood patterns, and current water levels. This proactive approach allows emergency services to position resources strategically before they are urgently needed.
B. Communication and Coordination
Effective disaster response requires seamless coordination among multiple agencies, including police, fire departments, medical services, and utility companies. Traditionally, this coordination has been hampered by incompatible communication systems and the overwhelming volume of information during crises. AI-powered command and control systems are solving this problem by serving as centralized hubs that integrate information from all sources and distribute it to relevant parties automatically.
These systems employ natural language processing to monitor communications across different channels, identifying critical information and prioritizing urgent requests. When a police officer reports a collapsed building with trapped survivors, the AI system immediately alerts the nearest rescue teams, notifies hospitals to prepare for casualties, and updates the common operating picture available to all agencies. This automated information flow eliminates the delays that previously occurred when messages had to be manually relayed between different organizations.
C. Damage Assessment
Assessing the extent of damage following a disaster is essential for planning recovery efforts, but surveying large affected areas traditionally required days or even weeks. AI has dramatically accelerated this process through the analysis of satellite imagery and drone footage. Computer vision algorithms can examine thousands of images, identifying damaged buildings, blocked roads, and other critical infrastructure issues within hours.
Following Hurricane Maria in Puerto Rico in 2017, AI systems analyzed satellite images to assess damage across the island, providing emergency managers with detailed maps showing which areas needed the most urgent attention. The technology could distinguish between minor and severe structural damage, identify buildings that had lost their roofs, and even detect areas where flooding persisted. This comprehensive assessment was completed in a fraction of the time that manual surveys would have required, enabling responders to prioritize their efforts more effectively.
D. Medical Triage
In mass casualty events, medical resources are quickly overwhelmed. Determining which patients need immediate treatment and which can safely wait is a agonizing but necessary decision that emergency medical teams must make. AI systems are now assisting with this triage process by analyzing patient data to predict outcomes and identify those at greatest risk.
These systems consider vital signs, injury descriptions, and patient characteristics to calculate survival probabilities under different treatment scenarios. While human medical professionals make the final decisions, AI provides them with evidence-based recommendations that can improve the accuracy and speed of triage. Studies have shown that AI-assisted triage can reduce mortality rates in mass casualty situations by up to 15% compared to traditional methods.
E. Vulnerable Population Identification
Not all community members are equally affected by disasters. Elderly individuals, people with disabilities, and those with chronic medical conditions often face heightened risks during emergencies. AI systems can identify and locate these vulnerable populations before disaster strikes, enabling emergency services to develop targeted assistance plans.
By analyzing data from various sources including healthcare records, social services databases, and census information, AI creates vulnerability maps showing where at-risk individuals are concentrated. When evacuations are ordered, emergency services can use these maps to ensure that vulnerable people receive the help they need. Some systems even provide personalized evacuation assistance, considering factors like mobility limitations and medical equipment dependencies.
F. Psychological Impact and Mental Health
An often overlooked aspect of disaster response is addressing the psychological trauma experienced by survivors and first responders. AI-powered mental health monitoring systems use various indicators to identify individuals who may be experiencing severe psychological distress. These systems analyze communication patterns, social media activity, and self-reported symptoms to flag people who might benefit from psychological support.
The implementation of AI in emergency response is not without ethical considerations. Questions arise about data privacy, algorithmic bias, and the appropriate balance between automated decision-making and human judgment. As these systems become more sophisticated and widely adopted, ongoing dialogue among technologists, emergency management professionals, and ethicists is essential to ensure they serve the public good while respecting individual rights.
G. Future Developments
Emerging technologies promise to make AI disaster response systems even more capable. Augmented reality interfaces will allow emergency workers to visualize critical information overlaid on their physical environment. Swarm robotics coordinated by AI will enable rapid search and rescue in dangerous areas where human responders cannot safely operate. As these technologies mature, the integration of AI into disaster response will deepen, creating systems that are increasingly autonomous yet accountable.
Questions 14-20
The passage has seven sections, A-G.
Which section contains the following information?
Write the correct letter, A-G.
- Information about how AI helps medical teams decide treatment priorities
- A description of ethical issues surrounding AI use in emergencies
- An example of a city that reduced emergency response times using AI
- Details about how AI identifies at-risk groups before disasters occur
- An explanation of how AI improves coordination between different agencies
- Information about future technologies that will enhance disaster response
- A description of how AI speeds up the process of evaluating disaster damage
Questions 21-24
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
AI systems help emergency services make better decisions about resource deployment by analyzing multiple 21) __ at once. Barcelona’s “Emergency Response Optimizer” uses 22) __ to forecast where resources will be needed. The system considers information such as 23) __, flood history, and water levels to predict which areas will need evacuation help. This allows emergency teams to position resources 24) __ rather than waiting for urgent calls.
Questions 25-26
Choose TWO letters, A-E.
Which TWO of the following are mentioned as benefits of AI damage assessment?
A) It requires more personnel than traditional methods
B) It can analyze images from satellites and drones
C) It takes the same time as manual surveys
D) It can distinguish between different levels of damage
E) It only works for hurricane damage
PASSAGE 3 – The Algorithmic Future: Machine Learning and Disaster Resilience
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The paradigm shift toward integrating artificial intelligence into disaster management represents far more than a mere technological upgrade; it constitutes a fundamental reconceptualization of humanity’s relationship with natural hazards. While previous sections have explored the immediate operational benefits of AI in prediction and response, this analysis delves into the more profound implications of machine learning systems for building long-term disaster resilience. The capacity of AI to process unprecedented volumes of environmental data, identify subtle correlations that elude human cognition, and generate predictive models of increasing sophistication suggests we are approaching an inflection point in our ability to mitigate disaster impacts.
Contemporary discourse around disaster resilience emphasizes the need to move beyond reactive response toward proactive adaptation. AI systems contribute to this objective through several interconnected mechanisms. First, they enable comprehensive risk assessment at scales previously unattainable. Traditional risk modeling relied on historical disaster records and relatively simple statistical extrapolations, producing assessments that often failed to capture the complexity of cascading risks and compound hazards. Machine learning algorithms, by contrast, can incorporate multidimensional datasets encompassing climate patterns, urbanization trends, infrastructure interdependencies, and socioeconomic vulnerabilities to generate nuanced risk profiles for specific locations and populations.
The methodological sophistication of these systems is particularly evident in their handling of non-linear relationships and feedback loops within complex adaptive systems. Natural disaster impacts rarely result from single causal factors; rather, they emerge from the interaction of multiple environmental, infrastructural, and social variables. For instance, the severity of flood damage in a given area depends not only on precipitation levels but also on soil permeability, drainage infrastructure capacity, land use patterns, and the socioeconomic status of residents, which influences both building quality and recovery capacity. AI systems employing deep neural networks can model these intricate interdependencies, identifying vulnerability hotspots that conventional analysis might overlook.
A particularly compelling application of machine learning in disaster resilience involves scenario modeling for urban planning and infrastructure development. As cities expand and climate patterns shift, planners face the challenge of designing urban environments that can withstand future disasters whose characteristics remain uncertain. AI-powered scenario generators address this epistemological challenge by creating thousands of plausible future disaster scenarios based on varying assumptions about climate change trajectories, population growth, and development patterns. Urban planners can then test different infrastructure configurations and policy interventions against this ensemble of scenarios, identifying strategies that prove robust across multiple futures.
The city of Rotterdam, confronting the existential threat of sea-level rise and increased flood risk, has pioneered the use of AI in climate adaptation planning. The city’s “Climate Adaptation Strategy” employs machine learning models that integrate hydrological modeling, urban development projections, and climate change scenarios to evaluate the effectiveness of various adaptation measures. These include green infrastructure installations, modifications to drainage systems, and changes to building codes. The AI system can assess not only the physical effectiveness of these interventions but also their cost-efficiency, social acceptability, and co-benefits such as improved air quality or urban aesthetics. This holistic assessment capability enables decision-makers to pursue adaptation strategies that maximize multiple objectives simultaneously.
Temporal dynamics represent another dimension where AI contributes to disaster resilience. Disasters do not exist as discrete events but rather as processes unfolding over extended time periods, from the gradual accumulation of risk factors through the acute emergency phase to long-term recovery and reconstruction. Machine learning systems can track communities through this entire disaster cycle, identifying critical junctures where interventions could substantially alter outcomes. For example, AI analysis of post-disaster recovery following Hurricane Katrina revealed that the speed of initial assistance strongly predicted long-term recovery success, suggesting that resource frontloading in the immediate aftermath produces disproportionate benefits.
The application of reinforcement learning—a machine learning approach where algorithms learn optimal strategies through trial and error—shows particular promise for disaster management policy. These systems can simulate thousands of disaster scenarios and test different policy responses, learning which approaches most effectively reduce casualties, minimize economic losses, and promote rapid recovery. Unlike traditional policy analysis, which typically evaluates a handful of predetermined options, reinforcement learning can explore vast policy spaces, potentially discovering non-obvious strategies that human planners might never consider.
However, the integration of AI into disaster resilience efforts raises substantive concerns that extend beyond technical performance to fundamental questions about governance, equity, and human agency. The opacity of advanced machine learning models—often characterized as “black boxes” because their decision-making processes are not readily interpretable—poses challenges for democratic accountability. When an AI system recommends evacuating certain neighborhoods while designating others as safe, on what basis are these determinations made? If the algorithmic logic remains inscrutable to affected communities and even to emergency managers, how can citizens meaningfully participate in decisions about their safety?
Algorithmic bias represents an equally serious concern. Machine learning systems learn from historical data, which inevitably reflects societal inequalities and past injustices. If disaster response in certain communities has historically been slower or less comprehensive due to discrimination, AI systems trained on this data may perpetuate and amplify these disparities. Research has documented cases where resource allocation algorithms systematically underserved marginalized populations because the training data reflected historically inequitable distribution patterns. Addressing this challenge requires not merely technical solutions but also critical examination of the data used to train AI systems and deliberate efforts to correct for historical biases.
The question of human expertise and professional judgment in an increasingly automated disaster management landscape also warrants careful consideration. While AI systems can process information at scales beyond human capacity, they lack the contextual understanding, ethical reasoning, and adaptive creativity that experienced emergency managers bring to crisis situations. The optimal relationship between human and machine intelligence remains a subject of ongoing debate, with perspectives ranging from techno-optimists who envision largely autonomous AI systems to skeptics who advocate for technology that augments rather than replaces human decision-making.
Institutional and infrastructural prerequisites for effective AI deployment in disaster management present practical challenges, particularly in resource-constrained settings where disaster risks are often greatest. Implementing sophisticated AI systems requires not only substantial financial investment in technology but also robust data infrastructure, reliable communications networks, and personnel trained to operate and maintain these systems. The digital divide between wealthy and poor nations risks creating a two-tiered global disaster management system, where technologically advanced countries enjoy increasingly effective protection while vulnerable populations in less developed regions remain exposed to unmitigated risks.
Looking forward, the trajectory of AI in disaster management will likely be shaped by advances in several complementary technologies. The proliferation of Internet of Things (IoT) devices will provide increasingly granular real-time data about environmental conditions and infrastructure status. Quantum computing, still in its nascent stages, promises computational power that could enable disaster simulations of unprecedented complexity and accuracy. Federated learning approaches may address privacy concerns by allowing AI systems to learn from distributed datasets without centralizing sensitive information. As these technologies mature and converge, they will expand the frontier of what is possible in disaster prediction, response, and resilience building.
Yet technology alone cannot solve the disaster challenge. Fundamentally, disasters are social phenomena—their impacts determined not merely by natural hazards but by human decisions about where and how to build, which risks to prioritize, and how to distribute protective resources across populations. AI can inform these decisions with unprecedented analytical power, but the decisions themselves remain inherently political, reflecting collective values and priorities. The most effective path forward likely involves socio-technical systems that combine AI capabilities with robust democratic institutions, strong professional expertise, and meaningful community engagement, ensuring that technology serves humanity’s collective interest in a more disaster-resilient future.
Questions 27-31
Choose the correct letter, A, B, C or D.
-
According to the passage, what is the main advantage of AI risk assessment over traditional methods?
- A) It is less expensive to implement
- B) It can incorporate multiple types of data to create detailed risk profiles
- C) It only uses historical disaster records
- D) It requires fewer skilled workers
-
The Rotterdam example illustrates how AI:
- A) Can replace urban planners entirely
- B) Only focuses on flood prevention
- C) Evaluates adaptation measures across multiple criteria
- D) Has solved all climate change problems
-
What does the passage suggest about reinforcement learning in disaster policy?
- A) It can only test a few policy options
- B) It is less effective than traditional policy analysis
- C) It can explore many possible strategies and find unexpected solutions
- D) It has no practical applications
-
The “black box” problem refers to:
- A) The color of computer equipment
- B) The difficulty in understanding how AI makes decisions
- C) A type of data storage device
- D) Emergency response vehicles
-
According to the passage, algorithmic bias occurs when:
- A) Computers make random errors
- B) AI systems reflect inequalities present in their training data
- C) Technology is too expensive
- D) Humans deliberately program discrimination
Questions 32-36
Complete the summary using the list of phrases, A-J, below.
Machine learning contributes to disaster resilience through several mechanisms. It enables 32) __ that captures the complexity of multiple interacting risks. AI systems can model 33) __ within complex systems, identifying vulnerable areas that traditional analysis might miss. In urban planning, AI creates 34) __ that help planners test different strategies. The technology can track communities through 35) __, identifying when interventions would be most effective. However, concerns exist about 36) __ in AI decision-making processes.
A) simple calculations
B) comprehensive risk assessment
C) the entire disaster cycle
D) thousands of future scenarios
E) only natural factors
F) non-linear relationships
G) transparency and accountability
H) reduced funding
I) single-cause explanations
J) historical buildings
Questions 37-40
Do the following statements agree with the claims of the writer in Passage 3?
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
- AI systems trained on biased historical data may continue patterns of inequality in disaster response.
- All emergency managers believe AI should completely replace human decision-making.
- Wealthy nations and poor nations currently have equal access to AI disaster management technology.
- Technology alone is sufficient to solve all disaster-related challenges facing humanity.
3. Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- FALSE
- TRUE
- FALSE
- FALSE
- TRUE
- FALSE
- thousands
- variations / changes
- ocean conditions
- complacency
- B
- C
- C
PASSAGE 2: Questions 14-26
- D
- F
- A
- E
- B
- G
- C
- data streams
- predictive analytics
- topographical data
- strategically / proactively
25-26. B, D (in any order)
PASSAGE 3: Questions 27-40
- B
- C
- C
- B
- B
- B
- F
- D
- C
- G
- YES
- NO
- NO
- NO
4. Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Traditional methods, faster, modern AI systems
- Vị trí trong bài: Đoạn 2, dòng 4-6
- Giải thích: Bài đọc nói rõ phương pháp truyền thống “time-consuming” (tốn thời gian) trong khi AI có thể xử lý thông tin “within seconds” (trong vài giây). Câu hỏi khẳng định phương pháp truyền thống nhanh hơn, điều này mâu thuẫn với thông tin trong bài.
Câu 2: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Machine learning algorithms, learn, data, previous disasters
- Vị trí trong bài: Đoạn 3, dòng 2-3
- Giải thích: Bài viết nói “Machine learning algorithms are trained using historical data from past disasters” – khớp chính xác với nội dung câu hỏi, chỉ paraphrase “previous disasters” thành “past disasters”.
Câu 5: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Social media information, authorities, scale of disaster
- Vị trí trong bài: Đoạn 6, dòng 3-5
- Giải thích: Bài viết nói rõ “This crowdsourced information can provide authorities with valuable insights into the scope and impact of an event”, trong đó “scope and impact” được paraphrase thành “scale” trong câu hỏi.
Câu 7: thousands
- Dạng câu hỏi: Sentence Completion
- Từ khóa: AI systems, examine data, sources, at the same time
- Vị trí trong bài: Đoạn 2, dòng 6-7
- Giải thích: Câu gốc: “can process information from thousands of sources simultaneously” – “simultaneously” được paraphrase thành “at the same time” trong câu hỏi.
Câu 11: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main advantage, AI, human analysts
- Vị trí trong bài: Đoạn 2, dòng 4-7
- Giải thích: Bài viết so sánh phương pháp truyền thống với AI, nhấn mạnh AI “can process information from thousands of sources simultaneously” – đây là lợi thế chính. Các đáp án khác không được đề cập hoặc sai sự thật (A – không nói về giá, C – AI cần training, D – AI không thể ngăn chặn tất cả thảm họa).
Passage 2 – Giải Thích
Câu 14: D
- Dạng câu hỏi: Matching Information
- Từ khóa: AI helps medical teams, treatment priorities
- Vị trí trong bài: Section D – “Medical Triage”
- Giải thích: Section D nói cụ thể về việc AI hỗ trợ “triage process” – quá trình phân loại bệnh nhân để quyết định ai cần điều trị khẩn cấp, tương ứng với “treatment priorities” trong câu hỏi.
Câu 16: A
- Dạng câu hỏi: Matching Information
- Từ khóa: city, reduced emergency response times, using AI
- Vị trí trong bài: Section A, đoạn 2
- Giải thích: Barcelona được đề cập cụ thể với con số “reduced average response times by 25%” nhờ hệ thống “Emergency Response Optimizer”.
Câu 21: data streams
- Dạng câu hỏi: Summary Completion
- Từ khóa: analyzing multiple, at once
- Vị trí trong bài: Section A, đoạn 1, dòng 3-4
- Giải thích: Câu gốc: “analyzing multiple data streams simultaneously” – câu hỏi paraphrase “simultaneously” thành “at once”.
Câu 25-26: B, D
- Dạng câu hỏi: Multiple Choice (chọn 2 đáp án)
- Từ khóa: benefits, AI damage assessment
- Vị trí trong bài: Section C
- Giải thích:
- B đúng: “analysis of satellite imagery and drone footage”
- D đúng: “distinguish between minor and severe structural damage”
- A sai: cần ít người hơn, không phải nhiều hơn
- C sai: nhanh hơn nhiều so với thủ công
- E sai: không chỉ dùng cho bão
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main advantage, AI risk assessment, traditional methods
- Vị trí trong bài: Đoạn 2, dòng 3-9
- Giải thích: Bài viết giải thích rằng AI có thể “incorporate multidimensional datasets encompassing climate patterns, urbanization trends, infrastructure interdependencies, and socioeconomic vulnerabilities to generate nuanced risk profiles”. Đây chính là việc kết hợp nhiều loại dữ liệu để tạo ra các hồ sơ rủi ro chi tiết.
Câu 30: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: “black box” problem
- Vị trí trong bài: Đoạn 8, dòng 2-4
- Giải thích: Bài viết giải thích “black boxes” là “their decision-making processes are not readily interpretable” – tức là khó hiểu cách AI đưa ra quyết định.
Câu 37: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: AI systems, biased historical data, patterns of inequality
- Vị trí trong bài: Đoạn 9, dòng 2-5
- Giải thích: Tác giả khẳng định rõ ràng: “If disaster response in certain communities has historically been slower or less comprehensive due to discrimination, AI systems trained on this data may perpetuate and amplify these disparities.”
Câu 38: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: all emergency managers, AI, completely replace human decision-making
- Vị trí trong bài: Đoạn 10, dòng 3-6
- Giải thích: Tác giả nói rõ “perspectives ranging from techno-optimists who envision largely autonomous AI systems to skeptics who advocate for technology that augments rather than replaces human decision-making” – tức là có nhiều quan điểm khác nhau, không phải tất cả đều tin AI nên thay thế hoàn toàn con người.
Câu 40: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: technology alone, sufficient, solve all disaster challenges
- Vị trí trong bài: Đoạn cuối, dòng 1-2
- Giải thích: Tác giả nói rõ ràng “Yet technology alone cannot solve the disaster challenge” – mâu thuẫn trực tiếp với câu hỏi.
Hệ thống AI dự báo thiên nhiên với màn hình hiển thị dữ liệu thời tiết và cảnh báo sớm IELTS Reading
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 impact/effect/consequences |
| disaster management | n phrase | /dɪˈzɑːstə ˈmænɪdʒmənt/ | quản lý thảm họa | the landscape of disaster management | disaster management system/strategy |
| vast amounts | n phrase | /vɑːst əˈmaʊnts/ | số lượng khổng lồ | vast amounts of data | vast amounts of information/resources |
| time-consuming | adj | /taɪm kənˈsjuːmɪŋ/ | tốn thời gian | a process that was time-consuming | time-consuming process/task/method |
| prone to error | adj phrase | /prəʊn tuː ˈerə/ | dễ mắc lỗi | prone to error | prone to mistakes/failure/accidents |
| machine learning | n | /məˈʃiːn ˈlɜːnɪŋ/ | học máy | machine learning algorithms | machine learning model/system/technique |
| minute variations | n phrase | /maɪˈnjuːt ˌveəriˈeɪʃənz/ | biến đổi nhỏ | detect minute variations | minute changes/differences/details |
| sophisticated network | n phrase | /səˈfɪstɪkeɪtɪd ˈnetwɜːk/ | mạng lưới tinh vi | sophisticated network of sensors | sophisticated system/technology/approach |
| undoubtedly | adv | /ʌnˈdaʊtɪdli/ | không nghi ngờ gì | undoubtedly saving lives | undoubtedly important/true/effective |
| crucial | adj | /ˈkruːʃəl/ | cực kỳ quan trọng | crucial for maintaining trust | crucial role/factor/importance |
| crowdsourced | adj | /ˈkraʊdsɔːst/ | được cung cấp bởi cộng đồng | crowdsourced information | crowdsourced data/content/intelligence |
| actionable | adj | /ˈækʃənəbəl/ | có thể hành động được | actionable information | actionable insights/recommendations/data |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| absolutely critical | adj phrase | /ˈæbsəluːtli ˈkrɪtɪkəl/ | cực kỳ quan trọng | the first few hours are absolutely critical | absolutely essential/necessary/crucial |
| golden window | n phrase | /ˈɡəʊldən ˈwɪndəʊ/ | khoảng thời gian vàng | during this golden window | golden opportunity/period/moment |
| optimizing | v | /ˈɒptɪmaɪzɪŋ/ | tối ưu hóa | optimizing resource allocation | optimize performance/efficiency/results |
| incomplete information | n phrase | /ˌɪnkəmˈpliːt ˌɪnfəˈmeɪʃən/ | thông tin không đầy đủ | with incomplete information | incomplete data/knowledge/picture |
| logistics systems | n phrase | /ləˈdʒɪstɪks ˈsɪstəmz/ | hệ thống hậu cần | AI-powered logistics systems | logistics network/operations/management |
| predictive analytics | n phrase | /prɪˈdɪktɪv ˌænəˈlɪtɪks/ | phân tích dự báo | uses predictive analytics | predictive modeling/algorithms/capabilities |
| topographical data | n phrase | /ˌtɒpəˈɡræfɪkəl ˈdeɪtə/ | dữ liệu địa hình | based on topographical data | topographical maps/features/information |
| proactive approach | n phrase | /prəʊˈæktɪv əˈprəʊtʃ/ | cách tiếp cận chủ động | this proactive approach | proactive measures/strategy/response |
| incompatible | adj | /ˌɪnkəmˈpætəbəl/ | không tương thích | incompatible communication systems | incompatible systems/formats/technologies |
| centralized hubs | n phrase | /ˈsentrəlaɪzd hʌbz/ | trung tâm tập trung | serving as centralized hubs | centralized system/control/database |
| dramatically accelerated | v phrase | /drəˈmætɪkəli əkˈseləreɪtɪd/ | tăng tốc đáng kể | has dramatically accelerated | dramatically increase/improve/change |
| computer vision | n phrase | /kəmˈpjuːtə ˈvɪʒən/ | thị giác máy tính | computer vision algorithms | computer vision technology/system/application |
| comprehensive assessment | n phrase | /ˌkɒmprɪˈhensɪv əˈsesmənt/ | đánh giá toàn diện | this comprehensive assessment | comprehensive analysis/review/evaluation |
| agonizing | adj | /ˈæɡənaɪzɪŋ/ | đau đớn, khó khăn | agonizing but necessary | agonizing decision/choice/process |
| evidence-based | adj | /ˈevɪdəns beɪst/ | dựa trên bằng chứng | evidence-based recommendations | evidence-based practice/approach/policy |
Phân tích dữ liệu thiên tai bằng AI với biểu đồ và bản đồ nhiệt cho IELTS Reading Practice
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| paradigm shift | n phrase | /ˈpærədaɪm ʃɪft/ | thay đổi mô hình tư duy | represents a paradigm shift | paradigm shift in thinking/approach |
| fundamental reconceptualization | n phrase | /ˌfʌndəˈmentəl ˌriːkənˌseptʃuəlaɪˈzeɪʃən/ | tái khái niệm hóa cơ bản | constitutes a fundamental reconceptualization | fundamental change/transformation/rethinking |
| unprecedented volumes | n phrase | /ʌnˈpresɪdentɪd ˈvɒljuːmz/ | khối lượng chưa từng có | process unprecedented volumes | unprecedented levels/scale/amounts |
| subtle correlations | n phrase | /ˈsʌtəl ˌkɒrəˈleɪʃənz/ | mối tương quan tinh vi | identify subtle correlations | subtle differences/patterns/changes |
| inflection point | n phrase | /ɪnˈflekʃən pɔɪnt/ | điểm uốn, điểm chuyển biến | approaching an inflection point | critical inflection point/turning point |
| cascading risks | n phrase | /kæˈskeɪdɪŋ rɪsks/ | rủi ro dây chuyền | capture cascading risks | cascading failures/effects/impacts |
| compound hazards | n phrase | /ˈkɒmpaʊnd ˈhæzədz/ | mối nguy phức hợp | compound hazards | compound disasters/emergencies/threats |
| multidimensional datasets | n phrase | /ˌmʌltiˌdɪˈmenʃənəl ˈdeɪtəsets/ | bộ dữ liệu đa chiều | incorporate multidimensional datasets | multidimensional analysis/approach/model |
| nuanced risk profiles | n phrase | /ˈnjuːɑːnst rɪsk ˈprəʊfaɪlz/ | hồ sơ rủi ro sắc thái | generate nuanced risk profiles | nuanced understanding/perspective/analysis |
| methodological sophistication | n phrase | /ˌmeθədəˈlɒdʒɪkəl səˌfɪstɪˈkeɪʃən/ | sự tinh vi về phương pháp luận | methodological sophistication | methodological approach/framework/rigor |
| non-linear relationships | n phrase | /nɒn ˈlɪniə rɪˈleɪʃənʃɪps/ | mối quan hệ phi tuyến | handling non-linear relationships | non-linear dynamics/patterns/processes |
| feedback loops | n phrase | /ˈfiːdbæk luːps/ | vòng phản hồi | feedback loops within systems | positive/negative feedback loops |
| complex adaptive systems | n phrase | /ˈkɒmpleks əˈdæptɪv ˈsɪstəmz/ | hệ thống thích nghi phức tạp | within complex adaptive systems | complex adaptive behavior/networks |
| intricate interdependencies | n phrase | /ˈɪntrɪkət ˌɪntədɪˈpendənsiz/ | sự phụ thuộc lẫn nhau phức tạp | model intricate interdependencies | intricate connections/relationships/patterns |
| vulnerability hotspots | n phrase | /ˌvʌlnərəˈbɪləti ˈhɒtspɒts/ | điểm nóng dễ bị tổn thương | identifying vulnerability hotspots | vulnerability assessment/analysis/mapping |
| epistemological challenge | n phrase | /ɪˌpɪstəməˈlɒdʒɪkəl ˈtʃælɪndʒ/ | thách thức nhận thức luận | address this epistemological challenge | epistemological questions/issues/problems |
| ensemble of scenarios | n phrase | /ɒnˈsɒmbəl əv səˈnɑːriəʊz/ | tập hợp các kịch bản | ensemble of scenarios | ensemble modeling/approach/forecast |
| robust across | adj phrase | /rəʊˈbʌst əˈkrɒs/ | mạnh mẽ trong nhiều trường hợp | prove robust across multiple futures | robust against/under/to |
| algorithmic logic | n phrase | /ˌælɡəˈrɪðmɪk ˈlɒdʒɪk/ | logic thuật toán | if the algorithmic logic remains | algorithmic decision-making/bias/fairness |
| perpetuate and amplify | v phrase | /pəˈpetʃueɪt ənd ˈæmplɪfaɪ/ | duy trì và khuếch đại | may perpetuate and amplify disparities | perpetuate inequalities/stereotypes/problems |
| socio-technical systems | n phrase | /ˌsəʊsiəʊ ˈteknɪkəl ˈsɪstəmz/ | hệ thống xã hội-kỹ thuật | socio-technical systems | socio-technical approach/framework/integration |
Kết bài
Chủ đề về vai trò của AI trong quản lý thảm họa thiên nhiên không chỉ phản ánh xu hướng công nghệ đương đại mà còn là một trong những chủ đề xuất hiện thường xuyên trong IELTS Reading những năm gần đây. Ba passages trong bài thi mẫu này đã cung cấp cho bạn một cái nhìn toàn diện từ các ứng dụng cơ bản của AI trong cảnh báo sớm (Passage 1), đến việc phối hợp ứng phó khẩn cấp (Passage 2), và cuối cùng là những vấn đề phức tạp về đạo đức và xã hội khi triển khai công nghệ này (Passage 3).
Độ khó tăng dần của các passages giúp bạn làm quen với cách IELTS Reading test được cấu trúc, từ những câu hỏi dễ tìm thông tin trực tiếp đến những câu hỏi đòi hỏi suy luận và phân tích sâu. Đáp án chi tiết kèm theo giải thích cụ thể về vị trí thông tin và kỹ thuật paraphrase sẽ giúp bạn hiểu rõ cách tiếp cận từng dạng câu hỏi, đặc biệt là những dạng khó như Matching Headings và Yes/No/Not Given.
Bảng từ vựng tổng hợp hơn 40 từ và cụm từ quan trọng sẽ giúp bạn không chỉ hiểu sâu hơn về chủ đề mà còn áp dụng những từ vựng học thuật này vào phần thi Writing Task 2 và Speaking Part 3. Hãy thực hành đề thi này trong điều kiện thi thật (60 phút, không tra từ điển) để đánh giá chính xác trình độ hiện tại của mình. Sau đó, dành thời gian phân tích kỹ những câu trả lời sai để rút kinh nghiệm cho các lần làm bài tiếp theo.
Chúc bạn luyện tập hiệu quả và đạt band điểm mục tiêu trong kỳ thi IELTS sắp tới.
