IELTS Reading: Ứng Dụng AI Trong Nghiên Cứu Biến Đổi Khí Hậu – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Biến đổi khí hậu là một trong những thách thức lớn nhất của nhân loại, và trí tuệ nhân tạo (AI) đang trở thành công cụ mạnh mẽ trong việc nghiên cứu và ứng phó với vấn đề này. Chủ đề “What Are The Implications Of AI In Climate Change Research?” xuất hiện ngày càng thường xuyên trong các đề thi IELTS Reading gần đây, phản ánh tầm quan trọng của xu hướng công nghệ và môi trường toàn cầu.

Bài viết này cung cấp một đề thi IELTS Reading hoàn chỉnh với 3 passages được thiết kế theo đúng chuẩn Cambridge IELTS, từ mức độ dễ đến khó. Bạn sẽ được luyện tập với 40 câu hỏi đa dạng bao gồm Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion và nhiều dạng khác. Mỗi câu hỏi đều có đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin và kỹ thuật paraphrase.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với văn phong học thuật về công nghệ và môi trường – hai chủ đề hot trong IELTS. Bạn cũng sẽ học được hơn 40 từ vựng quan trọng với collocation và ví dụ thực tế từ bài đọc.

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. Độ khó tăng dần từ Passage 1 đến Passage 3, đòi hỏi bạn phải phân bổ thời gian hợp lý:

• 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 không có thời gian bổ sung để chuyển đáp án sang answer sheet, vì vậy bạn cần ghi đáp án trực tiếp trong khi làm bài.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Chọn đáp án đúng từ các phương án cho sẵn
2. True/False/Not Given – Xác định tính đúng sai của thông tin so với bài đọc
3. Matching Information – Ghép thông tin với đoạn văn tương ứng
4. Matching Headings – Chọn tiêu đề phù hợp cho mỗi đoạn văn
5. Summary Completion – Điền từ vào chỗ trống để hoàn thành đoạn tóm tắt
6. Matching Features – Ghép đặc điểm với các yếu tố được liệt kê
7. Short-answer Questions – Trả lời câu hỏi bằng từ ngữ từ bài đọc

IELTS Reading Practice Test

PASSAGE 1 – Artificial Intelligence: A New Tool for Climate Scientists

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Climate change represents one of the most pressing challenges facing humanity today. As global temperatures continue to rise and extreme weather events become more frequent, scientists are turning to cutting-edge technology to better understand and predict these changes. Among the most promising tools in this effort is artificial intelligence (AI), which is revolutionising the way researchers collect, analyse, and interpret climate data.

Traditional climate research has always been data-intensive, requiring scientists to process vast amounts of information from weather stations, satellites, and ocean sensors. However, the sheer volume of data available today far exceeds what human researchers can effectively handle. This is where AI excels. Machine learning algorithms, a subset of AI, can analyse millions of data points in minutes, identifying patterns and correlations that might take humans years to discover.

One of the most significant applications of AI in climate science is in improving weather forecasting. Modern weather prediction relies on complex mathematical models that simulate the Earth’s atmosphere. These models divide the atmosphere into a three-dimensional grid and calculate how temperature, pressure, and humidity change over time. While powerful, these traditional models require enormous computational resources and still struggle with certain predictions, particularly for localised weather phenomena like thunderstorms or fog.

AI systems are now being trained to enhance these predictions. By learning from decades of historical weather data, neural networks can identify subtle patterns that traditional models miss. For example, researchers at the University of Oxford have developed an AI system that can predict rainfall with greater accuracy than conventional methods, particularly for short-term forecasts of one to six hours ahead. This improved precision is crucial for issuing timely warnings about floods or other weather-related disasters.

Beyond weather forecasting, AI is also helping scientists understand long-term climate trends. Climate models attempt to project how the Earth’s climate will change over decades or centuries based on different scenarios of greenhouse gas emissions. However, these models involve countless variables and complex interactions between the atmosphere, oceans, ice sheets, and ecosystems. AI can help by identifying which factors are most important and how they interact, making the models more accurate and computationally efficient.

Another valuable application is in monitoring environmental changes. Satellites orbiting Earth collect terabytes of imagery every day, showing changes in forest cover, ice sheets, ocean temperatures, and urban development. Analysing these images manually would be impossible, but AI-powered computer vision systems can automatically detect changes, such as deforestation in the Amazon or melting ice in the Arctic. These systems can alert researchers to changes as they happen, enabling faster response to environmental threats.

AI is also being used to optimise the placement of climate monitoring equipment. The Earth’s climate system is monitored by thousands of sensors, but deciding where to place new sensors to get the most valuable data is challenging. AI algorithms can analyse existing data to identify gaps in coverage and suggest optimal locations for new monitoring stations, ensuring that research funding is used most effectively.

Despite these promising applications, experts caution that AI is not a silver bullet for climate research. The technology has limitations and potential drawbacks. AI systems require large amounts of training data, and if this data contains biases or errors, the AI will perpetuate these problems. Additionally, the most powerful AI systems are “black boxes” – they can make accurate predictions, but scientists don’t always understand why the AI reached a particular conclusion. This lack of transparency can be problematic in scientific research, where understanding the underlying mechanisms is just as important as making predictions.

Furthermore, training large AI models requires significant computational power, which in turn consumes considerable energy. Some critics argue that the carbon footprint of AI research could partially offset the benefits it provides in fighting climate change. However, proponents counter that the insights gained from AI-assisted research will ultimately lead to much greater reductions in emissions than the energy used to develop the technology.

Looking forward, the integration of AI into climate science is likely to accelerate. As AI technology continues to improve and become more accessible, researchers expect it to play an increasingly central role in understanding and addressing climate change. The key will be using AI as a tool to augment, rather than replace, human expertise and ensuring that the technology is developed and deployed responsibly.

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

1. Climate scientists traditionally had difficulty processing all the data available to them.
2. AI can identify patterns in climate data faster than human researchers.
3. The University of Oxford’s AI system is more accurate than traditional methods for all types of weather prediction.
4. Climate models need to consider interactions between different components of Earth’s systems.
5. AI-powered systems can only analyse satellite images after humans have identified areas of interest.
6. Some critics believe that AI research may contribute to climate change through energy consumption.

Questions 7-10

Complete the sentences below.

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

7. Traditional weather prediction models divide the atmosphere into a __ grid.
8. Neural networks can identify __ that conventional forecasting methods overlook.
9. AI helps climate scientists determine which __ are most significant in long-term climate projections.
10. The lack of __ in how AI reaches conclusions can be problematic for scientific research.

Questions 11-13

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

11. According to the passage, what is the main advantage of using AI for weather forecasting?

    • A. It completely replaces traditional forecasting methods
    • B. It can process data more quickly than conventional approaches
    • C. It eliminates the need for weather monitoring equipment
    • D. It predicts weather changes months in advance

12. The passage suggests that AI is particularly useful for monitoring environmental changes because:

    • A. satellites cannot collect enough data without AI assistance
    • B. the volume of satellite imagery is too large for manual analysis
    • C. environmental changes happen too quickly for human observation
    • D. AI can predict future environmental changes before they occur

13. What is the author’s overall attitude toward using AI in climate research?

    • A. Entirely enthusiastic and without reservation
    • B. Completely sceptical about its potential benefits
    • C. Cautiously optimistic but aware of limitations
    • D. Uncertain about whether it will be useful

---

PASSAGE 2 – Machine Learning Applications in Climate Modelling

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The application of machine learning techniques to climate science represents a paradigm shift in how researchers approach one of the most complex problems facing humanity. While the potential benefits are substantial, the integration of these technologies into climate modelling raises important questions about methodology, validation, and interpretation that scientists are only beginning to address.

A. Climate models are mathematical representations of the Earth’s climate system, incorporating physical equations that describe how energy and matter move through the atmosphere, oceans, land surface, and ice sheets. These General Circulation Models (GCMs) have been the cornerstone of climate science for decades, enabling scientists to project future climate scenarios based on different levels of greenhouse gas emissions. However, GCMs face inherent limitations. The climate system involves processes occurring at scales ranging from molecular interactions in cloud droplets to global atmospheric circulation patterns spanning thousands of kilometres. No computer, regardless of power, can simulate all these processes simultaneously at full resolution.

B. To overcome computational limitations, climate models must parametrise smaller-scale processes – essentially, they use simplified mathematical approximations rather than simulating the actual physics. For example, clouds play a crucial role in regulating Earth’s temperature, but individual clouds are far too small to be represented in global climate models. Instead, modellers use parametrisation schemes that estimate how clouds at a given location will behave based on larger-scale variables like temperature and humidity. These parametrisations introduce uncertainties into model predictions, and improving them has been a major focus of climate science for years.

C. Machine learning offers a novel approach to this parametrisation challenge. Rather than deriving parametrisations from physical principles alone, researchers can train neural networks on high-resolution simulations or observational data to learn how small-scale processes behave. The neural network essentially becomes a sophisticated parametrisation scheme that captures complex, non-linear relationships between variables. Several research groups have demonstrated that AI-based parametrisations can match or exceed the accuracy of traditional approaches while being computationally cheaper to run.

D. One particularly promising application involves using machine learning to improve the representation of clouds and convection in climate models. Researchers at the National Center for Atmospheric Research in the United States trained a neural network on data from high-resolution simulations that explicitly modelled cloud formation. The trained network could then predict cloud behaviour in lower-resolution climate models with remarkable accuracy. When incorporated into a full climate model, the AI-based cloud parametrisation produced temperature and precipitation patterns that more closely matched real-world observations than traditional parametrisations.

E. Beyond improving parametrisations, machine learning is being used to accelerate climate simulations themselves. Running a comprehensive climate model to simulate a century of climate change can take months, even on powerful supercomputers. This computational burden limits how many scenarios scientists can explore and how much they can refine their models. Machine learning offers a potential solution through emulation – training a neural network to mimic the behaviour of a full climate model but at a fraction of the computational cost. These AI emulators can run thousands of times faster than the original models, enabling researchers to explore a much wider range of scenarios and better quantify uncertainties in their projections.

F. However, the use of machine learning in climate modelling is not without controversy. A fundamental principle of traditional climate models is that they are based on physical laws – conservation of energy, momentum, and mass. This physics-based foundation gives scientists confidence that the models will behave reasonably even when simulating conditions different from the present, such as the climate of the distant past or future. Machine learning models, by contrast, learn patterns from data without necessarily understanding the underlying physics. Critics worry that AI-based models might produce accurate results when simulating familiar conditions but fail unpredictably when confronted with unprecedented scenarios.

G. This concern is particularly acute for tipping points – critical thresholds beyond which the climate system might change dramatically and irreversibly. Historical data may contain little or no information about such extreme transitions, meaning a machine learning model trained on past observations might fail to anticipate them. To address this limitation, researchers are developing hybrid approaches that combine physics-based models with machine learning components. The idea is to use AI to handle aspects of the climate system where it excels – recognising complex patterns in large datasets – while retaining physical constraints that ensure the model behaves consistently with known laws of nature.

H. Another important application of machine learning in climate science is detection and attribution – determining whether observed changes in climate are caused by human activities or natural variability. The Earth’s climate naturally varies on multiple timescales, from daily weather fluctuations to multi-decadal oscillations like El Niño. Distinguishing the human-caused climate change signal from this natural noise is challenging. Machine learning algorithms, particularly deep learning networks, excel at pattern recognition tasks and can be trained to identify the characteristic “fingerprints” of different climate forcing factors, such as greenhouse gases, volcanic eruptions, or changes in solar radiation.

I. Researchers have used these techniques to analyse temperature records, showing that the warming pattern observed over the past century is consistent with the expected effects of increasing greenhouse gases and inconsistent with natural variability alone. Machine learning has also been applied to detect human influence on extreme weather events, helping to determine how much climate change has increased the probability or intensity of specific hurricanes, heatwaves, or droughts. This attribution science has important implications for climate policy and litigation, as it helps establish the connection between greenhouse gas emissions and specific damages.

The integration of machine learning into climate science is still in its early stages, and many challenges remain. As these technologies mature, they promise to enhance scientists’ ability to understand, predict, and ultimately mitigate climate change. However, realising this potential will require careful development that respects the complexity of the climate system and the importance of physical understanding in making reliable projections about our planet’s future.

Sơ đồ minh họa ứng dụng trí tuệ nhân tạo trong mô hình hóa khí hậu toàn cầu và dự báo thời tiết

Questions 14-20

The passage has nine paragraphs, A-I.

Which paragraph contains the following information?

Write the correct letter, A-I.

14. A description of how machine learning can identify human causes of climate change
15. An explanation of why traditional climate models cannot simulate all processes in detail
16. A concern about AI models performing poorly in unprecedented situations
17. An example of how AI improved the accuracy of temperature and precipitation predictions
18. A description of how AI can make climate simulations faster
19. The main limitation that traditional climate models have addressed for many years
20. A combination approach that uses both AI and physical principles

Questions 21-24

Complete the summary below.

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

Climate models use parametrisation schemes to handle processes that are too small to be simulated directly. Machine learning offers a new way to develop these schemes by training 21. __ on detailed simulation data. The result is an AI-based system that can capture 22. __ between different variables. This approach has been particularly successful for representing 23. __ in climate models. Additionally, machine learning enables the creation of 24. __ that can run much faster than full climate models, allowing researchers to test more scenarios.

Questions 25-26

Choose TWO letters, A-E.

Which TWO advantages of machine learning in climate science are mentioned in the passage?

A. It eliminates all uncertainty from climate predictions
B. It can reduce the computational cost of simulations
C. It completely replaces the need for physical understanding
D. It can help identify patterns characteristic of different climate factors
E. It guarantees accurate predictions for all future climate scenarios

---

PASSAGE 3 – The Transformative Potential and Ethical Dimensions of AI in Climate Research

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The confluence of artificial intelligence and climate science represents not merely an incremental technological advancement but rather a potentially transformative epistemological shift in humanity’s capacity to understand and respond to environmental change. Yet, as with many disruptive technologies, the integration of AI into climate research is accompanied by a constellation of ethical, methodological, and societal considerations that extend far beyond the technical domain. A comprehensive assessment of AI’s implications for climate science must therefore encompass not only its capabilities and limitations but also its broader ramifications for scientific practice, environmental governance, and social equity.

The epistemic challenges posed by machine learning in climate science are particularly profound. Traditional scientific methodology emphasises mechanistic understanding – the ability to explain phenomena through reference to underlying physical principles and causal relationships. This approach has been the bedrock of climate science, with researchers striving to understand precisely how greenhouse gases trap heat, how ocean currents transport thermal energy, and how feedback mechanisms amplify or dampen climate responses. Machine learning, particularly in its deep learning incarnations, operates according to a fundamentally different logic. These systems identify statistical regularities in data that enable accurate predictions, but they do so through architectures of such complexity that the reasoning process remains largely opaque, even to their creators.

This interpretability problem raises important questions about the nature of scientific understanding. If an AI system can accurately predict how the climate will respond to increasing greenhouse gas concentrations but cannot explain why in terms of physical mechanisms, have we achieved genuine understanding, or merely predictive capability? Some philosophers of science argue that explanation and prediction are distinct epistemic goals, both valuable but serving different purposes. Others contend that truly understanding a system requires the ability to explain its behaviour, not simply forecast it. This debate has tangible implications for climate science: if we rely heavily on AI predictions without mechanistic understanding, we may be ill-equipped to recognise when these predictions are likely to fail or to devise novel interventions in the climate system.

The data requirements of machine learning systems present another significant challenge, particularly in the context of climate change where, by definition, we are concerned with unprecedented conditions. Machine learning algorithms generally perform best when the conditions they are asked to predict closely resemble those in their training data. However, Earth’s climate is entering a state with no close analogue in the historical record – atmospheric carbon dioxide concentrations are higher than at any point in the past three million years, and global temperatures are rising at geologically unprecedented rates. This extrapolation problem is especially acute for extreme events and non-linear transitions in the climate system, precisely the phenomena that are often most consequential for human societies and ecosystems.

Several methodological strategies have been proposed to address this limitation. Transfer learning techniques enable AI systems to apply knowledge learned in one context to different but related scenarios, potentially improving performance in extrapolation tasks. Physics-informed neural networks incorporate known physical constraints and conservation laws into the learning process, ensuring that AI predictions remain consistent with fundamental principles even when extrapolating beyond training data. Hybrid modelling frameworks combine process-based climate models with machine learning components, leveraging the strengths of each approach while mitigating their respective weaknesses. These techniques represent promising directions, though their effectiveness in genuinely unprecedented conditions remains to be fully validated.

Beyond these technical considerations, the deployment of AI in climate research raises important questions about resource allocation and environmental justice. The development and application of sophisticated machine learning systems require substantial computational infrastructure, with the training of large transformer models consuming energy equivalent to the lifetime emissions of several cars. While this computational carbon footprint is modest compared to the potential benefits of improved climate predictions and mitigation strategies, it highlights a broader equity concern: the institutions and nations with the greatest computational resources will be best positioned to benefit from AI advances in climate science.

This technological disparity has geopolitical implications. Climate change disproportionately affects economically disadvantaged nations and communities, yet these same populations often have limited access to the advanced technological tools needed to predict and prepare for climate impacts. If AI-powered climate prediction becomes the gold standard, a “climate intelligence gap” could emerge, with well-resourced nations having access to superior forecasts while vulnerable populations rely on less accurate predictions. Addressing this concern will require intentional efforts to democratise access to AI climate tools, through open-source software development, capacity building in under-resourced institutions, and international cooperation frameworks.

The integration of AI into climate research also intersects with questions of algorithmic governance and epistemic authority. As governments, corporations, and international bodies increasingly rely on AI-powered climate projections to inform consequential decisions about infrastructure investment, resource allocation, and regulatory policy, questions arise about who controls these systems, how they are validated, and what recourse exists when they produce erroneous guidance. The proprietary nature of some AI systems means that key aspects of their operation may be opaque to independent scrutiny, potentially concentrating epistemic power in the hands of a small number of technology companies or well-funded research institutions.

Furthermore, the persuasive power of AI predictions may create new forms of epistemic complacency. The impressive performance of machine learning systems in narrow domains has generated considerable enthusiasm and, in some quarters, an almost uncritical faith in AI capabilities. There is a risk that policymakers and the public may accord excessive confidence to AI-generated climate projections, underestimating their uncertainties and limitations. This could lead to misplaced certainty in planning and decision-making, with potentially catastrophic consequences if AI predictions fail in critical scenarios.

To harness the potential of AI in climate research while navigating these challenges will require a multi-faceted approach. Scientifically, this means developing rigorous frameworks for validating AI models, particularly their performance under extrapolation conditions. It necessitates maintaining strong programmes of mechanistic research alongside data-driven approaches, ensuring that predictive tools are complemented by deep understanding of climate processes. Institutionally, it demands efforts to democratise access to AI climate tools and to ensure that their development and deployment are guided by diverse perspectives, including voices from communities most affected by climate change.

Ethically, it requires ongoing reflection on the appropriate role of AI in scientific practice and environmental governance. AI should be viewed as a powerful tool that augments human expertise rather than as a replacement for scientific judgment and democratic deliberation. The most promising path forward likely involves a synergistic integration of human insight and machine intelligence, leveraging computational power to identify patterns and possibilities while relying on human understanding to interpret findings, recognise limitations, and guide decisions.

The implications of AI in climate change research thus extend far beyond technical performance metrics. They encompass fundamental questions about the nature of scientific knowledge, the distribution of technological capabilities across societies, and the governance structures appropriate for an era of algorithmically-mediated environmental understanding. Addressing these implications thoughtfully will be essential to ensuring that AI serves as a genuinely beneficial force in humanity’s response to the climate crisis, rather than introducing new forms of inequality, opacity, or misplaced confidence into an already complex challenge.

Minh họa các thách thức đạo đức và phương pháp luận của AI trong nghiên cứu biến đổi khí hậu toàn cầu

Questions 27-31

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

27. According to the passage, the main epistemic challenge of using machine learning in climate science is that:

    • A. AI systems cannot make accurate predictions about climate
    • B. AI predictions may lack mechanistic explanations based on physical principles
    • C. AI requires too much computational power to be practical
    • D. AI cannot analyse large volumes of climate data effectively

28. The “extrapolation problem” mentioned in the passage refers to:

    • A. the difficulty of obtaining sufficient historical climate data
    • B. the challenge of predicting unprecedented climate conditions
    • C. the problem of transferring AI technology between countries
    • D. the issue of training AI systems efficiently

29. The author’s view on physics-informed neural networks is that they:

    • A. completely solve the extrapolation problem
    • B. are ineffective for climate prediction tasks
    • C. represent a promising but not yet fully validated approach
    • D. should replace traditional climate models entirely

30. According to the passage, the “climate intelligence gap” refers to:

    • A. differences in climate understanding between scientists and the public
    • B. unequal access to advanced AI climate prediction tools
    • C. the gap between AI predictions and actual climate outcomes
    • D. insufficient funding for climate research globally

31. The author’s overall stance on AI in climate research can best be described as:

    • A. enthusiastically supportive without reservations
    • B. entirely opposed due to ethical concerns
    • C. cautiously optimistic but emphasising need for careful governance
    • D. neutral and unwilling to take a position

Questions 32-36

Complete the summary below.

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

Traditional climate science emphasises 32. __, which involves explaining phenomena through physical principles and causal relationships. However, deep learning systems operate through identifying 33. __ in data, but their reasoning process remains 34. __ even to their creators. This raises questions about whether accurate predictions constitute genuine understanding. The passage suggests that the most effective approach likely involves a 35. __ of human insight and machine intelligence. Such integration would use AI to identify patterns while relying on 36. __ to interpret findings and guide decisions.

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

37. The computational energy required to train large AI models is greater than the potential environmental benefits from improved climate predictions.

38. Some AI climate systems are controlled by private companies, limiting independent verification.

39. Policymakers may place too much trust in AI climate projections without fully understanding their limitations.

40. AI will eventually be able to completely replace the need for human climate scientists.

---

Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. TRUE
2. TRUE
3. FALSE
4. TRUE
5. FALSE
6. TRUE
7. three-dimensional
8. subtle patterns
9. factors / variables
10. transparency
11. B
12. B
13. C

PASSAGE 2: Questions 14-26

14. H
15. A
16. F
17. D
18. E
19. B
20. G
21. neural networks
22. non-linear relationships
23. clouds and convection / clouds
24. AI emulators / emulators
    25-26. B, D (in either order)

PASSAGE 3: Questions 27-40

27. B
28. B
29. C
30. B
31. C
32. mechanistic understanding
33. statistical regularities
34. opaque
35. synergistic integration
36. human understanding / scientific judgment
37. NO
38. YES
39. YES
40. NO

---

Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Climate scientists, traditionally, difficulty, processing data
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Bài đọc nói “the sheer volume of data available today far exceeds what human researchers can effectively handle” – khối lượng dữ liệu vượt quá khả năng xử lý của con người. Điều này đồng nghĩa với việc các nhà khoa học gặp khó khăn trong xử lý dữ liệu.

Câu 2: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI, identify patterns, faster than, human researchers
• Vị trí trong bài: Đoạn 2, dòng 4-6
• Giải thích: “Machine learning algorithms can analyse millions of data points in minutes, identifying patterns and correlations that might take humans years to discover”. Cụm “in minutes” vs “years” cho thấy AI nhanh hơn nhiều so với con người.

Câu 3: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: University of Oxford, AI system, more accurate, all types
• Vị trí trong bài: Đoạn 4, dòng 3-5
• Giải thích: Bài đọc nói hệ thống AI “can predict rainfall with greater accuracy than conventional methods, particularly for short-term forecasts of one to six hours ahead”. Từ “particularly” cho thấy độ chính xác cao hơn chỉ áp dụng cho dự báo ngắn hạn, không phải tất cả các loại dự báo thời tiết. Câu hỏi nói “all types” nên đáp án là FALSE.

Câu 4: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Climate models, interactions, different components, Earth’s systems
• Vị trí trong bài: Đoạn 5, dòng 2-4
• Giải thích: “these models involve countless variables and complex interactions between the atmosphere, oceans, ice sheets, and ecosystems” – mô hình khí hậu cần xem xét tương tác phức tạp giữa các thành phần của hệ thống Trái Đất.

Câu 5: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI-powered systems, analyse satellite images, after humans, identified areas
• Vị trí trong bài: Đoạn 6, dòng 3-6
• Giải thích: “AI-powered computer vision systems can automatically detect changes” – hệ thống AI tự động phát hiện thay đổi, không cần con người xác định trước vùng cần quan tâm. Câu hỏi nói “only after humans have identified” là sai.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: critics, AI research, contribute to climate change, energy consumption
• Vị trí trong bài: Đoạn 9, dòng 1-3
• Giải thích: “Some critics argue that the carbon footprint of AI research could partially offset the benefits it provides in fighting climate change” – một số nhà phê bình cho rằng lượng khí thải carbon từ nghiên cứu AI có thể triệt tiêu một phần lợi ích của nó.

Câu 7: three-dimensional

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Traditional weather prediction models, divide atmosphere
• Vị trí trong bài: Đoạn 3, dòng 2-3
• Giải thích: “These models divide the atmosphere into a three-dimensional grid”. Đáp án chính xác là “three-dimensional”.

Câu 8: subtle patterns

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Neural networks, identify, conventional forecasting methods, overlook
• Vị trí trong bài: Đoạn 4, dòng 2-3
• Giải thích: “neural networks can identify subtle patterns that traditional models miss”. Paraphrase: “miss” = “overlook”, “traditional models” = “conventional forecasting methods”.

Câu 9: factors / variables

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI, climate scientists, determine, most significant, long-term climate projections
• Vị trí trong bài: Đoạn 5, dòng 4-5
• Giải thích: “AI can help by identifying which factors are most important”. Có thể dùng “factors” hoặc “variables” (cũng được nhắc trong đoạn).

Câu 10: transparency

• Dạng câu hỏi: Sentence Completion
• Từ khóa: lack of, AI reaches conclusions, problematic, scientific research
• Vị trí trong bài: Đoạn 8, dòng 4-6
• Giải thích: “This lack of transparency can be problematic in scientific research”. Đáp án là “transparency”.

Câu 11: B

• Dạng câu hỏi: Multiple Choice
• Giải thích: Đoạn 2 và 4 nhấn mạnh AI có thể xử lý “millions of data points in minutes” và phát hiện patterns nhanh hơn. Đáp án B “It can process data more quickly than conventional approaches” là chính xác nhất.

Câu 12: B

• Dạng câu hỏi: Multiple Choice
• Giải thích: Đoạn 6 nói “Satellites collect terabytes of imagery every day” và “Analysing these images manually would be impossible”. Đáp án B “the volume of satellite imagery is too large for manual analysis” phản ánh đúng ý này.

Câu 13: C

• Dạng câu hỏi: Multiple Choice
• Giải thích: Tác giả thừa nhận AI có nhiều ứng dụng hữu ích (đoạn 3-7) nhưng cũng cảnh báo về hạn chế (đoạn 8-9): “AI is not a silver bullet”, có vấn đề về transparency và carbon footprint. Đáp án C “Cautiously optimistic but aware of limitations” phản ánh đúng thái độ cân bằng này.

Hướng dẫn kỹ thuật làm bài IELTS Reading dạng True False Not Given về trí tuệ nhân tạo và khí hậu

Passage 2 – Giải Thích

Câu 14: H

• Dạng câu hỏi: Matching Information
• Từ khóa: machine learning, identify human causes, climate change
• Giải thích: Đoạn H nói về “detection and attribution” – xác định liệu thay đổi khí hậu có do con người gây ra hay không, và việc ML có thể “identify the characteristic fingerprints of different climate forcing factors”.

Câu 15: A

• Dạng câu hỏi: Matching Information
• Từ khóa: traditional climate models, cannot simulate all processes, in detail
• Giải thích: Đoạn A giải thích rằng “No computer, regardless of power, can simulate all these processes simultaneously at full resolution” do quy mô từ tương tác phân tử đến tuần hoàn khí quyển toàn cầu.

Câu 16: F

• Dạng câu hỏi: Matching Information
• Từ khóa: concern, AI models, performing poorly, unprecedented situations
• Giải thích: Đoạn F nói về lo ngại rằng “AI-based models might produce accurate results when simulating familiar conditions but fail unpredictably when confronted with unprecedented scenarios”.

Câu 17: D

• Dạng câu hỏi: Matching Information
• Từ khóa: example, AI improved accuracy, temperature, precipitation predictions
• Giải thích: Đoạn D mô tả ví dụ cụ thể từ National Center for Atmospheric Research, nơi “the AI-based cloud parametrisation produced temperature and precipitation patterns that more closely matched real-world observations”.

Câu 18: E

• Dạng câu hỏi: Matching Information
• Từ khóa: AI, make climate simulations faster
• Giải thích: Đoạn E nói về “emulation” – việc AI có thể “run thousands of times faster than the original models”.

Câu 19: B

• Dạng câu hỏi: Matching Information
• Từ khóa: main limitation, traditional climate models, addressed, many years
• Giải thích: Đoạn B thảo luận về parametrisation và nói “improving them has been a major focus of climate science for years”.

Câu 20: G

• Dạng câu hỏi: Matching Information
• Từ khóa: combination approach, AI, physical principles
• Giải thích: Đoạn G mô tả “hybrid approaches that combine physics-based models with machine learning components”.

Câu 21: neural networks

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn C, “researchers can train neural networks on high-resolution simulations”
• Giải thích: Từ cần điền mô tả công cụ được training trên dữ liệu mô phỏng.

Câu 22: non-linear relationships

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn C, “captures complex, non-linear relationships between variables”
• Giải thích: AI có thể nắm bắt các mối quan hệ phi tuyến phức tạp.

Câu 23: clouds and convection / clouds

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn D, “improve the representation of clouds and convection in climate models”
• Giải thích: Ứng dụng thành công đặc biệt liên quan đến mây và đối lưu. Cả hai đáp án đều chấp nhận được.

Câu 24: AI emulators / emulators

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn E, “These AI emulators can run thousands of times faster”
• Giải thích: Công cụ chạy nhanh hơn được gọi là emulator.

Câu 25-26: B, D

• Dạng câu hỏi: Multiple Choice (chọn 2 đáp án)
• Giải thích:
  • B (đúng): Đoạn C và E nhấn mạnh AI “computationally cheaper” và có thể “run thousands of times faster”
  • D (đúng): Đoạn H nói ML có thể “identify the characteristic fingerprints of different climate forcing factors”
  • A (sai): Không có nơi nào nói AI loại bỏ tất cả uncertainty
  • C (sai): Passage nhấn mạnh cần kết hợp AI với hiểu biết vật lý, không thay thế hoàn toàn
  • E (sai): Passage thảo luận về hạn chế của AI trong dự báo, không đảm bảo độ chính xác cho mọi tình huống

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Vị trí: Đoạn 2
• Giải thích: Đoạn 2 giải thích rằng thách thức nhận thức chính là AI “identify statistical regularities” nhưng “the reasoning process remains largely opaque” và không thể giải thích dựa trên cơ chế vật lý. Câu hỏi “have we achieved genuine understanding, or merely predictive capability?” làm rõ vấn đề này.

Câu 28: B

• Dạng câu hỏi: Multiple Choice
• Vị trí: Đoạn 4
• Giải thích: “Extrapolation problem” được mô tả là thách thức khi “Earth’s climate is entering a state with no close analogue in the historical record” – khí hậu đang đi vào trạng thái chưa từng có. AI được training trên dữ liệu quá khứ có thể gặp khó khăn khi dự báo điều kiện chưa từng thấy.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Vị trí: Đoạn 5
• Giải thích: Đoạn 5 mô tả physics-informed neural networks là một trong những “methodological strategies” và “represent promising directions, though their effectiveness in genuinely unprecedented conditions remains to be fully validated” – đầy hứa hẹn nhưng chưa được kiểm chứng hoàn toàn.

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Vị trí: Đoạn 7
• Giải thích: Đoạn 7 định nghĩa “climate intelligence gap” là khi “well-resourced nations having access to superior forecasts while vulnerable populations rely on less accurate predictions” – bất bình đẳng về khả năng tiếp cận công cụ dự báo AI tiên tiến.

Câu 31: C

• Dạng câu hỏi: Multiple Choice
• Giải thích: Xuyên suốt bài, tác giả thừa nhận tiềm năng to lớn của AI (“transformative potential”, “promising directions”) nhưng cũng chỉ ra nhiều thách thức và lo ngại (epistemic challenges, equity concerns, algorithmic governance). Kết luận ở đoạn cuối nhấn mạnh cần “rigorous frameworks”, “democratic deliberation”, và “thoughtful” approach – phản ánh thái độ thận trọng và nhấn mạnh quản trị cẩn thận.

Câu 32: mechanistic understanding

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn 2, “Traditional scientific methodology emphasises mechanistic understanding”
• Giải thích: Phương pháp khoa học truyền thống nhấn mạnh hiểu biết về cơ chế.

Câu 33: statistical regularities

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn 2, “These systems identify statistical regularities in data”
• Giải thích: Deep learning hoạt động bằng cách nhận diện các quy luật thống kê.

Câu 34: opaque

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn 2, “the reasoning process remains largely opaque”
• Giải thích: Quá trình suy luận của AI vẫn mờ đục, không minh bạch.

Câu 35: synergistic integration

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn 11, “The most promising path forward likely involves a synergistic integration of human insight and machine intelligence”
• Giải thích: Cách tiếp cận hiệu quả nhất là sự tích hợp tương tác giữa con người và AI.

Câu 36: human understanding / scientific judgment

• Dạng câu hỏi: Summary Completion
• Vị trí: Đoạn 11, “relying on human understanding to interpret findings” hoặc “replacement for scientific judgment”
• Giải thích: Cả hai đáp án đều được nhắc đến như vai trò thiết yếu của con người trong việc diễn giải kết quả và đưa ra quyết định.

Câu 37: NO

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí: Đoạn 9, câu cuối
• Giải thích: Bài viết nói “proponents counter that the insights gained from AI-assisted research will ultimately lead to much greater reductions in emissions than the energy used to develop the technology” – lợi ích lớn hơn chi phí năng lượng. Câu hỏi nói ngược lại nên đáp án là NO.

Câu 38: YES

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí: Đoạn 8
• Giải thích: “The proprietary nature of some AI systems means that key aspects of their operation may be opaque to independent scrutiny” – tính chất độc quyền của một số hệ thống AI khiến chúng không thể được kiểm tra độc lập, ngụ ý rằng các công ty tư nhân kiểm soát chúng.

Câu 39: YES

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí: Đoạn 9
• Giải thích: “There is a risk that policymakers and the public may accord excessive confidence to AI-generated climate projections, underestimating their uncertainties and limitations” – đúng với ý câu hỏi rằng các nhà hoạch định chính sách có thể tin tưởng quá mức.

Câu 40: NO

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí: Đoạn 11
• Giải thích: Bài viết rõ ràng nói “AI should be viewed as a powerful tool that augments human expertise rather than as a replacement for scientific judgment” – AI là công cụ hỗ trợ chứ không thay thế nhà khoa học.

---

Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
pressing	adj	/ˈpresɪŋ/	cấp bách, cấp thiết	pressing challenges	pressing issue, pressing need, pressing concern
revolutionise	v	/ˌrevəˈluːʃənaɪz/	cách mạng hóa	revolutionising the way researchers collect data	revolutionise the industry, revolutionise thinking
vast	adj	/vɑːst/	rộng lớn, khổng lồ	vast amounts of information	vast majority, vast expanse, vast experience
sheer	adj	/ʃɪə(r)/	hoàn toàn, thuần túy (nhấn mạnh quy mô)	the sheer volume of data	sheer size, sheer number, sheer force
correlation	n	/ˌkɒrəˈleɪʃn/	sự tương quan	identifying patterns and correlations	positive correlation, strong correlation, correlation between
localised	adj	/ˈləʊkəlaɪzd/	cục bộ, địa phương	localised weather phenomena	localised flooding, localised outbreak
precision	n	/prɪˈsɪʒn/	độ chính xác	improved precision	with precision, precision instruments, surgical precision
scenario	n	/səˈnɑːriəʊ/	kịch bản, tình huống	scenarios of greenhouse gas emissions	worst-case scenario, plausible scenario, scenario planning
countless	adj	/ˈkaʊntləs/	vô số, không đếm được	countless variables	countless hours, countless opportunities
deforestation	n	/ˌdiːˌfɒrɪˈsteɪʃn/	sự tàn phá rừng	deforestation in the Amazon	illegal deforestation, rampant deforestation
silver bullet	n	/ˌsɪlvə ˈbʊlɪt/	giải pháp hoàn hảo (thành ngữ)	AI is not a silver bullet	no silver bullet, search for a silver bullet
perpetuate	v	/pəˈpetʃueɪt/	duy trì, kéo dài (thường mang nghĩa tiêu cực)	the AI will perpetuate these problems	perpetuate stereotypes, perpetuate inequality

Passage 2 – 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 tư duy	represents a paradigm shift	undergo a paradigm shift, trigger a paradigm shift
cornerstone	n	/ˈkɔːnəstəʊn/	nền tảng, trụ cột	the cornerstone of climate science	cornerstone of democracy, cornerstone policy
molecular	adj	/məˈlekjələ(r)/	thuộc phân tử	molecular interactions	molecular structure, molecular level, molecular biology
parametrise	v	/pəˈræmɪtraɪz/	tham số hóa	models must parametrise smaller-scale processes	parametrise the system, parametrise variables
parametrisation	n	/pəˌræmɪtraɪˈzeɪʃn/	sự tham số hóa	parametrisation schemes	cloud parametrisation, model parametrisation
uncertainty	n	/ʌnˈsɜːtnti/	sự không chắc chắn	introduce uncertainties	scientific uncertainty, uncertainty about, reduce uncertainty
non-linear	adj	/nɒn ˈlɪniə(r)/	phi tuyến tính	non-linear relationships	non-linear dynamics, non-linear equation
remarkable	adj	/rɪˈmɑːkəbl/	đáng chú ý, xuất sắc	with remarkable accuracy	remarkable achievement, remarkable success
accelerate	v	/əkˈseləreɪt/	tăng tốc, đẩy nhanh	accelerate climate simulations	accelerate growth, accelerate the process
emulation	n	/ˌemjuˈleɪʃn/	sự mô phỏng, bắt chước	through emulation	emulation of nature, in emulation of
controversy	n	/ˈkɒntrəvɜːsi/	tranh cãi, bất đồng	not without controversy	spark controversy, generate controversy, subject of controversy
tipping point	n	/ˈtɪpɪŋ pɔɪnt/	điểm bùng phát, ngưỡng quan trọng	critical tipping points	reach a tipping point, climate tipping point
hybrid	adj	/ˈhaɪbrɪd/	lai, kết hợp	hybrid approaches	hybrid model, hybrid system, hybrid vehicle
detection	n	/dɪˈtekʃn/	sự phát hiện	detection and attribution	early detection, detection system, fraud detection
attribution	n	/ˌætrɪˈbjuːʃn/	sự quy cho, xác định nguyên nhân	attribution science	attribution of blame, attribution analysis

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
incremental	adj	/ˌɪŋkrəˈmentl/	tăng dần, từng bước	incremental technological advancement	incremental change, incremental improvement, incremental progress
transformative	adj	/trænsˈfɔːmətɪv/	mang tính chuyển đổi	transformative potential	transformative power, transformative impact, transformative experience
epistemological	adj	/ɪˌpɪstəməˈlɒdʒɪkl/	thuộc nhận thức luận	epistemological shift	epistemological framework, epistemological question
disruptive	adj	/dɪsˈrʌptɪv/	mang tính đột phá, gây gián đoạn	disruptive technologies	disruptive innovation, disruptive change
constellation	n	/ˌkɒnstəˈleɪʃn/	chòm sao (ở đây nghĩa bóng: tập hợp)	constellation of considerations	constellation of factors, constellation of issues
epistemic	adj	/ˌepɪˈstiːmɪk/	thuộc tri thức, nhận thức	epistemic challenges	epistemic authority, epistemic value, epistemic community
profound	adj	/prəˈfaʊnd/	sâu sắc, nghiêm trọng	particularly profound	profound impact, profound effect, profound change
mechanistic	adj	/ˌmekəˈnɪstɪk/	theo cơ chế, máy móc	mechanistic understanding	mechanistic approach, mechanistic view
bedrock	n	/ˈbedrɒk/	nền tảng căn bản	the bedrock of climate science	bedrock of society, bedrock principle
incarnation	n	/ˌɪnkɑːˈneɪʃn/	hiện thân, phiên bản	deep learning incarnations	previous incarnation, modern incarnation
opaque	adj	/əʊˈpeɪk/	mờ đục, không minh bạch	reasoning process remains opaque	opaque system, deliberately opaque
tangible	adj	/ˈtændʒəbl/	hữu hình, rõ ràng	tangible implications	tangible benefits, tangible results, tangible evidence
devise	v	/dɪˈvaɪz/	nghĩ ra, đưa ra	devise novel interventions	devise a plan, devise a strategy, devise a solution
unprecedented	adj	/ʌnˈpresɪdentɪd/	chưa từng có	unprecedented conditions	unprecedented scale, unprecedented level, unprecedented crisis
geologically	adv	/ˌdʒiːəˈlɒdʒɪkli/	về mặt địa chất	geologically unprecedented	geologically speaking, geologically recent
extrapolation	n	/ɪkˌstræpəˈleɪʃn/	sự ngoại suy	extrapolation problem	extrapolation from data, linear extrapolation
acute	adj	/əˈkjuːt/	nghiêm trọng, gay gắt	particularly acute	acute shortage, acute problem, acute awareness
validate	v	/ˈvælɪdeɪt/	xác thực, kiểm chứng	remains to be fully validated	validate a theory, validate findings, validate assumptions
geopolitical	adj	/ˌdʒiːəʊpəˈlɪtɪkl/	thuộc địa chính trị	geopolitical implications	geopolitical tensions, geopolitical landscape
disparity	n	/dɪˈspærəti/	sự chênh lệch, bất bình đẳng	technological disparity	income disparity, wealth disparity, disparity between
intentional	adj	/ɪnˈtenʃənl/	cố ý, có chủ đích	intentional efforts	intentional act, intentional harm
algorithmic	adj	/ˌælɡəˈrɪðmɪk/	thuộc thuật toán	algorithmic governance	algorithmic bias, algorithmic trading, algorithmic decision-making
epistemic authority	n phrase	/ˌepɪˈstiːmɪk ɔːˈθɒrəti/	quyền lực tri thức	epistemic authority	claim epistemic authority, challenge epistemic authority
proprietary	adj	/prəˈpraɪətri/	độc quyền, thuộc sở hữu riêng	proprietary nature	proprietary information, proprietary technology, proprietary software
scrutiny	n	/ˈskruːtəni/	sự xem xét kỹ lưỡng	independent scrutiny	public scrutiny, close scrutiny, under scrutiny
complacency	n	/kəmˈpleɪsnsi/	sự tự mãn	epistemic complacency	sense of complacency, avoid complacency, breed complacency
uncritical	adj	/ʌnˈkrɪtɪkl/	thiếu phản biện, chấp nhận mù quáng	uncritical faith	uncritical acceptance, uncritical approach
catastrophic	adj	/ˌkætəˈstrɒfɪk/	thảm khốc	catastrophic consequences	catastrophic failure, catastrophic event, catastrophic loss
harness	v	/ˈhɑːnɪs/	khai thác, tận dụng	harness the potential	harness energy, harness technology, harness resources
multi-faceted	adj	/ˌmʌlti ˈfæsɪtɪd/	nhiều mặt, đa chiều	multi-faceted approach	multi-faceted problem, multi-faceted strategy
synergistic	adj	/ˌsɪnəˈdʒɪstɪk/	có tính cộng hưởng	synergistic integration	synergistic effect, synergistic relationship
algorithmically-mediated	adj phrase	/ˌælɡəˈrɪðmɪkli ˈmiːdieɪtɪd/	được trung gian bởi thuật toán	algorithmically-mediated understanding	algorithmically-mediated experience, algorithmically-mediated interaction
opacity	n	/əʊˈpæsəti/	sự mờ đục, không rõ ràng	opacity in decision-making	opacity of the system, transparency vs opacity

Bảng từ vựng IELTS Reading chủ đề trí tuệ nhân tạo và biến đổi khí hậu với phiên âm và ví dụ

Kết Bài

Qua bài thi IELTS Reading mẫu về chủ đề “What are the implications of AI in climate change research?”, bạn đã được luyện tập với một đề thi hoàn chỉnh bao gồm 3 passages với độ khó tăng dần từ band 5.0-6.5 đến 7.0-9.0. Chủ đề này không chỉ xuất hiện thường xuyên trong các đề thi IELTS gần đây mà còn phản ánh xu hướng toàn cầu về công nghệ và môi trường.

Passage 1 giúp bạn làm quen với các ứng dụng cơ bản của AI trong nghiên cứu khí hậu, với ngôn ngữ dễ tiếp cận và cấu trúc câu đơn giản. Passage 2 đi sâu vào các kỹ thuật machine learning cụ thể như parametrisation và emulation, yêu cầu khả năng hiểu paraphrase và suy luận cao hơn. Passage 3 thách thức bạn với các khái niệm triết học khoa học và những vấn đề đạo đức phức tạp, đòi hỏi kỹ năng phân tích văn bản học thuật ở trình độ cao.

Đáp án chi tiết kèm giải thích về vị trí thông tin và kỹ thuật paraphrase sẽ giúp bạn hiểu rõ tại sao một đáp án đúng và cách nhận diện nó trong bài đọc. Đây là kỹ năng thiết yếu để đạt band điểm cao trong IELTS Reading. Hơn 40 từ vựng quan trọng được phân loại theo passage với phiên âm, nghĩa tiếng Việt, ví dụ thực tế và collocation sẽ giúp bạn xây dựng vốn từ học thuật vững chắc.

Hãy luyện tập đề thi này nhiều lần, chú ý đến thời gian làm bài và phân tích kỹ những câu trả lời sai để cải thiện kỹ năng. Chúc bạn đạt kết quả cao trong kỳ thi IELTS sắp tới!