IELTS Reading: AI Bảo Tồn Động Vật Hoang Dã – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở bài

Chủ đề về công nghệ trí tuệ nhân tạo (AI) trong bảo tồn động vật hoang dã đang trở thành một trong những đề tài phổ biến trong kỳ thi IELTS Reading những năm gần đây. Với sự phát triển vượt bậc của công nghệ và nhu cầu cấp thiết về bảo vệ môi trường, chủ đề “How Is AI Being Used In Wildlife Conservation?” xuất hiện với tần suất ngày càng cao trong các đề thi thực tế từ Cambridge IELTS series 14 trở đi.

Bài viết này cung cấp cho bạn một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages theo đúng format thi thật, bao gồm 40 câu hỏi đa dạng từ dễ đến khó. Bạn sẽ được luyện tập với các dạng câu hỏi như Multiple Choice, True/False/Not Given, Matching Information, Summary Completion và nhiều dạng khác. Đặc biệt, mỗi câu hỏi đều có đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin, cách paraphrase và kỹ thuật làm bài hiệu quả.

Đề thi này phù hợp cho học viên có trình độ từ band 5.0 trở lên, đặc biệt hữu ích cho những bạn đang nhắm đến band điểm 6.5-8.0. Thông qua việc luyện tập với đề thi này, bạn không chỉ làm quen với chủ đề công nghệ-môi trường mà còn nắm vững chiến lược làm bài Reading một cách bài bản và khoa học.

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 bị trừ điểm khi sai. Độ khó của các passages tăng dần từ Passage 1 đến Passage 3.

Phân bổ thời gian khuyến nghị:

• Passage 1 (Easy): 15-17 phút – Dành cho việc làm quen với đề thi, nội dung tương đối dễ hiểu
• Passage 2 (Medium): 18-20 phút – Yêu cầu kỹ năng đọc hiểu và phân tích cao hơn
• Passage 3 (Hard): 23-25 phút – Nội dung học thuật, từ vựng chuyên sâu, cần thời gian suy luận

Lưu ý quan trọng: Bạn nên dành 2-3 phút cuối để chuyển đáp án vào Answer Sheet, đảm bảo không mắc lỗi chính tả hoặc ghi sai vị trí.

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 – Câu hỏi trắc nghiệm nhiều lựa chọn
2. True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập
3. Matching Information – Nối thông tin với đoạn văn tương ứng
4. Sentence Completion – Hoàn thành câu với thông tin từ bài đọc
5. Matching Headings – Nối tiêu đề với đoạn văn phù hợp
6. Summary Completion – Điền từ vào đoạn tóm tắt
7. Short-answer Questions – Trả lời ngắn theo yêu cầu

Mỗi dạng câu hỏi đòi hỏi kỹ năng đọc và chiến lược làm bài khác nhau, vì vậy việc làm quen với tất cả các dạng này là vô cùng quan trọng.

2. IELTS Reading Practice Test

PASSAGE 1 – Artificial Intelligence Meets Wildlife Protection

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The world of wildlife conservation is undergoing a remarkable transformation thanks to artificial intelligence (AI). For decades, conservationists have relied on traditional methods such as manual tracking, physical surveys, and direct observation to monitor animal populations and protect endangered species. However, these approaches are often time-consuming, labour-intensive, and limited in scope. The introduction of AI technology has opened up new possibilities that were previously unimaginable, allowing researchers to collect and analyze vast amounts of data with unprecedented speed and accuracy.

One of the most significant applications of AI in wildlife conservation is in camera trap analysis. Camera traps are motion-activated cameras placed in natural habitats to capture images of passing animals. Traditionally, researchers had to manually review thousands of photographs to identify species, count individuals, and track animal behaviour. This process could take months or even years. Today, AI-powered image recognition software can automatically identify different species in photographs with over 95% accuracy in many cases. The technology uses machine learning algorithms that have been trained on millions of wildlife images, enabling them to distinguish between similar-looking species and even identify individual animals based on unique markings.

AI is also revolutionizing the fight against illegal poaching. In many African national parks, conservation teams are using predictive analytics to anticipate where poachers are most likely to strike next. By analyzing historical poaching data, weather patterns, moon phases, and other variables, AI systems can generate heat maps showing high-risk areas. This allows park rangers to deploy their limited resources more effectively, positioning patrols in locations where they are most needed. Some parks have reported up to a 70% reduction in poaching incidents after implementing these AI-driven patrol strategies.

Camera bẫy AI tự động nhận diện động vật hoang dã trong môi trường tự nhiên

Another breakthrough application involves using AI to analyze acoustic data. Many endangered species, particularly birds and marine mammals, can be monitored through their vocalizations. Traditional methods required trained specialists to listen to hours of audio recordings to identify species calls. Now, AI-powered acoustic monitoring systems can automatically detect and classify animal sounds in real-time. These systems are particularly valuable in dense rainforests or underwater environments where visual observation is difficult. For example, researchers studying whale populations can now deploy underwater microphones connected to AI systems that can identify individual whales by their unique songs and track their migration patterns across vast ocean areas.

Drones equipped with AI technology are providing conservationists with powerful new tools for monitoring wildlife from above. These unmanned aerial vehicles (UAVs) can cover large areas quickly and access remote locations that would be difficult or dangerous for humans to reach. AI algorithms process the aerial footage in real-time, counting animals, detecting changes in habitat, and even identifying signs of disease in populations. In some projects, thermal imaging cameras on drones are used to locate animals at night, with AI software distinguishing between different species based on their heat signatures. This technology has proven especially useful for counting large herds of elephants or monitoring orangutan populations in dense forest canopies.

The use of AI in wildlife conservation extends to tracking animal movements on a global scale. Satellite imagery combined with machine learning can monitor habitat changes, deforestation rates, and the impact of climate change on ecosystems. Conservation organizations are using these insights to make data-driven decisions about where to focus their protection efforts and how to create wildlife corridors that connect fragmented habitats. Some AI systems can even predict how animal ranges might shift in response to climate change, helping conservationists plan for future challenges.

Despite these impressive advances, experts emphasize that AI is a tool to augment human efforts, not replace them. The technology works best when combined with traditional field expertise and local knowledge. Conservation remains a deeply human endeavor that requires passion, dedication, and an understanding of complex ecological relationships that no algorithm can fully capture. However, as AI technology continues to improve and become more accessible, it is clear that it will play an increasingly vital role in the race to protect Earth’s biodiversity before it is too late.

Questions 1-13

Questions 1-5: Multiple Choice

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

1. According to the passage, traditional wildlife conservation methods were:
   A) More accurate than modern techniques
   B) Time-consuming and limited in coverage
   C) Preferred by most researchers
   D) Impossible to implement properly

2. AI-powered image recognition software can:
   A) Replace all human researchers
   B) Only work in perfect lighting conditions
   C) Identify species with over 95% accuracy
   D) Only recognize common animals

3. Predictive analytics in anti-poaching efforts helps by:
   A) Catching poachers automatically
   B) Creating maps of high-risk poaching areas
   C) Eliminating poaching completely
   D) Training more park rangers

4. Acoustic monitoring systems are particularly useful in:
   A) Open grasslands
   B) Urban environments
   C) Dense rainforests and underwater settings
   D) Desert regions

5. According to the passage, AI in conservation should:
   A) Completely replace human conservationists
   B) Work alongside traditional expertise
   C) Only be used for data collection
   D) Be avoided due to complexity

Questions 6-9: True/False/Not Given

Do the following statements agree with the information given in the passage?

Write:

• TRUE if the statement agrees with the information
• FALSE if the statement contradicts the information
• NOT GIVEN if there is no information on this

6. Manual review of camera trap photographs used to take several months or years.
7. All African national parks have implemented AI-driven patrol strategies.
8. AI systems can identify individual whales by their unique songs.
9. Drones with AI technology are cheaper than traditional monitoring methods.

Questions 10-13: Sentence Completion

Complete the sentences below.

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

10. Camera traps are activated by __ to photograph animals in their habitats.
11. Some parks have seen a __ decrease in poaching after using AI patrol strategies.
12. Drones with thermal imaging can identify species by their __.
13. Satellite imagery helps monitor the effects of __ on ecosystems.

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PASSAGE 2 – The Technical Revolution in Species Monitoring

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The integration of artificial intelligence into wildlife conservation has ushered in what many experts consider a paradigm shift in how we understand and protect biodiversity. This technological revolution is not merely about automating existing processes; rather, it represents a fundamental reimagining of conservation methodology, enabling researchers to tackle problems that were previously considered intractable due to the sheer scale and complexity of the data involved.

A At the forefront of this transformation is the application of deep learning neural networks to species identification tasks. Unlike traditional computer vision approaches that rely on hand-crafted features, deep learning systems can automatically learn to recognize distinguishing characteristics from raw data. Convolutional Neural Networks (CNNs), a specific type of deep learning architecture particularly well-suited for image analysis, have achieved remarkable success in wildlife identification challenges. These networks consist of multiple layers of processing units that progressively extract increasingly abstract features from images—from simple edges and textures in early layers to complex patterns like stripes, spots, or facial structures in deeper layers. The most advanced systems can now identify not only species but also individuals within populations, age categories, and even behavioral states from single photographs.

B The implications of this capability extend far beyond simple census-taking. Individual identification enables researchers to track life histories, monitor breeding success, understand social dynamics, and assess population health with unprecedented granularity. For instance, projects focusing on endangered chimpanzee communities have utilized facial recognition AI trained specifically on primate features to identify and track individuals across years of camera trap footage. This has revealed previously unknown information about territorial behaviors, family group compositions, and genetic diversity within populations. Such detailed, long-term data would be virtually impossible to obtain through traditional observation methods, which are constrained by human presence potentially altering animal behavior and the practical limitations of maintaining continuous field observations.

C Another groundbreaking application involves the use of AI in analyzing satellite and drone imagery for habitat monitoring. Deforestation, habitat fragmentation, and land-use changes are among the most significant threats to wildlife worldwide, yet tracking these changes across vast geographic areas has historically been challenging. Modern AI systems can process satellite images covering millions of square kilometers, automatically detecting changes in vegetation cover, identifying illegal logging activities, and mapping the expansion of human settlements into wildlife areas. Some systems employ temporal analysis, comparing images taken at different times to identify subtle changes that might indicate ecosystem degradation before it becomes irreversible. This early warning capability is crucial for proactive conservation, allowing interventions before critical thresholds are crossed.

Vệ tinh và AI giám sát phá rừng bảo vệ môi trương sống động vật hoang dã

D The marriage of AI with bioacoustic monitoring has opened particularly exciting possibilities for studying elusive or nocturnal species. Many animals are more easily heard than seen, and their vocalizations carry rich information about species presence, population density, and behavioral patterns. However, the volume of audio data generated by continuous recording devices deployed in the field is staggering—a single monitoring station might produce thousands of hours of recordings annually. AI-powered audio classification systems can process this data far more rapidly than human analysts, identifying not just species calls but also detecting vocalization patterns that might indicate stress, mating behavior, or territorial disputes. Advanced systems incorporate natural language processing techniques adapted for animal communication, potentially uncovering previously unrecognized patterns in how different species use sound to communicate.

E Perhaps the most sophisticated application of AI in conservation involves predictive modeling for proactive threat mitigation. These systems integrate diverse data streams—including satellite imagery, weather data, human activity patterns, and historical wildlife observations—to forecast future scenarios. Machine learning algorithms identify correlations and patterns that human analysts might miss, generating predictions about where human-wildlife conflicts are likely to occur, which populations face the greatest risk, and what interventions might prove most effective. Some conservation organizations are using ensemble models that combine multiple AI approaches to improve prediction accuracy, acknowledging that no single algorithm performs optimally under all conditions.

F However, the implementation of AI in conservation is not without challenges. The technology requires substantial computational resources, technical expertise, and high-quality training data, which may be scarce for rare or poorly studied species. There are also concerns about the interpretability of AI decisions—understanding why a system made a particular classification or prediction can be difficult with complex deep learning models, yet this understanding is crucial for making conservation decisions that affect ecosystems and human communities. Furthermore, there is a risk that over-reliance on technology might lead to devaluing traditional ecological knowledge and the nuanced understanding that comes from long-term field experience.

G Despite these challenges, the trajectory is clear: AI is becoming an indispensable tool in the conservation toolkit. As algorithms improve, computational costs decrease, and more conservation professionals gain technical skills, the technology’s impact will only grow. The key to maximizing this potential lies in maintaining a balanced approach that leverages AI’s analytical power while preserving the irreplaceable elements of conservation work—the fieldwork, the local partnerships, and the human commitment to protecting the natural world for future generations.

Questions 14-26

Questions 14-18: Matching Headings

The passage has seven paragraphs, A-G.

Choose the correct heading for each paragraph from the list of headings below.

List of Headings:
i. The challenges of implementing AI technology in conservation
ii. Using sound analysis to study hidden wildlife
iii. The future balance between technology and traditional methods
iv. How deep learning identifies individual animals
v. Predicting conservation threats before they occur
vi. Monitoring habitat destruction from space
vii. The fundamental change AI brings to conservation
viii. Training requirements for conservation AI systems
ix. Individual tracking reveals population secrets

14. Paragraph A
15. Paragraph B
16. Paragraph C
17. Paragraph D
18. Paragraph E

Questions 19-23: Yes/No/Not Given

Do the following statements agree with the views of the writer in the passage?

Write:

• YES if the statement agrees with the 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

19. Deep learning represents merely an automation of existing conservation processes.
20. Facial recognition AI has revealed new information about chimpanzee social structures.
21. Satellite monitoring systems are only effective after significant habitat damage has occurred.
22. AI audio systems can potentially decode complex animal communication patterns.
23. All conservation organizations should immediately adopt AI technology.

Questions 24-26: Summary Completion

Complete the summary below.

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

AI applications in conservation face several challenges. The technology demands significant 24 __ and specialized knowledge to operate effectively. Another concern is the 25 __ of AI decisions, as understanding the reasoning behind complex models can be difficult. There is also a risk that excessive focus on technology might lead to undervaluing 26 __ and the insights gained from extended field experience.

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PASSAGE 3 – Ethical Dimensions and Future Trajectories of AI-Driven Conservation

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The accelerating adoption of artificial intelligence in wildlife conservation has precipitated a complex discourse surrounding not only its technical efficacy but also its epistemological implications, ethical ramifications, and potential to fundamentally reconfigure the human-nature relationship. While the pragmatic benefits of AI—enhanced monitoring capabilities, improved analytical precision, and operational efficiency gains—are increasingly well-documented, the more subtle and profound impacts of this technological integration warrant careful scholarly attention. The deployment of AI systems in conservation contexts raises questions that extend beyond mere technical optimization, touching upon issues of environmental governance, data sovereignty, algorithmic accountability, and the very nature of conservation practice in an increasingly digitized world.

One particularly salient concern revolves around what scholars have termed “technological solutionism“—the tendency to frame complex socio-ecological challenges primarily as technical problems amenable to technological fixes. Wildlife conservation is inherently multifaceted, involving intricate webs of ecological processes, socioeconomic factors, cultural dimensions, and political dynamics. The appeal of AI lies partly in its promise to render these messy complexities more tractable through quantification, pattern recognition, and prediction. However, critics caution that an overemphasis on technical solutions may obscure the root causes of biodiversity loss, which often stem from systemic issues such as unsustainable economic models, consumption patterns, and inequitable resource distribution. There is a risk that AI could facilitate a form of “precision conservation” that optimizes interventions within existing paradigms without challenging the underlying structural factors driving ecological degradation.

The question of data governance presents another layer of complexity. AI systems are fundamentally data-dependent, requiring vast quantities of information for training and operation. In conservation contexts, this data often pertains to the locations and behaviors of endangered species, raising significant security concerns. Poaching syndicates and wildlife traffickers are increasingly sophisticated, and the potential for sensitive wildlife data to be compromised or misused is not merely hypothetical. Several high-profile cases have demonstrated how geotagged photographs and location data shared on social media or in academic publications have been exploited by those seeking to harm wildlife. The centralization of wildlife data in AI systems potentially creates single points of vulnerability that could be targeted by malicious actors. Moreover, questions arise regarding data ownership and access rights, particularly when conservation projects operate across jurisdictions with different legal frameworks or involve indigenous communities whose traditional knowledge contributes to the datasets being used.

Hệ thống bảo mật dữ liệu AI bảo vệ thông tin động vật hoang dã khỏi săn trộm

The algorithmic accountability challenge is particularly acute in conservation because the stakes are exceptionally high. When AI systems inform decisions about resource allocation, habitat management, or threat response, errors or biases can have irreversible consequences for endangered species and ecosystems. Yet many advanced AI models, particularly deep neural networks, function as “black boxes,” producing accurate predictions without providing transparent reasoning. This opacity is problematic from both practical and ethical standpoints. Conservation practitioners need to understand why a system recommends a particular action to assess its appropriateness in context and to maintain professional judgment. Furthermore, AI systems can perpetuate and amplify biases present in training data. If certain species, regions, or conservation challenges are underrepresented in datasets—often the case for less charismatic species or under-resourced regions—AI models may perform poorly for precisely those entities most in need of conservation attention, potentially exacerbating existing conservation inequities.

The integration of AI into conservation also prompts reflection on the ontological dimensions of conservation practice and the human relationship with nature. Traditional conservation has often emphasized direct encounter and embodied knowledge—the insights that come from spending time in wild places, observing animals in their habitats, and developing what might be called an intuitive understanding of ecological systems. There is concern that mediation of conservation through AI interfaces and data visualizations might foster a more detached, abstract relationship with nature, where wildlife becomes primarily datasets and algorithms rather than living beings deserving of respect and care in their own right. Some philosophers of technology argue that different modes of technological engagement with nature cultivate different ethical sensibilities, and that an overly technocratic approach might inadvertently undermine the affective connections and sense of moral responsibility that motivate conservation efforts.

Conversely, proponents argue that AI could actually democratize conservation and foster new forms of connection between humans and wildlife. Citizen science projects incorporating AI enable members of the public to contribute meaningfully to conservation research through activities like classifying camera trap images or identifying species in photographs. These participatory platforms have engaged millions of people worldwide, creating what researchers describe as “virtual fieldwork” that connects urban populations to distant ecosystems. AI-powered apps that identify species from photographs are making biodiversity accessible to curious individuals who lack formal training, potentially cultivating what theorist Richard Louv terms “nature literacy” in increasingly urbanized populations. From this perspective, AI serves not as a replacement for direct experience but as a gateway that might ultimately inspire deeper engagement with the natural world.

Looking forward, the trajectory of AI in conservation will likely be shaped by several emerging trends. The development of edge computing capabilities allowing AI processing on field devices without constant internet connectivity could make the technology more accessible in remote areas with limited infrastructure. Federated learning approaches, where AI models are trained across distributed datasets without centralizing sensitive information, may help address data security concerns. There is also growing interest in “explainable AI” techniques that provide more transparent reasoning, potentially addressing accountability challenges. Perhaps most significantly, there is increasing recognition of the need for interdisciplinary collaboration that brings together computer scientists, ecologists, social scientists, ethicists, and local communities to ensure AI development is guided by ecological understanding, social justice principles, and ethical considerations rather than technical possibilities alone.

The ultimate question may not be whether AI should be used in conservation, but rather how it can be deployed in ways that augment human capabilities without displacing human wisdom, that empower local communities rather than marginalizing traditional knowledge, and that serve the intrinsic value of biodiversity rather than merely optimizing conservation efficiency. As with any powerful technology, AI’s impact on conservation will depend not on the technology itself but on the values, institutions, and decision-making frameworks that guide its application. The challenge for the conservation community is to harness AI’s potential while remaining vigilant about its limitations and attentive to its broader implications for how humanity relates to the rest of the living world.

Questions 27-40

Questions 27-31: Multiple Choice

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

27. According to the passage, “technological solutionism” refers to:
    A) Using the most advanced technology available
    B) Treating complex issues as purely technical problems
    C) Developing new solutions for conservation
    D) Replacing human workers with machines

28. The author suggests that data security concerns in conservation are:
    A) Purely theoretical with no real-world examples
    B) Only relevant for certain species
    C) Demonstrated by actual incidents of data misuse
    D) Easily solved through encryption

29. The “black box” problem with AI systems means:
    A) They are physically inaccessible
    B) They only work in darkness
    C) Their reasoning process is not transparent
    D) They contain secret information

30. According to the passage, traditional conservation emphasizes:
    A) Data collection and analysis
    B) Direct experience and embodied knowledge
    C) Technological innovation
    D) Remote monitoring systems

31. The author’s overall position on AI in conservation can best be described as:
    A) Completely opposed due to ethical concerns
    B) Enthusiastically supportive without reservations
    C) Cautiously optimistic with awareness of complexities
    D) Neutral and purely descriptive

Questions 32-36: Matching Features

Match each concept with the correct description.

Choose the correct letter, A-H.

Concepts:
32. Edge computing
33. Federated learning
34. Explainable AI
35. Citizen science projects
36. Nature literacy

Descriptions:
A. Training AI across distributed datasets without centralizing information
B. Processing data on local devices without internet requirements
C. Public participation in conservation research using technology
D. Providing transparent reasoning for AI decisions
E. Understanding ecosystems through direct observation only
F. Securing wildlife data through military-grade encryption
G. Knowledge about biodiversity accessible to non-experts
H. Replacing field researchers with automated systems

Questions 37-40: Short-answer Questions

Answer the questions below.

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

37. What might an overemphasis on AI solutions obscure in conservation?
38. What do critics worry AI-mediated conservation might undermine between humans and nature?
39. What type of knowledge might be marginalized if AI is not deployed carefully?
40. According to the passage, AI’s conservation impact depends on the values and what else that guide its use?

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. B
6. TRUE
7. NOT GIVEN
8. TRUE
9. NOT GIVEN
10. motion
11. 70% / seventy percent
12. heat signatures
13. climate change

PASSAGE 2: Questions 14-26

14. iv
15. ix
16. vi
17. ii
18. v
19. NO
20. YES
21. NO
22. YES
23. NOT GIVEN
24. computational resources
25. interpretability
26. traditional (ecological) knowledge

PASSAGE 3: Questions 27-40

27. B
28. C
29. C
30. B
31. C
32. B
33. A
34. D
35. C
36. G
37. root causes
38. affective connections
39. traditional knowledge
40. decision-making frameworks

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: traditional wildlife conservation methods
• Vị trí trong bài: Đoạn 1, dòng 3-5
• Giải thích: Bài đọc nêu rõ “these approaches are often time-consuming, labour-intensive, and limited in scope” – các phương pháp truyền thống tốn thời gian và có phạm vi hạn chế. Đây là paraphrase của đáp án B “Time-consuming and limited in coverage”.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI-powered image recognition software
• Vị trí trong bài: Đoạn 2, dòng 5-6
• Giải thích: Văn bản đề cập “can automatically identify different species in photographs with over 95% accuracy” – phần mềm có thể nhận diện với độ chính xác trên 95%, đúng với đáp án C.

Câu 3: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Predictive analytics, anti-poaching
• Vị trí trong bài: Đoạn 3, dòng 3-5
• Giải thích: Bài viết nói “AI systems can generate heat maps showing high-risk areas” – tạo ra bản đồ nhiệt về các khu vực nguy cơ cao, tương ứng với đáp án B.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Manual review, camera trap photographs, months or years
• Vị trí trong bài: Đoạn 2, dòng 3-4
• Giải thích: “This process could take months or even years” khớp chính xác với thông tin trong câu hỏi.

Câu 7: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: All African national parks, AI-driven patrol strategies
• Vị trí trong bài: Đoạn 3
• Giải thích: Bài chỉ nói “In many African national parks” chứ không phải tất cả, và không có thông tin xác nhận tất cả đã áp dụng.

Câu 10: motion

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Camera traps, activated
• Vị trí trong bài: Đoạn 2, dòng 1-2
• Giải thích: “Camera traps are motion-activated cameras” – camera bẫy được kích hoạt bởi chuyển động.

Câu 13: climate change

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Satellite imagery, monitor effects
• Vị trí trong bài: Đoạn 6, dòng 2-3
• Giải thích: “monitor… the impact of climate change on ecosystems” – theo dõi tác động của biến đổi khí hậu.

Passage 2 – Giải Thích

Câu 14: iv (How deep learning identifies individual animals)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Paragraph A
• Giải thích: Đoạn văn tập trung vào việc giải thích cách deep learning neural networks và CNNs hoạt động để nhận diện đặc điểm của động vật từ hình ảnh, từ các đặc điểm đơn giản đến phức tạp.

Câu 15: ix (Individual tracking reveals population secrets)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Paragraph B
• Giải thích: Đoạn này nói về cách nhận diện cá thể giúp tiết lộ thông tin về life histories, breeding success, social dynamics – những bí mật về quần thể trước đây không biết.

Câu 16: vi (Monitoring habitat destruction from space)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Paragraph C
• Giải thích: Đoạn văn tập trung vào việc sử dụng satellite và drone imagery để theo dõi deforestation, habitat fragmentation và land-use changes từ không gian.

Câu 19: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Deep learning, automation of existing processes
• Vị trí trong bài: Đoạn 1, dòng 2-3
• Giải thích: Bài viết nói rõ “not merely about automating existing processes; rather, it represents a fundamental reimagining” – không chỉ là tự động hóa mà là sự tái tưởng tượng căn bản, mâu thuẫn với câu hỏi.

Câu 20: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Facial recognition AI, chimpanzee, social structures
• Vị trí trong bài: Paragraph B, dòng 5-7
• Giải thích: “revealed previously unknown information about territorial behaviors, family group compositions, and genetic diversity” – đúng với nhận định trong câu hỏi.

Câu 24: computational resources

• Dạng câu hỏi: Summary Completion
• Từ khóa: technology demands
• Vị trí trong bài: Paragraph F, dòng 2
• Giải thích: “requires substantial computational resources” – yêu cầu nguồn lực tính toán đáng kể.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: technological solutionism
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Định nghĩa rõ ràng: “the tendency to frame complex socio-ecological challenges primarily as technical problems amenable to technological fixes” – xu hướng coi các vấn đề phức tạp chủ yếu là vấn đề kỹ thuật.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: data security concerns
• Vị trí trong bài: Đoạn 3, dòng 6-8
• Giải thích: “Several high-profile cases have demonstrated how geotagged photographs and location data… have been exploited” – có các trường hợp thực tế đã chứng minh việc lạm dụng dữ liệu.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: black box problem
• Vị trí trong bài: Đoạn 4, dòng 3-4
• Giải thích: “function as ‘black boxes,’ producing accurate predictions without providing transparent reasoning” – cho kết quả chính xác nhưng không cung cấp lý do minh bạch.

Câu 31: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: author’s position
• Vị trí trong bài: Toàn bộ passage, đặc biệt đoạn cuối
• Giải thích: Tác giả trình bày cả lợi ích và thách thức, kết luận với “cautiously optimistic” approach – thừa nhận tiềm năng nhưng cảnh báo về các vấn đề phức tạp cần xem xét.

Câu 37: root causes

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: overemphasis on AI, obscure
• Vị trí trong bài: Đoạn 2, dòng 6-7
• Giải thích: “may obscure the root causes of biodiversity loss” – có thể che khuất các nguyên nhân gốc rễ.

Câu 38: affective connections

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: AI-mediated conservation, undermine
• Vị trí trong bài: Đoạn 5, dòng cuối
• Giải thích: “might inadvertently undermine the affective connections and sense of moral responsibility” – có thể làm suy yếu các kết nối cảm xúc.

Câu 40: decision-making frameworks

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: AI’s impact, depends on values
• Vị trí trong bài: Đoạn cuối, dòng 2-3
• Giải thích: “will depend not on the technology itself but on the values, institutions, and decision-making frameworks” – phụ thuộc vào giá trị và khung ra quyết định.

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5. Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
remarkable transformation	n.phrase	/rɪˈmɑːkəbl ˌtrænsfəˈmeɪʃən/	Sự chuyển đổi đáng chú ý	The world of wildlife conservation is undergoing a remarkable transformation	undergo a transformation
time-consuming	adj	/ˈtaɪm kənˌsjuːmɪŋ/	Tốn thời gian	Traditional methods are time-consuming	time-consuming process
labour-intensive	adj	/ˈleɪbər ɪnˈtensɪv/	Tốn nhiều công sức	These approaches are labour-intensive	labour-intensive work
unprecedented	adj	/ʌnˈpresɪdentɪd/	Chưa từng có	With unprecedented speed and accuracy	unprecedented accuracy
image recognition	n.phrase	/ˈɪmɪdʒ ˌrekəgˈnɪʃən/	Nhận dạng hình ảnh	AI-powered image recognition software	image recognition technology
machine learning	n.phrase	/məˈʃiːn ˈlɜːnɪŋ/	Học máy	Using machine learning algorithms	machine learning model
illegal poaching	n.phrase	/ɪˈliːgəl ˈpəʊtʃɪŋ/	Săn trộm bất hợp pháp	AI is revolutionizing the fight against illegal poaching	combat illegal poaching
predictive analytics	n.phrase	/prɪˈdɪktɪv ˌænəˈlɪtɪks/	Phân tích dự đoán	Using predictive analytics to anticipate poachers	predictive analytics system
heat maps	n.phrase	/hiːt mæps/	Bản đồ nhiệt	AI systems can generate heat maps	generate heat maps
patrol strategies	n.phrase	/pəˈtrəʊl ˈstrætədʒiz/	Chiến lược tuần tra	AI-driven patrol strategies	implement patrol strategies
breakthrough application	n.phrase	/ˈbreɪkθruː ˌæplɪˈkeɪʃən/	Ứng dụng đột phá	Another breakthrough application involves AI	breakthrough technology
unmanned aerial vehicles	n.phrase	/ʌnˈmænd ˈeəriəl ˈviːɪklz/	Thiết bị bay không người lái	These unmanned aerial vehicles can cover large areas	deploy unmanned vehicles

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
paradigm shift	n.phrase	/ˈpærədaɪm ʃɪft/	Sự thay đổi mô hình tư duy	A paradigm shift in how we protect biodiversity	undergo a paradigm shift
intractable	adj	/ɪnˈtræktəbl/	Khó giải quyết	Problems previously considered intractable	intractable problem
deep learning	n.phrase	/diːp ˈlɜːnɪŋ/	Học sâu	Deep learning neural networks	deep learning model
convolutional neural networks	n.phrase	/ˌkɒnvəˈluːʃənl ˈnjʊərəl ˈnetwɜːks/	Mạng nơ-ron tích chập	CNNs have achieved remarkable success	train neural networks
abstract features	n.phrase	/ˈæbstrækt ˈfiːtʃəz/	Đặc điểm trừu tượng	Extract increasingly abstract features	identify abstract features
census-taking	n	/ˈsensəs ˌteɪkɪŋ/	Điều tra dân số, kiểm kê	Beyond simple census-taking	conduct census-taking
unprecedented granularity	n.phrase	/ʌnˈpresɪdentɪd ˌgrænjuˈlærəti/	Độ chi tiết chưa từng có	With unprecedented granularity	achieve unprecedented detail
facial recognition	n.phrase	/ˈfeɪʃəl ˌrekəgˈnɪʃən/	Nhận dạng khuôn mặt	Facial recognition AI trained on primate features	facial recognition system
territorial behaviors	n.phrase	/ˌterɪˈtɔːriəl bɪˈheɪvjəz/	Hành vi lãnh thổ	Information about territorial behaviors	display territorial behavior
habitat fragmentation	n.phrase	/ˈhæbɪtæt ˌfrægmənˈteɪʃən/	Sự phân mảnh môi trường sống	Deforestation and habitat fragmentation	prevent habitat fragmentation
temporal analysis	n.phrase	/ˈtempərəl əˈnæləsɪs/	Phân tích theo thời gian	Systems employ temporal analysis	conduct temporal analysis
ecosystem degradation	n.phrase	/ˈiːkəʊˌsɪstəm ˌdegrəˈdeɪʃən/	Sự suy thoái hệ sinh thái	Detect changes indicating ecosystem degradation	prevent ecosystem degradation
early warning capability	n.phrase	/ˈɜːli ˈwɔːnɪŋ ˌkeɪpəˈbɪləti/	Khả năng cảnh báo sớm	This early warning capability is crucial	develop warning capability
bioacoustic monitoring	n.phrase	/ˌbaɪəʊəˈkuːstɪk ˈmɒnɪtərɪŋ/	Giám sát âm thanh sinh học	AI with bioacoustic monitoring	bioacoustic analysis
ensemble models	n.phrase	/ɒnˈsɒmbl ˈmɒdlz/	Mô hình tổng hợp	Using ensemble models to improve accuracy	develop ensemble models

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
epistemological implications	n.phrase	/ɪˌpɪstɪməˈlɒdʒɪkəl ˌɪmplɪˈkeɪʃənz/	Hàm ý nhận thức luận	Its epistemological implications	explore epistemological questions
ethical ramifications	n.phrase	/ˈeθɪkəl ˌræmɪfɪˈkeɪʃənz/	Hệ quả đạo đức	Ethical ramifications of AI use	consider ethical ramifications
environmental governance	n.phrase	/ɪnˌvaɪrənˈmentl ˈgʌvənəns/	Quản trị môi trường	Issues of environmental governance	improve environmental governance
algorithmic accountability	n.phrase	/ˌælgəˈrɪðmɪk əˌkaʊntəˈbɪləti/	Trách nhiệm giải trình thuật toán	The challenge of algorithmic accountability	ensure algorithmic accountability
technological solutionism	n.phrase	/ˌteknəˈlɒdʒɪkəl səˈluːʃənɪzəm/	Chủ nghĩa giải pháp công nghệ	What scholars term technological solutionism	critique technological solutionism
multifaceted	adj	/ˌmʌltɪˈfæsɪtɪd/	Đa diện	Wildlife conservation is multifaceted	multifaceted problem
systemic issues	n.phrase	/sɪˈstemɪk ˈɪʃuːz/	Vấn đề hệ thống	Root causes stem from systemic issues	address systemic issues
precision conservation	n.phrase	/prɪˈsɪʒən ˌkɒnsəˈveɪʃən/	Bảo tồn chính xác	A form of precision conservation	implement precision conservation
ecological degradation	n.phrase	/ˌiːkəˈlɒdʒɪkəl ˌdegrəˈdeɪʃən/	Suy thoái sinh thái	Structural factors driving ecological degradation	prevent ecological degradation
data governance	n.phrase	/ˈdeɪtə ˈgʌvənəns/	Quản trị dữ liệu	The question of data governance	establish data governance
poaching syndicates	n.phrase	/ˈpəʊtʃɪŋ ˈsɪndɪkəts/	Tổ chức săn trộm	Poaching syndicates are sophisticated	dismantle poaching syndicates
geotagged photographs	n.phrase	/ˈdʒiːəʊtægd ˈfəʊtəgrɑːfs/	Ảnh được gắn thẻ địa lý	How geotagged photographs have been exploited	share geotagged photographs
single points of vulnerability	n.phrase	/ˈsɪŋgl pɔɪnts əv ˌvʌlnərəˈbɪləti/	Điểm dễ bị tấn công duy nhất	Creates single points of vulnerability	eliminate vulnerability points
indigenous communities	n.phrase	/ɪnˈdɪdʒənəs kəˈmjuːnətiz/	Cộng đồng bản địa	Involve indigenous communities	support indigenous communities
black boxes	n.phrase	/blæk ˈbɒksɪz/	Hộp đen (không rõ cơ chế)	Function as black boxes	operate as black boxes
perpetuate and amplify biases	v.phrase	/pəˈpetʃueɪt ənd ˈæmplɪfaɪ ˈbaɪəsɪz/	Duy trì và khuếch đại thiên kiến	Can perpetuate and amplify biases	avoid perpetuating biases
ontological dimensions	n.phrase	/ˌɒntəˈlɒdʒɪkəl daɪˈmenʃənz/	Khía cạnh bản thể luận	The ontological dimensions of conservation	explore ontological questions
embodied knowledge	n.phrase	/ɪmˈbɒdid ˈnɒlɪdʒ/	Kiến thức được thể hiện qua hành động	Direct encounter and embodied knowledge	value embodied knowledge
technocratic approach	n.phrase	/ˌteknəˈkrætɪk əˈprəʊtʃ/	Cách tiếp cận kỹ trị	An overly technocratic approach	avoid technocratic solutions
citizen science projects	n.phrase	/ˈsɪtɪzən ˈsaɪəns ˈprɒdʒekts/	Dự án khoa học công dân	Citizen science projects incorporating AI	participate in citizen science
edge computing	n.phrase	/edʒ kəmˈpjuːtɪŋ/	Điện toán biên	Development of edge computing capabilities	implement edge computing
federated learning	n.phrase	/ˈfedəreɪtɪd ˈlɜːnɪŋ/	Học liên kết	Federated learning approaches	adopt federated learning
explainable AI	n.phrase	/ɪkˈspleɪnəbl eɪ aɪ/	AI có thể giải thích	Interest in explainable AI techniques	develop explainable AI
interdisciplinary collaboration	n.phrase	/ˌɪntədɪsɪˈplɪnəri kəˌlæbəˈreɪʃən/	Hợp tác liên ngành	Need for interdisciplinary collaboration	foster interdisciplinary work

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Kết bài

Qua bộ đề thi IELTS Reading hoàn chỉnh về chủ đề “How is AI being used in wildlife conservation?”, bạn đã được trải nghiệm một đề thi mô phỏng chính xác cấu trúc và độ khó của kỳ thi thực tế. Ba passages với độ khó tăng dần từ Easy đến Hard đã cung cấp góc nhìn toàn diện về việc ứng dụng AI trong bảo tồn động vật hoang dã, từ những ứng dụng cơ bản như camera bẫy và giám sát âm thanh, đến các vấn đề phức tạp về đạo đức, quản trị dữ liệu và tương lai của công nghệ này.

Với 40 câu hỏi đa dạng bao gồm Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion và nhiều dạng khác, bạn đã có cơ hội luyện tập toàn diện các kỹ năng làm bài Reading. Đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin và cách paraphrase sẽ giúp bạn hiểu rõ cách tiếp cận từng dạng câu hỏi một cách khoa học và hiệu quả.

Đặc biệt, phần từ vựng được tổng hợp theo từng passage không chỉ giúp bạn mở rộng vốn từ học thuật mà còn cung cấp collocations và cách sử dụng trong ngữ cảnh thực tế. Những từ vựng này rất có khả năng xuất hiện trong các đề thi IELTS Reading về chủ đề công nghệ, môi trường và khoa học.

Để đạt kết quả tốt nhất, hãy làm bài trong điều kiện thi thật với giới hạn thời gian 60 phút, sau đó đối chiếu đáp án và đọc kỹ phần giải thích để hiểu rõ lý do tại sao một đáp án đúng hoặc sai. Hãy nhớ rằng, IELTS Reading không chỉ kiểm tra khả năng đọc hiểu mà còn đánh giá kỹ năng quản lý thời gian, xác định thông tin quan trọng và suy luận logic. Chúc bạn ôn tập hiệu quả và đạt được band điểm mong muốn trong kỳ thi IELTS sắp tới.

Để tìm hiểu thêm về những ứng dụng cụ thể của công nghệ AI trong lĩnh vực môi trường, bạn có thể tham khảo bài viết How AI is transforming wildlife conservation để có cái nhìn sâu sắc hơn về sự chuyển đổi mà AI mang lại. Ngoài ra, nếu bạn quan tâm đến các khía cạnh rộng hơn, đừng bỏ lỡ nội dung về How is AI being used in environmental conservation? với những phân tích toàn diện về vai trò của trí tuệ nhân tạo trong bảo vệ môi trường toàn cầu.