Mở bài
Công nghệ trí tuệ nhân tạo (AI) đang tạo nên cuộc cách mạng trong lĩnh vực bảo tồn động vật hoang dã, một chủ đề ngày càng phổ biến trong các kỳ thi IELTS Reading gần đây. Sự kết hợp giữa khoa học công nghệ và môi trường tự nhiên không chỉ phản ánh xu hướng toàn cầu mà còn đòi hỏi người học IELTS phải nắm vững vốn từ vựng chuyên ngành đa dạng.
Bài viết này cung cấp một đề thi IELTS Reading hoàn chỉnh với 3 passages từ dễ đến khó, bao gồm 40 câu hỏi đa dạng giống như trong kỳ thi thật. Bạn sẽ học được cách xử lý các dạng câu hỏi phổ biến như Multiple Choice, True/False/Not Given, Matching Headings và Summary Completion. Đặc biệt, phần đáp án chi tiết kèm giải thích sẽ giúp bạn hiểu rõ kỹ thuật paraphrase và cách định vị thông tin chính xác trong bài đọc.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, muốn rèn luyện khả năng đọc hiểu học thuật và mở rộng vốn từ vựng về công nghệ, môi trường và bảo tồn thiên nhiên – những chủ đề thường xuyên xuất hiện trong IELTS Academic Reading.
Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test là bài kiểm tra kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi passage có độ dài khoảng 700-900 từ và độ khó tăng dần từ Passage 1 đến Passage 3.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (câu hỏi 1-13)
- Passage 2: 18-20 phút (câu hỏi 14-26)
- Passage 3: 23-25 phút (câu hỏi 27-40)
Lưu ý quan trọng: Không có thời gian thêm để chép đáp án, vì vậy bạn cần viết đáp án trực tiếp vào phiếu trả lời trong 60 phút.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Chọn đáp án đúng từ các phương án cho sẵn
- True/False/Not Given – Xác định thông tin đúng, sai hoặc không được nhắc đến
- Yes/No/Not Given – Xác định ý kiến của tác giả
- Matching Headings – Ghép tiêu đề phù hợp với các đoạn văn
- Summary Completion – Hoàn thành đoạn tóm tắt
- Matching Features – Ghép thông tin với các đặc điểm
- Short-answer Questions – Trả lời câu hỏi ngắn
IELTS Reading Practice Test
PASSAGE 1 – The Rise of AI in Wildlife Monitoring
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
Wildlife conservation has entered a new era with the integration of artificial intelligence (AI) into monitoring and protection programs. Traditional methods of tracking animals, which often relied on human observers spending countless hours in the field, are being revolutionized by smart technology. This transformation is not only making conservation work more efficient but also more accurate and far-reaching than ever before.
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 photograph animals as they pass by. Previously, conservationists had to manually review thousands of images to identify species, count individuals, and understand animal behavior. This process was extremely time-consuming and prone to human error. Now, AI-powered software can automatically analyze these images, identifying species with remarkable accuracy in a fraction of the time. For example, a program developed by conservation scientists can process 3.2 million images in just 48 hours – a task that would take a human team several months to complete.
Acoustic monitoring represents another area where AI is making a substantial impact. Many animals, particularly birds and marine mammals, can be identified by their unique calls and sounds. AI systems equipped with machine learning algorithms can now listen to recordings from remote locations and automatically identify species based on their vocalizations. This technology has proven especially valuable in monitoring populations of endangered species in dense forests or deep oceans where visual observation is difficult or impossible. Researchers studying whale populations, for instance, use AI to analyze underwater recordings, detecting and classifying different whale species by their songs and calls.
The use of drones equipped with AI has opened up new possibilities for wildlife surveys and anti-poaching operations. These unmanned aerial vehicles can cover vast areas quickly, capturing high-resolution images and videos. AI algorithms then analyze this aerial footage to count animals, map their movements, and even detect potential threats such as poachers or illegal logging activities. In African national parks, rangers use AI-powered drones to monitor elephant herds and identify suspicious human activity in real-time, allowing them to respond quickly to poaching threats.
However, the implementation of AI in conservation is not without challenges. One major concern is the digital divide between well-funded conservation projects in developed countries and resource-limited programs in biodiversity hotspots, often located in developing nations. Access to the necessary technology, internet connectivity, and technical expertise varies greatly across different regions. Additionally, AI systems require substantial amounts of data to train effectively, and gathering this data in remote wilderness areas can be logistically difficult and expensive.
Despite these challenges, the conservation community remains optimistic about AI’s potential. Many organizations are working to make AI tools more accessible to conservationists worldwide through open-source software and training programs. Furthermore, as AI technology continues to improve and become more affordable, its adoption in wildlife conservation is expected to accelerate. The combination of AI with other emerging technologies, such as satellite imagery and Internet of Things (IoT) sensors, promises to create even more comprehensive monitoring systems in the future.
The ethical implications of using AI in conservation also deserve attention. Questions about data privacy, the role of human expertise, and the potential for technology to distance people from nature are being actively discussed. Conservationists emphasize that AI should complement, not replace, human knowledge and field experience. The most effective conservation strategies integrate technological tools with traditional ecological knowledge and the irreplaceable insights of local communities who have lived alongside wildlife for generations.
Questions 1-6: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. According to the passage, what is the main advantage of using AI in camera trap analysis?
A. It produces higher quality images
B. It processes images much faster than humans
C. It can work in more remote locations
D. It costs less than traditional cameras
2. Acoustic monitoring is particularly useful for
A. animals that live in groups
B. species that are easy to see
C. creatures in hard-to-observe environments
D. animals that migrate long distances
3. AI-powered drones in African national parks are used to
A. feed elephant populations
B. track animal movements and detect threats
C. transport rangers to remote areas
D. communicate with local communities
4. What is described as a major challenge for implementing AI in conservation?
A. The technology is too complicated
B. Animals are frightened by drones
C. Unequal access to technology and resources
D. AI makes too many mistakes
5. The passage suggests that the future of AI in conservation will involve
A. replacing all human conservationists
B. focusing only on endangered species
C. combining AI with other technologies
D. limiting its use to developed countries
6. According to the final paragraph, the most effective conservation approach
A. relies entirely on AI systems
B. ignores traditional methods
C. combines technology with human expertise
D. focuses on data collection only
Questions 7-10: 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
7. Manual analysis of camera trap images is more accurate than AI analysis.
8. AI can identify whale species by analyzing their underwater sounds.
9. All conservation projects now have equal access to AI technology.
10. Some conservationists are concerned about the ethical aspects of using AI.
Questions 11-13: Sentence Completion
Complete the sentences below. Choose NO MORE THAN THREE WORDS from the passage for each answer.
11. AI-powered software can identify species from camera trap images with __ in much less time than humans.
12. Many conservation organizations are developing __ to make AI tools available to more conservationists globally.
13. Effective conservation strategies should integrate technology with __ and insights from local communities.
PASSAGE 2 – Machine Learning Algorithms Transform Species Protection
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The exponential growth of artificial intelligence capabilities has catalyzed a paradigm shift in how scientists approach wildlife conservation. Unlike earlier technological interventions that merely digitized existing practices, contemporary AI applications are fundamentally reimagining conservation methodologies. Through sophisticated machine learning algorithms, conservationists can now predict threats, optimize resource allocation, and develop proactive strategies rather than simply reacting to crises as they emerge.
Predictive modeling represents one of the most transformative applications of AI in conservation science. By analyzing vast datasets encompassing climate patterns, habitat changes, human activity, and historical population trends, machine learning systems can forecast where and when wildlife populations face the greatest risks. This capability enables conservation agencies to preemptively deploy resources to areas where intervention will have maximum impact. A notable example is the work conducted in Southeast Asian rainforests, where AI models successfully predicted illegal logging hotspots with 89% accuracy three months in advance, allowing authorities to position rangers strategically and prevent significant habitat destruction.
The identification and tracking of individual animals within populations has been revolutionized through computer vision technology. Traditional methods required physical tags or invasive procedures, causing stress to animals and limiting the scale of monitoring programs. Modern AI systems can recognize individual animals through biometric features – stripe patterns in tigers, spot configurations in leopards, facial features in primates, and even the unique tail shapes of whales. This non-invasive approach not only improves animal welfare but also enables researchers to monitor exponentially larger populations. The implications for understanding animal behavior, migration patterns, and population dynamics are profound, providing insights that were previously unattainable.
Combating wildlife trafficking, a multi-billion dollar illegal industry, has become significantly more effective through AI integration. Machine learning algorithms now scan millions of online marketplace listings, social media posts, and shipping records to identify potential illegal wildlife trade. These systems can detect subtle patterns and code words that human monitors might miss, flagging suspicious activities for investigation. Furthermore, AI-powered analysis of seizure data helps authorities understand trafficking networks, identify key players, and predict future smuggling routes. In one remarkable case, AI analysis of confiscated ivory DNA helped investigators map elephant poaching networks across three countries, leading to multiple arrests and the dismantling of a major criminal organization.
The application of natural language processing (NLP) extends AI’s conservation impact beyond physical monitoring. NLP systems analyze scientific literature, conservation reports, and field notes to extract and synthesize information that would take researchers years to compile manually. This accelerates knowledge discovery and helps identify research gaps or emerging threats. Additionally, NLP tools monitor news sources and social media in multiple languages to detect early warnings of environmental crimes or conflicts between humans and wildlife. This real-time intelligence gathering enables rapid response to developing situations.
However, the efficacy of AI conservation tools depends critically on data quality and quantity. Machine learning models require extensive training data to achieve reliable performance, yet comprehensive datasets for many endangered species simply do not exist. This creates a troubling paradox: the species most in need of protection often have the least data available for AI analysis. Addressing this data scarcity requires innovative approaches, including transfer learning techniques that adapt models trained on data-rich species to data-poor ones, and citizen science initiatives that crowdsource observations from the public.
The computational demands of sophisticated AI systems present another significant challenge, particularly for field-based conservation work in remote areas with limited infrastructure. While cloud-based processing offers powerful capabilities, it requires reliable internet connectivity that may not exist in wilderness locations. Researchers are developing edge computing solutions – AI systems that can operate on local devices without constant internet access – but these typically have reduced capabilities compared to their cloud-based counterparts. Balancing computational power with practical deployment requirements remains an ongoing technical challenge.
Algorithmic bias represents a subtle but potentially serious concern in AI-assisted conservation. If training data overrepresents certain species, habitats, or geographic regions, AI models may perform poorly in underrepresented contexts. For instance, a model trained predominantly on images from African savannas might struggle to identify animals in Asian forests. Ensuring diversity in training data and rigorously testing AI systems across varied conditions are essential to prevent such biases from undermining conservation efforts. Moreover, the transparency and explainability of AI decision-making processes require careful attention, particularly when these systems influence critical resource allocation decisions.
Ứng dụng công nghệ trí tuệ nhân tạo AI trong giám sát và bảo vệ động vật hoang dã đang thay đổi phương pháp bảo tồn thiên nhiên
Despite these challenges, the trajectory of AI development suggests increasingly sophisticated and accessible conservation tools in the coming years. Collaborative efforts between technology companies, academic institutions, and conservation organizations are accelerating innovation while working to ensure equitable access to these powerful capabilities. As AI systems become more refined and conservation practitioners gain experience in their effective deployment, the potential for technology to turn the tide against biodiversity loss grows stronger.
Questions 14-18: Yes/No/Not Given
Do the following statements agree with the views of the writer in the passage? Write:
- YES if the statement agrees with the views of the writer
- NO if the statement contradicts the views of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
14. Modern AI applications in conservation are simply digital versions of traditional methods.
15. Predictive modeling allows conservation agencies to take action before problems occur.
16. Physical tagging methods are more accurate than AI-based individual identification.
17. Natural language processing can help identify conservation research gaps.
18. Edge computing solutions are more powerful than cloud-based AI systems.
Questions 19-23: Matching Headings
The passage has eight paragraphs. Choose the correct heading for paragraphs B-F from the list of headings below.
List of Headings:
i. The problem of unequal data availability
ii. Financial costs of implementing AI systems
iii. Forecasting threats before they materialize
iv. Identifying animals without physical contact
v. Combating illegal wildlife trade through AI
vi. Processing conservation literature automatically
vii. The need for diverse training data
viii. Balancing power and practicality in remote locations
ix. Future collaboration and development
19. Paragraph B (Predictive modeling represents…)
20. Paragraph C (The identification and tracking…)
21. Paragraph D (Combating wildlife trafficking…)
22. Paragraph E (The application of natural language…)
23. Paragraph F (However, the efficacy…)
Questions 24-26: Summary Completion
Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.
AI has transformed wildlife conservation through multiple applications. Computer vision enables the recognition of individual animals using 24. __ such as stripe patterns or facial features. This approach avoids causing stress to animals. In fighting wildlife crime, AI systems can detect 25. __ in online marketplaces that humans might overlook. However, AI effectiveness depends on data quality, and many endangered species suffer from 26. __, making it difficult to train effective models.
PASSAGE 3 – The Ethical and Epistemological Dimensions of AI-Mediated Conservation
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The ascendancy of artificial intelligence in wildlife conservation has precipitated not only methodological innovations but also profound philosophical questions regarding the nature of conservation itself, the relationship between humanity and the natural world, and the epistemological frameworks through which we understand and interact with biodiversity. While the practical benefits of AI applications are increasingly well-documented, the deeper ontological and ethical implications warrant equally rigorous examination, particularly as these technologies become more deeply embedded in conservation practice and policy.
Central to these considerations is the question of how AI mediates and potentially transforms our understanding of wildlife populations and ecosystems. Traditional ecological fieldwork, though limited in scale and scope, provided direct phenomenological engagement with the natural world – an embodied experience that shaped not only scientific understanding but also the development of conservation values and environmental ethics. Contemporary AI-powered monitoring systems, by contrast, interpose computational layers between observers and observed, generating representations of wildlife that are algorithmically constructed rather than directly perceived. This shift raises fundamental questions about the nature of ecological knowledge: Does comprehensive quantitative data obtained through automated systems constitute a fundamentally different kind of knowledge than that acquired through prolonged direct observation? Moreover, how might this epistemological transition influence both scientific understanding and the affective dimensions of conservation – the emotional connections to wildlife that often motivate conservation action?
The issue of algorithmic opacity presents particularly vexing challenges in conservation contexts. Many advanced AI systems, especially those employing deep learning neural networks, function as “black boxes” – generating predictions or classifications with remarkable accuracy but through computational processes that resist human interpretation. When such systems recommend resource allocation decisions or identify conservation priorities, the inscrutability of their decision-making processes creates accountability concerns. Conservation practitioners must balance the demonstrable effectiveness of these tools against the importance of understanding and being able to justify the rationale underlying conservation actions, particularly when these involve controversial interventions or trade-offs between competing conservation objectives. The question of whether to trust AI recommendations that cannot be fully explained represents a significant challenge for conservation governance.
The deployment of AI conservation technologies also intersects with complex issues of environmental justice and postcolonial power dynamics. The development of these sophisticated systems occurs predominantly in institutions located in wealthy nations of the Global North, while their application often targets biodiversity hotspots in the Global South. This geographical disjuncture risks perpetuating extractive relationships wherein data and knowledge flow from biodiversity-rich but economically disadvantaged regions to technology-rich institutions elsewhere, with limited reciprocal benefit to local communities. Furthermore, AI systems trained primarily on data from particular ecological contexts may encode biases that render them less effective or appropriate in different environments, potentially leading to suboptimal conservation outcomes in already marginalized regions.
The question of technological determinism – the tendency for technology adoption to follow a seemingly inevitable trajectory regardless of whether it represents the most appropriate solution – merits careful consideration in conservation contexts. As AI capabilities expand and these tools become increasingly normalized in conservation practice, there exists a risk that problems come to be defined in terms amenable to AI solutions rather than on the basis of ecological or social realities. This technological framing might direct attention and resources toward challenges that AI can address while marginalizing conservation issues less susceptible to technological intervention, potentially creating systemic biases in conservation priorities. The allure of technological solutions may also overshadow the fundamental drivers of biodiversity loss – habitat destruction, climate change, overconsumption – which require primarily social, political, and economic rather than technological responses.
The integration of AI into conservation decision-making raises important questions about the changing role of human expertise and indigenous knowledge systems. While proponents emphasize that AI should augment rather than replace human judgment, the practical reality of resource-constrained conservation organizations may lead to de-skilling as technological systems assume functions previously performed by human experts. This trend risks eroding valuable forms of expertise, particularly the tacit knowledge developed through extensive field experience and the traditional ecological knowledge held by indigenous and local communities. These knowledge systems often encompass understandings of ecosystem dynamics, animal behavior, and environmental change that cannot be easily codified in datasets or captured by algorithmic analysis. The challenge lies in developing integrative approaches that genuinely synthesize technological capabilities with diverse forms of human knowledge rather than allowing one to supplant the other.
The temporal dimensions of AI-mediated conservation also warrant examination. The unprecedented scale and speed of data collection and analysis enabled by AI systems provides near-real-time insights into ecosystem dynamics, potentially enabling more responsive and adaptive conservation management. However, this temporal compression may also encourage a focus on immediate, measurable outcomes at the expense of longer-term processes that unfold over decades or centuries. Ecological succession, evolutionary adaptation, and the complex feedbacks between species and their environments operate on timescales that may be incommensurable with the rapid data cycles of AI systems. Balancing the tactical advantages of real-time information with the strategic imperatives of long-term ecological processes represents an ongoing challenge for AI-assisted conservation.
Furthermore, the question of technological lock-in poses long-term risks for conservation organizations. As institutions invest substantially in particular AI platforms and develop workflows dependent on specific systems, they may become constrained by these technological choices even if superior alternatives emerge or if the chosen systems prove inadequate for evolving conservation needs. The proprietary nature of many AI technologies may create dependencies on commercial vendors whose priorities and timelines do not necessarily align with conservation objectives. Developing sustainable technological infrastructures that maintain flexibility and avoid problematic dependencies requires careful planning and ongoing attention to governance structures.
Despite these substantial concerns, many conservation scientists maintain that the existential threats facing biodiversity – accelerating species extinctions, ecosystem collapse, climate disruption – demand the mobilization of every available tool, including AI technologies. From this perspective, while philosophical concerns merit consideration, they cannot justify forgoing technological capabilities that might enhance conservation effectiveness during a critical period for planetary biodiversity. The challenge, these advocates argue, lies not in whether to employ AI in conservation but in doing so thoughtfully, with appropriate attention to ethical implications, equitable access, and integration with existing knowledge systems. The coming decades will likely determine whether artificial intelligence becomes a transformative force for conservation success or represents another instance of technological optimism that fails to address the fundamental social and political dimensions of environmental crisis.
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, traditional ecological fieldwork differed from AI monitoring because it
A. was less accurate in collecting data
B. provided direct physical contact with nature
C. required more expensive equipment
D. focused on fewer species
28. The term “black boxes” refers to AI systems that
A. are painted black for camouflage
B. store data in secure containers
C. make decisions through unclear processes
D. only work in dark environments
29. The passage suggests that AI development and application create concerns about
A. animals becoming extinct more quickly
B. technology being too expensive
C. unequal power relationships between regions
D. data being completely inaccurate
30. “Technological determinism” in conservation means
A. technology always improves conservation
B. problems are defined to suit available technology
C. all conservationists must use AI
D. traditional methods no longer work
31. According to the final paragraph, conservation scientists who support AI believe
A. philosophical concerns are unimportant
B. AI will solve all conservation problems
C. biodiversity threats are too serious to avoid using AI
D. traditional knowledge should be completely replaced
Questions 32-36: Matching Features
Match each concern (Questions 32-36) with the correct aspect of AI in conservation (A-G).
Concerns:
32. AI systems may not work effectively in different environments
33. Conservation organizations may become dependent on specific platforms
34. Valuable traditional knowledge might be lost
35. Focus may shift toward short-term measurable results
36. Understanding ecosystem processes becomes algorithmically mediated
Aspects:
A. Epistemological changes
B. Algorithmic opacity
C. Environmental justice
D. Technological determinism
E. Human expertise and indigenous knowledge
F. Temporal dimensions
G. Technological lock-in
Questions 37-40: Short-answer Questions
Answer the questions below. Choose NO MORE THAN THREE WORDS from the passage for each answer.
37. What type of neural networks does the passage mention as being particularly difficult to interpret?
38. What flows from biodiversity-rich regions to technology-rich institutions, according to the passage?
39. What type of knowledge is developed through extensive field experience that cannot be easily captured by algorithms?
40. What three examples of existential threats facing biodiversity are mentioned in the final paragraph?
Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- B
- C
- C
- C
- FALSE
- TRUE
- FALSE
- TRUE
- remarkable accuracy
- open-source software / training programs
- traditional ecological knowledge
PASSAGE 2: Questions 14-26
- NO
- YES
- NOT GIVEN
- YES
- NO
- iii
- iv
- v
- vi
- i
- biometric features
- subtle patterns / code words
- data scarcity
PASSAGE 3: Questions 27-40
- B
- C
- C
- B
- C
- C
- G
- E
- F
- A
- deep learning (neural networks)
- data and knowledge
- tacit knowledge
- species extinctions / ecosystem collapse / climate disruption
Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main advantage, AI, camera trap analysis
- Vị trí trong bài: Đoạn 2, dòng 5-8
- Giải thích: Bài đọc nói rõ “AI-powered software can automatically analyze these images, identifying species with remarkable accuracy in a fraction of the time” và đưa ra ví dụ cụ thể về việc xử lý 3.2 triệu ảnh trong 48 giờ thay vì mất vài tháng. Đây là paraphrase của “processes images much faster than humans”.
Câu 2: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: acoustic monitoring, particularly useful
- Vị trí trong bài: Đoạn 3, dòng 4-6
- Giải thích: Bài viết chỉ ra “This technology has proven especially valuable in monitoring populations of endangered species in dense forests or deep oceans where visual observation is difficult or impossible” – tương ứng với đáp án C về môi trường khó quan sát.
Câu 7: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: manual analysis, more accurate, AI analysis
- Vị trí trong bài: Đoạn 2, dòng 3-4
- Giải thích: Bài đọc nói rằng phương pháp thủ công “was extremely time-consuming and prone to human error” (dễ mắc lỗi), ngược lại với việc khẳng định nó chính xác hơn AI.
Câu 8: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: AI, identify whale species, underwater sounds
- Vị trí trong bài: Đoạn 3, dòng 7-9
- Giải thích: “Researchers studying whale populations…use AI to analyze underwater recordings, detecting and classifying different whale species by their songs and calls” khớp chính xác với phát biểu.
Câu 11: remarkable accuracy
- Dạng câu hỏi: Sentence Completion
- Từ khóa: identify species, camera trap images
- Vị trí trong bài: Đoạn 2, dòng 5-6
- Giải thích: Cụm từ xuất hiện nguyên văn trong câu “AI-powered software can automatically analyze these images, identifying species with remarkable accuracy in a fraction of the time.”
Câu 13: traditional ecological knowledge
- Dạng câu hỏi: Sentence Completion
- Từ khóa: integrate technology, insights from local communities
- Vị trí trong bài: Đoạn 7, dòng 3-5
- Giải thích: Đoạn cuối nhấn mạnh “The most effective conservation strategies integrate technological tools with traditional ecological knowledge and the irreplaceable insights of local communities.”
Passage 2 – Giải Thích
Câu 14: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: modern AI applications, digital versions, traditional methods
- Vị trí trong bài: Đoạn A, dòng 2-4
- Giải thích: Tác giả rõ ràng phản bác ý này khi viết “Unlike earlier technological interventions that merely digitized existing practices, contemporary AI applications are fundamentally reimagining conservation methodologies” – AI hiện đại đang tạo ra cách làm hoàn toàn mới, không chỉ số hóa phương pháp cũ.
Câu 15: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: predictive modeling, take action before problems occur
- Vị trí trong bài: Đoạn A, dòng 5-6 và Đoạn B
- Giải thích: Tác giả khẳng định conservationists có thể “develop proactive strategies rather than simply reacting to crises” và “preemptively deploy resources”, cho thấy quan điểm ủng hộ việc hành động trước.
Câu 19: iii
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn B (Predictive modeling represents…)
- Giải thích: Đoạn văn tập trung vào “predictive modeling” và khả năng “forecast where and when wildlife populations face the greatest risks”, khớp với heading “Forecasting threats before they materialize”.
Câu 20: iv
- Dạng câu hỏi: Matching Headings
- Vị trí: Đoạn C (The identification and tracking…)
- Giải thích: Đoạn này nói về “non-invasive approach” sử dụng “biometric features” để nhận diện động vật mà không cần “physical tags or invasive procedures”, phù hợp với “Identifying animals without physical contact”.
Câu 24: biometric features
- Dạng câu hỏi: Summary Completion
- Vị trí: Đoạn C, dòng 3-5
- Giải thích: Bài đọc liệt kê cụ thể “Modern AI systems can recognize individual animals through biometric features – stripe patterns in tigers, spot configurations in leopards, facial features in primates”.
Câu 26: data scarcity
- Dạng câu hỏi: Summary Completion
- Vị trí: Đoạn F, dòng 4-5
- Giải thích: Đoạn văn chỉ ra vấn đề “the species most in need of protection often have the least data available” và đề cập trực tiếp đến việc “Addressing this data scarcity”.
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: traditional ecological fieldwork, differed from AI monitoring
- Vị trí trong bài: Đoạn B, dòng 2-4
- Giải thích: Bài viết nhấn mạnh “Traditional ecological fieldwork…provided direct phenomenological engagement with the natural world – an embodied experience” so với AI systems “interpose computational layers between observers and observed”. Từ khóa “direct” và “embodied experience” tương ứng với “provided direct physical contact with nature”.
Câu 28: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: black boxes
- Vị trí trong bài: Đoạn C, dòng 2-3
- Giải thích: Định nghĩa được đưa ra rõ ràng: “function as ‘black boxes’ – generating predictions or classifications with remarkable accuracy but through computational processes that resist human interpretation”, nghĩa là quy trình ra quyết định không rõ ràng.
Câu 29: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI development and application, concerns
- Vị trí trong bài: Đoạn D, toàn đoạn
- Giải thích: Đoạn D nói về “environmental justice and postcolonial power dynamics”, “geographical disjuncture risks perpetuating extractive relationships” giữa Global North và Global South, thể hiện mối quan hệ quyền lực bất bình đẳng giữa các vùng.
Câu 32: C (Environmental justice)
- Dạng câu hỏi: Matching Features
- Vị trí: Đoạn D, dòng 5-7
- Giải thích: “AI systems trained primarily on data from particular ecological contexts may encode biases that render them less effective or appropriate in different environments” – vấn đề này thuộc phần environmental justice khi bàn về hiệu quả không đồng đều của AI ở các môi trường khác nhau.
Câu 34: E (Human expertise and indigenous knowledge)
- Dạng câu hỏi: Matching Features
- Vị trí: Đoạn F, dòng 4-8
- Giải thích: Đoạn văn cảnh báo “risks eroding valuable forms of expertise, particularly the tacit knowledge developed through extensive field experience and the traditional ecological knowledge held by indigenous and local communities” – kiến thức truyền thống có nguy cơ bị mất đi.
Câu 37: deep learning (neural networks)
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: neural networks, difficult to interpret
- Vị trí: Đoạn C, dòng 2
- Giải thích: Câu trả lời xuất hiện trực tiếp: “especially those employing deep learning neural networks, function as ‘black boxes'”.
Câu 39: tacit knowledge
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: developed through extensive field experience, cannot be easily captured
- Vị trí: Đoạn F, dòng 5-6
- Giải thích: Bài viết nói rõ “particularly the tacit knowledge developed through extensive field experience…that cannot be easily codified in datasets or captured by algorithmic analysis”.
Câu 40: species extinctions / ecosystem collapse / climate disruption
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: three examples, existential threats
- Vị trí: Đoạn I, dòng 1-2
- Giải thích: Ba mối đe dọa được liệt kê rõ ràng: “accelerating species extinctions, ecosystem collapse, climate disruption”. Lựa chọn bất kỳ ba trong số này đều đúng.
Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| integration | n | /ˌɪntɪˈɡreɪʃn/ | sự tích hợp, hợp nhất | the integration of artificial intelligence into monitoring programs | integration of technology, seamless integration |
| revolutionized | v | /ˌrevəˈluːʃənaɪzd/ | đã cách mạng hóa | Traditional methods are being revolutionized by smart technology | revolutionize the industry, completely revolutionized |
| time-consuming | adj | /ˈtaɪm kənˌsuːmɪŋ/ | tốn thời gian | This process was extremely time-consuming | time-consuming task, time-consuming process |
| remarkable accuracy | n phrase | /rɪˈmɑːkəbl ˈækjərəsi/ | độ chính xác đáng kinh ngạc | identifying species with remarkable accuracy | achieve remarkable accuracy, with remarkable accuracy |
| acoustic monitoring | n phrase | /əˈkuːstɪk ˈmɒnɪtərɪŋ/ | giám sát bằng âm thanh | Acoustic monitoring represents another area | acoustic monitoring system, acoustic monitoring technology |
| machine learning algorithms | n phrase | /məˈʃiːn ˈlɜːnɪŋ ˈælɡərɪðəmz/ | thuật toán học máy | AI systems equipped with machine learning algorithms | advanced machine learning algorithms, apply machine learning algorithms |
| anti-poaching operations | n phrase | /ˈænti ˈpəʊtʃɪŋ ˌɒpəˈreɪʃnz/ | hoạt động chống săn trộm | drones for anti-poaching operations | conduct anti-poaching operations, anti-poaching operations team |
| digital divide | n phrase | /ˈdɪdʒɪtl dɪˈvaɪd/ | khoảng cách số | One major concern is the digital divide | bridge the digital divide, widen the digital divide |
| substantial amounts | n phrase | /səbˈstænʃl əˈmaʊnts/ | số lượng đáng kể | require substantial amounts of data | substantial amounts of money, provide substantial amounts |
| accessible | adj | /əkˈsesəbl/ | có thể tiếp cận được | make AI tools more accessible | easily accessible, freely accessible |
| comprehensive monitoring systems | n phrase | /ˌkɒmprɪˈhensɪv ˈmɒnɪtərɪŋ ˈsɪstəmz/ | hệ thống giám sát toàn diện | create comprehensive monitoring systems | develop comprehensive monitoring systems, comprehensive monitoring systems approach |
| ethical implications | n phrase | /ˈeθɪkl ˌɪmplɪˈkeɪʃnz/ | tác động đạo đức | The ethical implications of using AI | consider ethical implications, ethical implications of research |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| exponential growth | n phrase | /ˌekspəˈnenʃl ɡrəʊθ/ | tăng trưởng theo cấp số nhân | The exponential growth of AI capabilities | experience exponential growth, exponential growth rate |
| paradigm shift | n phrase | /ˈpærədaɪm ʃɪft/ | sự chuyển đổi mô hình tư duy | catalyzed a paradigm shift in conservation | represent a paradigm shift, undergo a paradigm shift |
| proactive strategies | n phrase | /prəʊˈæktɪv ˈstrætədʒiz/ | chiến lược chủ động | develop proactive strategies | implement proactive strategies, adopt proactive strategies |
| predictive modeling | n phrase | /prɪˈdɪktɪv ˈmɒdlɪŋ/ | mô hình dự đoán | Predictive modeling represents one of the most transformative applications | use predictive modeling, predictive modeling techniques |
| preemptively deploy | v phrase | /priˈemptɪvli dɪˈplɔɪ/ | triển khai phòng ngừa | preemptively deploy resources | preemptively deploy forces, preemptively deploy measures |
| computer vision technology | n phrase | /kəmˈpjuːtə ˈvɪʒn tekˈnɒlədʒi/ | công nghệ thị giác máy tính | revolutionized through computer vision technology | advanced computer vision technology, computer vision technology applications |
| biometric features | n phrase | /ˌbaɪəʊˈmetrɪk ˈfiːtʃəz/ | đặc điểm sinh trắc học | recognize animals through biometric features | unique biometric features, identify biometric features |
| non-invasive approach | n phrase | /nɒn ɪnˈveɪsɪv əˈprəʊtʃ/ | phương pháp không xâm lấn | This non-invasive approach improves animal welfare | adopt a non-invasive approach, non-invasive approach to treatment |
| subtle patterns | n phrase | /ˈsʌtl ˈpætənz/ | các mẫu tinh vi | detect subtle patterns and code words | identify subtle patterns, recognize subtle patterns |
| dismantling | n | /dɪsˈmæntlɪŋ/ | việc phá hủy, giải tán | the dismantling of a major criminal organization | dismantling of networks, complete dismantling |
| natural language processing | n phrase | /ˈnætʃrəl ˈlæŋɡwɪdʒ ˈprəʊsesɪŋ/ | xử lý ngôn ngữ tự nhiên (NLP) | The application of natural language processing | apply natural language processing, natural language processing techniques |
| accelerates knowledge discovery | v phrase | /əkˈseləreɪts ˈnɒlɪdʒ dɪsˈkʌvəri/ | đẩy nhanh việc khám phá kiến thức | This accelerates knowledge discovery | accelerate knowledge discovery process, significantly accelerates knowledge discovery |
| data scarcity | n phrase | /ˈdeɪtə ˈskeəsəti/ | sự khan hiếm dữ liệu | Addressing this data scarcity requires innovative approaches | overcome data scarcity, data scarcity problem |
| transfer learning | n phrase | /ˈtrænsfɜː ˈlɜːnɪŋ/ | học chuyển giao | transfer learning techniques that adapt models | use transfer learning, transfer learning approach |
| algorithmic bias | n phrase | /ˌælɡəˈrɪðmɪk ˈbaɪəs/ | thiên kiến thuật toán | Algorithmic bias represents a serious concern | reduce algorithmic bias, address algorithmic bias |
Công nghệ AI giám sát động vật hoang dã theo thời gian thực giúp bảo vệ các loài nguy cấp khỏi nạn săn trộm
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| ascendancy | n | /əˈsendənsi/ | sự thống trị, vượt trội | The ascendancy of artificial intelligence in conservation | rise to ascendancy, gain ascendancy |
| epistemological frameworks | n phrase | /ɪˌpɪstɪməˈlɒdʒɪkl ˈfreɪmwɜːks/ | khung nhận thức luận | the epistemological frameworks through which we understand | develop epistemological frameworks, epistemological frameworks of science |
| ontological | adj | /ˌɒntəˈlɒdʒɪkl/ | thuộc về bản thể luận | deeper ontological and ethical implications | ontological questions, ontological assumptions |
| phenomenological engagement | n phrase | /fɪˌnɒmɪnəˈlɒdʒɪkl ɪnˈɡeɪdʒmənt/ | sự tương tác hiện tượng học | provided direct phenomenological engagement with nature | phenomenological engagement with reality, deep phenomenological engagement |
| algorithmically constructed | adj phrase | /ˌælɡəˈrɪðmɪkli kənˈstrʌktɪd/ | được xây dựng bằng thuật toán | representations that are algorithmically constructed | algorithmically constructed models, algorithmically constructed solutions |
| affective dimensions | n phrase | /əˈfektɪv daɪˈmenʃnz/ | chiều kích cảm xúc | the affective dimensions of conservation | affective dimensions of learning, explore affective dimensions |
| algorithmic opacity | n phrase | /ˌælɡəˈrɪðmɪk əʊˈpæsəti/ | sự mờ đục của thuật toán | The issue of algorithmic opacity | address algorithmic opacity, algorithmic opacity problem |
| deep learning neural networks | n phrase | /diːp ˈlɜːnɪŋ ˈnjʊərəl ˈnetwɜːks/ | mạng nơ-ron học sâu | those employing deep learning neural networks | advanced deep learning neural networks, train deep learning neural networks |
| inscrutability | n | /ɪnˌskruːtəˈbɪləti/ | tính không thể hiểu được | the inscrutability of their decision-making processes | inscrutability of nature, complete inscrutability |
| postcolonial power dynamics | n phrase | /pəʊstˈkɒləniəl ˈpaʊə daɪˈnæmɪks/ | động lực quyền lực hậu thuộc địa | environmental justice and postcolonial power dynamics | examine postcolonial power dynamics, postcolonial power dynamics in research |
| geographical disjuncture | n phrase | /ˌdʒiːəˈɡræfɪkl dɪsˈdʒʌŋktʃə/ | sự gián đoạn địa lý | This geographical disjuncture risks perpetuating | geographical disjuncture between regions, create geographical disjuncture |
| perpetuating extractive relationships | v phrase | /pəˈpetʃueɪtɪŋ ɪkˈstræktɪv rɪˈleɪʃnʃɪps/ | duy trì các mối quan hệ bóc lột | risks perpetuating extractive relationships | avoid perpetuating extractive relationships, perpetuating extractive relationships with communities |
| technological determinism | n phrase | /ˌteknəˈlɒdʒɪkl dɪˈtɜːmɪnɪzəm/ | chủ nghĩa quyết định công nghệ | The question of technological determinism | critique technological determinism, technological determinism theory |
| tacit knowledge | n phrase | /ˈtæsɪt ˈnɒlɪdʒ/ | kiến thức ngầm | the tacit knowledge developed through field experience | transfer tacit knowledge, preserve tacit knowledge |
| codified | v | /ˈkɒdɪfaɪd/ | được mã hóa, hệ thống hóa | cannot be easily codified in datasets | codified in law, clearly codified |
| integrative approaches | n phrase | /ˈɪntɪɡrətɪv əˈprəʊtʃɪz/ | cách tiếp cận tích hợp | developing integrative approaches | adopt integrative approaches, integrative approaches to management |
| incommensurable | adj | /ˌɪnkəˈmenʃərəbl/ | không thể đo lường chung | timescales that may be incommensurable | incommensurable values, incommensurable with each other |
| technological lock-in | n phrase | /ˌteknəˈlɒdʒɪkl lɒk ɪn/ | bị khóa vào công nghệ | the question of technological lock-in | avoid technological lock-in, risk of technological lock-in |
Kết bài
Chủ đề “How AI Is Transforming Wildlife Conservation” không chỉ phản ánh xu hướng công nghệ hiện đại mà còn thể hiện mối quan hệ phức tạp giữa con người, công nghệ và thiên nhiên – một góc nhìn thường xuất hiện trong các đề thi IELTS Reading Academic gần đây. Qua ba passages với độ khó tăng dần, bạn đã được tiếp cận với đầy đủ các dạng câu hỏi từ cơ bản đến nâng cao, bao gồm Multiple Choice, True/False/Not Given, Yes/No/Not Given, Matching Headings, Summary Completion, Matching Features và Short-answer Questions.
Đề thi mẫu này được thiết kế theo chuẩn Cambridge IELTS, với Passage 1 tập trung vào thông tin cơ bản dễ định vị, Passage 2 yêu cầu khả năng paraphrase và hiểu sâu hơn, còn Passage 3 đòi hỏi tư duy phân tích cao về các khía cạnh đạo đức và triết học của vấn đề. Phần đáp án chi tiết kèm giải thích vị trí và cách paraphrase sẽ giúp bạn tự đánh giá chính xác và học hỏi từ những sai lầm.
Đặc biệt, bảng từ vựng được phân loại theo từng passage với phiên âm, nghĩa tiếng Việt và collocations thực tế sẽ giúp bạn mở rộng vốn từ học thuật về công nghệ, môi trường và khoa học bảo tồn – những lĩnh vực xuất hiện thường xuyên trong IELTS Reading. Hãy sử dụng những từ vựng này trong Writing Task 2 và Speaking Part 3 để tăng band điểm tổng thể.
Tương tự như How is AI being used in environmental conservation?, chủ đề này cho thấy IELTS ngày càng hướng đến các vấn đề toàn cầu đương đại. Để hiểu rõ hơn về vai trò của công nghệ trong bảo vệ môi trường, bạn có thể tham khảo The role of green buildings in reducing urban heat, nơi công nghệ xanh cũng đang tạo ra những thay đổi tích cực. Đối với những ai quan tâm đến Green spaces in urban planning roles, nội dung về sự kết hợp giữa công nghệ và thiên nhiên trong bảo tồn động vật hoang dã sẽ cung cấp góc nhìn mở rộng về tương tác giữa phát triển đô thị và môi trường tự nhiên.
Hãy luyện tập đề thi này trong điều kiện giống thi thật – 60 phút không ngắt quãng – để đánh giá chính xác năng lực hiện tại và xác định những kỹ năng cần cải thiện. Chúc bạn đạt band điểm mục tiêu trong kỳ thi IELTS sắp tới!
