Mở Bài
Trí tuệ nhân tạo (AI) đã và đang trở thành một trong những chủ đề “nóng” nhất trong các kỳ thi IELTS Reading những năm gần đây. Chủ đề “The Role Of Artificial Intelligence In Daily Life” xuất hiện với tần suất ngày càng cao, phản ánh sự quan tâm toàn cầu về công nghệ này. Với hơn 20 năm kinh nghiệm giảng dạy IELTS, tôi nhận thấy nhiều học viên Việt Nam gặp khó khăn với các bài đọc về công nghệ do từ vựng chuyên ngành và cấu trúc câu phức tạp.
Bài viết này cung cấp một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages theo đúng chuẩn thi thật, từ mức độ dễ đến khó. Bạn sẽ được luyện tập với đầy đủ 40 câu hỏi đa dạng các dạng bài, kèm theo đáp án chi tiết và giải thích cụ thể. Đặc biệt, bài viết còn cung cấp hệ thống từ vựng quan trọng và những kỹ thuật làm bài thực chiến giúp bạn tự tin đạt band điểm mục tiêu. Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với format thi thật và nâng cao khả năng đọc hiểu về chủ đề công nghệ – một trong những chủ đề core của IELTS Reading hiện nay.
Hướng Dẫn Làm Bài IELTS Reading
Tổng Quan Về IELTS Reading Test
IELTS Reading Test kéo dài trong 60 phút với 3 passages và tổng cộng 40 câu hỏi. Đây là bài thi đòi hỏi kỹ năng quản lý thời gian chặt chẽ và khả năng đọc hiểu đa dạng thể loại văn bản.
Phân bổ thời gian khuyến nghị:
- Passage 1 (Easy): 15-17 phút – Đây là passage dễ nhất, giúp bạn khởi động tốt
- Passage 2 (Medium): 18-20 phút – Độ khó tăng lên, cần tập trung cao hơn
- Passage 3 (Hard): 23-25 phút – Passage khó nhất, cần thời gian suy luận và phân tích
Lưu ý quan trọng: Bạn cần tự ghi đáp án vào Answer Sheet trong 60 phút, không có thời gian bổ sung như phần Listening.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi mẫu này bao gồm 8 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Câu hỏi trắc nghiệm
- True/False/Not Given – Xác định thông tin đúng/sai/không có
- Matching Information – Nối thông tin với đoạn văn
- Sentence Completion – Hoàn thành câu
- Yes/No/Not Given – Xác định quan điểm tác giả
- Matching Headings – Nối tiêu đề với đoạn văn
- Summary Completion – Hoàn thành đoạn tóm tắt
- Short-answer Questions – Câu hỏi trả lời ngắn
Mỗi dạng câu hỏi yêu cầu kỹ năng khác nhau, từ scanning thông tin cụ thể đến skimming ý chính và phân tích sâu.
IELTS Reading Practice Test
PASSAGE 1 – AI in Everyday Consumer Technology
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
Artificial intelligence has seamlessly integrated into our daily routines, often in ways we barely notice. From the moment we wake up to the time we go to sleep, AI-powered systems work behind the scenes to make our lives more convenient and efficient. Understanding how these technologies function can help us appreciate the profound changes taking place in modern society.
Smartphones represent perhaps the most obvious example of AI in daily life. Every time you unlock your phone using facial recognition, you are utilizing sophisticated AI algorithms that can identify your unique features in milliseconds. These systems have become so advanced that they can recognize you even when you wear glasses, change your hairstyle, or are in different lighting conditions. The virtual assistants built into smartphones, such as Siri, Google Assistant, and Alexa, use natural language processing (NLP) to understand spoken commands and respond appropriately. These assistants can set reminders, answer questions, play music, and even control other smart devices in your home.
Social media platforms rely heavily on AI to curate content for users. When you scroll through your Facebook, Instagram, or TikTok feed, you are not seeing posts in chronological order. Instead, complex AI algorithms analyze your past behavior—what you like, share, comment on, and how long you view certain posts—to predict what content will most interest you. This personalization keeps users engaged but has also raised concerns about filter bubbles, where people only see information that reinforces their existing beliefs.
Online shopping has been transformed by AI-powered recommendation systems. When Amazon suggests products you might like, or Netflix recommends shows based on your viewing history, these are AI systems at work. These recommendation engines analyze vast amounts of data about user preferences and purchasing patterns to make increasingly accurate predictions. Studies show that approximately 35% of Amazon’s revenue comes from its recommendation system, demonstrating the commercial effectiveness of this technology.
In transportation, AI is making significant impacts through navigation apps like Google Maps and Waze. These applications use AI to analyze real-time traffic data from millions of users, identifying the fastest routes and predicting traffic conditions. The algorithms consider multiple variables: current traffic flow, historical patterns for the time of day, road construction, accidents, and even weather conditions. This real-time analysis helps drivers save time and reduce fuel consumption.
Smart home devices represent another growing application of AI in daily life. Thermostats like Nest learn your temperature preferences and schedule, automatically adjusting heating and cooling to optimize comfort while reducing energy costs. Smart lighting systems can detect when rooms are occupied and adjust brightness based on the time of day and available natural light. These systems collectively contribute to making homes more energy-efficient and comfortable.
Email services use AI to filter spam and organize your inbox. Gmail’s spam filter, for example, uses machine learning to identify unwanted emails with over 99% accuracy. The system continuously learns from user behavior—when you mark something as spam or move it to a specific folder—improving its performance over time. Priority Inbox features use AI to determine which emails are most important based on your past interactions.
In healthcare, AI applications are becoming increasingly common in consumer devices. Wearable fitness trackers like Fitbit and Apple Watch use AI algorithms to monitor heart rate patterns, detect irregular rhythms that might indicate health problems, and provide insights about sleep quality. These devices can alert users to potential health issues, prompting them to seek medical attention before conditions become serious.
Photography has been revolutionized by AI-enhanced cameras in smartphones. Modern phones use AI to automatically adjust settings like exposure, focus, and color balance to produce better photos. Portrait mode features use AI to identify the subject and blur the background, creating professional-looking images. Some phones can even enhance photos taken in low light by using AI to reduce noise and improve clarity.
The banking sector employs AI for fraud detection and customer service. When you receive an alert about suspicious activity on your credit card, that is likely an AI system that detected unusual purchasing patterns. Chatbots on banking websites and apps use AI to answer customer questions and help with basic transactions, providing 24/7 service without human intervention.
Despite these benefits, the proliferation of AI in daily life raises important questions about privacy and data security. These systems require access to personal information to function effectively, creating potential vulnerabilities. As AI becomes more embedded in everyday objects and services, users must balance the convenience these technologies offer against concerns about how their data is collected, stored, and used. Understanding both the capabilities and limitations of AI helps individuals make informed decisions about which technologies to embrace in their daily lives.
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
1. According to the passage, smartphone facial recognition systems can:
A. only work in perfect lighting conditions
B. recognize users despite changes in appearance
C. function exclusively with virtual assistants
D. identify users’ emotions accurately
2. Social media platforms use AI algorithms primarily to:
A. display posts in the order they were created
B. prevent users from seeing controversial content
C. show content predicted to interest each user
D. limit the time users spend on the platform
3. The passage states that Amazon’s recommendation system:
A. accounts for over one-third of the company’s sales
B. is less effective than Netflix’s system
C. only works for frequent shoppers
D. reduces customer satisfaction
4. Smart home thermostats like Nest help users by:
A. requiring manual temperature adjustments
B. learning preferences and adjusting automatically
C. working only during specific times of day
D. eliminating the need for heating systems
5. Gmail’s spam filter achieves its high accuracy by:
A. employing human reviewers for every email
B. blocking all emails from unknown senders
C. continuously learning from user behavior
D. using simple keyword matching
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. Navigation apps like Google Maps only consider current traffic when suggesting routes.
7. Wearable fitness trackers can identify potential health problems before they become serious.
8. AI-enhanced smartphone cameras are more expensive than traditional cameras.
9. Banking AI systems can detect unusual purchasing patterns that might indicate fraud.
Questions 10-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
10. Virtual assistants use __ __ to understand and respond to spoken commands.
11. AI-driven content curation on social media has created concerns about __ __, where users only see information confirming their existing views.
12. Smart lighting systems can __ when people are in rooms and adjust accordingly.
13. The widespread use of AI in everyday life creates potential __ related to personal data.
PASSAGE 2 – The Evolution and Implementation of AI Systems
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The trajectory of artificial intelligence development has been marked by alternating periods of optimism and disappointment, commonly referred to as “AI summers” and “AI winters.” Understanding this historical context is essential for comprehending the current proliferation of AI applications and the realistic expectations we should hold for future developments. The technology we now encounter daily is the result of decades of research, incremental improvements, and breakthrough innovations that have finally made practical AI systems commercially viable.
A. The foundational concepts of artificial intelligence emerged in the 1950s, when computer scientists began exploring whether machines could simulate human intelligence. Early pioneers like Alan Turing posed fundamental questions about machine thinking and developed tests to evaluate computer intelligence. The 1956 Dartmouth Conference is widely considered the birthplace of AI as a formal field of study, where researchers optimistically predicted that machines with human-level intelligence would be created within a generation. However, these early predictions proved overly ambitious, as the complexity of human cognition was vastly underestimated and computing power remained severely limited.
B. The first AI winter occurred in the 1970s when initial enthusiasm gave way to disillusionment. Early AI systems excelled at specific, narrowly defined tasks but failed to achieve the general intelligence researchers had envisioned. Funding agencies became skeptical about the field’s potential, leading to reduced investment and slower progress. This period taught important lessons about the difference between narrow AI—systems designed for specific tasks—and artificial general intelligence (AGI), which remains largely theoretical even today.
C. A resurgence of interest occurred in the 1980s with the development of expert systems, which used rule-based programming to replicate human expertise in specific domains. Companies invested heavily in these systems for applications like medical diagnosis and financial analysis. However, another winter followed in the late 1980s and early 1990s when expert systems proved expensive to maintain and difficult to scale. The systems required constant updating by human experts and couldn’t learn from new information independently.
D. The current AI spring began in the early 2010s, driven by three critical factors: exponential increases in computing power, the availability of vast amounts of data, and breakthroughs in machine learning techniques, particularly deep learning. Deep learning uses artificial neural networks with multiple layers to progressively extract higher-level features from raw input. For example, in image recognition, lower layers might identify edges, middle layers might recognize shapes, and higher layers might identify complete objects like faces or cars.
E. The transformer architecture, introduced in 2017, represented another pivotal advancement. Transformers excel at processing sequential data and understanding context, making them particularly effective for natural language tasks. This architecture underpins modern language models like GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers), which have revolutionized how computers understand and generate human language. These models are pre-trained on enormous text corpora and can then be fine-tuned for specific applications, making them remarkably versatile.
F. Implementation challenges remain significant despite these technical advances. One major issue is the “black box” problem: deep learning systems often produce accurate results, but their decision-making processes are opaque, even to their creators. When an AI system denies a loan application or recommends a medical treatment, understanding the reasoning behind these decisions is crucial for accountability and fairness. Researchers are developing “explainable AI” techniques to make these systems more transparent, but this remains an active area of investigation.
G. Bias in AI systems has emerged as another critical concern. Because machine learning systems learn from historical data, they can perpetuate and amplify existing societal biases. For instance, facial recognition systems have shown lower accuracy rates for people with darker skin tones because training datasets disproportionately featured lighter-skinned individuals. Similarly, hiring algorithms have sometimes discriminated against women because they were trained on data from companies where men predominately held certain positions. Addressing these biases requires careful curation of training data and ongoing monitoring of system performance across different demographic groups.
H. The computational resources required for training state-of-the-art AI models have also become a concern. Training large language models can consume as much energy as several cars use over their entire lifetimes, raising questions about the environmental sustainability of AI development. Some researchers argue that the field needs to focus more on creating efficient algorithms rather than simply scaling up existing approaches with more data and computing power.
I. Deployment of AI systems in real-world environments presents unique challenges. Systems that perform excellently in controlled testing environments may encounter unexpected situations that degrade their performance. Autonomous vehicles, for example, must handle countless edge cases—unusual situations that rarely occur but require appropriate responses. A self-driving car might need to interpret a police officer’s hand signals, recognize objects it has never seen before, or navigate through construction zones with temporarily modified road configurations.
J. The regulatory landscape for AI is still evolving, with different countries taking varied approaches. The European Union has proposed comprehensive AI regulations that would classify systems by risk level and impose corresponding requirements. High-risk applications, such as those used in critical infrastructure or law enforcement, would face stringent requirements for transparency, accuracy, and human oversight. Meanwhile, other jurisdictions are taking more industry-led approaches, relying on companies to self-regulate. How these regulatory frameworks develop will significantly shape the future implementation of AI technologies in society.
Questions 14-18: Matching Headings
The passage has ten paragraphs, A-J.
Choose the correct heading for paragraphs D-H from the list of headings below.
List of Headings:
i. Environmental costs of advanced AI training
ii. The problem of inherited social prejudices
iii. Early unrealistic expectations about AI
iv. Factors enabling the current AI boom
v. Difficulties in understanding AI decisions
vi. The rise and fall of rule-based systems
vii. Different national strategies for AI control
viii. Challenges in real-world AI applications
ix. Revolutionary advances in language processing
14. Paragraph D
15. Paragraph E
16. Paragraph F
17. Paragraph G
18. Paragraph H
Questions 19-23: Yes/No/Not Given
Do the following statements agree with the claims 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. The 1956 Dartmouth Conference participants accurately predicted the timeline for developing human-level AI.
20. Expert systems in the 1980s could independently learn from new information.
21. Deep learning systems process information through multiple layers of increasing complexity.
22. All researchers agree that focusing on larger models is the best approach for AI development.
23. The European Union’s proposed AI regulations would apply the same requirements to all AI systems.
Questions 24-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
Artificial intelligence has experienced periods of excitement and disappointment throughout its history. The current success of AI is built on three main factors: increased computing power, large amounts of data, and advances in 24. __ __ techniques. However, significant challenges remain, including the “black box” problem where AI 25. __ __ are difficult to understand. Additionally, AI systems can perpetuate societal 26. __ when trained on historical data that reflects past discrimination.
PASSAGE 3 – Philosophical and Societal Implications of Artificial Intelligence Integration
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The pervasive integration of artificial intelligence into the fabric of contemporary society necessitates a rigorous examination of its profound philosophical implications and potential to fundamentally alter human existence. While discussions of AI often gravitate toward technical capabilities and practical applications, the more salient questions concern how these technologies reshape our understanding of consciousness, agency, responsibility, and what it means to be human in an age where machines increasingly perform tasks once considered uniquely human domains.
The question of machine consciousness represents one of the most intractable problems at the intersection of philosophy, computer science, and cognitive science. Contemporary AI systems, despite their impressive capabilities, operate through statistical pattern recognition and mathematical optimization rather than anything resembling subjective experience or phenomenal consciousness—what philosophers call “qualia,” the subjective, qualitative properties of experiences. A deep learning system that identifies images of cats has no phenomenological experience of “seeing” a cat; it merely processes pixel values through mathematical transformations optimized to produce the correct classification. However, as AI systems become more sophisticated and exhibit increasingly complex behaviors, the demarcation line between genuine understanding and sophisticated simulation becomes philosophically ambiguous.
Minh họa triết học về nhận thức trí tuệ nhân tạo và ý thức máy móc trong IELTS Reading
The thought experiment known as the “Chinese Room,” proposed by philosopher John Searle in 1980, illuminates this distinction. Imagine a person who speaks only English sitting in a room, following elaborate rules for manipulating Chinese characters without understanding their meaning. If the rules are sufficiently comprehensive, this person could produce responses indistinguishable from those of a native Chinese speaker, yet possess no genuine comprehension of the language. Searle argued that computers are fundamentally analogous to this scenario—they manipulate symbols according to rules without understanding meaning. Critics contend that this argument underestimates how understanding might emerge from complex information processing, but the debate remains unresolved and increasingly relevant as AI systems demonstrate more sophisticated language capabilities.
The epistemological implications of AI extend beyond questions of machine consciousness to fundamental issues about knowledge itself. Traditional epistemology concerns how humans acquire, justify, and utilize knowledge. However, modern AI systems increasingly generate knowledge through processes opaque to human understanding. When a deep learning system discovers that particular patterns in medical images correlate with disease, has it produced knowledge? The system cannot explain its reasoning in terms humans find comprehensible, yet its predictions may be more accurate than human experts. This phenomenon challenges conventional notions that knowledge requires justification and understanding, suggesting we may need expanded epistemological frameworks that accommodate non-human knowledge generation.
The concept of agency—the capacity for autonomous action and decision-making—undergoes radical reconceptualization in the AI era. Traditionally, agency has been considered an exclusively human (or possibly animal) attribute, intrinsically linked to consciousness, intentionality, and free will. However, AI systems now make consequential decisions affecting human lives: approving or denying loans, determining prison sentences through risk assessment algorithms, selecting candidates for job interviews, and even influencing political opinions through targeted content delivery. These systems exhibit what might be termed “functional agency“—the capacity to make decisions and take actions—without possessing the consciousness or moral reasoning traditionally associated with genuine agency.
This disjunction between functional and conscious agency creates significant ethical quandaries regarding responsibility and accountability. When an autonomous vehicle causes an accident, who bears responsibility? The car’s owner, the manufacturer, the software engineers, or the AI system itself? Traditional legal and ethical frameworks presuppose that moral responsibility correlates with conscious intentionality and the capacity for moral reasoning. However, when AI systems make decisions through processes their creators cannot fully explain or predict, these frameworks become inadequate. Some scholars argue for distributed responsibility models where accountability is shared among all stakeholders in an AI system’s development and deployment, while others suggest we may need to recognize forms of “machine responsibility” distinct from human moral agency.
The automation of cognitive labor raises existential questions about human purpose and identity. Throughout history, work has served not merely as a means of economic survival but as a source of meaning, identity, and social connection. As AI systems demonstrate competence in domains previously requiring advanced education and specialized expertise—including legal research, medical diagnosis, artistic creation, and even scientific discovery—concerns arise about technological unemployment and the erosion of human purpose. Some envision utopian scenarios where AI liberation from labor enables humans to pursue creative and intellectual fulfillment. Others foresee dystopian outcomes where most humans become economically superfluous, leading to unprecedented social stratification and existential malaise.
Cognitive scientists and philosophers have long debated whether human intelligence is fundamentally different from artificial intelligence or merely more complex. The “embodied cognition” perspective argues that human intelligence is inextricably linked to our physical bodies and sensorimotor experiences, suggesting that disembodied AI could never replicate truly human-like understanding. A computer might process information about “running” or “pain,” but without a body that runs or feels pain, it allegedly lacks genuine comprehension of these concepts. Conversely, functionalists argue that what matters is the computational structure of information processing, not its physical substrate—intelligence is intelligence whether instantiated in biological neurons or silicon circuits.
The potential development of artificial general intelligence (AGI)—systems with human-level intelligence across diverse domains—and eventually artificial superintelligence (ASI)—systems far exceeding human cognitive capabilities—intensifies these philosophical concerns. Some researchers, including prominent figures like Nick Bostrom, warn that ASI could pose existential risks to humanity if its objectives are not perfectly aligned with human values. The “alignment problem“—ensuring that advanced AI systems pursue goals compatible with human welfare—represents perhaps the most critical challenge in AI development. However, specifying human values precisely enough to program them into an AI system proves extraordinarily difficult, given the complexity, context-dependence, and cultural variability of human ethical systems.
The epistemological authority that society grants to AI systems also merits scrutiny. When AI recommendations influence judicial decisions, medical treatments, or educational opportunities, these systems acquire a form of epistemic authority traditionally reserved for human experts. This transference of authority occurs often without adequate consideration of the systems’ limitations, biases, or the appropriateness of quantitative optimization for inherently qualitative human decisions. The seductive precision of algorithmic outputs can obscure the subjective choices embedded in their design—what data to include, which variables to prioritize, how to operationalize complex concepts like “risk” or “merit.”
Ultimately, the integration of AI into daily life represents not merely a technological shift but a fundamental transformation in the human condition. These technologies challenge us to reconsider what distinguishes humans from machines, whether consciousness and understanding can exist in non-biological forms, and how to maintain human dignity and purpose in a world where many traditionally human capabilities can be replicated or surpassed by artificial systems. Navigating this transformation requires not only technical expertise but also philosophical wisdom, ethical thoughtfulness, and a commitment to ensuring that AI development serves genuinely human flourishing rather than merely technical optimization or economic efficiency.
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
27. According to the passage, contemporary AI systems:
A. possess phenomenal consciousness similar to humans
B. process information through statistical pattern recognition
C. have subjective experiences when identifying objects
D. understand the meaning behind their classifications
28. The Chinese Room thought experiment suggests that:
A. computers can understand Chinese better than English
B. following rules is sufficient for genuine understanding
C. symbol manipulation differs from true comprehension
D. all critics agree with Searle’s conclusions
29. The passage indicates that modern AI-generated knowledge:
A. always requires human justification to be valid
B. challenges traditional epistemological frameworks
C. is easily explainable to human experts
D. cannot be more accurate than human predictions
30. When discussing responsibility for AI decisions, the passage suggests:
A. AI systems themselves should be held legally responsible
B. only software engineers should bear accountability
C. traditional frameworks adequately address these issues
D. current ethical models are insufficient for AI contexts
31. The embodied cognition perspective argues that:
A. artificial intelligence is fundamentally identical to human intelligence
B. physical sensorimotor experiences are essential for genuine understanding
C. disembodied AI can fully replicate human cognition
D. computational structure is more important than physical form
Questions 32-36: Matching Features
Match each researcher or concept (32-36) with the correct description (A-H).
Researchers/Concepts:
32. John Searle
33. Nick Bostrom
34. Qualia
35. Functionalists
36. Epistemological authority
Descriptions:
A. Warns about existential risks from superintelligent AI
B. Believes computational structure matters more than physical form
C. The subjective, qualitative properties of conscious experience
D. Proposed the Chinese Room thought experiment
E. Developed the transformer architecture for AI
F. The credibility and trust society places in AI recommendations
G. Created the first expert systems in the 1980s
H. Argued that AI will inevitably develop consciousness
32. __
33. __
34. __
35. __
36. __
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 agency do AI systems demonstrate when making decisions without consciousness?
38. What problem refers to ensuring advanced AI systems pursue goals compatible with human welfare?
39. What model do some scholars propose for sharing accountability among those involved in AI development?
40. According to the passage, what does AI integration represent beyond just a technological change?
Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- B
- C
- A
- B
- C
- FALSE
- TRUE
- NOT GIVEN
- TRUE
- natural language
- filter bubbles
- detect
- vulnerabilities
PASSAGE 2: Questions 14-26
- iv
- ix
- v
- ii
- i
- NO
- NO
- YES
- NO
- NO
- machine learning / deep learning
- decision-making processes
- biases
PASSAGE 3: Questions 27-40
- B
- C
- B
- D
- B
- D
- A
- C
- B
- F
- functional agency
- alignment problem
- distributed responsibility
- fundamental transformation
Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: B – recognize users despite changes in appearance
- Dạng câu hỏi: Multiple Choice
- Từ khóa: smartphone facial recognition systems, can
- Vị trí trong bài: Đoạn 2, dòng 2-4
- Giải thích: Bài đọc nói rõ “These systems have become so advanced that they can recognize you even when you wear glasses, change your hairstyle, or are in different lighting conditions.” Đây là paraphrase trực tiếp của đáp án B. Đáp án A sai vì hệ thống hoạt động được trong “different lighting conditions”, C và D không được đề cập.
Câu 2: C – show content predicted to interest each user
- Dạng câu hỏi: Multiple Choice
- Từ khóa: social media platforms, AI algorithms, primarily
- Vị trí trong bài: Đoạn 3, dòng 2-4
- Giải thích: Bài viết giải thích “complex AI algorithms analyze your past behavior… to predict what content will most interest you.” Từ “predict what content will most interest you” được paraphrase thành “show content predicted to interest each user” trong đáp án C.
Câu 3: A – accounts for over one-third of the company’s sales
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Amazon’s recommendation system
- Vị trí trong bài: Đoạn 4, dòng cuối
- Giải thích: Đoạn văn nêu rõ “approximately 35% of Amazon’s revenue comes from its recommendation system”. 35% chính là “over one-third” (hơn 1/3), và “revenue” được paraphrase thành “sales”.
Câu 6: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Navigation apps, Google Maps, only consider current traffic
- Vị trí trong bài: Đoạn 5, dòng 3-5
- Giải thích: Bài đọc liệt kê rất nhiều yếu tố: “current traffic flow, historical patterns for the time of day, road construction, accidents, and even weather conditions.” Câu hỏi nói “only consider current traffic” mâu thuẫn với thông tin này, nên đáp án là FALSE.
Câu 7: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Wearable fitness trackers, identify potential health problems
- Vị trí trong bài: Đoạn 8, dòng cuối
- Giải thích: Bài viết nói “These devices can alert users to potential health issues, prompting them to seek medical attention before conditions become serious.” Điều này khớp hoàn toàn với ý của câu hỏi.
Câu 10: natural language
- Dạng câu hỏi: Sentence Completion
- Từ khóa: Virtual assistants, understand and respond, spoken commands
- Vị trí trong bài: Đoạn 2, dòng 5-6
- Giải thích: Câu gốc: “The virtual assistants… use natural language processing (NLP) to understand spoken commands.” Đáp án cần điền vào chỗ trống là “natural language”.
Câu 11: filter bubbles
- Dạng câu hỏi: Sentence Completion
- Từ khóa: concerns about, users only see information confirming existing views
- Vị trí trong bài: Đoạn 3, dòng cuối
- Giải thích: Bài viết đề cập “raised concerns about filter bubbles, where people only see information that reinforces their existing beliefs.” “Reinforces their existing beliefs” được paraphrase thành “confirming their existing views”.
Passage 2 – Giải Thích
Câu 14: iv – Factors enabling the current AI boom
- Dạng câu hỏi: Matching Headings
- Vị trí trong bài: Đoạn D
- Giải thích: Đoạn D bắt đầu với “The current AI spring began in the early 2010s, driven by three critical factors” và liệt kê ba yếu tố: computing power, data availability, và machine learning breakthroughs. Đây chính xác là các yếu tố tạo nên sự bùng nổ AI hiện tại.
Câu 15: ix – Revolutionary advances in language processing
- Dạng câu hỏi: Matching Headings
- Vị trí trong bài: Đoạn E
- Giải thích: Đoạn này tập trung vào “transformer architecture” và cách nó “revolutionized how computers understand and generate human language.” Từ “revolutionized” trong bài khớp với “revolutionary advances” trong heading.
Câu 16: v – Difficulties in understanding AI decisions
- Dạng câu hỏi: Matching Headings
- Vị trí trong bài: Đoạn F
- Giải thích: Đoạn F thảo luận về “black box problem” – việc các hệ thống AI có “decision-making processes” that are “opaque”. Đây chính là khó khăn trong việc hiểu các quyết định của AI.
Câu 19: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: 1956 Dartmouth Conference, accurately predicted, timeline
- Vị trí trong bài: Đoạn A, dòng cuối
- Giải thích: Bài viết nói rõ “However, these early predictions proved overly ambitious” – các dự đoán là quá tham vọng, tức là không chính xác. Đây là mâu thuẫn trực tiếp với câu hỏi.
Câu 21: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Deep learning systems, multiple layers, increasing complexity
- Vị trí trong bài: Đoạn D, giữa đoạn
- Giải thích: Bài viết giải thích “Deep learning uses artificial neural networks with multiple layers to progressively extract higher-level features” và cho ví dụ về image recognition với các layers từ đơn giản đến phức tạp. Điều này khớp với claims trong câu hỏi.
Câu 24: machine learning / deep learning
- Dạng câu hỏi: Summary Completion
- Từ khóa: advances in, techniques
- Vị trí trong bài: Đoạn D, dòng đầu
- Giải thích: Ba yếu tố được liệt kê là “computing power, the availability of vast amounts of data, and breakthroughs in machine learning techniques, particularly deep learning.” Cả hai từ đều chấp nhận được.
Passage 3 – Giải Thích
Câu 27: B – process information through statistical pattern recognition
- Dạng câu hỏi: Multiple Choice
- Từ khóa: contemporary AI systems
- Vị trí trong bài: Đoạn 2, dòng 2-3
- Giải thích: Bài viết nói rõ “Contemporary AI systems, despite their impressive capabilities, operate through statistical pattern recognition and mathematical optimization.” Đây là match chính xác với đáp án B.
Câu 28: C – symbol manipulation differs from true comprehension
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Chinese Room thought experiment, suggests
- Vị trí trong bài: Đoạn 3
- Giải thích: Searle lập luận rằng “computers are fundamentally analogous to this scenario—they manipulate symbols according to rules without understanding meaning.” “Without understanding meaning” chính là việc thiếu “true comprehension”.
Câu 31: B – physical sensorimotor experiences are essential for genuine understanding
- Dạng câu hỏi: Multiple Choice
- Từ khóa: embodied cognition perspective
- Vị trí trong bài: Đoạn 8, dòng đầu
- Giải thích: Đoạn văn giải thích “The ’embodied cognition’ perspective argues that human intelligence is inextricably linked to our physical bodies and sensorimotor experiences, suggesting that disembodied AI could never replicate truly human-like understanding.”
Câu 32: D – Proposed the Chinese Room thought experiment
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 3, dòng đầu
- Giải thích: Bài viết nêu rõ “The thought experiment known as the ‘Chinese Room,’ proposed by philosopher John Searle in 1980.”
Câu 37: functional agency
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: type of agency, AI systems, making decisions without consciousness
- Vị trí trong bài: Đoạn 5, dòng cuối
- Giải thích: Bài viết định nghĩa “These systems exhibit what might be termed ‘functional agency’—the capacity to make decisions and take actions—without possessing the consciousness.”
Câu 38: alignment problem
- Dạng câu hỏi: Short-answer Questions
- Từ khóa: ensuring advanced AI systems, pursue goals compatible with human welfare
- Vị trí trong bài: Đoạn 9, giữa đoạn
- Giải thích: Câu gốc: “The ‘alignment problem’—ensuring that advanced AI systems pursue goals compatible with human welfare—represents perhaps the most critical challenge.”
Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| seamlessly integrated | adv + v | /ˈsiːmləsli ˈɪntɪɡreɪtɪd/ | tích hợp liền mạch | AI has seamlessly integrated into our daily routines | integrate seamlessly into |
| utilize | v | /ˈjuːtɪlaɪz/ | sử dụng, tận dụng | You are utilizing sophisticated AI algorithms | utilize technology/resources |
| curate | v | /kjʊəˈreɪt/ | tuyển chọn, sắp xếp | Social media platforms rely heavily on AI to curate content | curate content/information |
| chronological order | n | /ˌkrɒnəˈlɒdʒɪkl ˈɔːdə/ | thứ tự thời gian | You are not seeing posts in chronological order | in chronological order |
| filter bubbles | n | /ˈfɪltə ˈbʌblz/ | bong bóng lọc (hiện tượng chỉ tiếp nhận thông tin phù hợp quan điểm) | Raised concerns about filter bubbles | create/exist in filter bubbles |
| recommendation engines | n | /ˌrekəmenˈdeɪʃn ˈendʒɪnz/ | công cụ gợi ý | These recommendation engines analyze vast amounts of data | powerful recommendation engines |
| vast amounts | n | /vɑːst əˈmaʊnts/ | lượng lớn | Analyze vast amounts of data | vast amounts of data/information |
| real-time analysis | n | /ˈrɪəl taɪm əˈnæləsɪs/ | phân tích thời gian thực | This real-time analysis helps drivers save time | conduct real-time analysis |
| automatically adjusting | adv + v | /ˌɔːtəˈmætɪkli əˈdʒʌstɪŋ/ | tự động điều chỉnh | Automatically adjusting heating and cooling | automatically adjust settings |
| machine learning | n | /məˈʃiːn ˈlɜːnɪŋ/ | học máy | Gmail’s spam filter uses machine learning | apply/use machine learning |
| proliferation | n | /prəˌlɪfəˈreɪʃn/ | sự gia tăng nhanh chóng | The proliferation of AI in daily life | proliferation of technology/weapons |
| vulnerabilities | n | /ˌvʌlnərəˈbɪlətiz/ | lỗ hổng, điểm yếu | Creating potential vulnerabilities | security vulnerabilities |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| trajectory | n | /trəˈdʒektəri/ | quỹ đạo phát triển | The trajectory of AI development | career/development trajectory |
| alternating periods | n | /ˈɔːltəneɪtɪŋ ˈpɪəriədz/ | các giai đoạn xen kẽ | Marked by alternating periods of optimism | alternating periods of growth |
| incremental improvements | n | /ˌɪŋkrəˈmentl ɪmˈpruːvmənts/ | cải tiến từng bước | Result of incremental improvements | make incremental improvements |
| commercially viable | adj | /kəˈmɜːʃəli ˈvaɪəbl/ | khả thi về mặt thương mại | Made practical AI systems commercially viable | commercially viable products |
| foundational concepts | n | /faʊnˈdeɪʃənl ˈkɒnsepts/ | các khái niệm nền tảng | The foundational concepts of AI emerged | foundational concepts/principles |
| overly ambitious | adj | /ˈəʊvəli æmˈbɪʃəs/ | quá tham vọng | These predictions proved overly ambitious | overly ambitious goals/plans |
| severely limited | adv + adj | /sɪˈvɪəli ˈlɪmɪtɪd/ | bị giới hạn nghiêm trọng | Computing power remained severely limited | severely limited resources |
| gave way to | v phrase | /ɡeɪv weɪ tuː/ | nhường chỗ cho | Initial enthusiasm gave way to disillusionment | give way to pressure/demands |
| narrowly defined tasks | adj + v + n | /ˈnærəʊli dɪˈfaɪnd tɑːsks/ | các nhiệm vụ được định nghĩa hẹp | Excelled at specific, narrowly defined tasks | narrowly defined scope/role |
| resurgence | n | /rɪˈsɜːdʒəns/ | sự hồi sinh, trở lại | A resurgence of interest occurred | experience a resurgence |
| exponential increases | n | /ˌekspəˈnenʃl ˈɪnkriːsɪz/ | sự tăng trưởng theo cấp số nhân | Driven by exponential increases in computing power | exponential increases/growth |
| breakthroughs | n | /ˈbreɪkθruːz/ | đột phá | Breakthroughs in machine learning techniques | major/significant breakthroughs |
| underpins | v | /ˌʌndəˈpɪnz/ | làm nền tảng, hỗ trợ | This architecture underpins modern language models | underpin theories/systems |
| black box problem | n | /blæk bɒks ˈprɒbləm/ | vấn đề hộp đen | One major issue is the black box problem | solve the black box problem |
| perpetuate | v | /pəˈpetʃueɪt/ | duy trì, kéo dài | They can perpetuate existing societal biases | perpetuate stereotypes/myths |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| pervasive integration | adj + n | /pəˈveɪsɪv ˌɪntɪˈɡreɪʃn/ | sự tích hợp lan rộng | The pervasive integration of AI | pervasive integration/influence |
| necessitates | v | /nəˈsesɪteɪts/ | đòi hỏi, cần thiết | Necessitates a rigorous examination | necessitate changes/action |
| salient | adj | /ˈseɪliənt/ | nổi bật, quan trọng | The more salient questions concern | salient features/points |
| intractable | adj | /ɪnˈtræktəbl/ | khó giải quyết | One of the most intractable problems | intractable problems/conflicts |
| phenomenal consciousness | n | /fɪˈnɒmɪnl ˈkɒnʃəsnəs/ | ý thức hiện tượng học | Anything resembling phenomenal consciousness | possess phenomenal consciousness |
| qualia | n | /ˈkwɑːliə/ | cảm giác chủ quan | What philosophers call “qualia” | experience/possess qualia |
| demarcation line | n | /ˌdiːmɑːˈkeɪʃn laɪn/ | ranh giới phân định | The demarcation line becomes ambiguous | draw a demarcation line |
| epistemological implications | n | /ɪˌpɪstəməˈlɒdʒɪkl ˌɪmplɪˈkeɪʃnz/ | hàm ý nhận thức luận | The epistemological implications of AI | epistemological implications/questions |
| opaque | adj | /əʊˈpeɪk/ | mờ đục, không rõ ràng | Processes opaque to human understanding | opaque reasoning/processes |
| reconceptualization | n | /ˌriːkənˌsepʃuəlaɪˈzeɪʃn/ | sự khái niệm hóa lại | Agency undergoes radical reconceptualization | require reconceptualization |
| intrinsically linked | adv + v | /ɪnˈtrɪnsɪkli lɪŋkt/ | gắn liền về bản chất | Intrinsically linked to consciousness | intrinsically linked/connected |
| disjunction | n | /dɪsˈdʒʌŋkʃn/ | sự tách rời | This disjunction between functional and conscious agency | disjunction between theory/practice |
| ethical quandaries | n | /ˈeθɪkl ˈkwɒndəriz/ | tình huống khó xử về đạo đức | Creates significant ethical quandaries | face ethical quandaries |
| dystopian outcomes | adj + n | /dɪsˈtəʊpiən ˈaʊtkʌmz/ | kết quả u ám | Others foresee dystopian outcomes | dystopian outcomes/scenarios |
| embodied cognition | adj + n | /ɪmˈbɒdid kɒɡˈnɪʃn/ | nhận thức được thể hiện (qua cơ thể) | The embodied cognition perspective | embodied cognition theory |
| existential risks | adj + n | /ˌeɡzɪˈstenʃl rɪsks/ | rủi ro hiện sinh | ASI could pose existential risks | pose existential risks/threats |
| alignment problem | n | /əˈlaɪnmənt ˈprɒbləm/ | vấn đề căn chỉnh | The alignment problem represents the most critical challenge | solve the alignment problem |
| epistemic authority | adj + n | /ɪˈpɪstemɪk ɔːˈθɒrəti/ | quyền uy nhận thức | The epistemological authority that society grants | epistemic authority/power |
Kết Bài
Chủ đề “The role of artificial intelligence in daily life” không chỉ là một trong những chủ đề phổ biến nhất trong IELTS Reading mà còn phản ánh xu hướng phát triển công nghệ đang định hình lại cuộc sống chúng ta. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ ba mức độ khó từ cơ bản đến nâng cao, giúp bạn làm quen với cách IELTS Reading test xây dựng độ khó tăng dần một cách tự nhiên.
Ba passages trong bài đã cung cấp góc nhìn toàn diện về AI: từ ứng dụng thực tế trong đời sống hàng ngày (Passage 1), lịch sử phát triển và thách thức triển khai (Passage 2), đến những vấn đề triết học và xã hội sâu sắc (Passage 3). Tương tự như Impact of smart technologies on urban living, chủ đề AI đòi hỏi bạn không chỉ hiểu từ vựng chuyên ngành mà còn nắm vững kỹ năng paraphrase và suy luận logic.
Học viên luyện tập đề thi IELTS Reading về trí tuệ nhân tạo với đáp án chi tiết
Các đáp án chi tiết kèm giải thích vị trí và cách paraphrase sẽ giúp bạn tự đánh giá chính xác năng lực hiện tại và xác định những điểm cần cải thiện. Đặc biệt, hệ thống từ vựng được phân loại theo từng passage giúp bạn học từ vựng theo ngữ cảnh – phương pháp hiệu quả nhất để ghi nhớ lâu dài. Để mở rộng vốn từ về công nghệ, bạn có thể tham khảo thêm bài viết về How technology is reshaping the financial industry.
Những kỹ thuật làm bài được chia sẻ trong phần giải thích đáp án – như cách xác định từ khóa, tìm vị trí thông tin nhanh, và nhận diện các dạng paraphrase phổ biến – là kinh nghiệm thực chiến từ hàng ngàn học viên đã đạt band điểm cao. Hãy áp dụng chúng thường xuyên trong quá trình luyện tập.
Những thách thức về công nghệ cũng liên quan mật thiết đến các vấn đề xã hội rộng lớn hơn. Nếu bạn quan tâm đến cách công nghệ ảnh hưởng đến quyền tự do ngôn luận, đừng bỏ qua bài viết What are the implications of social media for freedom of speech?. Tương tự, để hiểu rõ hơn về vai trò của công nghệ trong phát triển bền vững, bạn có thể khám phá The role of renewable energy in rural electrification.
Hãy nhớ rằng, IELTS Reading không chỉ kiểm tra khả năng đọc hiểu mà còn đánh giá kỹ năng quản lý thời gian và khả năng xử lý thông tin dưới áp lực. Thực hành đều đặn với các đề thi đầy đủ như thế này sẽ giúp bạn xây dựng sự tự tin và phản xạ cần thiết để đạt band điểm mục tiêu trong kỳ thi thật. Một khía cạnh khác của công nghệ mà bạn nên chú ý là Challenges in managing global water resources, nơi AI đóng vai trò quan trọng trong giải quyết các vấn đề môi trường toàn cầu.
Chúc bạn học tập hiệu quả và đạt được kết quả cao trong kỳ thi IELTS sắp tới!
