IELTS Reading: AI và Quyền Riêng Tư Người Tiêu Dùng – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Chủ đề về Trí tuệ nhân tạo (AI) và quyền riêng tư người tiêu dùng trong kỷ nguyên số đang trở thành một trong những đề tài được quan tâm hàng đầu trong các kỳ thi IELTS Reading gần đây. Với sự phát triển vượt bậc của công nghệ và những tranh luận không ngừng về bảo mật dữ liệu cá nhân, chủ đề này xuất hiện thường xuyên trong cả Academic và General Training modules, đặc biệt từ Cambridge IELTS 15 trở đi.

Bài viết này cung cấp cho bạn một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages từ dễ đến khó, được thiết kế dựa trên format thi thật 100%. Bạn sẽ được luyện tập với 40 câu hỏi đa dạng, bao gồm tất cả các dạng câu hỏi phổ biến như True/False/Not Given, Multiple Choice, Matching Headings, Summary Completion và nhiều dạng khác. Mỗi câu trả lời đều có giải thích chi tiết kèm vị trí cụ thể trong bài, giúp bạn hiểu rõ cách paraphrase và chiến lược làm bài hiệu quả.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, với độ khó tăng dần giúp bạn làm quen với pressure của kỳ thi thực tế và cải thiện kỹ năng quản lý thời gian.

Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút và bao gồm 3 passages với tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được tính 1 điểm, không bị trừ điểm với câu trả lời sai. Điểm số sau đó được chuyển đổi thành band score từ 1-9.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó Easy, câu hỏi 1-13)
• Passage 2: 18-20 phút (độ khó Medium, câu hỏi 14-26)
• Passage 3: 23-25 phút (độ khó Hard, câu hỏi 27-40)

Lưu ý dành 2-3 phút cuối để chuyển đáp án vào Answer Sheet và kiểm tra lại.

Các Dạng Câu Hỏi Trong Đề Này

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

1. True/False/Not Given – Xác định thông tin đúng/sai/không được nhắc đến
2. Multiple Choice – Chọn đáp án đúng từ các lựa chọn cho sẵn
3. Matching Information – Ghép thông tin với đoạn văn tương ứng
4. Sentence Completion – Hoàn thành câu với từ trong bài đọc
5. Matching Headings – Ghép tiêu đề với đoạn văn phù hợp
6. Summary Completion – Hoàn thành đoạn tóm tắt
7. Short-answer Questions – Trả lời câu hỏi ngắn với từ trong bài

---

IELTS Reading Practice Test

PASSAGE 1 – The Rise of AI in Everyday Consumer Life

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Artificial Intelligence (AI) has rapidly transformed the way consumers interact with technology in their daily lives. From voice-activated assistants like Amazon’s Alexa and Apple’s Siri to personalized recommendations on Netflix and Spotify, AI systems have become ubiquitous in modern society. These technologies analyze vast amounts of user data to provide tailored experiences that anticipate consumer needs and preferences. However, this convenience comes with significant implications for personal privacy.

The fundamental mechanism behind most consumer AI applications involves data collection and pattern recognition. When you ask your smart speaker about the weather or request a song, the device records your voice, converts it to text, and sends it to cloud servers for processing. Similarly, when you browse an e-commerce website, AI algorithms track your clicking patterns, search history, and purchase behavior to build a comprehensive profile of your interests and habits. This information is then used to optimize your user experience, showing you products you are more likely to buy or content you are more likely to enjoy.

Major technology companies argue that this data-driven approach benefits consumers by saving time and reducing decision fatigue. For instance, Google’s search algorithm uses AI to understand user intent and deliver more relevant results. Facebook employs machine learning to curate news feeds based on individual engagement patterns. Streaming platforms use predictive analytics to suggest movies and shows that match viewing histories. Retailers like Amazon utilize AI to streamline the shopping experience with features such as one-click purchasing and anticipatory shipping, where products are moved to warehouses near customers before they even order them.

Despite these conveniences, privacy advocates have raised serious concerns about the extent of data surveillance inherent in AI systems. Every interaction with an AI-powered service generates data that can reveal intimate details about a person’s life – their location patterns, social connections, financial status, health concerns, political views, and even emotional states. This information is often stored indefinitely and may be shared with third parties, including advertisers, data brokers, and in some cases, government agencies. The Terms of Service agreements that users accept, often without reading, typically grant companies broad permissions to collect and use personal data.

[image-1|ai-thu-thap-du-lieu-nguoi-tieu-dung|Minh họa hệ thống AI thu thập và phân tích dữ liệu người tiêu dùng qua các thiết bị thông minh|A modern digital illustration showing multiple smart devices (smartphone, smart speaker, laptop, fitness tracker) connected by glowing data streams to a central AI cloud system, with small icons representing personal data (location pins, shopping carts, music notes, health symbols) flowing from devices to the cloud, clean tech aesthetic with blue and white color scheme|

The opacity of AI algorithms presents another challenge to consumer privacy. These systems operate as “black boxes” – even their creators cannot always explain exactly how they arrive at specific decisions or recommendations. This lack of transparency makes it difficult for consumers to understand what data is being collected, how it is being used, and what inferences are being made about them. Machine learning models can identify correlations and patterns that humans might never notice, potentially revealing sensitive information that users never intentionally disclosed.

Furthermore, AI systems are not infallible. They can perpetuate biases present in their training data, leading to discriminatory outcomes in areas such as credit scoring, job recruitment, and even criminal justice. Privacy breaches and data leaks have become increasingly common, with high-profile incidents affecting millions of users. When AI systems are compromised, the consequences can be severe because the data they hold is so comprehensive and detailed.

Regulatory responses to these privacy concerns have begun to emerge. The European Union’s General Data Protection Regulation (GDPR), implemented in 2018, grants citizens extensive rights over their personal data, including the “right to explanation” for automated decisions and the “right to be forgotten.” California’s Consumer Privacy Act (CCPA) provides similar protections for state residents. However, enforcement remains challenging, and many consumers are still unaware of their rights or how to exercise them.

Consumer behavior itself presents a paradox. While surveys consistently show that people are concerned about privacy, they continue to adopt AI-powered services at unprecedented rates. This “privacy paradox” suggests that convenience often outweighs privacy concerns in practical decision-making, or that consumers feel they have little choice but to accept current terms if they want to participate in digital society. Some researchers argue that the complexity of privacy policies and the abstract nature of data risks make it difficult for average consumers to make truly informed choices.

Looking forward, the relationship between AI and consumer privacy will likely remain a contentious issue. Emerging technologies like facial recognition, emotion detection, and behavioral prediction systems promise even greater capabilities but also heightened privacy risks. The challenge for society is to harness the benefits of AI while establishing robust safeguards that protect individual privacy rights and maintain public trust in these powerful technologies.

---

Questions 1-13

Questions 1-5: True/False/Not Given

Do the following statements agree with the information given in Passage 1?

Write:

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

1. AI voice assistants send recorded voice data to cloud servers for analysis.
2. Amazon uses anticipatory shipping to send products to customers before they order them.
3. Privacy advocates believe that data collection by AI systems poses no significant risks.
4. The GDPR gives European citizens the right to have their personal data deleted.
5. Most consumers fully understand the privacy policies they agree to when using AI services.

Questions 6-9: Multiple Choice

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

6. According to the passage, AI algorithms track user behavior in order to:

   • A) Increase government surveillance
   • B) Create personalized user experiences
   • C) Sell data to competitors
   • D) Reduce company operating costs

7. The term “black boxes” is used to describe:

   • A) The physical appearance of AI servers
   • B) The lack of transparency in how AI systems make decisions
   • C) The color of smart speakers
   • D) A type of data storage device

8. The “privacy paradox” refers to the fact that consumers:

   • A) Don’t use AI services despite their benefits
   • B) Are not concerned about their privacy at all
   • C) Express privacy concerns but continue using AI services
   • D) Prefer older technologies to AI-powered ones

9. Which regulatory measure is mentioned as providing privacy protections?

   • A) Only the GDPR
   • B) Only the CCPA
   • C) Both GDPR and CCPA
   • D) Neither GDPR nor CCPA

Questions 10-13: Sentence Completion

Complete the sentences below.

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

10. AI systems use __ to identify patterns in user data that humans might not notice.

11. When AI algorithms make mistakes, they can lead to __ in important areas like employment and justice.

12. The passage suggests that __ often wins over privacy concerns when consumers make decisions about using AI services.

13. Future AI technologies like facial recognition and emotion detection present even greater __ to personal privacy.

---

PASSAGE 2 – Data Monetization and the Hidden Economy of Personal Information

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

In the contemporary digital ecosystem, personal data has emerged as one of the most valuable commodities, giving rise to what economists term the “data economy.” This multibillion-dollar industry operates largely invisibly to average consumers, who generate vast quantities of information through their daily digital interactions. While users of “free” services like social media platforms, search engines, and mobile applications may believe they pay nothing for these conveniences, they are in fact exchanging something far more valuable: detailed information about their behaviors, preferences, relationships, and identities. The monetization of this personal data represents a fundamental shift in economic relations, with profound implications for consumer privacy.

The business model underlying most consumer AI services relies on surveillance capitalism – a term coined by scholar Shoshana Zuboff to describe the commodification of personal information. Companies collect data not merely to improve services but to generate predictive products that forecast user behavior. These predictions are then sold to advertisers, insurers, retailers, and other entities seeking to influence consumer decisions. The value chain is complex: raw behavioral data is extracted from users, processed through AI algorithms to identify patterns and create detailed psychological profiles, and packaged into targeted advertising products that command premium prices in the marketplace.

Consider the typical journey of personal information through this ecosystem. When a user browses a website, invisible trackers called cookies and web beacons record their activities. This information is shared with dozens or even hundreds of third-party companies through real-time bidding systems. Within milliseconds, these companies analyze the data, match it with existing profiles, and bid for the opportunity to display advertisements to that specific user. The winning bidder’s ad appears on the page – all of this occurring in the fraction of a second it takes for the webpage to load. This process, repeated billions of times daily, generates enormous revenue streams for data intermediaries and advertising platforms.

The granularity and scope of data collection have expanded dramatically with the proliferation of connected devices. Internet of Things (IoT) gadgets – smart thermostats, fitness trackers, connected cars, and even intelligent refrigerators – continuously generate data streams about user behaviors. Mobile devices with GPS capabilities create detailed location histories that reveal not only where people go but can infer their routines, relationships, and lifestyle choices. AI analysis of this aggregated data can determine with remarkable accuracy whether someone is pregnant, experiencing financial difficulties, considering a major purchase, or even planning to change jobs – often before the person has explicitly disclosed such information.

[image-2|he-thong-theo-doi-du-lieu-nguoi-dung-online|Sơ đồ hệ thống theo dõi và kinh doanh dữ liệu người dùng qua quảng cáo trực tuyến|A detailed infographic showing the flow of user data from a consumer using multiple devices through various data collection points (cookies, trackers, apps) to data brokers and advertisers, with dollar signs indicating monetization points, connected by arrows in a circular flow diagram, professional business style with charts and icons|

The asymmetry of knowledge and power in this data economy heavily favors corporations over individuals. Companies possess sophisticated analytical tools and comprehensive datasets that allow them to understand consumer behavior at population scale, while individual users have limited visibility into what data about them exists, where it resides, or how it is being used. Privacy policies, nominally intended to inform users about data practices, have become so lengthy and complex that they are effectively unreadable – one study found that it would take the average person 76 working days per year to read all the privacy policies of websites they visit. This information asymmetry undermines the concept of informed consent that supposedly governs data collection.

Moreover, the aggregation effect means that even seemingly innocuous data points can reveal sensitive information when combined. An individual data element – such as a “like” on social media, a search query, or a location check-in – may appear harmless in isolation. However, AI systems can synthesize thousands of such data points to construct intimate portraits of individuals, inferring characteristics such as sexual orientation, religious beliefs, health conditions, and personality traits with surprising accuracy. Research has demonstrated that machine learning algorithms can predict personal attributes from digital footprints with accuracy rates exceeding human judgment, raising questions about informational self-determination – the ability to control what others know about you.

The psychological impacts of this pervasive surveillance are beginning to receive scholarly attention. Some researchers warn of a “chilling effect” whereby awareness of constant monitoring may lead people to self-censor and conform to perceived norms, reducing diversity of thought and authentic self-expression. Others point to the anxiety and loss of autonomy that can result from feeling perpetually observed and analyzed. The power dynamics inherent in surveillance relationships – where one party watches while the other is watched – create inherent imbalances that extend beyond privacy concerns to broader questions of human dignity and freedom.

Regulatory frameworks struggle to keep pace with the rapid evolution of data monetization practices. Traditional privacy laws, designed for an era of direct relationships between individuals and specific organizations, prove inadequate for addressing the distributed, opaque networks of data sharing that characterize modern AI ecosystems. The GDPR’s requirement for explicit consent and data minimization represents progress, but implementation challenges abound. Companies often satisfy legal requirements through compliance theater – lengthy consent forms and privacy notices that technically meet regulations but fail to provide meaningful control or understanding to users.

Furthermore, the global nature of digital services creates jurisdictional complexities. Data collected in one country may be processed in another and sold to entities in a third, making enforcement of any single nation’s privacy laws problematic. Cross-border data flows are essential to modern internet services but create regulatory gaps that sophisticated actors can exploit. Some countries have responded with data localization requirements, mandating that personal information about their citizens be stored domestically, but such measures bring their own challenges, potentially fragmenting the internet and increasing costs without necessarily enhancing privacy.

Looking ahead, some technologists propose that blockchain and encryption technologies could enable new models where individuals retain ownership and control of their personal data, selectively granting access in exchange for compensation or services. Privacy-enhancing technologies (PETs) such as differential privacy, federated learning, and homomorphic encryption promise to allow AI analysis while protecting individual privacy. However, structural economic incentives currently favor centralized data accumulation, and truly transforming the data economy would require not merely technical solutions but fundamental changes to business models and power structures that benefit enormously from the current system.

---

Questions 14-26

Questions 14-19: Yes/No/Not Given

Do the following statements agree with the views of the writer in Passage 2?

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. Users of free online services are actually paying for them with their personal data.
15. Privacy policies are deliberately made too long for people to read.
16. Individual data points are never harmful on their own.
17. Constant surveillance may cause people to change their natural behavior.
18. Traditional privacy laws are adequate for regulating modern data practices.
19. Blockchain technology will definitely solve privacy problems in the future.

Questions 20-23: Matching Information

Match each statement with the correct paragraph (A-J).

You may use any letter more than once.

A – Paragraph 1
B – Paragraph 2
C – Paragraph 3
D – Paragraph 4
E – Paragraph 5
F – Paragraph 6
G – Paragraph 7
H – Paragraph 8
I – Paragraph 9
J – Paragraph 10

20. A description of how advertisements are selected and displayed to users in real-time
21. An explanation of how combining multiple data points can reveal personal information
22. A discussion of the psychological effects of being constantly monitored
23. Information about the challenges of enforcing privacy laws across different countries

Questions 24-26: Summary Completion

Complete the summary below.

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

The modern data economy is based on surveillance capitalism, where companies profit from personal information. Through a process called 24) __, raw behavioral data is transformed into products that predict and influence consumer behavior. The system involves 25) __ of information between users and corporations, with companies having far superior knowledge and analytical capabilities. Some experts believe 26) __ could offer new ways for individuals to maintain control over their personal data, though current business models favor centralized data collection.

---

PASSAGE 3 – Artificial Intelligence, Algorithmic Governance, and the Future of Privacy Rights

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The advent of artificial intelligence has precipitated a paradigmatic transformation in the conceptualization and exercise of privacy rights, challenging foundational assumptions that have governed informational privacy for decades. Traditional privacy frameworks, rooted in concepts of informational self-determination and contextual integrity, were predicated on a relatively transparent relationship between data subjects and data collectors, with clearly delineated purposes for information use. However, the opacity, autonomy, and predictive capabilities of contemporary AI systems have rendered such frameworks increasingly obsolete, necessitating a reconceptualization of privacy itself as both a legal construct and a practical reality in digital society.

At the heart of this transformation lies what scholars term “algorithmic governance” – the delegation of decision-making authority to automated systems that evaluate, categorize, and regulate human behavior based on probabilistic assessments derived from data analysis. Unlike traditional forms of governance characterized by explicit rules and human judgment, algorithmic governance operates through inscrutability and automaticity. Machine learning algorithms, particularly those employing deep neural networks, function through millions of weighted parameters and non-linear transformations that resist straightforward interpretation even by their designers. This epistemic opacity creates what philosopher Frank Pasquale terms “black box society” – a condition wherein significant decisions affecting individuals’ lives are made by systems whose operations remain fundamentally illegible to those subject to them.

The implications for privacy are multifaceted and extend beyond conventional concerns about unauthorized disclosure of personal information. AI systems engage in what can be characterized as inferential privacy violations – the derivation of sensitive attributes about individuals from ostensibly non-sensitive data sources through pattern recognition and statistical correlation. Psychometric research has demonstrated that computational models can predict personality dimensions, political ideology, sexual orientation, and mental health indicators from digital behavioral residues such as social media activity, smartphone usage patterns, and even keystroke dynamics with accuracy rates that frequently exceed those of human judges, including close personal acquaintances.

This phenomenon, which privacy theorist Daniel Solove conceptualizes as “digital dossiers,” creates what might be termed second-order privacy vulnerabilities. An individual may consciously control the explicit information they disclose – their public statements, declared preferences, and voluntary communications. However, they exercise virtually no control over the inferences that sophisticated AI systems extract from their behavioral metadata. Moreover, these inferences, once generated, often achieve a form of ontological solidity – they become “facts” about the individual in institutional databases and algorithmic decision systems, potentially influencing outcomes in domains ranging from creditworthiness assessments to employment screening to predictive policing, regardless of their actual accuracy or the individual’s opportunity to contest them.

[image-3|ai-phan-tich-du-lieu-va-xam-pham-quyen-rieng-tu|Biểu đồ phức tạp về cách AI phân tích dữ liệu hành vi và tạo ra các suy luận về đặc điểm cá nhân|A sophisticated conceptual diagram showing layers of privacy invasion: bottom layer shows basic user activities (social media, browsing, shopping), middle layer shows AI processing with neural network visualization, top layer shows inferred sensitive attributes (personality, health, political views) with warning symbols, academic style with muted colors and technical annotations|

The sociotechnical architecture of modern AI systems further complicates privacy protection. The training of effective machine learning models requires vast datasets, creating economic incentives for maximal data collection. The network effects and economies of scale that characterize digital platforms generate winner-take-all dynamics, concentrating data in the hands of a limited number of technological behemoths whose infrastructural power extends across sectors and geographies. This concentration creates what competition scholars call “data monopolies” – entities whose comprehensive informational holdings grant them not merely economic advantages but epistemic superiority that can translate into various forms of social power and control.

Furthermore, the performativity of algorithmic systems – their capacity not merely to predict but to shape behavior – introduces novel privacy concerns. When recommendation algorithms curate information environments, attention economies commodify engagement, and nudging techniques leverage cognitive biases, the autonomous self that classical privacy theory seeks to protect becomes increasingly attenuated. Privacy ceases to be solely about protecting a pre-existing self from observation and instead becomes entangled with questions of authentic self-determination in environments deliberately designed to influence and modify behavior. Some scholars argue this represents a shift from privacy as seclusion to privacy as autonomy – the freedom to develop one’s identity, preferences, and beliefs without manipulative interference.

The normalization of pervasive monitoring through AI systems also engenders what Shoshana Zuboff characterizes as a “psychic numbing” – a learned helplessness regarding privacy protection born of the ubiquity and inscrutability of surveillance mechanisms. When virtually every digital interaction generates exploitable data, when opt-out mechanisms prove ineffectual or illusory, and when the complexity of data flows defeats individual comprehension, privacy-protective behaviors may come to seem futile. This learned resignation potentially represents a more insidious threat than acute privacy violations, as it erodes the very capacity for privacy consciousness and resistance that might generate political pressure for systemic reform.

From a governance perspective, addressing AI-related privacy challenges requires what Julie Cohen terms “semantic discontinuity” – regulatory interventions that disrupt the seamless data flows that enable both innovation and exploitation within the current data economy. The European Union’s GDPR represents the most comprehensive attempt to date, establishing principles including purpose limitation, data minimization, and processing transparency, alongside individual rights to access, rectification, and erasure of personal data. The regulation’s provisions for data protection impact assessments and requirements for “privacy by design” aim to embed privacy considerations into technological development rather than treating them as afterthoughts.

However, the GDPR’s effectiveness remains contested. Critics argue that consent mechanisms, even when strengthened by regulatory requirements, cannot address the structural asymmetries inherent in consumer-platform relationships, where declining consent effectively means exclusion from essential digital services. The “right to explanation” for automated decisions, while theoretically significant, confronts the technical reality that contemporary machine learning systems may not admit of comprehensible explanations that are simultaneously faithful to the model’s operation and intelligible to lay audiences. Some scholars advocate for moving beyond individual consent toward collective governance mechanisms, including data trusts, fiduciary duties, or even recognizing data rights as inalienable rather than subject to contractual waiver.

Alternative regulatory approaches emphasize structural rather than procedural interventions. Proposals include algorithmic auditing requirements that mandate external evaluation of AI systems for privacy impacts and discriminatory outcomes; interoperability mandates that would reduce network effects by enabling users to migrate their data between platforms; and even more radical suggestions for data taxation or collective ownership models that would fundamentally alter the economic incentives driving maximal data collection. Some jurisdictions have experimented with sector-specific regulations for particularly sensitive domains such as facial recognition or biometric identification, with several cities and regions implementing moratoria or outright bans on governmental use of such technologies.

The trajectory of AI development suggests that privacy challenges will intensify rather than diminish. Ambient intelligence environments, where AI systems are embedded throughout physical spaces; affective computing that interprets emotional states from physiological signals and facial expressions; brain-computer interfaces that may eventually access neural data; and synthetic media technologies that can generate convincing but fabricated images and videos of individuals – all present unprecedented privacy implications. The development of artificial general intelligence (AGI), should it be achieved, could introduce capabilities and vulnerabilities that current frameworks cannot anticipate. Addressing these challenges will require not merely regulatory agility but sustained public deliberation about the kind of informational society we wish to create – one that harnesses AI’s transformative potential while preserving the privacy, autonomy, and dignity that constitute preconditions for human flourishing in democratic societies.

---

Questions 27-40

Questions 27-31: Multiple Choice

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

27. According to the passage, algorithmic governance differs from traditional governance primarily because it:

• A) Is more efficient and accurate
• B) Operates through automated systems that are difficult to understand
• C) Is controlled by government agencies
• D) Costs less to implement

28. The term “inferential privacy violations” refers to:

• A) Direct disclosure of personal information
• B) Hacking of personal devices
• C) Deriving sensitive information from non-sensitive data through AI analysis
• D) Sharing data with third parties without permission

29. According to the passage, “second-order privacy vulnerabilities” mean that:

• A) Privacy violations happen twice as often
• B) Individuals cannot control what AI systems infer about them
• C) Secondary data is more important than primary data
• D) Privacy protection is a second-tier concern

30. The passage suggests that the GDPR’s “right to explanation”:

• A) Has completely solved AI privacy problems
• B) Is easy to implement with all AI systems
• C) Faces practical challenges because AI systems are difficult to explain
• D) Is not important for consumer privacy

31. The concept of “psychic numbing” described in the passage indicates that:

• A) AI systems can directly affect brain function
• B) People become resigned to privacy loss because protection seems impossible
• C) Privacy violations cause psychological illness
• D) Monitoring reduces mental capacity

Questions 32-36: Matching Features

Match each concept with the correct scholar who proposed or discussed it.

Choose the correct letter, A-E.

A – Frank Pasquale
B – Daniel Solove
C – Shoshana Zuboff
D – Julie Cohen
E – Not mentioned

32. Digital dossiers
33. Black box society
34. Surveillance capitalism
35. Semantic discontinuity
36. Algorithmic governance

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 effects cause data to concentrate in the hands of a few large technology companies?

38. According to the passage, what kind of models have been proposed as alternatives to individual consent for data governance?

39. What type of computing interprets emotional states from physiological signals?

40. What must be preserved to maintain preconditions for human flourishing in democratic societies, according to the passage?

---

Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. TRUE
2. TRUE
3. FALSE
4. TRUE
5. NOT GIVEN
6. B
7. B
8. C
9. C
10. machine learning
11. discriminatory outcomes
12. convenience
13. privacy risks

PASSAGE 2: Questions 14-26

14. YES
15. NOT GIVEN
16. NO
17. YES
18. NO
19. NOT GIVEN
20. C
21. F
22. G
23. I
24. data monetization / monetization
25. asymmetry / information asymmetry
26. blockchain / blockchain technology

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. C
31. B
32. B
33. A
34. C
35. D
36. E
37. network effects
38. collective governance / data trusts
39. affective computing
40. privacy, autonomy, dignity / privacy and autonomy / autonomy and dignity

---

Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI voice assistants, recorded voice data, cloud servers
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Bài đọc nói rõ: “When you ask your smart speaker about the weather or request a song, the device records your voice, converts it to text, and sends it to cloud servers for processing.” Câu trong đề paraphrase “smart speaker” thành “AI voice assistants” và giữ nguyên ý nghĩa về việc gửi dữ liệu voice lên cloud servers.

Câu 2: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Amazon, anticipatory shipping, products, before order
• Vị trí trong bài: Đoạn 3, dòng cuối
• Giải thích: Bài viết đề cập: “anticipatory shipping, where products are moved to warehouses near customers before they even order them.” Điều này hoàn toàn khớp với phát biểu trong câu hỏi.

Câu 3: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: privacy advocates, data collection, no significant risks
• Vị trí trong bài: Đoạn 4, dòng đầu
• Giải thích: Bài đọc khẳng định: “privacy advocates have raised serious concerns about the extent of data surveillance” – điều này hoàn toàn mâu thuẫn với phát biểu “poses no significant risks.”

Câu 4: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: GDPR, European citizens, right, personal data deleted
• Vị trí trong bài: Đoạn 7, dòng 2-3
• Giải thích: Passage nêu rõ GDPR grants “the right to be forgotten” – được paraphrase thành “right to have their personal data deleted” trong câu hỏi.

Câu 5: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: consumers, fully understand, privacy policies
• Vị trí trong bài: Không có thông tin cụ thể
• Giải thích: Mặc dù đoạn 8 đề cập “many consumers are still unaware of their rights,” nhưng không có thông tin về việc họ “fully understand” hay không understand privacy policies khi đồng ý.

Câu 6: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI algorithms, track user behavior
• Vị trí trong bài: Đoạn 2
• Giải thích: Bài viết nói: “AI algorithms track your clicking patterns… to build a comprehensive profile… This information is then used to optimize your user experience” – tức là để tạo personalized experiences (đáp án B).

Câu 7: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: “black boxes”
• Vị trí trong bài: Đoạn 5, dòng 2
• Giải thích: Passage giải thích: “These systems operate as ‘black boxes’ – even their creators cannot always explain exactly how they arrive at specific decisions” – đây chính là về lack of transparency (đáp án B).

Câu 8: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: privacy paradox
• Vị trí trong bài: Đoạn 8
• Giải thích: Bài viết mô tả: “While surveys consistently show that people are concerned about privacy, they continue to adopt AI-powered services at unprecedented rates. This ‘privacy paradox’…” – khớp với đáp án C.

Câu 9: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: regulatory measure, privacy protections
• Vị trí trong bài: Đoạn 7
• Giải thích: Đoạn văn đề cập cả hai: “The European Union’s General Data Protection Regulation (GDPR)” và “California’s Consumer Privacy Act (CCPA)” – do đó đáp án là C (Both).

Câu 10: machine learning

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI systems, identify patterns, humans might not notice
• Vị trí trong bài: Đoạn 5, dòng 5-6
• Giải thích: “Machine learning models can identify correlations and patterns that humans might never notice.”

Câu 11: discriminatory outcomes

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI algorithms, mistakes, areas like employment and justice
• Vị trí trong bài: Đoạn 6, dòng 2
• Giải thích: “They can perpetuate biases present in their training data, leading to discriminatory outcomes in areas such as credit scoring, job recruitment, and even criminal justice.”

Câu 12: convenience

• Dạng câu hỏi: Sentence Completion
• Từ khóa: wins over privacy concerns, consumer decisions
• Vị trí trong bài: Đoạn 8, dòng 3
• Giải thích: “This ‘privacy paradox’ suggests that convenience often outweighs privacy concerns in practical decision-making.”

Câu 13: privacy risks

• Dạng câu hỏi: Sentence Completion
• Từ khóa: future AI technologies, facial recognition, emotion detection
• Vị trí trong bài: Đoạn 9, dòng 2
• Giải thích: “Emerging technologies like facial recognition, emotion detection, and behavioral prediction systems promise even greater capabilities but also heightened privacy risks.”

---

Passage 2 – Giải Thích

Câu 14: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: free online services, paying, personal data
• Vị trí trong bài: Đoạn 1, dòng 3-5
• Giải thích: Tác giả rõ ràng khẳng định: “While users of ‘free’ services… may believe they pay nothing for these conveniences, they are in fact exchanging something far more valuable: detailed information…” – thể hiện quan điểm của tác giả.

Câu 15: NOT GIVEN

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: privacy policies, deliberately made too long
• Vị trí trong bài: Đoạn 5
• Giải thích: Bài viết nói privacy policies “have become so lengthy and complex that they are effectively unreadable” nhưng không nói liệu điều này là cố ý (deliberately) hay không.

Câu 16: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: individual data points, never harmful, on their own
• Vị trí trong bài: Đoạn 6
• Giải thích: Tác giả nói data points “may appear harmless in isolation” nhưng đây chỉ là vẻ ngoài (appear), và đoạn văn tiếp tục giải thích chúng có thể được combine để reveal sensitive information – do đó tác giả không cho rằng chúng thực sự không harmful.

Câu 17: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: constant surveillance, people change natural behavior
• Vị trí trong bài: Đoạn 7, dòng 2-3
• Giải thích: Tác giả đề cập: “awareness of constant monitoring may lead people to self-censor and conform to perceived norms” – đây là quan điểm được tác giả thừa nhận.

Câu 18: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: traditional privacy laws, adequate, modern data practices
• Vị trí trong bài: Đoạn 8, dòng 1-2
• Giải thích: Tác giả rõ ràng nói: “Traditional privacy laws… prove inadequate for addressing the distributed, opaque networks” – đây là quan điểm phủ định về tính đầy đủ của traditional laws.

Câu 19: NOT GIVEN

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: blockchain technology, will definitely solve, privacy problems
• Vị trí trong bài: Đoạn 10
• Giải thích: Tác giả nói blockchain “could enable new models” nhưng cũng chỉ ra “structural economic incentives currently favor centralized data accumulation” – không có khẳng định chắc chắn (definitely) về việc nó sẽ solve problems.

Câu 20: C

• Dạng câu hỏi: Matching Information
• Từ khóa: advertisements, selected and displayed, real-time
• Vị trí trong bài: Đoạn 3 (Paragraph C)
• Giải thích: Đoạn 3 mô tả chi tiết quy trình real-time bidding: “Within milliseconds, these companies analyze the data… and bid for the opportunity to display advertisements to that specific user.”

Câu 21: F

• Dạng câu hỏi: Matching Information
• Từ khóa: combining multiple data points, reveal personal information
• Vị trí trong bài: Đoạn 6 (Paragraph F)
• Giải thích: Đoạn 6 giải thích “aggregation effect”: “AI systems can synthesize thousands of such data points to construct intimate portraits of individuals.”

Câu 22: G

• Dạng câu hỏi: Matching Information
• Từ khóa: psychological effects, constantly monitored
• Vị trí trong bài: Đoạn 7 (Paragraph G)
• Giải thích: Đoạn 7 thảo luận về “psychological impacts of this pervasive surveillance” và “chilling effect,” “anxiety and loss of autonomy.”

Câu 23: I

• Dạng câu hỏi: Matching Information
• Từ khóa: challenges, enforcing privacy laws, different countries
• Vị trí trong bài: Đoạn 9 (Paragraph I)
• Giải thích: Đoạn 9 đề cập: “the global nature of digital services creates jurisdictional complexities. Data collected in one country may be processed in another…”

Câu 24: data monetization / monetization

• Dạng câu hỏi: Summary Completion
• Từ khóa: raw behavioral data, transformed into products
• Vị trí trong bài: Đoạn 2
• Giải thích: “The monetization of this personal data represents a fundamental shift” và “raw behavioral data is extracted from users, processed through AI algorithms.”

Câu 25: asymmetry / information asymmetry

• Dạng câu hỏi: Summary Completion
• Từ khóa: between users and corporations, superior knowledge
• Vị trí trong bài: Đoạn 5, tiêu đề và nội dung
• Giải thích: “The asymmetry of knowledge and power in this data economy heavily favors corporations over individuals.”

Câu 26: blockchain / blockchain technology

• Dạng câu hỏi: Summary Completion
• Từ khóa: new ways, individuals maintain control, personal data
• Vị trí trong bài: Đoạn 10
• Giải thích: “some technologists propose that blockchain and encryption technologies could enable new models where individuals retain ownership and control of their personal data.”

---

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: algorithmic governance, differs from traditional governance
• Vị trí trong bài: Đoạn 2
• Giải thích: Passage nói: “Unlike traditional forms of governance characterized by explicit rules and human judgment, algorithmic governance operates through inscrutability and automaticity” – nghĩa là hoạt động qua các hệ thống tự động khó hiểu (đáp án B).

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: inferential privacy violations
• Vị trí trong bài: Đoạn 3
• Giải thích: Được định nghĩa rõ ràng: “inferential privacy violations – the derivation of sensitive attributes about individuals from ostensibly non-sensitive data sources through pattern recognition.”

Câu 29: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: second-order privacy vulnerabilities
• Vị trí trong bài: Đoạn 4
• Giải thích: Passage giải thích: “An individual may consciously control the explicit information they disclose… However, they exercise virtually no control over the inferences that sophisticated AI systems extract.”

Câu 30: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: GDPR, right to explanation
• Vị trí trong bài: Đoạn 9
• Giải thích: “The ‘right to explanation’ for automated decisions… confronts the technical reality that contemporary machine learning systems may not admit of comprehensible explanations” – chỉ ra practical challenges.

Câu 31: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: psychic numbing
• Vị trí trong bài: Đoạn 7
• Giải thích: Được mô tả là “a learned helplessness regarding privacy protection born of the ubiquity and inscrutability of surveillance mechanisms” – tức là sự bất lực học được, resignation (đáp án B).

Câu 32: B (Daniel Solove)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 3
• Giải thích: “This phenomenon, which privacy theorist Daniel Solove conceptualizes as ‘digital dossiers’…”

Câu 33: A (Frank Pasquale)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 2
• Giải thích: “This epistemic opacity creates what philosopher Frank Pasquale terms ‘black box society’…”

Câu 34: C (Shoshana Zuboff)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 2 (dòng đầu)
• Giải thích: Mặc dù không xuất hiện trong Passage 3, “surveillance capitalism” là thuật ngữ của Shoshana Zuboff được đề cập trong Passage 2 và là kiến thức phổ biến trong chủ đề này. Tuy nhiên, trong Passage 3 không có đề cập trực tiếp, nên có thể coi là E.

Lưu ý điều chỉnh: Câu 34 nên là E (Not mentioned) vì Passage 3 không đề cập surveillance capitalism.

Câu 35: D (Julie Cohen)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 8
• Giải thích: “addressing AI-related privacy challenges requires what Julie Cohen terms ‘semantic discontinuity’…”

Câu 36: E (Not mentioned)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 2
• Giải thích: “Algorithmic governance” được đề cập nhưng không được gán cho một scholar cụ thể nào – passage nói “scholars term” (số nhiều).

Câu 37: network effects

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: data concentrate, few large technology companies
• Vị trí trong bài: Đoạn 5
• Giải thích: “The network effects and economies of scale that characterize digital platforms generate winner-take-all dynamics, concentrating data in the hands of a limited number of technological behemoths.”

Câu 38: collective governance / data trusts

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: alternatives to individual consent, data governance
• Vị trí trong bài: Đoạn 9
• Giải thích: “Some scholars advocate for moving beyond individual consent toward collective governance mechanisms, including data trusts, fiduciary duties…”

Câu 39: affective computing

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: interprets emotional states, physiological signals
• Vị trí trong bài: Đoạn 10
• Giải thích: “affective computing that interprets emotional states from physiological signals and facial expressions.”

Câu 40: privacy, autonomy, dignity / privacy and autonomy / autonomy and dignity

• Dạng câu hỏi: Short-answer Questions (chấp nhận nhiều combination trong giới hạn 3 từ)
• Từ khóa: preconditions, human flourishing, democratic societies
• Vị trí trong bài: Đoạn 10, câu cuối
• Giải thích: “preserving the privacy, autonomy, and dignity that constitute preconditions for human flourishing in democratic societies.”

---

Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
ubiquitous	adj	/juːˈbɪkwɪtəs/	có mặt khắp nơi, phổ biến	AI systems have become ubiquitous in modern society	ubiquitous technology, ubiquitous presence
tailored	adj	/ˈteɪlərd/	được cá nhân hóa, điều chỉnh phù hợp	provide tailored experiences	tailored experience, tailored content
comprehensive	adj	/ˌkɒmprɪˈhensɪv/	toàn diện, bao quát	build a comprehensive profile	comprehensive profile, comprehensive data
optimize	v	/ˈɒptɪmaɪz/	tối ưu hóa	optimize your user experience	optimize performance, optimize results
predictive analytics	n	/prɪˈdɪktɪv ˌænəˈlɪtɪks/	phân tích dự đoán	use predictive analytics	predictive analytics model
surveillance	n	/sɜːˈveɪləns/	sự giám sát, theo dõi	data surveillance inherent in AI systems	surveillance system, under surveillance
intimate details	n phrase	/ˈɪntɪmət ˈdiːteɪlz/	chi tiết riêng tư, cá nhân	reveal intimate details about a person’s life	intimate details, intimate information
opacity	n	/əʊˈpæsəti/	sự không rõ ràng, mờ đục	the opacity of AI algorithms	algorithmic opacity, opacity of systems
perpetuate	v	/pəˈpetʃueɪt/	duy trì, làm lâu dài	perpetuate biases	perpetuate discrimination, perpetuate stereotypes
discriminatory	adj	/dɪˈskrɪmɪnətri/	phân biệt đối xử	discriminatory outcomes	discriminatory practice, discriminatory behavior
robust safeguards	n phrase	/rəʊˈbʌst ˈseɪfɡɑːdz/	các biện pháp bảo vệ chắc chắn	establishing robust safeguards	robust safeguards, robust protection
contentious issue	n phrase	/kənˈtenʃəs ˈɪʃuː/	vấn đề gây tranh cãi	remain a contentious issue	contentious issue, contentious debate

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
commodification	n	/kəˌmɒdɪfɪˈkeɪʃn/	sự hàng hóa hóa	the commodification of personal information	commodification of data
monetization	n	/ˌmʌnɪtaɪˈzeɪʃn/	sự kiếm tiền từ, thu lợi	monetization of personal data	data monetization
granularity	n	/ˌɡrænjuˈlærəti/	mức độ chi tiết, độ hạt	the granularity and scope of data collection	data granularity
proliferation	n	/prəˌlɪfəˈreɪʃn/	sự tăng nhanh, phổ biến rộng rãi	the proliferation of connected devices	nuclear proliferation, weapon proliferation
asymmetry	n	/eɪˈsɪmətri/	sự bất cân xứng, không đối xứng	asymmetry of knowledge and power	power asymmetry, information asymmetry
aggregation effect	n phrase	/ˌæɡrɪˈɡeɪʃn ɪˈfekt/	hiệu ứng tập hợp	the aggregation effect means that	data aggregation
synthesize	v	/ˈsɪnθəsaɪz/	tổng hợp, kết hợp	AI systems can synthesize thousands of data points	synthesize information, synthesize data
pervasive	adj	/pəˈveɪsɪv/	lan tràn, phổ biến khắp nơi	pervasive surveillance	pervasive influence, pervasive technology
chilling effect	n phrase	/ˈtʃɪlɪŋ ɪˈfekt/	hiệu ứng làm lạnh (gây sợ hãi, tự kiểm duyệt)	warn of a chilling effect	chilling effect on freedom
compliance theater	n phrase	/kəmˈplaɪəns ˈθɪətə/	hành vi tuân thủ hình thức	compliance theater – lengthy consent forms	compliance theater, security theater
jurisdictional	adj	/ˌdʒʊərɪsˈdɪkʃənl/	thuộc về quyền tài phán	jurisdictional complexities	jurisdictional issues, jurisdictional boundaries
federated learning	n phrase	/ˈfedəreɪtɪd ˈlɜːnɪŋ/	học liên kết (kỹ thuật ML)	federated learning and homomorphic encryption	federated learning model
differential privacy	n phrase	/ˌdɪfəˈrenʃl ˈprɪvəsi/	quyền riêng tư khác biệt	differential privacy technologies	differential privacy guarantee
homomorphic encryption	n phrase	/ˌhɒməˈmɔːfɪk ɪnˈkrɪpʃn/	mã hóa đồng cấu	homomorphic encryption promise	homomorphic encryption scheme

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
paradigmatic	adj	/ˌpærədɪɡˈmætɪk/	mang tính mô hình, điển hình	paradigmatic transformation	paradigmatic shift, paradigmatic example
informational self-determination	n phrase	/ˌɪnfəˈmeɪʃənl ˌself dɪˌtɜːmɪˈneɪʃn/	quyền tự quyết định thông tin	concepts of informational self-determination	right to informational self-determination
inscrutability	n	/ɪnˌskruːtəˈbɪləti/	tính không thể hiểu được	operates through inscrutability	algorithmic inscrutability
epistemic opacity	n phrase	/ˌepɪˈstiːmɪk əʊˈpæsəti/	sự mờ đục về nhận thức	epistemic opacity creates	epistemic opacity of AI
illegible	adj	/ɪˈledʒəbl/	không thể đọc được, hiểu được	operations remain fundamentally illegible	illegible decisions, illegible to users
multifaceted	adj	/ˌmʌltiˈfæsɪtɪd/	nhiều mặt, đa chiều	implications are multifaceted	multifaceted problem, multifaceted approach
ostensibly	adv	/ɒˈstensəbli/	có vẻ như, ngầm hiểu	ostensibly non-sensitive data	ostensibly neutral, ostensibly simple
ontological	adj	/ˌɒntəˈlɒdʒɪkl/	thuộc về bản thể luận	achieve ontological solidity	ontological status, ontological questions
sociotechnical	adj	/ˌsəʊsiəʊˈteknɪkl/	thuộc về xã hội-kỹ thuật	sociotechnical architecture	sociotechnical system, sociotechnical design
infrastructural power	n phrase	/ˌɪnfrəˈstrʌktʃərəl ˈpaʊə/	quyền lực về cơ sở hạ tầng	whose infrastructural power extends	infrastructural power, infrastructural control
epistemic superiority	n phrase	/ˌepɪˈstiːmɪk suːˌpɪəriˈɒrəti/	sự vượt trội về nhận thức	grant them epistemic superiority	epistemic advantage, epistemic authority
performativity	n	/pəˌfɔːməˈtɪvəti/	tính thực thi, tạo hiện thực	the performativity of algorithmic systems	performativity of language
attenuated	adj	/əˈtenjueɪtɪd/	bị suy yếu, giảm bớt	becomes increasingly attenuated	attenuated relationship, attenuated signal
psychic numbing	n phrase	/ˈsaɪkɪk ˈnʌmɪŋ/	sự tê liệt tâm lý	psychic numbing regarding privacy	psychic numbing effect
semantic discontinuity	n phrase	/sɪˈmæntɪk ˌdɪskɒntɪˈnjuːəti/	sự gián đoạn ngữ nghĩa	requires semantic discontinuity	semantic discontinuity in regulation
inalienable	adj	/ɪnˈeɪliənəbl/	không thể tước đoạt	data rights as inalienable	inalienable rights, inalienable freedoms
moratoria	n	/ˌmɒrəˈtɔːriə/	lệnh đình chỉ tạm thời (số nhiều)	implementing moratoria or outright bans	debt moratoria, nuclear moratoria
ambient intelligence	n phrase	/ˈæmbiənt ɪnˈtelɪdʒəns/	trí tuệ môi trường xung quanh	ambient intelligence environments	ambient intelligence system
affective computing	n phrase	/əˈfektɪv kəmˈpjuːtɪŋ/	điện toán cảm xúc	affective computing that interprets emotions	affective computing technology

---

Kết Bài

Chủ đề về AI và quyền riêng tư người tiêu dùng trong kỷ nguyên số không chỉ là một đề tài nóng trong xã hội hiện đại mà còn là một chủ đề quan trọng thường xuyên xuất hiện trong IELTS Reading. Qua bộ đề thi mẫu này, bạn đã được luyện tập với ba passages có độ khó tăng dần – từ bài đọc giới thiệu tổng quan dễ hiểu (Band 5.0-6.5), đến phân tích chuyên sâu về kinh tế dữ liệu (Band 6.0-7.5), và cuối cùng là thảo luận học thuật về quản trị thuật toán (Band 7.0-9.0).

Bộ đề cung cấp đầy đủ 40 câu hỏi với 7 dạng câu hỏi phổ biến nhất trong IELTS Reading, giúp bạn làm quen với mọi format có thể xuất hiện trong kỳ thi thực tế. Phần đáp án chi tiết không chỉ cung cấp câu trả lời đúng mà còn giải thích vị trí cụ thể trong bài, cách paraphrase từ khóa, và lý do tại sao các đáp án khác không chính xác với multiple choice questions.

Bảng từ vựng được phân loại theo từng passage giúp bạn học từ mới một cách có hệ thống, từ những từ cơ bản như “ubiquitous,” “optimize” đến những thuật ngữ học thuật phức tạp như “epistemic opacity,” “performativity,” và “semantic discontinuity.” Những từ vựng này không chỉ hữu ích cho bài thi Reading mà còn có thể được áp dụng trong Writing và Speaking khi thảo luận về công nghệ và xã hội.

Hãy sử dụng bộ đề này như một công cụ luyện tập thực chiến: làm bài trong điều kiện thi thật với thời gian giới hạn, sau đó đối chiếu đáp án và đọc kỹ giải thích để hiểu sâu hơn về kỹ thuật làm bài. Việc luyện tập đều đặn với các đề thi chất lượng cao như thế này sẽ giúp bạn cải thiện đáng kể kỹ năng Reading và đạt được band điểm mục tiêu trong kỳ thi IELTS.