IELTS Reading: AI Trong Phân Tích Hành Vi Tiêu Dùng – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Công nghệ trí tuệ nhân tạo (AI) đang cách mạng hóa cách thức các doanh nghiệp hiểu và dự đoán hành vi người tiêu dùng. Chủ đề “How Is AI Being Used In Consumer Behavior Analysis?” không chỉ là một xu hướng công nghệ đương đại mà còn xuất hiện ngày càng thường xuyên trong các đề thi IELTS Reading gần đây, đặc biệt trong các passages về công nghệ, kinh doanh và xã hội học.

Đề thi mẫu này được thiết kế dựa trên cấu trúc chuẩn của Cambridge IELTS, bao gồm 3 passages với độ khó tăng dần từ Easy đến Hard. Bạn sẽ được trải nghiệm đầy đủ các dạng câu hỏi phổ biến như Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion và nhiều dạng khác.

Qua bài thi này, bạn sẽ học được cách tiếp cận từng loại câu hỏi một cách bài bản, làm quen với từ vựng chuyên ngành về AI và consumer behavior, đồng thời rèn luyện kỹ năng quản lý thời gian hiệu quả. Đề thi phù hợp cho học viên từ band 5.0 trở lên, với đáp án chi tiết kèm giải thích giúp bạn tự đánh giá và cải thiện năng lực Reading của mình.

1. 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 khi sai.

Phân bổ thời gian khuyến nghị:

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

Lưu ý dành 2-3 phút cuối để chuyển đáp án vào Answer Sheet, đảm bảo viết chính xác chính tả và format.

Các Dạng Câu Hỏi Trong Đề Này

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

1. Multiple Choice – Câu hỏi trắc nghiệm
2. True/False/Not Given – Xác định thông tin đúng/sai/không được nhắc đến
3. Matching Information – Ghép thông tin với đoạn văn
4. Yes/No/Not Given – Xác định quan điểm tác giả
5. Matching Headings – Ghép tiêu đề với đoạn văn
6. Summary Completion – Hoàn thành đoạn tóm tắt
7. Short-answer Questions – Câu hỏi trả lời ngắn

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2. IELTS Reading Practice Test

PASSAGE 1 – The Rise of AI in Understanding Customers

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

In recent years, artificial intelligence (AI) has become an increasingly important tool for businesses seeking to understand their customers better. Traditional methods of consumer behavior analysis, such as surveys and focus groups, while still valuable, are being supplemented and sometimes replaced by AI-powered technologies that can process vast amounts of data in a fraction of the time it would take human analysts.

The most basic application of AI in this field involves data collection and organization. Companies now gather information from multiple sources including purchase histories, website visits, social media interactions, and even in-store movements tracked through smartphone signals. AI systems can consolidate all this disparate data into comprehensive customer profiles, something that would be practically impossible to do manually given the sheer volume of information involved.

Machine learning algorithms, a subset of AI, are particularly useful for identifying patterns in consumer behavior. For example, an online retailer might use these algorithms to determine which products are frequently purchased together, or what time of day certain demographic groups prefer to shop. This information allows companies to optimize their marketing strategies and inventory management. A clothing retailer, for instance, might discover that customers who buy running shoes on Monday mornings are 60% more likely to purchase sports drinks within the next week, enabling targeted promotional campaigns.

Predictive analytics represents another significant application of AI in understanding consumer behavior. By analyzing historical data, AI systems can forecast future purchasing patterns with remarkable accuracy. Streaming services like Netflix and Spotify have pioneered this approach, using AI to predict what content users will enjoy based on their viewing or listening history. These recommendation engines have become so sophisticated that they often suggest content users didn’t know they wanted, effectively shaping consumer preferences as much as responding to them.

The retail sector has embraced chatbots and virtual assistants powered by AI to interact with customers in real-time. These tools do more than just answer questions; they gather valuable insights about customer needs, preferences, and pain points. Every interaction is analyzed and stored, contributing to an ever-growing database of consumer behavior information. Natural language processing, another AI technology, enables these systems to understand not just what customers say, but the sentiment and emotion behind their words.

Personalization has reached new heights thanks to AI. E-commerce platforms can now create unique shopping experiences for each visitor, displaying products and offers tailored to individual preferences. Amazon’s recommendation system, which generates 35% of the company’s revenue, is perhaps the most famous example of this technology in action. The system considers hundreds of factors, from past purchases to items left in shopping carts, to predict what each customer might want to buy next.

Physical stores are also leveraging AI to understand in-store behavior. Computer vision technology can track how customers move through a store, which displays attract the most attention, and even facial expressions that might indicate interest or confusion. This information helps retailers optimize store layouts and product placement. Some supermarkets have experimented with smart shelves equipped with sensors that detect when products are picked up and put back, providing insights into customer decision-making processes.

However, the use of AI in consumer behavior analysis is not without challenges. Privacy concerns have become increasingly prominent as consumers become more aware of how much data is being collected about them. Companies must balance the desire for detailed consumer insights with respect for individual privacy rights. Additionally, there is the risk of algorithmic bias, where AI systems might make incorrect assumptions based on flawed or incomplete data, potentially leading to discriminatory practices in marketing or service delivery.

Despite these challenges, the trend toward AI-powered consumer analysis seems irreversible. As the technology becomes more sophisticated and accessible, even small businesses can now afford tools that were once available only to large corporations. The key to success lies in using AI not just to collect data, but to generate actionable insights that improve customer satisfaction while maintaining ethical standards in data collection and use.

Questions 1-5

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

1. According to the passage, traditional methods of consumer behavior analysis are
   A. completely replaced by AI
   B. still used alongside AI technologies
   C. more effective than AI methods
   D. too expensive for most companies

2. Machine learning algorithms help companies to
   A. replace human employees
   B. reduce their product inventory
   C. recognize patterns in how consumers behave
   D. collect data from social media only

3. The example of Netflix and Spotify shows that AI can
   A. only respond to existing user preferences
   B. predict and influence what consumers want
   C. replace human content creators
   D. eliminate the need for marketing

4. What percentage of Amazon’s revenue comes from its recommendation system?
   A. 25%
   B. 30%
   C. 35%
   D. 40%

5. According to the passage, computer vision technology in physical stores can
   A. replace security cameras
   B. track customer movements and expressions
   C. automatically charge customers for products
   D. communicate with customers directly

Questions 6-9

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. AI systems can combine customer data from different sources faster than humans.

7. All retail companies now use smart shelves in their stores.

8. Privacy concerns have decreased as AI technology has improved.

9. Small businesses can now access AI tools that were previously only for large companies.

Questions 10-13

Complete the sentences below.

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

10. AI-powered chatbots use __ __ to understand the emotions in customer messages.

11. Some retailers use __ __ that can detect when products are touched by customers.

12. There is a risk of __ __ when AI makes wrong assumptions from bad data.

13. Companies need to create __ __ from data, not just collect it.

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PASSAGE 2 – Advanced Applications of AI in Consumer Psychology

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The intersection of artificial intelligence and consumer psychology represents one of the most fascinating developments in modern marketing. While basic AI applications focus on transactional data – what people buy, when, and how much they spend – more advanced systems are now capable of understanding the psychological motivations underlying consumer decisions. This deeper level of analysis is transforming not only how companies market their products but also how they conceptualize the entire customer journey.

A. Emotional AI, also known as affective computing, has emerged as a particularly powerful tool in this domain. These systems use facial recognition, voice analysis, and even biometric data to assess emotional states during the shopping experience. For instance, some automotive companies employ emotional AI during test drives, monitoring drivers’ stress levels and excitement through wearable devices. This data reveals which features genuinely delight customers versus those that merely meet expectations, insights that traditional surveys might miss due to response bias – the tendency of people to answer questions in ways they believe are socially acceptable rather than truthfully reflecting their feelings.

B. The concept of micro-moments – brief instances when consumers turn to devices for immediate answers – has been revolutionized by AI. Google’s research indicates that these moments are critical decision points in the purchasing process. AI systems can now identify and capitalize on these moments by delivering precisely targeted content within milliseconds. A person searching for “best running shoes” on their smartphone might receive different recommendations based on subtle factors like search history, time of day, current location, and even weather conditions. This level of contextual awareness was impossible before sophisticated AI algorithms could process multiple data streams simultaneously.

C. Neuromarketing, the study of brain responses to marketing stimuli, has found a powerful ally in AI. Traditional neuromarketing relied on expensive equipment like fMRI machines and required participants to visit specialized laboratories. Now, AI-enhanced systems can infer neurological responses from more accessible data. By analyzing eye-tracking patterns, pupil dilation, and micro-expressions, AI can estimate which advertisements or product designs trigger the reward centers of the brain. Major beverage companies have used these techniques to test packaging designs, discovering that certain color combinations activate pleasure responses before consumers even taste the product.

D. The application of AI to social network analysis has unveiled the complex ways that purchasing decisions spread through communities. Rather than viewing consumers as isolated individuals, AI systems map the intricate web of influence that exists within social groups. This network-based approach reveals opinion leaders – individuals whose choices disproportionately affect others’ purchasing behavior. Fashion brands, in particular, have leveraged this insight by identifying and courting these influential nodes in consumer networks, understanding that a single strategic partnership can cascade through an entire community.

E. Sentiment analysis powered by natural language processing has become increasingly sophisticated. Early systems could only categorize text as positive, negative, or neutral. Modern AI can detect subtle nuances in consumer feedback, recognizing sarcasm, disappointment disguised as politeness, and even cultural context that might change a statement’s meaning. When a restaurant chain analyzed social media mentions using advanced sentiment AI, they discovered that phrases like “it was fine” or “not bad” – which older systems classified as positive – actually indicated lukewarm experiences that predicted customers wouldn’t return.

F. The gamification of shopping experiences has been significantly enhanced by AI’s ability to understand individual motivation profiles. Psychological research identifies various motivational drivers – achievement, competition, collaboration, and exploration among them. AI systems can determine which drivers resonate with each consumer and adjust the shopping experience accordingly. An online learning platform discovered that some users responded well to competitive leaderboards, while others were motivated by collaborative challenges. By using AI to tailor gamification elements to individual psychological profiles, they increased engagement rates by 47%.

G. Perhaps most controversially, AI is being used to identify and target consumers during vulnerable moments. Algorithms can detect signs of emotional distress, major life changes, or other periods when people are more susceptible to persuasion. While this capability raises significant ethical questions, its existence is undeniable. Some critics argue that this represents a form of psychological manipulation that exploits human weakness for commercial gain. Regulatory frameworks are struggling to keep pace with these developments, leading to calls for stricter oversight of AI applications in marketing.

The future of AI in consumer psychology analysis points toward even more seamless integration between technology and human behavior understanding. Predictive models are becoming so advanced that they can anticipate needs before consumers themselves are consciously aware of them. As one marketing executive noted, “We’re moving from asking ‘What do customers want?’ to ‘What will they want, and how can we be there before they realize it?'” This proactive approach, while potentially beneficial in creating frictionless consumer experiences, also intensifies concerns about autonomy and manipulation in the marketplace. The challenge facing businesses and regulators alike is ensuring that these powerful tools enhance rather than exploit consumer welfare.

Questions 14-20

The passage has seven paragraphs, A-G.

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

List of Headings:

• i. The role of timing in modern consumer engagement
• ii. Understanding emotional responses without surveys
• iii. Mapping influence patterns in buying communities
• iv. Ethical concerns about targeting emotionally vulnerable consumers
• v. Reading between the lines of customer feedback
• vi. Customizing interactive elements based on personality types
• vii. Brain science meets artificial intelligence in marketing
• viii. Collecting basic purchase data for analysis
• ix. The future integration of predictive technology

14. Paragraph A
15. Paragraph B
16. Paragraph C
17. Paragraph D
18. Paragraph E
19. Paragraph F
20. Paragraph G

Questions 21-23

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

21. According to paragraph A, traditional surveys may be unreliable because
    A. they are too expensive to conduct regularly
    B. people give socially acceptable answers rather than honest ones
    C. they cannot collect biometric data
    D. consumers do not understand the questions

22. The automotive companies mentioned in paragraph A use emotional AI to
    A. replace human test drivers
    B. reduce the cost of vehicle testing
    C. understand which car features truly excite customers
    D. monitor traffic conditions

23. The restaurant chain’s sentiment analysis revealed that
    A. customers always express opinions directly
    B. positive words always indicate satisfaction
    C. mild praise often signals disappointment
    D. social media is unreliable for feedback

Questions 24-26

Complete the summary below.

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

AI has revolutionized understanding of consumer psychology. Emotional AI can assess people’s feelings using facial recognition and 24. __ __. In social networks, AI identifies 25. __ __ whose purchasing choices strongly affect others. Modern sentiment analysis can even detect 26. __ and cultural context in customer feedback, making it far more accurate than earlier systems.

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PASSAGE 3 – The Algorithmic Reshaping of Consumer Identity and Market Dynamics

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The proliferation of AI-driven consumer behavior analysis has precipitated a fundamental reconceptualization of the relationship between markets and individuals, challenging long-held assumptions about consumer autonomy, market efficiency, and the very nature of commercial exchange. What initially appeared to be merely a technological enhancement of existing marketing practices has evolved into a paradigm shift with profound implications for economic theory, individual privacy, and social structure. Contemporary scholars argue that we are witnessing not just an improvement in how companies understand consumers, but a radical transformation in how consumer identity itself is constructed and performed in the digital age.

The theoretical foundations of classical economics presupposed an idealized rational actor – the homo economicus – who makes purchasing decisions based on complete information and logical evaluation of alternatives. This model, while acknowledged as a simplification, served as a useful heuristic for understanding market behavior. However, AI-powered analysis has exposed the profound inadequacy of this framework. Behavioral economists have long recognized that humans employ cognitive shortcuts and are subject to systematic biases, but AI systems reveal that consumer behavior is far more malleable and context-dependent than previously imagined. The granularity of data now available – tracking not just purchases but the entire decision-making process including hesitations, abandoned choices, and sequential patterns across platforms – demonstrates that consumer preferences are not stable attributes but rather fluid constructs continuously shaped by environmental stimuli.

This understanding has given rise to what sociologist Shoshana Zuboff terms “surveillance capitalism” – an economic system predicated on the commodification of personal experience and behavior. In this model, the actual product being sold is not the consumer’s purchase but the consumer’s behavioral data, which is then used to create predictive models that can be sold to other businesses. The asymmetry is striking: while companies accumulate comprehensive dossiers on individual consumers, those individuals typically have minimal insight into what is known about them or how that information is being utilized. This informational imbalance represents a significant departure from traditional market dynamics, where both parties theoretically entered transactions with roughly equivalent knowledge.

The concept of “algorithmic curation” – the selective presentation of information and options based on predicted preferences – introduces subtle but consequential distortions into the consumer decision-making process. When a streaming service’s recommendation algorithm prioritizes certain content, or when an e-commerce platform displays products in a particular order, these choices shape the feasible set of options from which consumers select. Behavioral scientists have demonstrated that choice architecture – how options are presented – substantially influences final decisions, often more powerfully than the objective characteristics of the options themselves. AI systems, by dynamically adjusting this architecture for each individual in real-time, exercise a form of personalized persuasion that operates largely beneath conscious awareness.

The efficacy of these systems has led to what some researchers describe as a “persuasion arms race”, wherein companies continuously refine their AI models to become more effective at influencing purchasing behavior, while consumers (and the few advocate organizations representing their interests) attempt to maintain decisional autonomy. Regulatory frameworks developed in an era of mass marketing – where the same advertisement was broadcast to millions – prove inadequate for addressing individually tailored persuasion that adapts in real-time to each person’s psychological vulnerabilities. The European Union’s General Data Protection Regulation (GDPR) represents one attempt to address these concerns, establishing “the right to explanation” for algorithmic decisions, but critics argue that even when provided, such explanations are typically too technical and opaque to enable genuine informed consent.

From a macroeconomic perspective, the concentration of AI capabilities in the hands of a few large technology companies has created unprecedented market power. Traditional antitrust frameworks focused on monopolistic control of supply or predatory pricing practices. However, companies like Google, Amazon, and Alibaba derive their dominance not from controlling physical goods but from their superior information about consumer behavior. This “data moat” creates formidable barriers to entry; new competitors cannot match the predictive accuracy of established players who have been collecting data for years. The result is a form of economic concentration that may not be captured by conventional measures like market share or pricing power, yet substantially constrains competition and innovation.

The psychological ramifications of ubiquitous behavioral tracking are only beginning to be understood. Some researchers suggest that awareness of being constantly analyzed and categorized may lead to strategic self-presentation even in commercial contexts – a phenomenon known as “algorithmic conformity” where individuals modulate their behavior to align with perceived algorithmic preferences rather than expressing authentic desires. Others point to the “filter bubble” effect, where algorithmic curation insulates individuals from diverse perspectives and products, potentially leading to market fragmentation and reduced serendipitous discovery of unexpected products or services. There is also the question of algorithmic discrimination: if AI systems are trained on historical data that reflects past societal biases, they may perpetuate and amplify these biases, denying certain demographic groups access to favorable offers or product recommendations.

The normative questions raised by AI-driven consumer analysis resist easy resolution. Proponents argue that personalized experiences create genuine value by reducing search costs and matching consumers with products they truly want, leading to greater satisfaction and market efficiency. Moreover, they contend that concerns about manipulation are overblown; consumers retain ultimate veto power over purchases and become increasingly savvy about algorithmic influence over time. Critics counter that genuine autonomy requires not just the formal freedom to refuse a purchase but also substantive conditions for reflective decision-making – conditions that are systematically undermined by sophisticated persuasion technologies. They argue for stronger regulatory safeguards, greater algorithmic transparency, and potentially restrictions on certain types of behavioral targeting, particularly for vulnerable populations or concerning essential goods and services.

Looking forward, the trajectory of AI in consumer behavior analysis will likely be determined by the interplay of technological capability, regulatory intervention, and evolving social norms. Some envision a future of “ethical AI” where systems are designed with built-in protections for consumer welfare, perhaps including mandatory disclosure of persuasive intent or algorithmic designs that prioritize long-term consumer satisfaction over short-term conversion rates. Others are more pessimistic, suggesting that the economic incentives driving increasingly sophisticated behavioral analysis are so powerful that only comprehensive regulatory overhaul can ensure that these technologies serve genuine human flourishing rather than merely corporate profit maximization. What remains certain is that the questions raised by AI-driven consumer analysis transcend purely technical concerns, touching on fundamental issues of human agency, economic justice, and the kind of commercial society we collectively wish to create.

Questions 27-31

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

27. According to the first paragraph, AI in consumer analysis represents
    A. a simple improvement in existing marketing techniques
    B. a fundamental change in consumer-market relationships
    C. a temporary trend in technology
    D. a return to classical economic theory

28. The author mentions homo economicus to
    A. support classical economic theories
    B. introduce behavioral economics concepts
    C. contrast idealized models with complex reality
    D. criticize modern consumer behavior

29. Shoshana Zuboff’s term “surveillance capitalism” describes a system where
    A. consumers are the main purchasers of AI technology
    B. personal behavioral data becomes the commodity being sold
    C. companies only collect purchase information
    D. surveillance cameras monitor shopping behavior

30. According to the passage, “algorithmic curation” influences consumers by
    A. providing complete information about all products
    B. allowing unlimited choice in decision-making
    C. shaping which options are presented and how
    D. eliminating the need for consumer decisions

31. The GDPR’s “right to explanation” has been criticized because
    A. it is too expensive for companies to implement
    B. explanations provided are often too technical to understand
    C. consumers do not want algorithmic explanations
    D. it only applies to European companies

Questions 32-36

Complete the summary using the list of words/phrases, A-L, below.

AI-driven consumer analysis has created significant market power for large technology companies. Unlike traditional monopolies that controlled 32. __ or engaged in predatory pricing, modern tech giants dominate through superior consumer information. This “data moat” creates 33. __ that prevent new competitors from achieving comparable predictive accuracy. The psychological effects include “algorithmic conformity,” where people alter their behavior to match 34. __, and filter bubbles that limit exposure to 35. __. Additionally, AI systems trained on historical data may perpetuate 36. __, affecting which products different demographic groups are shown.

A. physical goods
B. advertising revenue
C. algorithmic preferences
D. market regulations
E. barriers to entry
F. diverse perspectives
G. societal biases
H. consumer complaints
I. personal desires
J. product quality
K. international trade
L. random recommendations

Questions 37-40

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

37. Consumer preferences are stable characteristics that remain constant across different contexts.

38. Traditional antitrust frameworks are sufficient for addressing market power based on data collection.

39. Proponents of personalized AI believe consumers eventually become more aware of algorithmic influence.

40. The future development of AI in consumer analysis will depend entirely on technological advances without social or regulatory factors.

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. B
6. TRUE
7. NOT GIVEN
8. FALSE
9. TRUE
10. natural language (processing)
11. smart shelves
12. algorithmic bias
13. actionable insights

PASSAGE 2: Questions 14-26

14. ii
15. i
16. vii
17. iii
18. v
19. vi
20. iv
21. B
22. C
23. C
24. biometric data
25. opinion leaders
26. sarcasm

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. C
31. B
32. A
33. E
34. C
35. F
36. G
37. NO
38. NO
39. YES
40. NO

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: traditional methods, consumer behavior analysis
• Vị trí trong bài: Đoạn 1, dòng 2-4
• Giải thích: Passage nói “Traditional methods…while still valuable, are being supplemented and sometimes replaced” – điều này có nghĩa là các phương pháp truyền thống vẫn được sử dụng cùng với AI, không bị thay thế hoàn toàn. Đáp án A (completely replaced) sai vì có từ “sometimes”. C và D không được đề cập.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: machine learning algorithms, help companies
• Vị trí trong bài: Đoạn 3, câu đầu
• Giải thích: Câu “Machine learning algorithms…are particularly useful for identifying patterns in consumer behavior” được paraphrase thành “recognize patterns in how consumers behave”. Các đáp án khác không được nhắc đến.

Câu 3: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Netflix, Spotify, shows
• Vị trí trong bài: Đoạn 4, dòng 4-6
• Giải thích: Passage viết “These recommendation engines…often suggest content users didn’t know they wanted, effectively shaping consumer preferences as much as responding to them” – có nghĩa AI vừa dự đoán vừa ảnh hưởng sở thích người dùng.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI systems, combine customer data, faster than humans
• Vị trí trong bài: Đoạn 2, câu cuối
• Giải thích: “AI systems can consolidate all this disparate data…something that would be practically impossible to do manually” – điều này khẳng định AI nhanh hơn con người rất nhiều trong việc kết hợp dữ liệu.

Câu 7: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: all retail companies, smart shelves
• Vị trí trong bài: Đoạn 7
• Giải thích: Passage chỉ nói “Some supermarkets have experimented with smart shelves” – không đề cập đến tất cả các công ty bán lẻ, nên không thể xác định được.

Câu 8: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: privacy concerns, decreased
• Vị trí trong bài: Đoạn 8, câu đầu
• Giải thích: “Privacy concerns have become increasingly prominent” – điều này mâu thuẫn trực tiếp với việc mối lo ngại giảm xuống.

Câu 10: natural language (processing)

• Dạng câu hỏi: Sentence Completion
• Từ khóa: chatbots, understand emotions
• Vị trí trong bài: Đoạn 5, câu cuối
• Giải thích: “Natural language processing…enables these systems to understand…the sentiment and emotion behind their words”

Câu 13: actionable insights

• Dạng câu hỏi: Sentence Completion
• Từ khóa: companies, create, data
• Vị trí trong bài: Đoạn 9, câu cuối
• Giải thích: “The key to success lies in using AI not just to collect data, but to generate actionable insights”

Hình minh họa bài thi IELTS Reading về phân tích hành vi người tiêu dùng bằng AI với các biểu đồ dữ liệu và công nghệ

Passage 2 – Giải Thích

Câu 14: ii (Paragraph A)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn A nói về Emotional AI có thể đánh giá cảm xúc thông qua facial recognition, voice analysis, và biometric data, giúp hiểu được cảm xúc thực sự của khách hàng mà không cần khảo sát. Tiêu đề “Understanding emotional responses without surveys” phù hợp nhất.

Câu 15: i (Paragraph B)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn B tập trung vào “micro-moments” – những khoảnh khắc quan trọng khi người tiêu dùng tìm kiếm thông tin, và AI có thể phân phối nội dung phù hợp trong thời gian cực ngắn. Timing là yếu tố then chốt được nhấn mạnh.

Câu 17: iii (Paragraph D)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn D nói về social network analysis và cách AI lập bản đồ các mạng lưới ảnh hưởng trong cộng đồng, xác định opinion leaders. Đây chính là “mapping influence patterns in buying communities”.

Câu 21: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: traditional surveys, unreliable
• Vị trí trong bài: Đoạn A, dòng 5-7
• Giải thích: “insights that traditional surveys might miss due to response bias – the tendency of people to answer questions in ways they believe are socially acceptable rather than truthfully reflecting their feelings”

Câu 23: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: restaurant chain, sentiment analysis, revealed
• Vị trí trong bài: Đoạn E, câu cuối
• Giải thích: Restaurant chain phát hiện “phrases like ‘it was fine’ or ‘not bad’…actually indicated lukewarm experiences” – lời khen nhẹ nhàng thường báo hiệu sự thất vọng.

Câu 24: biometric data

• Dạng câu hỏi: Summary Completion
• Từ khóa: Emotional AI, assess feelings, facial recognition and…
• Vị trí trong bài: Đoạn A, câu 2
• Giải thích: “These systems use facial recognition, voice analysis, and even biometric data to assess emotional states”

Câu 25: opinion leaders

• Dạng câu hỏi: Summary Completion
• Từ khóa: social networks, identifies, purchasing choices affect others
• Vị trí trong bài: Đoạn D
• Giải thích: “This network-based approach reveals opinion leaders – individuals whose choices disproportionately affect others’ purchasing behavior”

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI in consumer analysis, represents
• Vị trí trong bài: Đoạn 1, câu đầu
• Giải thích: Passage mở đầu bằng việc nói AI đã “precipitated a fundamental reconceptualization of the relationship between markets and individuals” – đây là sự thay đổi căn bản, không chỉ là cải tiến đơn thuần.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: homo economicus, author mentions
• Vị trí trong bài: Đoạn 2
• Giải thích: Tác giả giới thiệu homo economicus như một “idealized rational actor” rồi sau đó chỉ ra “profound inadequacy of this framework” khi so với thực tế phức tạp được AI tiết lộ – đây là sự đối chiếu giữa mô hình lý tưởng hóa và hiện thực.

Câu 29: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: surveillance capitalism, describes
• Vị trí trong bài: Đoạn 3, câu đầu
• Giải thích: “surveillance capitalism – an economic system predicated on the commodification of personal experience and behavior…the actual product being sold…is the consumer’s behavioral data”

Câu 31: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: GDPR, right to explanation, criticized
• Vị trí trong bài: Đoạn 5, câu cuối
• Giải thích: “critics argue that even when provided, such explanations are typically too technical and opaque to enable genuine informed consent”

Câu 32-36: Summary Completion

• Câu 32: A (physical goods) – “companies like Google, Amazon…derive their dominance not from controlling physical goods”
• Câu 33: E (barriers to entry) – “This ‘data moat’ creates formidable barriers to entry”
• Câu 34: C (algorithmic preferences) – “individuals modulate their behavior to align with perceived algorithmic preferences”
• Câu 35: F (diverse perspectives) – “algorithmic curation insulates individuals from diverse perspectives and products”
• Câu 36: G (societal biases) – “if AI systems are trained on historical data that reflects past societal biases, they may perpetuate and amplify these biases”

Câu 37: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: consumer preferences, stable characteristics
• Vị trí trong bài: Đoạn 2, cuối đoạn
• Giải thích: Passage khẳng định “consumer preferences are not stable attributes but rather fluid constructs” – điều này trái ngược với statement.

Câu 38: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: traditional antitrust frameworks, sufficient
• Vị trí trong bài: Đoạn 6
• Giải thích: “Traditional antitrust frameworks…prove inadequate for addressing” các vấn đề mới – tác giả cho rằng chúng không đủ.

Câu 39: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: proponents, consumers, aware, algorithmic influence
• Vị trí trong bài: Đoạn 8
• Giải thích: “Proponents argue…consumers…become increasingly savvy about algorithmic influence over time”

Câu 40: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: future development, depend entirely, technological advances
• Vị trí trong bài: Đoạn 9, câu đầu
• Giải thích: “the trajectory…will likely be determined by the interplay of technological capability, regulatory intervention, and evolving social norms” – tác giả nói sẽ phụ thuộc vào nhiều yếu tố, không chỉ công nghệ.

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5. Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
artificial intelligence	n	/ˌɑːtɪˈfɪʃəl ɪnˈtelɪdʒəns/	trí tuệ nhân tạo	AI has become an increasingly important tool	artificial intelligence systems, develop AI
consumer behavior	n	/kənˈsjuːmə bɪˈheɪvjə/	hành vi người tiêu dùng	traditional methods of consumer behavior analysis	analyze consumer behavior, track behavior
process data	v	/ˈprəʊses ˈdeɪtə/	xử lý dữ liệu	can process vast amounts of data	process information, data processing
consolidate	v	/kənˈsɒlɪdeɪt/	hợp nhất, tổng hợp	AI systems can consolidate all this data	consolidate information, consolidate resources
algorithm	n	/ˈælɡərɪðəm/	thuật toán	machine learning algorithms	complex algorithm, design algorithms
identify patterns	v	/aɪˈdentɪfaɪ ˈpætənz/	xác định các mô hình	useful for identifying patterns	identify trends, pattern recognition
predictive analytics	n	/prɪˈdɪktɪv ˌænəˈlɪtɪks/	phân tích dự đoán	predictive analytics represents another application	use predictive analytics, advanced analytics
recommendation engine	n	/ˌrekəmenˈdeɪʃən ˈendʒɪn/	công cụ đề xuất	these recommendation engines have become sophisticated	powerful engine, build recommendation systems
personalization	n	/ˌpɜːsənəlaɪˈzeɪʃən/	cá nhân hóa	personalization has reached new heights	product personalization, increase personalization
leverage	v	/ˈliːvərɪdʒ/	tận dụng	physical stores are also leveraging AI	leverage technology, leverage data
computer vision	n	/kəmˈpjuːtə ˈvɪʒən/	thị giác máy tính	computer vision technology can track customers	advanced computer vision, apply computer vision
privacy concerns	n	/ˈprɪvəsi kənˈsɜːnz/	mối lo ngại về quyền riêng tư	privacy concerns have become prominent	address privacy concerns, raise concerns

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
intersection	n	/ˌɪntəˈsekʃən/	giao điểm, sự kết hợp	the intersection of AI and consumer psychology	at the intersection of, intersection between
underlying	adj	/ˌʌndəˈlaɪɪŋ/	tiềm ẩn, nền tảng	the psychological motivations underlying decisions	underlying cause, underlying factors
affective computing	n	/əˈfektɪv kəmˈpjuːtɪŋ/	tính toán cảm xúc	emotional AI, also known as affective computing	develop affective computing, affective systems
biometric data	n	/ˌbaɪəʊˈmetrɪk ˈdeɪtə/	dữ liệu sinh trắc học	use facial recognition and biometric data	collect biometric data, biometric information
response bias	n	/rɪˈspɒns ˈbaɪəs/	thiên kiến phản hồi	insights that surveys might miss due to response bias	avoid response bias, reduce bias
micro-moments	n	/ˈmaɪkrəʊ ˈməʊmənts/	khoảnh khắc vi mô	the concept of micro-moments	capture micro-moments, critical moments
contextual awareness	n	/kənˈtekstʃuəl əˈweənəs/	nhận thức bối cảnh	this level of contextual awareness was impossible	improve contextual awareness, context-aware systems
neuromarketing	n	/ˌnjʊərəʊˈmɑːkɪtɪŋ/	tiếp thị thần kinh học	neuromarketing has found a powerful ally in AI	neuromarketing research, apply neuromarketing
pupil dilation	n	/ˈpjuːpəl daɪˈleɪʃən/	sự giãn đồng tử	by analyzing pupil dilation	measure pupil dilation, pupil response
opinion leaders	n	/əˈpɪnjən ˈliːdəz/	những người dẫn dắt dư luận	reveals opinion leaders whose choices affect others	identify opinion leaders, influential leaders
sentiment analysis	n	/ˈsentɪmənt əˈnæləsɪs/	phân tích cảm xúc	sentiment analysis powered by natural language processing	conduct sentiment analysis, sentiment detection
nuances	n	/ˈnjuːɑːnsɪz/	sắc thái, nét tinh tế	modern AI can detect subtle nuances	capture nuances, linguistic nuances
gamification	n	/ˌɡeɪmɪfɪˈkeɪʃən/	trò chơi hóa	the gamification of shopping experiences	implement gamification, gamification strategy
motivation profiles	n	/ˌməʊtɪˈveɪʃən ˈprəʊfaɪlz/	hồ sơ động lực	understand individual motivation profiles	create motivation profiles, user profiles
engagement rates	n	/ɪnˈɡeɪdʒmənt reɪts/	tỷ lệ tương tác	increased engagement rates by 47%	boost engagement rates, high engagement

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
proliferation	n	/prəˌlɪfəˈreɪʃən/	sự gia tăng nhanh chóng	the proliferation of AI-driven analysis	rapid proliferation, proliferation of technology
precipitate	v	/prɪˈsɪpɪteɪt/	gây ra, thúc đẩy	has precipitated a fundamental reconceptualization	precipitate change, precipitate crisis
paradigm shift	n	/ˈpærədaɪm ʃɪft/	sự chuyển đổi mô hình	evolved into a paradigm shift	major paradigm shift, represent a shift
presuppose	v	/ˌpriːsəˈpəʊz/	giả định trước	classical economics presupposed an idealized actor	presuppose knowledge, presuppose conditions
heuristic	n	/hjʊˈrɪstɪk/	phương pháp phát hiện	served as a useful heuristic	simple heuristic, heuristic approach
malleable	adj	/ˈmæliəbəl/	dễ uốn nắn, linh hoạt	consumer behavior is far more malleable	malleable preferences, highly malleable
granularity	n	/ˌɡrænjuˈlærəti/	độ chi tiết, mức độ tỉ mỉ	the granularity of data now available	data granularity, fine granularity
surveillance capitalism	n	/səˈveɪləns ˈkæpɪtəlɪzəm/	chủ nghĩa tư bản giám sát	Zuboff terms surveillance capitalism	under surveillance capitalism, rise of capitalism
commodification	n	/kəˌmɒdɪfɪˈkeɪʃən/	sự hàng hóa hóa	predicated on the commodification of experience	commodification of data, prevent commodification
asymmetry	n	/eɪˈsɪmətri/	sự bất cân xứng	the asymmetry is striking	information asymmetry, power asymmetry
algorithmic curation	n	/ˌælɡəˈrɪðmɪk kjʊəˈreɪʃən/	sự tuyển chọn thuật toán	the concept of algorithmic curation	through algorithmic curation, content curation
choice architecture	n	/tʃɔɪs ˈɑːkɪtektʃə/	kiến trúc lựa chọn	choice architecture substantially influences decisions	design choice architecture, influence architecture
decisional autonomy	n	/dɪˈsɪʒənəl ɔːˈtɒnəmi/	quyền tự chủ quyết định	attempt to maintain decisional autonomy	protect autonomy, individual autonomy
antitrust frameworks	n	/ˌæntiˈtrʌst ˈfreɪmwɜːks/	khung pháp lý chống độc quyền	regulatory frameworks developed for mass marketing	antitrust laws, enforce antitrust
data moat	n	/ˈdeɪtə məʊt/	hào rào dữ liệu	this data moat creates formidable barriers	build a data moat, competitive moat
algorithmic conformity	n	/ˌælɡəˈrɪðmɪk kənˈfɔːməti/	sự tuân thủ thuật toán	a phenomenon known as algorithmic conformity	pressure for conformity, social conformity
filter bubble	n	/ˈfɪltə ˈbʌbəl/	bong bóng lọc thông tin	the filter bubble effect	trapped in filter bubbles, burst the bubble
normative questions	n	/ˈnɔːmətɪv ˈkwestʃənz/	câu hỏi mang tính chuẩn mực	the normative questions raised	normative ethics, normative standards
veto power	n	/ˈviːtəʊ ˈpaʊə/	quyền phủ quyết	consumers retain ultimate veto power	exercise veto power, have veto rights

Một khía cạnh thú vị trong việc sử dụng AI để phân tích hành vi người tiêu dùng là nó cũng liên quan đến việc AI ảnh hưởng đến quyết định tài chính trong doanh nghiệp như thế nào, vì dữ liệu hành vi người dùng thường được sử dụng để tối ưu hóa các quyết định đầu tư và phân bổ nguồn lực trong công ty.

Bảng từ vựng quan trọng IELTS Reading về AI và phân tích hành vi tiêu dùng với các thuật ngữ chuyên ngành

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Kết Bài

Chủ đề “How is AI being used in consumer behavior analysis?” không chỉ phản ánh xu hướng công nghệ hiện đại mà còn là một trong những chủ đề xuất hiện thường xuyên trong IELTS Reading với tính ứng dụng cao. Đề thi mẫu này đã cung cấp cho bạn trải nghiệm hoàn chỉnh với ba passages tăng dần độ khó, từ giới thiệu cơ bản về AI trong phân tích tiêu dùng, đến các ứng dụng nâng cao trong tâm lý học người tiêu dùng, và cuối cùng là những vấn đề triết học và đạo đức sâu sắc mà công nghệ này đặt ra.

Ba passages với tổng cộng 40 câu hỏi đa dạng đã giúp bạn làm quen với đầy đủ các dạng bài thi IELTS Reading thực tế, từ Multiple Choice, True/False/Not Given, Matching Headings đến Summary Completion và Short-answer Questions. Đáp án chi tiết kèm giải thích không chỉ giúp bạn kiểm tra kết quả mà còn hiểu rõ logic làm bài, cách paraphrase và xác định vị trí thông tin trong passage.

Tuy nhiên, cần lưu ý rằng việc áp dụng AI trong nhiều lĩnh vực, đặc biệt là những lĩnh vực nhạy cảm, có thể dẫn đến những tranh cãi về mặt đạo đức và xã hội, chẳng hạn như những tác động xã hội của việc gia tăng sử dụng AI trong thực thi pháp luật cho thấy sự cần thiết phải cân nhắc kỹ lưỡng các vấn đề về quyền riêng tư và công bằng xã hội.

Bộ từ vựng phong phú từ cơ bản đến nâng cao về AI, công nghệ và hành vi tiêu dùng sẽ là tài sản quý giá giúp bạn tự tin hơn không chỉ trong phần Reading mà còn trong Writing và Speaking khi gặp các chủ đề liên quan. Hãy lưu lại những collocation và cấu trúc ngữ pháp được làm đậm trong các passages để áp dụng vào bài viết và lời nói của mình.

Hãy nhớ rằng việc luyện tập thường xuyên với các đề thi mẫu chất lượng cao như thế này, kết hợp với việc phân tích kỹ lưỡng đáp án và từ vựng, là chìa khóa để đạt được band điểm Reading mục tiêu trong kỳ thi IELTS của bạn. Chúc bạn học tập hiệu quả và đạt kết quả cao!