IELTS Reading: Impact of AI on Digital Marketing – Đề thi mẫu có đáp án chi tiết

Mở bài

Trí tuệ nhân tạo (AI) đang tạo nên cuộc cách mạng trong lĩnh vực digital marketing, thay đổi cách thức các doanh nghiệp tiếp cận, tương tác và chuyển đổi khách hàng. Chủ đề Impact Of AI On Digital Marketing xuất hiện ngày càng nhiều trong kỳ thi IELTS Reading, phản ánh tầm quan trọng và xu hướng phát triển không ngừng của công nghệ trong thế giới kinh doanh hiện đại.

Đề thi mẫu này được thiết kế dành riêng cho học viên Việt Nam đang ôn luyện IELTS từ band 5.0 trở lên. Bạn sẽ được trải nghiệm một bài thi IELTS Reading hoàn chỉnh với 3 passages có độ khó tăng dần, từ Easy (Band 5.0-6.5) đến Medium (Band 6.0-7.5) và Hard (Band 7.0-9.0). Mỗi passage được thiết kế với các dạng câu hỏi đa dạng hoàn toàn giống thi thật, kèm theo đáp án chi tiết có giải thích cụ thể về vị trí thông tin và cách paraphrase.

Ngoài ra, bài viết còn cung cấp bảng từ vựng chuyên ngành marketing và AI, giúp bạn nâng cao vốn từ học thuật và kỹ thuật làm bài hiệu quả. Hãy dành đúng 60 phút để hoàn thành 40 câu hỏi như trong kỳ thi thật để đánh giá chính xác trình độ hiện tại của mình.

Hướng dẫn làm bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test là một trong bốn phần thi quan trọng, đánh giá khả năng đọc hiểu tiếng Anh học thuật của thí sinh. Bài thi bao gồm:

• Thời gian: 60 phút cho tất cả 3 passages (không có thêm thời gian chuyển đáp án)
• Tổng số câu hỏi: 40 câu
• Điểm số: Mỗi câu đúng được 1 điểm, quy đổi thành band score từ 0-9

Phân bổ thời gian khuyến nghị:

• Passage 1 (Easy): 15-17 phút
• Passage 2 (Medium): 18-20 phút
• Passage 3 (Hard): 23-25 phút

Với đề thi này về impact of AI on digital marketing, bạn sẽ gặp nội dung liên quan đến công nghệ, kinh doanh và xu hướng marketing hiện đại – những chủ đề phổ biến trong IELTS Academic.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi thường xuyên xuất hiện trong IELTS Reading:

1. Multiple Choice – Câu hỏi trắc nghiệm nhiều lựa chọn
2. True/False/Not Given – Xác định thông tin đúng/sai/không được đề cập
3. Matching Information – Nối thông tin với đoạn văn tương ứng
4. Matching Headings – Nối tiêu đề phù hợp với đoạn văn
5. Summary Completion – Hoàn thành đoạn tóm tắt
6. Matching Features – Nối đặc điểm với các nhóm/người
7. Short-answer Questions – Câu hỏi trả lời ngắn

Bài thi IELTS Reading về tác động của trí tuệ nhân tạo đến marketing số

IELTS Reading Practice Test

PASSAGE 1 – The Rise of AI in Modern Marketing

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The marketing landscape has undergone a dramatic transformation in recent years, largely due to the integration of artificial intelligence (AI) into various aspects of digital marketing strategies. What was once considered science fiction has now become an essential component of how businesses interact with their customers and promote their products and services online.

At its core, AI in marketing refers to the use of intelligent technologies to collect data, understand customer behavior, and make automated decisions that enhance marketing effectiveness. These technologies include machine learning algorithms, natural language processing, and predictive analytics. The primary advantage of AI is its ability to process vast amounts of data much faster than humans ever could, identifying patterns and trends that might otherwise go unnoticed.

One of the most visible applications of AI in digital marketing is the personalization of content. Traditional marketing approaches often relied on broad demographic categories to target audiences, sending the same message to large groups of people. However, AI enables hyper-personalization, where each customer receives content specifically tailored to their preferences, browsing history, and past interactions with a brand. For instance, when you shop online and see product recommendations that seem remarkably relevant to your interests, that’s AI at work, analyzing your behavior and predicting what you might want to purchase next.

Chatbots represent another significant AI application that has revolutionized customer service in digital marketing. These intelligent virtual assistants can handle multiple customer inquiries simultaneously, providing instant responses 24 hours a day, seven days a week. Unlike human representatives who need breaks and can only assist one customer at a time, chatbots can manage thousands of conversations concurrently. They answer frequently asked questions, guide customers through purchasing processes, and even handle basic complaint resolution. As chatbot technology continues to improve, these systems are becoming increasingly sophisticated, capable of understanding context and engaging in more natural conversations.

Email marketing has also been transformed by AI capabilities. Instead of sending the same email to an entire mailing list, AI systems can determine the optimal time to send emails to each individual subscriber based on when they’re most likely to open and read them. These systems also test different subject lines, content variations, and calls-to-action to identify which combinations produce the best results. This level of optimization would be impossible to achieve manually, especially for companies with large customer databases.

Social media marketing has similarly benefited from AI integration. Platforms like Facebook, Instagram, and LinkedIn use AI algorithms to determine which sponsored content appears in users’ feeds and when. Marketers can leverage these algorithms by creating content that the AI identifies as engaging and relevant to target audiences. Additionally, AI tools help marketers analyze social media conversations, identifying trends, sentiment, and brand mentions across millions of posts. This social listening capability allows companies to understand public perception and respond quickly to both opportunities and potential reputation issues.

Despite these advantages, the rise of AI in digital marketing is not without challenges. One significant concern is data privacy. AI systems require access to substantial amounts of personal data to function effectively, raising questions about how this information is collected, stored, and used. Many consumers are becoming increasingly aware of and concerned about their digital footprints. Companies must balance the desire to leverage AI capabilities with the need to respect customer privacy and comply with regulations like the General Data Protection Regulation (GDPR) in Europe.

Another challenge is the potential loss of the human touch in marketing communications. While AI excels at data analysis and automation, some critics argue that it lacks the creativity and emotional intelligence that human marketers bring to campaigns. The most effective approach may be a hybrid model where AI handles data-driven tasks while humans focus on creative strategy and building genuine connections with customers.

Looking ahead, the impact of AI on digital marketing will likely continue to grow. Emerging technologies such as voice search optimization, visual search, and augmented reality are opening new frontiers for AI applications in marketing. Companies that successfully integrate these technologies while maintaining ethical standards and respecting customer preferences will be best positioned to thrive in an increasingly competitive digital marketplace.

Questions 1-13

Questions 1-5: Multiple Choice

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

1. According to the passage, what is the main advantage of AI in marketing?

   • A) It is cheaper than human workers
   • B) It can process large amounts of data quickly
   • C) It eliminates the need for human marketers
   • D) It works only with demographic data

2. How does AI-powered personalization differ from traditional marketing?

   • A) It uses the same message for everyone
   • B) It only focuses on age groups
   • C) It tailors content to individual preferences
   • D) It requires more human involvement

3. What is a key advantage of chatbots mentioned in the passage?

   • A) They can replace all human employees
   • B) They work limited hours each day
   • C) They can handle only one conversation at a time
   • D) They can manage multiple conversations simultaneously

4. According to the text, AI in email marketing helps by:

   • A) Writing all emails automatically
   • B) Determining the best time to send emails to individuals
   • C) Eliminating the need for subject lines
   • D) Sending emails to random recipients

5. What concern is raised about AI in digital marketing?

   • A) It is too expensive to implement
   • B) It cannot analyze social media
   • C) It raises data privacy issues
   • D) It works too slowly

Questions 6-9: True/False/Not Given

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

Write:

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

6. AI can identify patterns in data that humans might miss.
7. Chatbots can only handle simple customer questions.
8. All social media platforms use identical AI algorithms.
9. The GDPR is a European regulation related to data protection.

Questions 10-13: Sentence Completion

Complete the sentences below.

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

10. AI tools help marketers perform __ on social media to understand public opinion.
11. Critics argue that AI lacks the __ that human marketers possess.
12. The most effective marketing approach may be a __ that combines AI and human skills.
13. Companies must maintain __ while implementing AI technologies in marketing.

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PASSAGE 2 – AI-Driven Analytics and Consumer Behavior Prediction

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The capacity of artificial intelligence to revolutionize digital marketing extends far beyond simple automation and content personalization. At the heart of this transformation lies predictive analytics – a sophisticated application of AI that enables marketers to anticipate consumer behavior with unprecedented accuracy. This capability represents a paradigm shift from reactive marketing strategies, where businesses respond to existing trends, to proactive approaches that predict and influence future consumer actions.

Predictive analytics employs complex algorithms and machine learning models to analyze historical data, identify patterns, and forecast future outcomes. In the context of digital marketing, these systems examine multiple data points including purchase history, browsing patterns, social media interactions, demographic information, and even external factors such as seasonal trends and economic indicators. The aggregation and analysis of these multifaceted data sources allow AI systems to generate probabilistic predictions about individual consumer behavior, such as the likelihood of making a purchase, the optimal price point, or the risk of customer churn.

Consider the application of predictive analytics in customer lifetime value (CLV) calculation. Traditional methods of estimating CLV relied heavily on historical averages and broad assumptions about customer behavior. However, AI-powered systems can create individualized CLV predictions by analyzing hundreds of variables simultaneously. This granular understanding enables businesses to allocate marketing resources more efficiently, focusing efforts on customers with the highest potential value while implementing retention strategies for those showing signs of disengagement. Tương tự như the impact of social media on career development, việc phân tích hành vi người dùng trên các nền tảng số đã trở thành một yếu tố then chốt trong chiến lược tiếp thị hiện đại.

The precision of AI-driven predictions has also transformed dynamic pricing strategies. E-commerce platforms increasingly employ AI systems that adjust prices in real-time based on multiple factors: competitor pricing, inventory levels, demand fluctuations, and individual customer willingness to pay. Airlines and hotel booking platforms have utilized dynamic pricing for years, but AI has made this approach accessible and practical for businesses of all sizes. The algorithms can optimize pricing to maximize revenue while maintaining competitiveness, automatically responding to market changes faster than any human analyst could.

Another critical application emerges in content optimization and distribution strategies. AI systems analyze which types of content resonate most effectively with specific audience segments, determining not only what to publish but also when and where to distribute it. These systems track engagement metrics – including click-through rates, time spent on page, social shares, and conversion rates – across various channels and demographics. By identifying patterns in this data, AI can recommend content topics, formats, and distribution channels likely to generate the highest engagement and conversion rates. This data-driven approach to content strategy eliminates much of the guesswork traditionally associated with content marketing.

The application of natural language processing (NLP) has introduced another dimension to AI-powered marketing analytics. NLP enables systems to understand and analyze human language in customer reviews, social media posts, customer service interactions, and other unstructured text data. Through sentiment analysis, these systems can gauge public opinion about brands, products, or campaigns, providing marketers with nuanced insights into customer perceptions. More advanced NLP applications can identify emerging trends by detecting shifts in conversation topics and sentiment patterns before they become widely apparent.

However, the sophistication of predictive analytics raises important ethical considerations. The same capabilities that enable beneficial personalization can be used for manipulative practices. For instance, AI systems can identify when consumers are most vulnerable to persuasion – perhaps during certain times of day or emotional states inferred from online behavior. While using this information to provide timely assistance or relevant offers may be acceptable, exploiting psychological vulnerabilities crosses ethical boundaries. The lack of clear regulatory frameworks in many jurisdictions means that companies must establish their own ethical guidelines for AI use in marketing.

Transparency presents another challenge. As AI systems become more complex, understanding how they reach specific predictions or decisions becomes increasingly difficult, even for data scientists. This “black box” problem raises concerns about accountability. If an AI system makes a prediction that leads to discriminatory outcomes – such as showing high-value opportunities only to certain demographic groups – who bears responsibility? Moreover, how can companies ensure their AI systems don’t perpetuate or amplify biases present in historical data?

The future trajectory of AI in predictive marketing analytics points toward even greater integration and sophistication. Emerging technologies like emotion recognition through facial analysis and voice pattern analysis could provide marketers with unprecedented insights into consumer reactions. Quantum computing, while still in early stages, promises to dramatically increase the computational power available for processing and analyzing massive datasets. These advances will undoubtedly create new opportunities for understanding and predicting consumer behavior, but they will also intensify existing ethical debates about privacy, manipulation, and the appropriate boundaries of marketing technology.

Questions 14-26

Questions 14-18: Yes/No/Not Given

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

Write:

• YES if the statement agrees with the claims of the writer
• NO if the statement contradicts the claims of the writer
• NOT GIVEN if it is impossible to say what the writer thinks about this

14. Predictive analytics allows marketers to forecast consumer behavior with complete certainty.
15. AI-powered customer lifetime value calculations are more individualized than traditional methods.
16. Dynamic pricing was invented specifically for e-commerce platforms.
17. Natural language processing can analyze unstructured text data from various sources.
18. All companies have established comprehensive ethical guidelines for using AI in marketing.

Questions 19-22: Matching Headings

The passage has eight paragraphs (Paragraphs 1-8). Choose the correct heading for paragraphs 3, 5, 6, and 8 from the list of headings below.

List of Headings:

• i. The role of NLP in understanding customer sentiment
• ii. Dynamic pricing powered by artificial intelligence
• iii. Future developments in predictive marketing technology
• iv. The basics of how predictive analytics functions
• v. Calculating customer lifetime value with AI
• vi. Content strategy optimization through data analysis
• vii. Ethical concerns surrounding AI predictions
• viii. The cost of implementing AI systems

19. Paragraph 3
20. Paragraph 5
21. Paragraph 6
22. Paragraph 8

Questions 23-26: Summary Completion

Complete the summary below.

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

AI-driven predictive analytics represents a shift from reactive to (23)____ marketing strategies. These systems analyze various data sources including purchase history, browsing patterns, and even (24)____ like seasonal trends. One significant challenge with advanced AI systems is the (25)____ problem, which makes it difficult to understand how predictions are made. Looking ahead, technologies such as (26)____ could dramatically increase the power available for data analysis.

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PASSAGE 3 – The Socioeconomic Implications of AI-Dominated Marketing Ecosystems

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The proliferation of artificial intelligence in digital marketing has precipitated a fundamental reconceptualization of the relationship between consumers, businesses, and the technological infrastructure that mediates their interactions. While much attention has focused on the operational efficiencies and tactical advantages that AI confers upon marketing practitioners, less scrutiny has been directed toward the broader socioeconomic ramifications of increasingly algorithmically-mediated commercial environments. These implications extend far beyond questions of marketing effectiveness, touching upon issues of economic equity, labor market dynamics, cognitive autonomy, and the very nature of consumer agency in digitally-saturated societies.

From a macroeconomic perspective, the integration of AI into digital marketing both reflects and reinforces existing patterns of economic concentration. The development and deployment of sophisticated AI systems require substantial investments in computational infrastructure, specialized talent, and vast datasets – resources disproportionately available to large technology companies and well-capitalized enterprises. This creates structural advantages that perpetuate market dominance, as companies with superior AI capabilities can achieve more efficient customer acquisition, better retention rates, and higher returns on marketing investment. The resulting competitive dynamics increasingly favor incumbent players with established data advantages, potentially stifling innovation from smaller competitors who lack comparable technological resources. Điều này có điểm tương đồng với how does the growth of e-commerce affect traditional retail, khi sự phát triển công nghệ tạo ra những thách thức không nhỏ cho các doanh nghiệp nhỏ và vừa trong việc cạnh tranh với những tập đoàn lớn.

The labor market implications of AI-driven marketing represent another critical dimension of this transformation. While proponents emphasize AI’s capacity to augment human capabilities rather than replace them entirely, the reality exhibits greater complexity. Certain marketing functions – particularly those involving routine data analysis, campaign optimization, and performance monitoring – have become substantially automated, reducing demand for entry-level and mid-level marketing positions that previously provided career pathways into the profession. Simultaneously, AI has created demand for new roles requiring expertise in data science, machine learning, and AI strategy. However, these positions typically necessitate advanced technical qualifications that require significant educational investment, potentially exacerbating socioeconomic stratification within the marketing profession itself.

The epistemological dimensions of AI in marketing – concerning how knowledge about consumers is generated, validated, and applied – raise profound questions about the nature of commercial persuasion in contemporary society. Traditional marketing relied substantially on human intuition, creative insight, and interpretive understanding of consumer motivations. AI systems, conversely, operate through pattern recognition in behavioral data, identifying correlations that may lack causal explanations or meaningful interpretations. This shift toward purely empirical, correlation-based marketing introduces a form of “atheoretical” consumer understanding – effective in predicting behavior without necessarily understanding the underlying psychological or social mechanisms. The implications extend beyond marketing strategy to questions about the nature of human agency and rationality. If marketing systems can effectively predict and influence behavior through pattern matching without understanding intentionality or meaning, what does this suggest about the nature of consumer decision-making and free will?

Moreover, the feedback loops created by AI marketing systems raise concerns about self-fulfilling prophecies and the construction of consumer identities. When AI systems categorize individuals based on behavioral data and subsequently target them with content aligned to these categories, they may reinforce and amplify particular aspects of identity while marginalizing others. A person who purchases outdoor equipment might be algorithmically categorized as an “outdoor enthusiast” and subsequently exposed to disproportionate amounts of related content, potentially channeling their interests and consumption patterns in ways that wouldn’t have occurred absent the algorithmic intervention. These systems thus become not merely responsive to consumer preferences but actively constitutive of them – a distinction with significant implications for consumer autonomy and identity formation.

The asymmetric information dynamics created by sophisticated AI marketing systems present additional ethical challenges. While marketers gain increasingly granular insights into individual consumers through AI analysis, consumers typically possess limited knowledge about how their data is collected, analyzed, and applied. This information asymmetry creates power imbalances that may enable manipulative practices. More troubling still is the potential for what researcher Shoshana Zuboff terms “behavioral surplus extraction” – where the predictive models developed through marketing analytics are applied not merely to predict behavior but to modify it in ways that serve corporate interests potentially misaligned with consumer welfare.

The regulatory landscape surrounding AI in digital marketing remains fragmented and inadequate to address these multifaceted challenges. Existing frameworks like GDPR focus primarily on data protection and individual consent, but may be insufficient to address systemic concerns about economic concentration, cognitive manipulation, and algorithmic discrimination. The transnational nature of digital marketing platforms further complicates regulatory efforts, as businesses can potentially exploit jurisdictional arbitrage, operating from locations with more permissive regulatory environments. Developing effective governance frameworks requires international coordination and interdisciplinary expertise spanning technology, economics, psychology, and ethics – a formidable challenge given the rapid pace of technological evolution.

Looking forward, addressing these socioeconomic implications requires moving beyond narrow technocratic approaches that frame AI in marketing purely as an optimization challenge. Instead, society must engage in broader normative deliberation about the kind of commercial environment we wish to inhabit. This includes considering questions such as: What forms of behavioral influence are acceptable in commercial contexts? How should we balance marketing effectiveness against consumer autonomy? What obligations do companies have to ensure their AI systems don’t perpetuate social inequalities? How can we maintain competitive markets when AI capabilities concentrate among a few dominant players?

Scholars have proposed various approaches to addressing these challenges. Some advocate for “algorithmic transparency” mandates requiring companies to disclose how their AI systems function. Others suggest implementing “algorithmic impact assessments” similar to environmental impact assessments, requiring evaluation of potential societal effects before deploying AI marketing systems. More radical proposals include treating consumer data as a form of labor deserving compensation, or even establishing public ownership of data infrastructure to prevent private monopolization of the informational resources that power AI marketing. Đối với những ai quan tâm đến ethical concerns in facial recognition technology, những vấn đề về quyền riêng tư và đạo đức trong việc sử dụng AI cho mục đích phân tích hành vi khách hàng cũng đặt ra những câu hỏi tương tự về ranh giới của công nghệ.

The trajectory of AI in digital marketing will ultimately be determined not by technological capabilities alone, but by the regulatory frameworks, ethical norms, and social values that shape its deployment. As these systems become increasingly sophisticated and pervasive, the imperative for thoughtful, evidence-based governance grows correspondingly urgent. The challenge lies in harnessing the legitimate benefits of AI-driven marketing – including greater relevance, efficiency, and personalization – while mitigating its potential to undermine consumer autonomy, exacerbate inequalities, and concentrate economic power. Meeting this challenge successfully will require ongoing dialogue among technologists, policymakers, businesses, and citizens to collectively define the boundaries and expectations for AI in our commercial ecosystems.

Bảng điều khiển phân tích marketing AI với dữ liệu dự đoán hành vi người tiêu dùng

Questions 27-40

Questions 27-31: Multiple Choice

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

27. According to the passage, what is a major consequence of AI requiring substantial resources?

    • A) It makes marketing more affordable for small businesses
    • B) It creates advantages for large companies with existing data
    • C) It eliminates all competition in the market
    • D) It reduces the need for computational infrastructure

28. How does the passage describe the impact of AI on marketing jobs?

    • A) AI has completely replaced all marketing professionals
    • B) AI has created only entry-level positions
    • C) AI has automated some functions while creating demand for technical expertise
    • D) AI has had no effect on the marketing labor market

29. The “atheoretical” approach to consumer understanding mentioned in the passage refers to:

    • A) Marketing based on creative intuition
    • B) Predicting behavior through pattern recognition without understanding causes
    • C) Using only traditional psychological theories
    • D) Completely random marketing strategies

30. According to the passage, AI marketing systems may affect consumer identity by:

    • A) Having no influence on personal preferences
    • B) Reinforcing certain aspects while marginalizing others
    • C) Eliminating all personal choices
    • D) Randomly assigning interests to individuals

31. What does the passage suggest about current regulatory frameworks?

    • A) They are completely adequate for AI marketing
    • B) They focus mainly on data protection but may not address systemic issues
    • C) They have eliminated all ethical concerns
    • D) They only apply to small businesses

Questions 32-36: Matching Features

Match each proposal (32-36) with the correct description (A-H).

Write the correct letter, A-H, in boxes 32-36.

Proposals mentioned in the passage:
32. Algorithmic transparency mandates
33. Algorithmic impact assessments
34. Consumer data as labor
35. Public ownership of data infrastructure
36. GDPR regulations

Descriptions:

• A) Treating data contributions as work deserving payment
• B) Eliminating all AI systems from marketing
• C) Evaluating societal effects before deploying AI systems
• D) Requiring companies to explain how their AI functions
• E) Focusing primarily on data protection and consent
• F) Preventing private control of informational resources
• G) Banning all forms of digital marketing
• H) Limiting internet access to consumers

Questions 37-40: Short-answer Questions

Answer the questions below.

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

37. What term does researcher Shoshana Zuboff use to describe the extraction and use of predictive models for corporate interests?
38. What type of approach do scholars say is insufficient for addressing AI marketing challenges?
39. What kind of deliberation does the passage suggest society needs to engage in regarding commercial environments?
40. According to the passage, what three factors will ultimately determine the trajectory of AI in marketing besides technological capabilities?

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Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. D
4. B
5. C
6. TRUE
7. FALSE
8. NOT GIVEN
9. TRUE
10. social listening
11. emotional intelligence
12. hybrid model
13. ethical standards

PASSAGE 2: Questions 14-26

14. NO
15. YES
16. NOT GIVEN
17. YES
18. NO
19. v
20. vi
21. i
22. iii
23. proactive approaches
24. external factors
25. black box
26. quantum computing

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. B
31. B
32. D
33. C
34. A
35. F
36. E
37. behavioral surplus extraction
38. narrow technocratic approaches
39. broader normative deliberation
40. regulatory frameworks, ethical norms, social values

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Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main advantage, AI in marketing
• Vị trí trong bài: Đoạn 2, dòng 4-6
• Giải thích: Bài đọc nói rõ “The primary advantage of AI is its ability to process vast amounts of data much faster than humans ever could” – đây chính là paraphrase của đáp án B về việc xử lý lượng lớn dữ liệu nhanh chóng.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI-powered personalization, differ from traditional marketing
• Vị trí trong bài: Đoạn 3, dòng 3-7
• Giải thích: Passage đối chiếu rõ ràng: traditional marketing dùng “broad demographic categories” và “same message to large groups”, trong khi AI cho phép “hyper-personalization” với “content specifically tailored to their preferences” – chính là đáp án C.

Câu 5: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: concern, AI in digital marketing
• Vị trí trong bài: Đoạn 7, dòng 1-3
• Giải thích: Đoạn 7 mở đầu với “One significant concern is data privacy”, sau đó giải thích chi tiết về vấn đề này – đáp án C chính xác phản ánh điều này.

Câu 6: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: identify patterns, humans might miss
• Vị trí trong bài: Đoạn 2, dòng 6-8
• Giải thích: Bài viết khẳng định AI có khả năng “identifying patterns and trends that might otherwise go unnoticed” – “go unnoticed” có nghĩa là con người có thể bỏ lỡ, hoàn toàn khớp với câu hỏi.

Câu 7: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: chatbots, only handle simple questions
• Vị trí trong bài: Đoạn 4, dòng 7-9
• Giải thích: Passage nói rõ “As chatbot technology continues to improve, these systems are becoming increasingly sophisticated, capable of understanding context and engaging in more natural conversations” – điều này mâu thuẫn trực tiếp với việc chatbot chỉ xử lý câu hỏi đơn giản.

Câu 10: social listening

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI tools, social media, understand public opinion
• Vị trí trong bài: Đoạn 6, dòng 6-8
• Giải thích: Câu trong bài: “This social listening capability allows companies to understand public perception” – “social listening” chính xác là thuật ngữ được dùng, và “understand public perception” paraphrase “understand public opinion”.

Câu 12: hybrid model

• Dạng câu hỏi: Sentence Completion
• Từ khóa: most effective marketing approach, combines AI and human skills
• Vị trí trong bài: Đoạn 8, dòng 3-5
• Giải thích: Bài viết đề cập: “The most effective approach may be a hybrid model where AI handles data-driven tasks while humans focus on creative strategy” – “hybrid model” là đáp án chính xác.

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Passage 2 – Giải Thích

Câu 14: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: predictive analytics, forecast, complete certainty
• Vị trí trong bài: Đoạn 2, dòng 8-10
• Giải thích: Bài viết sử dụng cụm “probabilistic predictions” (dự đoán xác suất), điều này mâu thuẫn với “complete certainty” (chắc chắn hoàn toàn) trong câu hỏi. Probabilistic nghĩa là dựa trên xác suất, không phải 100% chắc chắn.

Câu 15: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI-powered CLV calculations, more individualized, traditional methods
• Vị trí trong bài: Đoạn 3, dòng 3-6
• Giải thích: Passage khẳng định: “Traditional methods…relied heavily on historical averages and broad assumptions” trong khi “AI-powered systems can create individualized CLV predictions by analyzing hundreds of variables simultaneously” – rõ ràng xác nhận AI individualized hơn.

Câu 17: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Natural language processing, analyze unstructured text data
• Vị trí trong bài: Đoạn 6, dòng 2-4
• Giải thích: Đoạn văn nói rõ: “NLP enables systems to understand and analyze human language in customer reviews, social media posts, customer service interactions, and other unstructured text data” – hoàn toàn khớp với câu hỏi.

Câu 19: v (Paragraph 3)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn 3 tập trung hoàn toàn vào việc thảo luận về “customer lifetime value (CLV) calculation” và cách AI tạo ra “individualized CLV predictions”. Heading v “Calculating customer lifetime value with AI” là phù hợp nhất.

Câu 20: vi (Paragraph 5)

• Dạng câu hỏi: Matching Headings
• Giải thích: Đoạn 5 bắt đầu với “Another critical application emerges in content optimization and distribution strategies” và thảo luận về cách AI phân tích loại content nào hiệu quả, khi nào và ở đâu nên phân phối. Heading vi “Content strategy optimization through data analysis” chính xác nhất.

Câu 23: proactive approaches

• Dạng câu hỏi: Summary Completion
• Từ khóa: shift from reactive to…
• Vị trí trong bài: Đoạn 1, dòng 4-5
• Giải thích: Bài viết nói về “a paradigm shift from reactive marketing strategies…to proactive approaches” – cụm “proactive approaches” chính là đáp án cần điền vào chỗ trống.

Câu 25: black box

• Dạng câu hỏi: Summary Completion
• Từ khóa: challenge, difficult to understand predictions
• Vị trí trong bài: Đoạn 8, dòng 2-4
• Giải thích: Passage đề cập: “This ‘black box’ problem raises concerns about accountability” trong ngữ cảnh thảo luận về việc khó hiểu cách AI đưa ra dự đoán – “black box” là thuật ngữ chính xác.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI requiring substantial resources, major consequence
• Vị trí trong bài: Đoạn 2, dòng 2-7
• Giải thích: Đoạn văn giải thích rằng việc phát triển AI cần “substantial investments” và các nguồn lực này “disproportionately available to large technology companies”, tạo ra “structural advantages that perpetuate market dominance”. Đáp án B chính xác phản ánh điều này.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: impact of AI, marketing jobs
• Vị trí trong bài: Đoạn 3, dòng 2-9
• Giải thích: Passage mô tả một bức tranh phức tạp: “Certain marketing functions…have become substantially automated, reducing demand for entry-level and mid-level marketing positions” nhưng “AI has created demand for new roles requiring expertise in data science, machine learning” – đáp án C tóm tắt chính xác cả hai khía cạnh này.

Câu 29: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: “atheoretical” approach, consumer understanding
• Vị trí trong bài: Đoạn 4, dòng 5-9
• Giải thích: Bài viết giải thích: “AI systems…operate through pattern recognition in behavioral data, identifying correlations that may lack causal explanations” và gọi đây là “atheoretical consumer understanding – effective in predicting behavior without necessarily understanding the underlying psychological or social mechanisms”. Đáp án B chính xác.

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: AI marketing systems, affect consumer identity
• Vị trí trong bài: Đoạn 5, dòng 3-8
• Giải thích: Passage mô tả: “they may reinforce and amplify particular aspects of identity while marginalizing others” và cho ví dụ về người mua thiết bị outdoor bị categorize và “subsequently exposed to disproportionate amounts of related content, potentially channeling their interests”. Đáp án B phản ánh chính xác quá trình này.

Câu 32: D (Algorithmic transparency mandates)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 9, dòng 4-5
• Giải thích: Bài viết nói: “Some advocate for ‘algorithmic transparency’ mandates requiring companies to disclose how their AI systems function” – description D “Requiring companies to explain how their AI functions” chính xác match với proposal này.

Câu 37: behavioral surplus extraction

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: Shoshana Zuboff, term, extraction, corporate interests
• Vị trí trong bài: Đoạn 6, dòng 5-8
• Giải thích: Câu trong bài: “More troubling still is the potential for what researcher Shoshana Zuboff terms ‘behavioral surplus extraction'” – đây chính xác là thuật ngữ mà Zuboff sử dụng (không quá 3 từ).

Câu 38: narrow technocratic approaches

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: scholars, insufficient, addressing AI marketing challenges
• Vị trí trong bài: Đoạn 8, dòng 1-2
• Giải thích: Passage khẳng định: “addressing these socioeconomic implications requires moving beyond narrow technocratic approaches” – đây là loại approach mà scholars cho là không đủ.

Câu 40: regulatory frameworks, ethical norms, social values (hoặc bất kỳ ba trong số này theo thứ tự bất kỳ)

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: three factors, determine trajectory, besides technological capabilities
• Vị trí trong bài: Đoạn 10, dòng 1-2
• Giải thích: Câu mở đầu đoạn 10: “The trajectory of AI in digital marketing will ultimately be determined not by technological capabilities alone, but by the regulatory frameworks, ethical norms, and social values” – ba yếu tố này được liệt kê rõ ràng.

Từ vựng chuyên ngành AI và marketing cho IELTS Reading

Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
integration	n	/ˌɪntɪˈɡreɪʃn/	sự tích hợp, hòa nhập	the integration of artificial intelligence into various aspects	technology integration, seamless integration
enhance	v	/ɪnˈhæns/	nâng cao, cải thiện	make automated decisions that enhance marketing effectiveness	enhance performance, enhance capabilities
vast	adj	/væst/	rộng lớn, khổng lồ	process vast amounts of data	vast majority, vast network
personalization	n	/ˌpɜːrsənəlaɪˈzeɪʃn/	cá nhân hóa	the personalization of content	content personalization, hyper-personalization
demographic	adj/n	/ˌdeməˈɡræfɪk/	nhân khẩu học, thuộc về dân số	broad demographic categories	demographic data, target demographic
revolutionize	v	/ˌrevəˈluːʃənaɪz/	cách mạng hóa	chatbots have revolutionized customer service	revolutionize the industry
simultaneously	adv	/ˌsaɪmlˈteɪniəsli/	đồng thời	handle multiple customer inquiries simultaneously	occur simultaneously
optimal	adj	/ˈɒptɪməl/	tối ưu, tốt nhất	determine the optimal time to send emails	optimal solution, optimal conditions
optimization	n	/ˌɒptɪmaɪˈzeɪʃn/	tối ưu hóa	this level of optimization would be impossible	search engine optimization, performance optimization
sentiment	n	/ˈsentɪmənt/	cảm xúc, quan điểm	identifying trends, sentiment, and brand mentions	public sentiment, consumer sentiment
leverage	v	/ˈlevərɪdʒ/	tận dụng, khai thác	leverage AI capabilities	leverage resources, leverage technology
genuine	adj	/ˈdʒenjuɪn/	chân thực, thực sự	building genuine connections with customers	genuine interest, genuine concern

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
predictive analytics	n phrase	/prɪˈdɪktɪv ˌænəˈlɪtɪks/	phân tích dự đoán	predictive analytics employs complex algorithms	advanced predictive analytics
anticipate	v	/ænˈtɪsɪpeɪt/	dự đoán, lường trước	enables marketers to anticipate consumer behavior	anticipate changes, anticipate demand
paradigm shift	n phrase	/ˈpærədaɪm ʃɪft/	sự thay đổi mô hình/tư duy căn bản	represents a paradigm shift from reactive marketing	major paradigm shift
aggregation	n	/ˌæɡrɪˈɡeɪʃn/	sự tổng hợp	the aggregation and analysis of these data sources	data aggregation
churn	n	/tʃɜːrn/	tỷ lệ khách hàng rời bỏ	the risk of customer churn	reduce churn, churn rate
granular	adj	/ˈɡrænjələr/	chi tiết, chi li	granular understanding enables businesses	granular data, granular level
retention	n	/rɪˈtenʃn/	giữ chân (khách hàng)	implementing retention strategies	customer retention, retention rate
dynamic pricing	n phrase	/daɪˈnæmɪk ˈpraɪsɪŋ/	định giá linh hoạt	AI has transformed dynamic pricing strategies	dynamic pricing model
willingness to pay	n phrase	/ˈwɪlɪŋnəs tuː peɪ/	mức sẵn lòng trả	individual customer willingness to pay	assess willingness to pay
resonate	v	/ˈrezəneɪt/	gây được tiếng vang, phù hợp	which types of content resonate most effectively	resonate with audience
sentiment analysis	n phrase	/ˈsentɪmənt əˈnæləsɪs/	phân tích cảm xúc	Through sentiment analysis, these systems can gauge public opinion	perform sentiment analysis
nuanced	adj	/ˈnuːɑːnst/	tinh tế, nhiều sắc thái	providing marketers with nuanced insights	nuanced understanding
vulnerable	adj	/ˈvʌlnərəbl/	dễ bị tổn thương, yếu đuối	when consumers are most vulnerable to persuasion	vulnerable population
perpetuate	v	/pərˈpetʃueɪt/	duy trì, làm kéo dài	perpetuate or amplify biases	perpetuate stereotypes
trajectory	n	/trəˈdʒektəri/	quỹ đạo phát triển	the future trajectory of AI in marketing	growth trajectory

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
proliferation	n	/prəˌlɪfəˈreɪʃn/	sự gia tăng nhanh chóng	the proliferation of artificial intelligence	nuclear proliferation, proliferation of data
precipitate	v	/prɪˈsɪpɪteɪt/	gây ra, thúc đẩy	has precipitated a fundamental reconceptualization	precipitate a crisis
ramification	n	/ˌræmɪfɪˈkeɪʃn/	hệ quả, tác động	broader socioeconomic ramifications	serious ramifications
mediate	v	/ˈmiːdieɪt/	trung gian, làm môi giới	technological infrastructure that mediates their interactions	mediate disputes
macroeconomic	adj	/ˌmækroʊˌiːkəˈnɒmɪk/	vĩ mô (kinh tế)	From a macroeconomic perspective	macroeconomic policy
perpetuate	v	/pərˈpetʃueɪt/	duy trì, làm kéo dài	reinforces existing patterns of economic concentration	perpetuate inequality
incumbent	adj/n	/ɪnˈkʌmbənt/	đương nhiệm, hiện hành	increasingly favor incumbent players	incumbent advantage
stifle	v	/ˈstaɪfl/	kìm hãm, dập tắt	potentially stifling innovation	stifle competition, stifle creativity
augment	v	/ɔːɡˈment/	tăng cường, bổ sung	AI’s capacity to augment human capabilities	augment reality, augment workforce
exacerbate	v	/ɪɡˈzæsərbeɪt/	làm trầm trọng thêm	potentially exacerbating socioeconomic stratification	exacerbate tensions
epistemological	adj	/ɪˌpɪstɪməˈlɒdʒɪkl/	nhận thức luận	the epistemological dimensions of AI	epistemological debate
correlation	n	/ˌkɒrəˈleɪʃn/	mối tương quan	identifying correlations that may lack causal explanations	strong correlation
atheoretical	adj	/ˌeɪθiːəˈretɪkl/	không có nền tảng lý thuyết	introduces a form of “atheoretical” consumer understanding	atheoretical approach
intentionality	n	/ɪnˌtenʃəˈnæləti/	tính chủ đích	without understanding intentionality or meaning	human intentionality
self-fulfilling prophecy	n phrase	/self fʊlˈfɪlɪŋ ˈprɒfəsi/	lời tiên tri tự ứng nghiệm	concerns about self-fulfilling prophecies	create a self-fulfilling prophecy
constitutive	adj	/kənˈstɪtjutɪv/	cấu thành, tạo nên	actively constitutive of them	constitutive element
asymmetric	adj	/ˌeɪsɪˈmetrɪk/	bất đối xứng	asymmetric information dynamics	asymmetric warfare
fragmented	adj	/ˈfræɡmentɪd/	phân mảnh, rời rạc	regulatory landscape remains fragmented and inadequate	fragmented market
jurisdictional arbitrage	n phrase	/ˌdʒʊrɪsˈdɪkʃənl ˈɑːrbɪtrɑːʒ/	lợi dụng chênh lệch pháp luật giữa các vùng	exploit jurisdictional arbitrage	engage in jurisdictional arbitrage
normative	adj	/ˈnɔːrmətɪv/	mang tính quy chuẩn	broader normative deliberation	normative framework
technocratic	adj	/ˌteknəˈkrætɪk/	theo chủ nghĩa kỹ trị	moving beyond narrow technocratic approaches	technocratic solution

Mẹo thực hành IELTS Reading hiệu quả với bài tập về AI và marketing

Kết bài

Qua bài thi mẫu hoàn chỉnh về chủ đề Impact of AI on digital marketing, bạn đã được trải nghiệm một đề thi IELTS Reading chuẩn với 40 câu hỏi đa dạng dạng, phản ánh chính xác cấu trúc và độ khó của kỳ thi thực tế. Chủ đề AI và marketing không chỉ phổ biến trong các đề thi IELTS gần đây mà còn mang tính thời sự cao, giúp bạn vừa luyện tập kỹ năng đọc hiểu vừa cập nhật kiến thức về công nghệ và kinh doanh hiện đại.

Ba passages với độ khó tăng dần từ Easy (Band 5.0-6.5) qua Medium (Band 6.0-7.5) đến Hard (Band 7.0-9.0) đã cung cấp một lộ trình rõ ràng để bạn đánh giá trình độ hiện tại và xác định những kỹ năng cần cải thiện. Passage 1 giúp bạn làm quen với các ứng dụng cơ bản của AI trong marketing, Passage 2 đi sâu vào phân tích dự đoán và hành vi người tiêu dùng, trong khi Passage 3 thách thức khả năng hiểu biết về các vấn đề phức tạp như tác động kinh tế xã hội và đạo đức công nghệ.

Đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin, cách paraphrase và chiến lược làm bài sẽ giúp bạn không chỉ biết đáp án đúng mà còn hiểu được lý do tại sao đúng và cách tìm ra chúng một cách có phương pháp. Đây chính là chìa khóa để nâng cao band điểm IELTS Reading từ 6.0 lên 7.0 hoặc thậm chí 8.0+.

Bảng từ vựng phân theo từng passage với phonetic transcription, nghĩa tiếng Việt, ví dụ cụ thể và collocations là tài liệu quý giá giúp bạn xây dựng vốn từ học thuật vững chắc – một yếu tố không thể thiếu để chinh phục IELTS Reading. Hãy dành thời gian học kỹ những từ vựng này và áp dụng chúng vào việc đọc các bài báo tiếng Anh về công nghệ và marketing.

Hãy nhớ rằng, thành công trong IELTS Reading không chỉ đến từ việc làm nhiều bài tập mà còn từ việc phân tích kỹ lưỡng từng câu trả lời, hiểu rõ các dạng câu hỏi và rèn luyện khả năng quản lý thời gian hiệu quả. Chúc bạn ôn tập hiệu quả và đạt được band điểm mong muốn trong kỳ thi IELTS sắp tới!