IELTS Reading: Vai Trò của AI trong Cải Thiện Hệ Thống Giáo Dục ở Các Nước Đang Phát Triển – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Chủ đề về trí tuệ nhân tạo (AI) và ứng dụng của nó trong giáo dục đang trở thành một trong những đề tài nóng hổi và thường xuyên xuất hiện trong bài thi IELTS Reading những năm gần đây. Đặc biệt, vai trò của AI trong việc cải thiện hệ thống giáo dục tại các quốc gia đang phát triển là một góc nhìn đa chiều, kết hợp giữa công nghệ, xã hội và phát triển bền vững – những yếu tố mà ban giám khảo IELTS rất ưa chuộng.

Trong bài viết này, bạn sẽ được trải nghiệm một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages có độ khó tăng dần từ Easy đến Hard. Đề thi được thiết kế dựa trên format chuẩn Cambridge IELTS, bao gồm 40 câu hỏi đa dạng với 7 dạng câu hỏi khác nhau. Bạn sẽ học được cách xử lý các loại thông tin từ cơ bản đến phức tạp, rèn luyện kỹ năng skimming, scanning và paraphrasing – những kỹ năng thiết yếu để đạt band điểm cao.

Ngoài đề thi mẫu, bài viết còn cung cấp đáp án chi tiết với giải thích từng câu, vị trí thông tin trong bài, và phân tích cách paraphrase. Đặc biệt, phần từ vựng học thuật được trích xuất từ các passages sẽ giúp bạn nâng cao vốn từ vựng chuyên ngành.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, đặc biệt hữu ích cho những bạn đang nhắm đến band 7.0-8.0 và muốn làm quen với các chủ đề học thuật hiện đại.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test bao gồm 3 passages với tổng cộng 40 câu hỏi, thời gian làm bài là 60 phút. Đây là bài kiểm tra khả năng đọc hiểu, phân tích và xử lý thông tin học thuật của bạn trong điều kiện áp lực thời gian.

Phân bổ thời gian khuyến nghị:

• Passage 1 (Easy): 15-17 phút – Đây là passage dễ nhất, giúp bạn “khởi động” và ghi điểm tối đa
• Passage 2 (Medium): 18-20 phút – Độ khó trung bình, yêu cầu kỹ năng paraphrase tốt
• Passage 3 (Hard): 23-25 phút – Passage khó nhất, cần thời gian suy luận và phân tích sâu

Lưu ý quan trọng: Hãy dành 2-3 phút cuối để chuyển đáp án lên answer sheet một cách cẩn thận.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất 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 nhắc đến
3. Matching Information – Ghép thông tin với đoạn văn tương ứng
4. Matching Headings – Ghép tiêu đề với đoạn văn
5. Sentence Completion – Hoàn thiện câu
6. Summary Completion – Hoàn thiện đoạn tóm tắt
7. Short-answer Questions – Câu hỏi trả lời ngắn

Mỗi dạng câu hỏi yêu cầu một kỹ thuật làm bài riêng, và chúng ta sẽ phân tích chi tiết trong phần giải thích đáp án.

Hướng dẫn chi tiết các dạng câu hỏi IELTS Reading về công nghệ AI trong giáo dục

2. IELTS Reading Practice Test

PASSAGE 1 – AI-Powered Learning Tools Transform Classrooms in Rural Areas

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

In many developing countries, access to quality education remains a significant challenge, particularly in remote and rural areas where resources are scarce and qualified teachers are difficult to recruit. However, the emergence of artificial intelligence (AI) is beginning to change this landscape dramatically. AI-powered educational tools are now reaching students in places that were previously considered too isolated for effective learning interventions.

One of the most transformative applications of AI in education is the development of intelligent tutoring systems. These systems can adapt to individual learning speeds and provide personalized feedback to students without requiring constant human supervision. In Kenya, for example, a tablet-based learning program uses AI algorithms to assess each student’s comprehension level and automatically adjusts the difficulty of questions accordingly. This adaptive learning approach has proven particularly effective in mathematics and science subjects, where students often struggle with foundational concepts.

The affordability factor is another crucial advantage of AI in educational contexts. Traditional education models require substantial investments in infrastructure, teacher training, and ongoing operational costs. AI-powered solutions, by contrast, can be deployed at scale with relatively minimal expense once the initial development is complete. A single AI teaching assistant can serve thousands of students simultaneously, something impossible for human teachers. In Bangladesh, an AI-based English learning app has reached over 500,000 students in rural areas, providing pronunciation correction and grammar guidance that would otherwise be unavailable.

Language barriers have historically prevented many students in developing countries from accessing quality educational content, which is predominantly available in English or other major languages. AI-powered translation and localization tools are helping to overcome this obstacle. These systems can instantly translate educational materials into local languages and even adjust cultural references to make content more relevant. In India, where over 22 major languages are spoken, AI translation has enabled students to learn in their mother tongue while still gaining exposure to international curricula.

However, the implementation of AI in education is not without challenges and limitations. The most obvious barrier is the digital divide – many rural areas lack reliable electricity and internet connectivity, which are essential for most AI applications. Solar-powered devices and offline AI systems are being developed to address this issue, but progress remains slow. Additionally, there is concern about the quality of AI-generated content and the potential for algorithmic bias to perpetuate existing educational inequalities.

Another significant consideration is the role of human teachers in an AI-enhanced educational environment. Rather than replacing teachers, the most successful implementations use AI to augment and support their work. AI handles routine tasks such as grading assignments and tracking student progress, freeing teachers to focus on higher-order pedagogical activities like facilitating discussions, providing emotional support, and developing critical thinking skills. In Rwanda, teachers using AI assistance report spending 40% less time on administrative tasks and correspondingly more time on direct student interaction.

The data collection capabilities of AI systems also offer unprecedented insights into learning patterns and educational outcomes. By analyzing how students interact with educational content, AI can identify common misconceptions, predict which students are at risk of falling behind, and suggest targeted interventions. This evidence-based approach to education policy is helping governments in developing countries make more informed decisions about resource allocation and curriculum development.

Despite these promising developments, experts emphasize that AI should be viewed as a complementary tool rather than a complete solution to educational challenges in developing countries. Issues such as poverty, malnutrition, and social instability continue to affect student learning regardless of technological interventions. Sustainable improvement in education systems requires a holistic approach that addresses both technological and socioeconomic factors.

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 challenge to education in rural areas of developing countries?
   A. Students’ lack of interest in learning
   B. Shortage of resources and qualified teachers
   C. Government policies
   D. Cultural resistance to modern education

2. The intelligent tutoring system in Kenya is particularly effective for:
   A. Language learning
   B. History and geography
   C. Mathematics and science
   D. Arts and music

3. What makes AI-powered education solutions cost-effective?
   A. They require no initial investment
   B. They can serve many students after initial development
   C. They are free to download
   D. They eliminate the need for schools

4. The AI-based English learning app in Bangladesh has reached:
   A. 50,000 students
   B. 100,000 students
   C. 500,000 students
   D. 1,000,000 students

5. According to the passage, what is the most appropriate role for AI in education?
   A. To replace teachers completely
   B. To complement and support teachers’ work
   C. To focus only on administrative tasks
   D. To be used exclusively in urban areas

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 translation tools can only translate educational materials into English.
7. The lack of electricity is a significant barrier to implementing AI in rural areas.
8. Teachers in Rwanda spend less time on administrative work after using AI assistance.
9. AI systems in education are more expensive than traditional teaching methods.

Questions 10-13: Sentence Completion

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

10. AI-powered learning tools provide __ to students without needing constant human supervision.
11. Solar-powered devices are being developed to address the problem of __ in rural areas.
12. AI can analyze student interactions to identify __ and predict learning difficulties.
13. Experts believe that sustainable improvement requires addressing both technological and __ factors.

PASSAGE 2 – The Pedagogical Revolution: How AI Addresses Educational Inequality

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The integration of artificial intelligence into education systems across developing nations represents more than a mere technological upgrade; it constitutes a fundamental reimagining of how knowledge can be delivered, assessed, and personalized. While developed countries have long enjoyed the benefits of well-resourced educational infrastructure, students in emerging economies have historically faced systemic disadvantages that perpetuate cycles of poverty and limited social mobility. AI offers a potential pathway to democratize access to high-quality education, though the reality is considerably more complex than its proponents sometimes suggest.

A. The Assessment Paradigm Shift

Traditional educational assessment methods in developing countries often rely on high-stakes examinations that occur infrequently and provide limited diagnostic value. These summative assessments tell educators and students whether learning objectives have been met but offer little insight into the specific cognitive obstacles that impede progress. AI-powered formative assessment tools represent a paradigmatic shift in this regard. By continuously monitoring student performance through sophisticated natural language processing and machine learning algorithms, these systems can identify gaps in understanding in real-time and provide immediate corrective feedback.

In the Philippines, a pilot program implementing AI-driven assessment revealed that students who received continuous automated feedback improved their performance by an average of 23% compared to control groups using traditional assessment methods. The system’s ability to disaggregate data by topic, skill level, and even time of day provided teachers with granular insights previously impossible to obtain. This level of analytical precision enables educators to intervene proactively rather than reactively, fundamentally altering the pedagogical relationship between instruction and assessment.

B. Overcoming Teacher Shortages

The chronic shortage of qualified educators in developing countries represents perhaps the most significant barrier to educational expansion. Sub-Saharan Africa alone requires an additional 15 million teachers to achieve universal primary education, a target that seems increasingly unattainable through conventional recruitment and training methods. AI-powered virtual teaching assistants and automated instructional systems offer a scalable alternative that, while not replacing human teachers, can dramatically extend their reach and effectiveness.

However, this technological solution introduces its own complications. Critics argue that over-reliance on automated instruction may deprive students of the social and emotional dimensions of learning that are crucial for holistic development. The constructivist theory of education emphasizes that knowledge is co-constructed through social interaction, not merely transmitted from teacher to student. AI systems, no matter how sophisticated, currently lack the empathetic capacity and contextual understanding that characterize effective human teaching.

C. Customization and Cultural Relevance

One of AI’s most promising applications in developing country contexts is its capacity for mass customization – the ability to tailor educational content to individual learners’ needs, preferences, and cultural backgrounds. Machine learning algorithms can analyze how students from different regions, linguistic groups, and socioeconomic backgrounds interact with educational content, then optimize delivery methods accordingly. This represents a significant advance over the one-size-fits-all curricula that have historically characterized education in resource-constrained environments.

In Indonesia, an AI-powered literacy program incorporates local folklore, historical narratives, and culturally relevant examples into its lessons, adapting content based on each student’s regional background. Early results suggest that this cultural contextualization significantly enhances engagement and retention compared to generic international curricula. The system learns from millions of student interactions to continuously refine its understanding of which pedagogical approaches work best for specific demographic groups.

D. Infrastructure and Sustainability Concerns

Despite these advantages, the infrastructural prerequisites for AI implementation remain substantial. Reliable electricity, internet connectivity, and hardware availability cannot be assumed in many developing regions. The environmental footprint of AI systems also deserves consideration; training large machine learning models consumes enormous amounts of energy, raising questions about sustainability in contexts where resources are already strained.

Furthermore, the data privacy implications of educational AI systems are particularly acute in developing countries, where regulatory frameworks may be weak or non-existent. Educational AI systems collect vast amounts of sensitive information about children’s learning patterns, cognitive abilities, and even emotional states. Without robust data protection mechanisms, this information could be misused or exploited.

E. The Economic Dimension

The economic case for AI in education extends beyond immediate cost savings. By improving educational outcomes, AI has the potential to enhance human capital development and drive long-term economic growth. Countries like Vietnam that have invested heavily in educational technology are beginning to see returns in the form of a more skilled workforce capable of competing in the global knowledge economy. However, these benefits take years to materialize and require sustained investment despite uncertain short-term returns.

The digital skills students acquire through interaction with AI systems may be as valuable as the subject matter knowledge itself. In an increasingly automated global economy, technological literacy is becoming essential for employability. AI-enhanced education thus serves a dual purpose: improving traditional academic outcomes while simultaneously preparing students for a digitally mediated future.

Questions 14-26

Questions 14-18: Matching Headings

The passage has five sections, A-E. Choose the correct heading for each section from the list of headings below.

List of Headings:
i. Economic benefits and workforce development
ii. Problems with electricity and data security
iii. New methods for testing and evaluating students
iv. The challenge of insufficient teaching staff
v. Adapting lessons to local cultures and individual needs
vi. Financial costs of implementing AI systems
vii. Comparison between developed and developing nations
viii. Student resistance to new technology

14. Section A
15. Section B
16. Section C
17. Section D
18. Section E

Questions 19-23: Yes/No/Not Given

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

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

19. AI assessment tools provide more detailed information about student learning than traditional exams.
20. Sub-Saharan Africa has successfully recruited enough teachers to achieve universal primary education.
21. AI systems can fully replace the emotional support provided by human teachers.
22. The Indonesian literacy program performs better when it includes culturally relevant content.
23. All developing countries have strong data protection laws for educational technology.

Questions 24-26: Summary Completion

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

AI technology offers significant advantages for education in developing countries, but implementation faces several challenges. While AI can help address the (24)__ of qualified educators, critics worry about losing the important (25)__ of learning. Additionally, the (26)__ for AI systems, including electricity and internet access, remain problematic in many regions.

PASSAGE 3 – Algorithmic Pedagogy and the Future of Educational Equity in the Global South

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The discourse surrounding artificial intelligence in education has largely been dominated by techno-optimistic narratives that position AI as an almost messianic solution to the intractable challenges facing educational systems in developing countries. However, a more nuanced analysis reveals a complex landscape where technological intervention intersects with deeply entrenched structural inequalities, colonial legacies, and epistemological questions about the nature of knowledge itself. The question is not simply whether AI can improve education in the Global South, but rather what kind of education AI promotes, whose interests it serves, and what paradigmatic assumptions about learning and human development are embedded in its algorithmic architecture.

The Epistemological Foundations of AI-Driven Education

Contemporary AI educational systems are predicated on behaviorist and cognitivist learning theories that emphasize measurable outcomes, standardized competencies, and quantifiable progress metrics. These systems excel at teaching skills that can be decomposed into discrete, sequential steps and assessed through objective criteria – precisely the kind of knowledge that is increasingly being automated away in the global labor market. Meanwhile, the higher-order cognitive capabilities that economists and sociologists identify as crucial for the 21st-century economy – creative problem-solving, critical thinking, ethical reasoning, and collaborative intelligence – remain largely peripheral to algorithmic pedagogical models.

This epistemological bias is not merely a technical limitation but reflects deeper assumptions about what constitutes valuable knowledge. The computational logic underlying AI systems inherently privileges information that can be digitized, categorized, and processed algorithmically. Knowledge that is tacit, context-dependent, or culturally embedded – precisely the kind of indigenous knowledge systems and local wisdom that might be most relevant to students in developing countries – often proves recalcitrant to algorithmic representation.

Professor Kwame Mfume of the University of Ghana argues that the wholesale adoption of AI educational technologies developed primarily in Western contexts risks perpetuating a form of “algorithmic colonialism.” The curricula, assessment metrics, and pedagogical approaches encoded in these systems reflect the educational priorities and cultural values of their designers, who are overwhelmingly located in wealthy countries. When deployed in radically different cultural contexts, these systems may inadvertently marginalize local knowledge traditions and reproduce Western-centric educational models that have historically proven poorly suited to local developmental needs.

The Political Economy of Educational AI

The economic structures undergirding AI development in education warrant careful examination. The most sophisticated AI educational systems are being developed by multinational technology corporations and venture capital-backed startups whose primary obligation is to shareholders rather than students. The commodification of education through AI-driven platforms creates asymmetrical power relationships where a small number of technology companies increasingly control the infrastructural foundation of global education.

This concentration of power has several troubling implications. First, it creates dependency relationships where developing countries become reliant on foreign technology providers, potentially compromising educational sovereignty. Second, the proprietary nature of commercial AI systems means that their algorithmic decision-making processes remain opaque, functioning as “black boxes” that educators and policymakers cannot inspect or modify. Third, the business models of educational technology companies often involve collecting vast amounts of student data, which becomes valuable intellectual property that companies can monetize through various channels, raising profound ethical questions about the exploitation of developing country children’s personal information.

Dr. Amrita Patel, a scholar of educational policy at the Mumbai Institute of Technology, observes that many developing countries lack the technical expertise and regulatory capacity to negotiate effectively with global technology companies. Contracts for educational AI systems are often signed with limited understanding of their long-term implications, and governments may find themselves locked into expensive licensing agreements that prove difficult to extricate from. The asymmetrical information dynamics inherent in these relationships mean that developing countries often operate at a systematic disadvantage.

Algorithmic Bias and the Reproduction of Inequality

Perhaps the most insidious concern regarding AI in education involves the potential for algorithmic bias to perpetuate and amplify existing inequalities. Machine learning systems learn from historical data, and when that data reflects societies characterized by systemic discrimination, the resulting algorithms may encode and automate those biases. In educational contexts, this could manifest in AI systems that consistently underestimate the potential of students from marginalized groups or recommend less challenging educational pathways for certain demographic categories.

Research by the MIT Media Lab has demonstrated that facial recognition algorithms – a technology increasingly incorporated into educational monitoring systems – perform significantly worse on darker-skinned faces, raising concerns about discriminatory performance across racial groups. Similarly, natural language processing systems may struggle with non-standard dialects and accented speech, potentially disadvantaging students who speak minority languages or regional variants of dominant languages.

The opacity of algorithmic decision-making compounds these problems. When an AI system recommends a particular educational intervention or assesses a student’s capabilities, the reasoning behind that recommendation is often inscrutable even to the system’s designers. This lack of transparency makes it difficult to identify and correct biases, creating what legal scholar Frank Pasquale terms the “black box society” – one in which consequential decisions affecting people’s lives are made by incomprehensible automated systems.

Toward a Critical AI Pedagogy

Despite these concerns, dismissing AI entirely would be both impractical and counterproductive. The technology offers genuine capabilities that, if deployed thoughtfully and equitably, could address real educational challenges. What is needed is not a rejection of AI but rather the development of what might be termed a “critical AI pedagogy” – an approach that harnesses AI’s capabilities while remaining vigilant about its limitations and potential harms.

Such an approach would involve several key elements. First, participatory design processes that meaningfully involve teachers, students, and communities from developing countries in the creation of AI educational systems. Second, open-source alternatives to proprietary AI systems, allowing for local adaptation and transparent inspection of algorithmic processes. Third, robust regulatory frameworks that protect student data, ensure algorithmic accountability, and prevent exploitative commercial practices. Fourth, substantial investments in local technical capacity, enabling developing countries to develop indigenous AI capabilities rather than remaining perpetually dependent on foreign technology.

Most fundamentally, a critical AI pedagogy requires ongoing reflection on the purposes of education itself. Is education primarily about credentialing and sorting individuals for the labor market, or about developing human potential and cultivating engaged citizenship? Does effective education mean transmitting standardized knowledge efficiently, or fostering critical consciousness and creative agency? These are not technical questions that AI can answer, but normative and political questions that societies must address collectively. AI is neither savior nor scourge for education in developing countries – it is a powerful tool whose ultimate impact will depend on the intentions, wisdom, and political will with which it is deployed.

Questions 27-40

Questions 27-31: Multiple Choice

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

27. According to the passage, current AI educational systems are primarily based on:
    A. Collaborative learning theories
    B. Behaviorist and cognitivist approaches
    C. Indigenous knowledge systems
    D. Constructivist educational philosophy

28. Professor Kwame Mfume’s concept of “algorithmic colonialism” refers to:
    A. The use of computers in former colonial territories
    B. The imposition of Western educational values through AI systems
    C. The colonization of Africa by technology companies
    D. The historical development of algorithms

29. What is the primary obligation of AI educational technology companies?
    A. Student welfare
    B. Government requirements
    C. Shareholder profits
    D. Teacher satisfaction

30. Research from the MIT Media Lab found that facial recognition technology:
    A. Works equally well for all racial groups
    B. Should not be used in education
    C. Performs worse on darker-skinned faces
    D. Is superior to human recognition

31. According to the passage, a “critical AI pedagogy” would include:
    A. Rejecting all AI technology in education
    B. Participatory design and open-source alternatives
    C. Exclusive use of proprietary systems
    D. Eliminating teacher involvement

Questions 32-36: Matching Features

Match each concern with the correct category. You may use any letter more than once.

Concerns:
32. Knowledge that cannot be easily digitized is ignored
33. Educational decisions made without transparency
34. Companies collect and profit from student information
35. Historical discrimination is automated
36. Countries become reliant on foreign providers

Categories:
A. Epistemological issues
B. Economic structures
C. Algorithmic bias
D. Political economy

Questions 37-40: Short-answer Questions

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

37. What type of knowledge does AI computational logic naturally favor?
38. What term describes the AI systems whose decision-making processes cannot be inspected?
39. What does Dr. Amrita Patel say many developing countries lack when negotiating with technology companies?
40. According to the passage, what two things will determine AI’s ultimate impact on education?

Minh họa công nghệ AI đang thay đổi hệ thống giáo dục tại các quốc gia đang phát triển

3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. B
6. FALSE
7. TRUE
8. TRUE
9. NOT GIVEN
10. personalized feedback
11. the digital divide / digital divide
12. common misconceptions
13. socioeconomic

PASSAGE 2: Questions 14-26

14. iii
15. iv
16. v
17. ii
18. i
19. YES
20. NO
21. NO
22. YES
23. NO
24. chronic shortage
25. social dimensions / emotional dimensions
26. infrastructural prerequisites

PASSAGE 3: Questions 27-40

27. B
28. B
29. C
30. C
31. B
32. A
33. C
34. B
35. C
36. D
37. digitized information / information digitized
38. black boxes
39. technical expertise / regulatory capacity
40. intentions, wisdom / political will

4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main challenge, education, rural areas, developing countries
• Vị trí trong bài: Đoạn 1, câu đầu tiên
• Giải thích: Câu đầu tiên của passage nói rõ “access to quality education remains a significant challenge, particularly in remote and rural areas where resources are scarce and qualified teachers are difficult to recruit“. Đây là paraphrase trực tiếp của đáp án B “Shortage of resources and qualified teachers”.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: intelligent tutoring system, Kenya, particularly effective
• Vị trí trong bài: Đoạn 2, câu cuối
• Giải thích: Passage nói rõ: “This adaptive learning approach has proven particularly effective in mathematics and science subjects“. Đáp án C là chính xác.

Câu 5: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: most appropriate role, AI, education
• Vị trí trong bài: Đoạn 6, câu đầu
• Giải thích: Passage nhấn mạnh: “Rather than replacing teachers, the most successful implementations use AI to augment and support their work”. Điều này tương đương với đáp án B “To complement and support teachers’ work”.

Câu 7: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: lack of electricity, significant barrier, AI, rural areas
• Vị trí trong bài: Đoạn 5, câu thứ 2
• Giải thích: Passage nói: “The most obvious barrier is the digital divide – many rural areas lack reliable electricity and internet connectivity, which are essential for most AI applications”. Câu này xác nhận thiếu điện là rào cản đáng kể.

Câu 10: personalized feedback

• Dạng câu hỏi: Sentence Completion (NO MORE THAN THREE WORDS)
• Từ khóa: AI-powered learning tools, provide, students, without constant human supervision
• Vị trí trong bài: Đoạn 2, câu đầu
• Giải thích: Câu gốc: “These systems can adapt to individual learning speeds and provide personalized feedback to students without requiring constant human supervision”. Đây là cụm từ chính xác từ passage.

Câu 12: common misconceptions

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI, analyze, student interactions, identify, predict learning difficulties
• Vị trí trong bài: Đoạn 7, câu thứ 2
• Giải thích: Passage viết: “By analyzing how students interact with educational content, AI can identify common misconceptions, predict which students are at risk of falling behind”. Đáp án phải là “common misconceptions”.

Passage 2 – Giải Thích

Câu 14: iii (Section A)

• Dạng câu hỏi: Matching Headings
• Giải thích: Section A tập trung vào việc AI thay đổi cách đánh giá học sinh, từ summative assessment sang formative assessment với continuous automated feedback. Heading “New methods for testing and evaluating students” phù hợp nhất.

Câu 15: iv (Section B)

• Dạng câu hỏi: Matching Headings
• Giải thích: Section B bắt đầu với “The chronic shortage of qualified educators” và nói về vấn đề thiếu 15 triệu giáo viên ở Sub-Saharan Africa. Heading “The challenge of insufficient teaching staff” chính xác mô tả nội dung này.

Câu 19: YES

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí trong bài: Section A, đoạn 2
• Giải thích: Passage nói rõ: “These summative assessments tell educators… but offer little insight into the specific cognitive obstacles” trong khi AI systems provide “granular insights previously impossible to obtain”. Điều này xác nhận AI cung cấp thông tin chi tiết hơn.

Câu 21: NO

• Dạng câu hỏi: Yes/No/Not Given
• Vị trí trong bài: Section B, đoạn 2
• Giải thích: Passage nói rõ: “AI systems, no matter how sophisticated, currently lack the empathetic capacity and contextual understanding that characterize effective human teaching”. Điều này mâu thuẫn với statement, nên đáp án là NO.

Câu 24: chronic shortage

• Dạng câu hỏi: Summary Completion (NO MORE THAN TWO WORDS)
• Từ khóa: qualified educators
• Vị trí trong bài: Section B, câu đầu
• Giải thích: Cụm từ chính xác từ passage là “The chronic shortage of qualified educators”.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: current AI educational systems, primarily based on
• Vị trí trong bài: Đoạn 2, câu đầu
• Giải thích: Passage nói rõ: “Contemporary AI educational systems are predicated on behaviorist and cognitivist learning theories“. Đáp án B chính xác.

Câu 28: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: algorithmic colonialism, Professor Kwame Mfume
• Vị trí trong bài: Đoạn 3, câu cuối
• Giải thích: Professor Mfume lập luận rằng việc áp dụng AI giáo dục phát triển ở phương Tây có nguy cơ tạo ra “algorithmic colonialism” vì “The curricula, assessment metrics, and pedagogical approaches encoded in these systems reflect the educational priorities and cultural values of their designers” và có thể “reproduce Western-centric educational models“. Đáp án B chính xác.

Câu 30: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: MIT Media Lab, facial recognition technology
• Vị trí trong bài: Đoạn 7, câu đầu
• Giải thích: Passage nói: “Research by the MIT Media Lab has demonstrated that facial recognition algorithms… perform significantly worse on darker-skinned faces“. Đáp án C chính xác.

Câu 32: A (Epistemological issues)

• Dạng câu hỏi: Matching Features
• Concern: Knowledge that cannot be easily digitized is ignored
• Vị trí trong bài: Đoạn 2, câu cuối
• Giải thích: Đoạn về “Epistemological Foundations” giải thích rằng “Knowledge that is tacit, context-dependent, or culturally embedded… often proves recalcitrant to algorithmic representation“. Đây là vấn đề epistemological.

Câu 33: C (Algorithmic bias)

• Dạng câu hỏi: Matching Features
• Concern: Educational decisions made without transparency
• Vị trí trong bài: Đoạn 8
• Giải thích: Section về algorithmic bias nói về “The opacity of algorithmic decision-making” và “the reasoning behind that recommendation is often inscrutable“. Đây là vấn đề về bias và lack of transparency.

Câu 38: black boxes

• Dạng câu hỏi: Short-answer Questions (NO MORE THAN THREE WORDS)
• Từ khóa: term describes, AI systems, decision-making processes, cannot be inspected
• Vị trí trong bài: Đoạn 5, câu thứ 2
• Giải thích: Passage sử dụng thuật ngữ: “the proprietary nature of commercial AI systems means that their algorithmic decision-making processes remain opaque, functioning as ‘black boxes’“. Đáp án chính xác là “black boxes”.

Câu 40: intentions, wisdom / political will

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: two things, determine, AI’s ultimate impact
• Vị trí trong bài: Đoạn cuối, câu cuối cùng
• Giải thích: Câu kết: “AI is neither savior nor scourge for education in developing countries – it is a powerful tool whose ultimate impact will depend on the intentions, wisdom, and political will with which it is deployed”. Có thể chọn bất kỳ 2 trong 3 yếu tố này.

Phân tích chi tiết đáp án và kỹ thuật paraphrase trong đề thi IELTS Reading về AI

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
transformative	adj	/trænsˈfɔːmətɪv/	Có tính chuyển đổi, mang tính cách mạng	One of the most transformative applications of AI	transformative change, transformative technology
adaptive	adj	/əˈdæptɪv/	Có khả năng thích ứng	This adaptive learning approach has proven effective	adaptive learning, adaptive system
deploy	v	/dɪˈplɔɪ/	Triển khai, áp dụng	AI-powered solutions can be deployed at scale	deploy technology, deploy resources
augment	v	/ɔːɡˈment/	Tăng cường, bổ sung	AI is used to augment and support teachers’ work	augment capabilities, augment income
complementary	adj	/ˌkɒmplɪˈmentri/	Bổ sung, bổ trợ	AI should be viewed as a complementary tool	complementary approach, complementary skills
intervention	n	/ˌɪntəˈvenʃn/	Sự can thiệp, biện pháp	Effective learning interventions	early intervention, targeted intervention
personalized	adj	/ˈpɜːsənəlaɪzd/	Được cá nhân hóa	Provide personalized feedback to students	personalized learning, personalized service
foundational	adj	/faʊnˈdeɪʃənl/	Thuộc về nền tảng, cơ bản	Foundational concepts in mathematics	foundational knowledge, foundational skills
barrier	n	/ˈbæriə(r)/	Rào cản, trở ngại	Language barriers have prevented many students	break down barriers, overcome barriers
evidence-based	adj	/ˈevɪdəns beɪst/	Dựa trên bằng chứng	Evidence-based approach to education policy	evidence-based practice, evidence-based research
holistic	adj	/həˈlɪstɪk/	Toàn diện, tổng thể	Holistic approach to education	holistic development, holistic view

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
pedagogical	adj	/ˌpedəˈɡɒdʒɪkl/	Thuộc về sư phạm, giáo dục học	Higher-order pedagogical activities	pedagogical approach, pedagogical methods
formative	adj	/ˈfɔːmətɪv/	Mang tính hình thành, định hình	Formative assessment tools	formative years, formative evaluation
summative	adj	/ˈsʌmətɪv/	Mang tính tổng kết	Summative assessments tell educators	summative assessment, summative evaluation
granular	adj	/ˈɡrænjələ(r)/	Chi tiết, tỉ mỉ	Granular insights previously impossible	granular data, granular level
chronic	adj	/ˈkrɒnɪk/	Kinh niên, mãn tính	Chronic shortage of qualified educators	chronic problem, chronic disease
scalable	adj	/ˈskeɪləbl/	Có khả năng mở rộng quy mô	Offer a scalable alternative	scalable solution, scalable model
empathetic	adj	/ˌempəˈθetɪk/	Đồng cảm, thấu hiểu	Lack the empathetic capacity	empathetic response, empathetic understanding
contextualization	n	/kənˌtekstʃuəlaɪˈzeɪʃn/	Sự đặt trong bối cảnh	Cultural contextualization enhances engagement	cultural contextualization
prerequisite	n	/priːˈrekwəzɪt/	Điều kiện tiên quyết	Infrastructural prerequisites for AI	prerequisite for success, meet prerequisites
regulatory	adj	/ˈreɡjələtri/	Thuộc về quy định, điều tiết	Regulatory frameworks may be weak	regulatory body, regulatory compliance
sustainability	n	/səˌsteɪnəˈbɪləti/	Tính bền vững	Sustainability concerns	environmental sustainability, long-term sustainability
disaggregate	v	/dɪsˈæɡrɪɡeɪt/	Tách rời, phân tách dữ liệu	The system’s ability to disaggregate data	disaggregate information, disaggregate statistics
over-reliance	n	/ˌəʊvər rɪˈlaɪəns/	Sự phụ thuộc quá mức	Over-reliance on automated instruction	over-reliance on technology

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
techno-optimistic	adj	/ˌteknəʊ ˌɒptɪˈmɪstɪk/	Lạc quan về công nghệ	Techno-optimistic narratives	techno-optimistic view
intractable	adj	/ɪnˈtræktəbl/	Khó giải quyết, bất trị	Intractable challenges facing education	intractable problem, intractable conflict
epistemological	adj	/ɪˌpɪstəməˈlɒdʒɪkl/	Thuộc về nhận thức luận	Epistemological questions about knowledge	epistemological foundations, epistemological approach
paradigmatic	adj	/ˌpærədɪɡˈmætɪk/	Thuộc về mô hình, khuôn mẫu	Paradigmatic assumptions about learning	paradigmatic shift, paradigmatic example
cognitivist	adj/n	/ˈkɒɡnətɪvɪst/	Thuộc về chủ nghĩa nhận thức	Cognitivist learning theories	cognitivist approach, cognitivist perspective
decomposed	v (past)	/ˌdiːkəmˈpəʊzd/	Được phân tách, chia nhỏ	Skills that can be decomposed into steps	decomposed into parts
recalcitrant	adj	/rɪˈkælsɪtrənt/	Khó xử lý, ngoan cố	Recalcitrant to algorithmic representation	recalcitrant problem, recalcitrant behavior
wholesale	adj	/ˈhəʊlseɪl/	Toàn bộ, đại trà	Wholesale adoption of AI technologies	wholesale changes, wholesale rejection
commodification	n	/kəˌmɒdɪfɪˈkeɪʃn/	Sự thương mại hóa	Commodification of education	commodification of culture
asymmetrical	adj	/ˌeɪsɪˈmetrɪkl/	Bất đối xứng	Asymmetrical power relationships	asymmetrical information, asymmetrical warfare
proprietary	adj	/prəˈpraɪətri/	Thuộc về sở hữu độc quyền	Proprietary nature of AI systems	proprietary technology, proprietary software
insidious	adj	/ɪnˈsɪdiəs/	Ngấm ngầm, âm hiểm	The most insidious concern	insidious threat, insidious influence
perpetuate	v	/pəˈpetʃueɪt/	Duy trì, làm tồn tại mãi	Perpetuate and amplify existing inequalities	perpetuate stereotypes, perpetuate injustice
inscrutable	adj	/ɪnˈskruːtəbl/	Khó hiểu, bí ẩn	Inscrutable even to the system’s designers	inscrutable logic, inscrutable expression
opacity	n	/əʊˈpæsəti/	Tính mờ đục, không minh bạch	Opacity of algorithmic decision-making	algorithmic opacity, lack of opacity
normative	adj	/ˈnɔːmətɪv/	Thuộc về chuẩn mực, quy phạm	Normative and political questions	normative values, normative framework
vigilant	adj	/ˈvɪdʒɪlənt/	Cảnh giác, tỉnh táo	Remaining vigilant about limitations	remain vigilant, vigilant monitoring

Bảng từ vựng học thuật quan trọng về AI và giáo dục cho IELTS Reading

Kết Bài

Chủ đề về vai trò của AI trong việc cải thiện hệ thống giáo dục tại các nước đang phát triển không chỉ phản ánh xu hướng công nghệ hiện đại mà còn đặt ra những câu hỏi sâu sắc về công bằng giáo dục, bản sắc văn hóa và tương lai của học tập. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ ba cấp độ khó từ Easy đến Hard, với tổng cộng 40 câu hỏi đa dạng giống như trong bài thi IELTS Reading thực tế.

Ba passages đã cung cấp góc nhìn toàn diện về chủ đề: từ những ứng dụng cơ bản và lợi ích của AI trong giáo dục (Passage 1), đến những thách thức về cơ sở hạ tầng và vấn đề sư phạm (Passage 2), và cuối cùng là những phân tích sâu sắc về các vấn đề nhận thức luận, chính trị-kinh tế và đạo đức xung quanh công nghệ này (Passage 3).

Phần đáp án chi tiết kèm theo giải thích từng câu sẽ giúp bạn hiểu rõ tại sao một đáp án là đúng, vị trí thông tin xuất hiện ở đâu trong bài, và cách paraphrase được sử dụng như thế nào. Đây là kỹ năng cốt lõi để đạt band điểm cao trong IELTS Reading. Đặc biệt, bảng từ vựng với hơn 40 từ học thuật quan trọng sẽ giúp bạn mở rộng vốn từ vựng chuyên ngành về công nghệ và giáo dục.

Hãy nhớ rằng, việc luyện tập với các đề thi mẫu như thế này không chỉ giúp bạn làm quen với format bài thi mà còn nâng cao khả năng đọc hiểu, phân tích thông tin và quản lý thời gian – những kỹ năng sẽ đồng hành cùng bạn không chỉ trong kỳ thi IELTS mà còn trong học tập và công việc sau này. Chúc bạn ôn tập hiệu quả và đạt được band điểm mong muốn!