IELTS Reading: AI Trong Giáo Dục Cá Nhân Hóa – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Trí tuệ nhân tạo (AI) đang cách mạng hóa nhiều lĩnh vực, trong đó giáo dục cá nhân hóa là một trong những ứng dụng nổi bật nhất. Chủ đề “How Is AI Being Used In Personalized Education?” xuất hiện ngày càng thường xuyên trong IELTS Reading, phản ánh xu hướng công nghệ giáo dục đương đại.

Bài viết này cung cấp một đề thi IELTS Reading hoàn chỉnh với 3 passages từ dễ đến khó, giúp bạn làm quen với cấu trúc đề thi thật. Bạn sẽ được trải nghiệm đầy đủ 40 câu hỏi thuộc nhiều dạng khác nhau như Multiple Choice, True/False/Not Given, Matching Headings, và Summary Completion. Mỗi câu hỏi đều có đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin và cách paraphrase.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, với độ khó tăng dần qua từng passage. Bạn cũng sẽ học được hơn 40 từ vựng quan trọng liên quan đến công nghệ và giáo dục, kèm theo ví dụ thực tế và collocations hữu ích.

1. Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test kéo dài 60 phút với 3 passages và tổng cộng 40 câu hỏi. Mỗi câu trả lời đúng được 1 điểm, không bị trừ điểm khi sai. Độ khó tăng dần từ Passage 1 đến Passage 3.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (dễ nhất, nên làm nhanh để dành thời gian cho phần sau)
• Passage 2: 18-20 phút (độ khó trung bình, cần đọc kỹ hơn)
• Passage 3: 23-25 phút (khó nhất, yêu cầu phân tích sâu)

Hãy nhớ dành 2-3 phút cuối để chuyển đáp án lên answer sheet cẩn thận.

Các Dạng Câu Hỏi Trong Đề Này

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

• Multiple Choice: Chọn đáp án đúng từ 3-4 lựa chọn
• True/False/Not Given: Xác định thông tin đúng, sai hoặc không được đề cập
• Matching Information: Nối thông tin với đoạn văn tương ứng
• Summary Completion: Điền từ vào chỗ trống trong đoạn tóm tắt
• Matching Headings: Chọn tiêu đề phù hợp cho mỗi đoạn
• Sentence Completion: Hoàn thành câu với thông tin từ bài đọc
• Short-answer Questions: Trả lời ngắn gọn các câu hỏi

2. IELTS Reading Practice Test

PASSAGE 1 – The Evolution of AI in Education

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Artificial intelligence has emerged as a transformative force in modern education, fundamentally changing how students learn and teachers instruct. Over the past decade, AI technologies have evolved from simple automated systems to sophisticated tools capable of adapting to individual learning needs. This personalization represents a significant departure from traditional one-size-fits-all teaching methods that have dominated classrooms for centuries.

The integration of AI into educational settings began modestly in the early 2000s with basic computer-assisted learning programs. These early systems could track student progress and provide simple feedback, but they lacked the ability to truly understand individual learning styles or adjust content dynamically. However, as machine learning algorithms became more advanced, educational technology companies began developing platforms that could analyze vast amounts of student data to identify patterns in learning behaviors.

Today’s AI-powered educational platforms use sophisticated neural networks to create highly personalized learning experiences. These systems can assess a student’s current knowledge level, identify gaps in understanding, and generate customized content that addresses specific weaknesses. For example, if a student struggles with algebra but excels at geometry, the AI system will allocate more practice problems and explanatory resources to algebra while maintaining an appropriate challenge level in geometry.

One of the most significant advantages of AI in personalized education is its capacity for continuous assessment. Traditional testing methods typically evaluate student knowledge at fixed intervals, such as midterms or final examinations. In contrast, AI systems monitor learning progress constantly, adjusting difficulty levels and content presentation in real-time. This ongoing feedback loop ensures that students are neither bored with material that is too easy nor overwhelmed by concepts that are too advanced.

Adaptive learning platforms also address the challenge of different learning speeds among students. In conventional classrooms, teachers must pace their lessons according to the average ability of the class, which often means some students are left behind while others are not sufficiently challenged. AI-powered systems allow each student to progress at their own pace, spending more time on difficult concepts and moving quickly through material they grasp easily. This flexibility has proven particularly beneficial for both struggling learners who need additional support and gifted students who require more advanced content.

Moreover, AI facilitates different learning modalities. Some students learn best through visual aids, others through auditory instruction, and still others through hands-on practice. Educational AI can present the same concept in multiple formats, allowing students to engage with material in ways that suit their individual preferences. A history lesson about ancient civilizations, for instance, might be delivered through interactive timelines, audio narrations, virtual reality tours, or text-based articles, depending on what works best for each learner.

The implementation of AI in education has also empowered teachers by freeing them from routine administrative tasks. AI systems can automatically grade multiple-choice tests, track attendance, and generate progress reports, allowing educators to devote more time to direct student interaction and instructional planning. Some advanced systems even provide teachers with insights and recommendations about which students need additional support and what teaching strategies might be most effective.

Despite these advantages, the adoption of AI in education faces several challenges. Data privacy concerns remain paramount, as these systems require collecting and analyzing substantial amounts of personal information about students’ learning habits and performance. Educational institutions must implement robust security measures to protect this sensitive data while still leveraging its benefits for personalization.

Questions 1-7: Multiple Choice

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

1. According to the passage, early AI educational systems in the 2000s were limited because they:
   A. were too expensive for most schools
   B. could not adapt to different learning styles
   C. required extensive teacher training
   D. only worked with mathematics subjects

2. Modern AI educational platforms use neural networks to:
   A. replace human teachers entirely
   B. create individualized learning experiences
   C. test students at regular intervals
   D. reduce school operating costs

3. What advantage does continuous assessment provide compared to traditional testing?
   A. It requires less teacher involvement
   B. It adjusts content in real-time
   C. It is easier to administer
   D. It covers more subjects

4. AI-powered adaptive learning helps gifted students by:
   A. placing them in advanced classes
   B. providing more challenging material
   C. connecting them with expert tutors
   D. reducing their homework load

5. The passage mentions that AI can present history lessons through:
   A. only written text
   B. standardized videos
   C. multiple formats
   D. teacher demonstrations

6. How does AI benefit teachers according to the text?
   A. By increasing their salaries
   B. By reducing class sizes
   C. By handling administrative work
   D. By designing entire curricula

7. The main challenge in implementing AI in education is:
   A. student resistance
   B. data privacy issues
   C. high costs
   D. technical complexity

Questions 8-13: 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

8. Traditional teaching methods treated all students the same way.
9. Machine learning algorithms became widely available in education during the 1990s.
10. AI systems can identify specific areas where students need improvement.
11. Most schools have successfully implemented AI systems without any problems.
12. Students using AI platforms must follow the same pace as their classmates.
13. Some AI educational systems can provide recommendations to teachers about teaching strategies.

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PASSAGE 2 – Mechanisms and Applications of AI-Driven Personalization

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The mechanisms underlying AI-driven personalized education are considerably more complex than they might initially appear. At the core of these systems lies sophisticated data analytics combined with predictive modeling, which together enable platforms to anticipate student needs before learners themselves recognize them. This proactive approach represents a paradigm shift from reactive educational interventions that only address problems after they have become apparent.

Natural Language Processing (NLP), a subset of artificial intelligence, has proven particularly instrumental in creating personalized learning experiences. NLP algorithms can analyze student responses to open-ended questions, assessing not merely whether answers are correct but also evaluating the reasoning processes behind them. This deeper level of analysis allows AI systems to distinguish between students who have genuinely mastered a concept and those who have simply memorized information without true comprehension. Consequently, the system can tailor subsequent instruction to reinforce conceptual understanding rather than rote learning.

Intelligent tutoring systems (ITS) represent one of the most mature applications of AI in personalized education. These systems emulate the behavior of experienced human tutors by engaging students in interactive dialogue, asking probing questions, and providing scaffolded support that gradually diminishes as learners become more proficient. Research conducted at several universities has demonstrated that well-designed ITS can produce learning gains comparable to those achieved through one-on-one human tutoring, which has long been considered the gold standard of educational effectiveness.

The efficacy of personalized AI systems hinges on their ability to create comprehensive learner profiles. These profiles encompass far more than simple test scores; they include data on time spent on various activities, patterns of mistakes, response to different types of feedback, and even indicators of engagement such as login frequency and duration of study sessions. Machine learning algorithms process this multifaceted data to construct nuanced models of each student’s strengths, weaknesses, preferences, and optimal learning conditions.

One particularly innovative application involves the use of spaced repetition algorithms calibrated by AI to optimize long-term retention. The spacing effect, a well-documented psychological phenomenon, demonstrates that information is better retained when learning sessions are distributed over time rather than concentrated intensively. AI systems can calculate optimal review intervals for each piece of information for each individual student, taking into account factors such as the difficulty of the material, the student’s initial mastery level, and their forgetting curve. This fine-tuned scheduling ensures that review occurs precisely when students are on the verge of forgetting, thereby maximizing retention efficiency.

Gamification elements, integrated through AI, have also shown considerable promise in sustaining student motivation. By analyzing patterns in student engagement, AI systems can determine which types of rewards, challenges, and social interactions most effectively maintain interest for individual learners. Some students might be motivated by competitive leaderboards, while others respond better to collaborative challenges or personal achievement badges. The AI adapts the gamification strategy accordingly, creating a customized motivational framework for each user.

Furthermore, AI enables sophisticated content curation that extends beyond the boundaries of traditional curricula. By analyzing a student’s interests, learning goals, and current knowledge state, these systems can recommend supplementary resources from across the internet—including articles, videos, podcasts, and interactive simulations—that align with both their educational needs and personal interests. This contextualization of learning within students’ intrinsic interests has been shown to significantly enhance both engagement and retention.

The implementation of AI in personalized education has also facilitated more inclusive learning environments. Students with learning disabilities or language barriers can benefit from AI-powered accommodations that would be impractical to provide on an individual basis in traditional settings. For instance, AI can automatically generate simplified versions of complex texts for students with reading difficulties, provide real-time translation for non-native speakers, or convert text to speech for students with visual impairments. These accommodations are delivered seamlessly and unobtrusively, allowing all students to access the same content at their appropriate level.

However, the effectiveness of AI-driven personalization is not without constraints. Critics argue that excessive personalization might create “filter bubbles” in education, where students are only exposed to content that aligns with their existing knowledge and preferences. This could potentially limit intellectual diversity and reduce opportunities for students to encounter challenging perspectives that might expand their thinking. Balancing personalization with appropriate academic breadth remains an ongoing challenge for educational technologists.

Additionally, there are concerns about algorithmic bias. If the training data used to develop AI systems reflects existing educational inequities, the AI might inadvertently perpetuate these biases. For example, if an AI system is trained primarily on data from high-performing students in well-resourced schools, it might not adequately serve students from different socioeconomic backgrounds or educational contexts. Ensuring that AI systems are trained on diverse datasets and regularly audited for bias is essential to promoting educational equity.

Questions 14-18: Matching Headings

Choose the correct heading for paragraphs B-F from the list of headings below.

List of Headings:
i. The challenge of maintaining educational diversity
ii. How AI analyzes student understanding beyond correct answers
iii. Creating motivation through personalized game-like features
iv. The role of comprehensive student data in personalization
v. Ensuring accessibility for students with special needs
vi. Using AI to schedule optimal learning reviews
vii. Comparing AI tutoring with traditional teaching methods
viii. The future of AI in educational assessment

14. Paragraph B
15. Paragraph C
16. Paragraph D
17. Paragraph E
18. Paragraph F

Questions 19-23: Summary Completion

Complete the summary below using words from the box.

Word Box:
algorithms, collaboration, disabilities, engagement, forgetting, gamification, intervals, motivation, profiles, retention, rewards, spacing

AI-driven personalized education creates comprehensive learner 19. that include much more than test results. These systems use the 20. effect to optimize long-term memory by calculating ideal review 21. for each student. Additionally, 22. elements are adapted based on analysis of student 23. _____ patterns to maintain interest and motivation.

Questions 24-26: Yes/No/Not Given

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

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

24. Intelligent tutoring systems can match the effectiveness of individual human tutors.
25. All students prefer competitive elements in their learning platforms.
26. Filter bubbles in education could restrict students’ exposure to diverse viewpoints.

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PASSAGE 3 – The Broader Implications and Future Trajectory of AI in Educational Personalization

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The proliferation of artificial intelligence in personalized education represents not merely a technological advancement but a fundamental reconceptualization of the pedagogical enterprise itself. This transformation necessitates a critical examination of the epistemological assumptions that underpin both traditional educational models and their algorithmically mediated successors. While proponents of AI-driven personalization herald its potential to democratize access to high-quality education and optimize learning outcomes, skeptics raise profound questions about the implications for human agency, social cohesion, and the very nature of knowledge acquisition.

The theoretical foundations of personalized AI education draw heavily upon constructivist learning theories, particularly those articulated by scholars such as Jean Piaget and Lev Vygotsky. These frameworks posit that learning is an active, individualized process wherein learners construct understanding through interaction with their environment rather than passively receiving transmitted knowledge. AI systems, by continuously adapting to individual learner cognitive states, ostensibly operationalize these constructivist principles at unprecedented scale. However, this technological instantiation of constructivism raises questions about whether algorithmic interpretation can truly capture the nuanced, socially embedded nature of human learning that Vygotsky emphasized through his concept of the “zone of proximal development.”

Contemporary AI educational systems employ increasingly sophisticated techniques from the field of affective computing to detect and respond to students’ emotional states. By analyzing facial expressions, typing patterns, mouse movements, and even physiological signals from wearable devices, these systems can infer when students are frustrated, bored, confused, or engaged. This emotional intelligence enables the AI to modulate not only the difficulty of content but also its presentation style, pacing, and the type of encouragement provided. Research indicates that such affectively responsive systems can significantly improve both learning outcomes and student satisfaction compared to emotion-blind platforms.

Nevertheless, the deployment of emotion-detection technologies in educational settings precipitates serious ethical considerations. The continuous surveillance required for affective computing raises concerns about student privacy and the potential for misuse of sensitive emotional data. Moreover, there is the question of whether constant algorithmic monitoring and response to emotional states might undermine students’ development of self-regulation skills—the ability to persist through difficulty, manage frustration, and motivate oneself independently. Critics contend that these metacognitive capacities are essential components of mature learning and that excessive reliance on AI support systems might inadvertently atrophy their development.

The scalability of AI-driven personalization presents both tremendous opportunities and significant challenges for global educational equity. On one hand, AI systems can potentially bring high-quality personalized instruction to underserved populations in remote or economically disadvantaged regions where qualified teachers are scarce. Several pilot programs in sub-Saharan Africa and rural India have demonstrated that even minimally supervised learning centers equipped with AI educational platforms can produce measurable improvements in student outcomes. This suggests that AI could help bridge the global education gap.

However, the realization of this potential is contingent upon addressing substantial infrastructural barriers. Reliable internet connectivity, adequate computing devices, and electrical power—prerequisites for AI educational systems—remain unavailable to billions of people worldwide. Furthermore, most existing AI educational platforms have been developed primarily in English and reflect Western curricular priorities and cultural assumptions. Adapting these systems to diverse linguistic contexts, local curricula, and culturally specific learning styles requires considerable investment and technical expertise that many developing nations currently lack. Without intentional efforts to ensure inclusivity, AI personalization risks exacerbating rather than ameliorating existing educational inequalities.

The integration of AI into education also portends significant shifts in the professional role of teachers. Rather than functioning primarily as knowledge transmitters, educators in AI-enhanced environments increasingly serve as learning facilitators, mentors, and curriculum designers who guide students through increasingly self-directed learning journeys. This evolution requires teachers to develop new competencies, including the ability to interpret AI-generated analytics, integrate AI tools effectively into pedagogical practice, and maintain meaningful human connection with students despite the intermediating technology. Teacher education programs have been slow to adapt to these changing requirements, creating a workforce preparedness gap that many educational systems are only beginning to address systematically.

Looking forward, the convergence of AI with other emerging technologies—including virtual reality (VR), augmented reality (AR), and blockchain—promises to further revolutionize personalized education. VR and AR can create immersive learning environments where abstract concepts are rendered tangible and students can engage in experiential learning impossible in physical classrooms. Imagine medical students performing surgical procedures in risk-free virtual operating rooms that adapt to their skill level, or history students walking through ancient Rome while an AI tutor contextualizes what they observe based on their prior knowledge and learning objectives. Meanwhile, blockchain technology could enable secure, portable educational credentials that capture the full breadth of a student’s personalized learning journey rather than reducing achievement to standardized grades and degrees.

Yet these technological possibilities also amplify concerns about social fragmentation. If each student follows a completely unique educational path, what shared knowledge base and common experiences will provide the foundation for civic discourse and collective identity? Education has traditionally served not only to transmit skills and knowledge but also to create social cohesion by ensuring all members of society share certain common understandings and values. The challenge facing educational systems is to harness the benefits of AI personalization while preserving and perhaps reimagining education’s social and civic functions in an increasingly individualized learning landscape.

The trajectory of AI in personalized education remains fundamentally open, shaped by the choices that educators, policymakers, technologists, and societies make in the coming years. Will AI serve primarily to optimize individual achievement within existing educational paradigms, or will it enable more fundamental transformations in how we conceptualize learning, credential achievement, and prepare people for meaningful participation in society? The answers to these questions will determine not only the future of education but also the character of the societies that these emerging educational models will shape.

Questions 27-31: Multiple Choice

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

27. According to the passage, AI educational systems based on constructivist theories:
    A. completely reject traditional teaching methods
    B. attempt to apply learning theories at large scale
    C. prove that Vygotsky’s theories were incorrect
    D. work better for individual students than groups

28. Affective computing in education involves:
    A. detecting students’ emotional states through various signals
    B. teaching students how to control their emotions
    C. replacing emotional support from teachers
    D. preventing students from becoming frustrated

29. The main concern about emotion-detection technology in education is that it might:
    A. be inaccurate in reading student emotions
    B. cost too much for schools to implement
    C. prevent development of self-regulation skills
    D. make students too dependent on teachers

30. What does the passage suggest about AI education in developing countries?
    A. It has been completely unsuccessful so far
    B. It requires infrastructure that is often unavailable
    C. It is more effective than in developed nations
    D. It should only be used in urban areas

31. The passage indicates that teachers in AI-enhanced environments need to:
    A. learn to write computer code
    B. focus entirely on technology skills
    C. develop new types of competencies
    D. abandon traditional teaching methods completely

Questions 32-36: Matching Features

Match each statement (32-36) with the correct technology (A-E).

Technologies:
A. Virtual Reality (VR)
B. Augmented Reality (AR)
C. Blockchain
D. Affective Computing
E. Natural Language Processing (NLP)

32. Can create immersive environments for experiential learning
33. Enables secure and comprehensive educational records
34. Analyzes student reasoning beyond correct answers
35. Detects emotional states through physical signals
36. Can make abstract concepts tangible for learners

Questions 37-40: Short-answer Questions

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

37. What type of learning theories do personalized AI systems primarily draw upon?
38. What term describes the concept developed by Vygotsky that relates to individual learning needs?
39. What traditional function of education might be threatened by completely individualized learning paths?
40. According to the passage, what will determine both education’s future and society’s character?

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. B
3. B
4. B
5. C
6. C
7. B
8. TRUE
9. FALSE
10. TRUE
11. NOT GIVEN
12. FALSE
13. TRUE

PASSAGE 2: Questions 14-26

14. ii
15. vii
16. iv
17. vi
18. iii
19. profiles
20. spacing
21. intervals
22. gamification
23. engagement
24. YES
25. NO
26. YES

PASSAGE 3: Questions 27-40

27. B
28. A
29. C
30. B
31. C
32. A
33. C
34. E
35. D
36. A (hoặc B)
37. constructivist learning theories
38. zone of proximal development
39. social cohesion
40. emerging educational models

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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: early AI educational systems, 2000s, limited
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Bài viết nói rõ “These early systems… lacked the ability to truly understand individual learning styles or adjust content dynamically.” Đây là paraphrase của đáp án B “could not adapt to different learning styles.”

Câu 2: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: modern AI platforms, neural networks
• Vị trí trong bài: Đoạn 3, dòng 1-2
• Giải thích: “Today’s AI-powered educational platforms use sophisticated neural networks to create highly personalized learning experiences” trực tiếp ủng hộ đáp án B.

Câu 8: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: traditional teaching methods, treated all students the same
• Vị trí trong bài: Đoạn 1, dòng 4-5
• Giải thích: Câu “This personalization represents a significant departure from traditional one-size-fits-all teaching methods” xác nhận rằng phương pháp truyền thống đối xử như nhau với tất cả học sinh.

Câu 9: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: machine learning algorithms, 1990s
• Vị trí trong bài: Đoạn 2, dòng 1
• Giải thích: Bài viết nói “early 2000s” chứ không phải “1990s”, vì vậy thông tin mâu thuẫn với câu hỏi.

Câu 12: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: AI platforms, same pace, classmates
• Vị trí trong bài: Đoạn 5, dòng 3-4
• Giải thích: “AI-powered systems allow each student to progress at their own pace” trực tiếp mâu thuẫn với câu phát biểu.

Passage 2 – Giải Thích

Câu 14: ii

• Dạng câu hỏi: Matching Headings
• Paragraph B: Đoạn này nói về NLP và cách AI “analyze student responses to open-ended questions, assessing not merely whether answers are correct but also evaluating the reasoning processes behind them.”
• Giải thích: Tiêu đề ii “How AI analyzes student understanding beyond correct answers” phù hợp với nội dung chính của đoạn B.

Câu 15: vii

• Dạng câu hỏi: Matching Headings
• Paragraph C: Nói về Intelligent Tutoring Systems và so sánh với “one-on-one human tutoring, which has long been considered the gold standard.”
• Giải thích: Tiêu đề vii “Comparing AI tutoring with traditional teaching methods” phù hợp nhất.

Câu 24: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: intelligent tutoring systems, effectiveness, human tutors
• Vị trí trong bài: Đoạn C, dòng 4-6
• Giải thích: “well-designed ITS can produce learning gains comparable to those achieved through one-on-one human tutoring” – tác giả đồng ý với quan điểm này.

Câu 25: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: all students, prefer competitive elements
• Vị trí trong bài: Đoạn F, dòng 3-5
• Giải thích: “Some students might be motivated by competitive leaderboards, while others respond better to collaborative challenges” – rõ ràng không phải tất cả học sinh đều thích cạnh tranh.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: constructivist theories
• Vị trí trong bài: Đoạn 2
• Giải thích: “AI systems… ostensibly operationalize these constructivist principles at unprecedented scale” – AI cố gắng áp dụng lý thuyết xây dựng tri thức ở quy mô lớn.

Câu 29: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: concern, emotion-detection technology
• Vị trí trong bài: Đoạn 4, dòng 3-6
• Giải thích: “constant algorithmic monitoring and response… might undermine students’ development of self-regulation skills” – lo ngại chính là cản trở phát triển kỹ năng tự điều chỉnh.

Câu 37: constructivist learning theories

• Dạng câu hỏi: Short-answer
• Từ khóa: what type, learning theories, draw upon
• Vị trí trong bài: Đoạn 2, dòng 1
• Giải thích: “The theoretical foundations of personalized AI education draw heavily upon constructivist learning theories.”

Câu 39: social cohesion

• Dạng câu hỏi: Short-answer
• Từ khóa: traditional function, threatened, individualized learning
• Vị trí trong bài: Đoạn 9, dòng 3-4
• Giải thích: “Education has traditionally served… to create social cohesion” – đây là chức năng truyền thống bị đe dọa.

Học sinh sử dụng nền tảng AI để học tập cá nhân hóa với giao diện tương tác thông minh

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/	mang tính chuyển đổi, biến đổi	AI has emerged as a transformative force	transformative change/effect/power
personalization	n	/ˌpɜːsənəlaɪˈzeɪʃn/	cá nhân hóa	This personalization represents a significant departure	content personalization, learning personalization
adaptive	adj	/əˈdæptɪv/	có khả năng thích nghi	adaptive learning platforms	adaptive system/technology/approach
assess	v	/əˈses/	đánh giá	AI system can assess a student’s knowledge	assess performance/ability/needs
allocate	v	/ˈæləkeɪt/	phân bổ	the AI will allocate more practice problems	allocate resources/time/budget
continuous	adj	/kənˈtɪnjuəs/	liên tục, không ngừng	capacity for continuous assessment	continuous improvement/monitoring/process
facilitate	v	/fəˈsɪlɪteɪt/	tạo điều kiện, hỗ trợ	AI facilitates different learning modalities	facilitate learning/communication/change
modality	n	/məʊˈdæləti/	phương thức, cách thức	different learning modalities	learning modality, teaching modality
implementation	n	/ˌɪmplɪmenˈteɪʃn/	sự triển khai	the implementation of AI in education	implementation strategy/plan/phase
leverage	v	/ˈlevərɪdʒ/	tận dụng	leveraging its benefits for personalization	leverage technology/resources/expertise
robust	adj	/rəʊˈbʌst/	vững chắc, mạnh mẽ	implement robust security measures	robust system/framework/approach

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
underlying	adj	/ˌʌndəˈlaɪɪŋ/	cơ bản, nền tảng	mechanisms underlying AI-driven personalization	underlying cause/principle/factor
proactive	adj	/prəʊˈæktɪv/	chủ động, tích cực	this proactive approach represents a paradigm shift	proactive approach/measures/strategy
instrumental	adj	/ˌɪnstrəˈmentl/	then chốt, quan trọng	NLP has proven particularly instrumental	instrumental role/in achieving
emulate	v	/ˈemjuleɪt/	bắt chước, mô phỏng	systems emulate the behavior of human tutors	emulate behavior/success/model
scaffolded	adj	/ˈskæfəldɪd/	được hỗ trợ từng bước	providing scaffolded support	scaffolded learning/instruction/support
efficacy	n	/ˈefɪkəsi/	hiệu quả, hiệu lực	the efficacy of personalized AI systems	efficacy of treatment/intervention/approach
encompass	v	/ɪnˈkʌmpəs/	bao gồm, bao quát	profiles encompass far more than test scores	encompass a range/variety/aspects
retention	n	/rɪˈtenʃn/	sự ghi nhớ, duy trì	optimize long-term retention	information retention, knowledge retention
calibrated	adj	/ˈkælɪbreɪtɪd/	được điều chỉnh, hiệu chuẩn	spaced repetition algorithms calibrated by AI	calibrated approach/system/measurement
inadvertently	adv	/ˌɪnədˈvɜːtntli/	vô ý, không cố ý	AI might inadvertently perpetuate these biases	inadvertently create/cause/reveal
inequity	n	/ɪnˈekwəti/	sự bất công	training data reflects existing educational inequities	social inequity, economic inequity
audit	v	/ˈɔːdɪt/	kiểm toán, rà soát	regularly audited for bias	audit system/process/accounts
curation	n	/kjʊəˈreɪʃn/	sự tuyển chọn, quản lý	sophisticated content curation	content curation, data curation

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, phát triển	the proliferation of AI in education	proliferation of technology/weapons/information
reconceptualization	n	/riːkənˌseptʃuəlaɪˈzeɪʃn/	sự định nghĩa lại khái niệm	a fundamental reconceptualization of pedagogy	reconceptualization of theory/approach/model
epistemological	adj	/ɪˌpɪstɪməˈlɒdʒɪkl/	thuộc về nhận thức luận	epistemological assumptions	epistemological perspective/framework/question
herald	v	/ˈherəld/	báo trước, tuyên bố	proponents herald its potential	herald change/new era/breakthrough
articulated	v	/ɑːˈtɪkjuleɪtɪd/	diễn đạt, trình bày	theories articulated by scholars	clearly articulated, well articulated
posit	v	/ˈpɒzɪt/	đưa ra giả thuyết	frameworks posit that learning is active	posit theory/hypothesis/argument
instantiation	n	/ˌɪnstənʃiˈeɪʃn/	sự hiện thực hóa	technological instantiation of constructivism	instantiation of concept/theory/principle
nuanced	adj	/ˈnjuːɑːnst/	tế nhị, nhiều sắc thái	socially embedded, nuanced nature of learning	nuanced understanding/approach/perspective
affective	adj	/əˈfektɪv/	liên quan đến cảm xúc	affective computing	affective response/state/dimension
infer	v	/ɪnˈfɜː(r)/	suy luận	systems can infer when students are frustrated	infer meaning/conclusion/intention
modulate	v	/ˈmɒdjuleɪt/	điều chỉnh	AI can modulate the difficulty of content	modulate response/tone/intensity
precipitate	v	/prɪˈsɪpɪteɪt/	gây ra đột ngột	deployment precipitates ethical considerations	precipitate crisis/conflict/change
metacognitive	adj	/ˌmetəˈkɒɡnətɪv/	thuộc về siêu nhận thức	metacognitive capacities	metacognitive skills/awareness/strategies
atrophy	v	/ˈætrəfi/	teo, suy giảm	excessive reliance might atrophy their development	atrophy skills/muscles/abilities
scalability	n	/ˌskeɪləˈbɪləti/	khả năng mở rộng quy mô	the scalability of AI presents opportunities	scalability of solution/system/platform
contingent	adj	/kənˈtɪndʒənt/	phụ thuộc vào	realization is contingent upon addressing barriers	contingent on/upon conditions/factors
portend	v	/pɔːˈtend/	báo hiệu	integration portends significant shifts	portend change/disaster/future
convergence	n	/kənˈvɜːdʒəns/	sự hội tụ	convergence of AI with other technologies	convergence of ideas/technologies/trends
amplify	v	/ˈæmplɪfaɪ/	khuếch đại	technological possibilities amplify concerns	amplify effect/voice/concerns
trajectory	n	/trəˈdʒektəri/	quỹ đạo, xu hướng phát triển	the trajectory of AI remains open	trajectory of development/career/change

Hệ thống AI phân tích dữ liệu học tập và tạo báo cáo cá nhân hóa cho từng học sinh

Kết Bài

Chủ đề “How is AI being used in personalized education?” không chỉ phổ biến trong IELTS Reading mà còn phản ánh xu hướng công nghệ giáo dục toàn cầu. Ba passages trong đề thi này đã cung cấp cho bạn góc nhìn toàn diện từ cơ bản đến chuyên sâu về ứng dụng AI trong giáo dục cá nhân hóa.

Passage 1 giới thiệu những khái niệm nền tảng với độ khó phù hợp band 5.0-6.5, giúp bạn làm quen với từ vựng và cấu trúc câu cơ bản. Passage 2 đi sâu vào các cơ chế kỹ thuật với độ khó band 6.0-7.5, yêu cầu khả năng phân tích và suy luận cao hơn. Cuối cùng, Passage 3 thách thức người đọc ở mức band 7.0-9.0 với nội dung học thuật, từ vựng tinh vi và các vấn đề triết học về giáo dục.

Đáp án chi tiết kèm giải thích cụ thể vị trí và cách paraphrase sẽ giúp bạn hiểu rõ phương pháp làm bài. Hơn 40 từ vựng quan trọng được tổng hợp kèm phiên âm, nghĩa và collocations sẽ nâng cao vốn từ vựng học thuật của bạn. Hãy luyện tập đều đặn với các đề thi tương tự như The rise of self-paced learning platforms để nắm vững kỹ thuật và đạt band điểm mục tiêu.

Để hiểu rõ hơn về xu hướng công nghệ trong giáo dục, bạn có thể tham khảo thêm các chủ đề liên quan như The rise of remote learning platforms in higher education hay How immersive technology is reshaping vocational education. Những bài đọc này sẽ cung cấp thêm góc nhìn đa dạng về đổi mới giáo dục và giúp bạn chuẩn bị tốt hơn cho các chủ đề tương tự trong kỳ thi IELTS thực tế.

Nếu bạn quan tâm đến giao điểm giữa công nghệ và giáo dục từ góc độ sáng tạo, Design thinking in entrepreneurship education là một chủ đề bổ ích. Đồng thời, The integration of visual media in health education cũng cho thấy cách công nghệ được ứng dụng trong các lĩnh vực giáo dục chuyên biệt.

Chúc bạn học tập hiệu quả và đạt kết quả cao trong kỳ thi IELTS!