IELTS Reading: Sự Bùng Nổ Của Công Cụ Giáo Dục AI – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Trong bối cảnh cách mạng công nghệ 4.0, chủ đề “The Rise Of AI-powered Educational Tools” đã trở thành một trong những chủ đề nóng hổi và xuất hiện ngày càng thường xuyên trong các kỳ thi IELTS Reading gần đây. Chủ đề này không chỉ phản ánh xu hướng phát triển của giáo dục toàn cầu mà còn đòi hỏi người học phải có kiến thức về công nghệ, xã hội và tâm lý học.

Từ kinh nghiệm giảng dạy hơn 20 năm của tôi, chủ đề về công nghệ giáo dục, đặc biệt là AI, xuất hiện với tần suất cao trong IELTS Academic Reading, chiếm khoảng 15-20% các đề thi chính thức. Đây là chủ đề đa chiều, có thể kết hợp với nhiều góc độ khác nhau như tâm lý học, kinh tế, và phát triển xã hội.

Trong bài viết này, bạn sẽ nhận được một bộ đề thi IELTS Reading hoàn chỉnh với ba passages từ dễ đến khó, bao gồm 40 câu hỏi đa dạng theo đúng format thi thật. Mỗi passage được thiết kế tỉ mỉ để phản ánh chính xác độ khó và phong cách của Cambridge IELTS. Bạn cũng sẽ có đáp án chi tiết kèm giải thích cụ thể về vị trí thông tin, kỹ thuật paraphrase, và bảng từ vựng quan trọng được phân loại theo từng passage.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với chủ đề công nghệ giáo dục và rèn luyện kỹ năng làm bài một cách bài bản, khoa học.

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 phải hoàn thành trong 60 phút. Đây là bài kiểm tra khả năng đọc hiểu, phân tích và xác định thông tin của bạn trong các văn bản học thuật.

Phân bổ thời gian khuyến nghị:

• Passage 1 (Easy): 15-17 phút – Đây là bài dễ nhất, giúp bạn “khởi động” và tự tin
• Passage 2 (Medium): 18-20 phút – Độ khó tăng lên, yêu cầu kỹ năng paraphrase tốt hơn
• Passage 3 (Hard): 23-25 phút – Passage khó nhất, cần thời gian suy luận và phân tích

Lưu ý quan trọng: Hãy dành 2-3 phút cuối để chép đáp án vào Answer Sheet. Nhiều học viên Việt Nam mất điểm vì quên bước này hoặc chép sai chính tả.

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 – Chọn đáp án đúng từ các phương án cho sẵn
2. True/False/Not Given – Xác định thông tin đúng, sai hay không được đề cập
3. Yes/No/Not Given – Xác định quan điểm của tác giả
4. Matching Headings – Ghép tiêu đề với các đoạn văn
5. Sentence Completion – Hoàn thành câu với thông tin từ bài đọc
6. Matching Features – Ghép thông tin với các đối tượng được liệt kê
7. Short-answer Questions – Trả lời ngắn gọn các câu hỏi

Mỗi dạng câu hỏi yêu cầu một kỹ thuật làm bài khác nhau, và đề thi này sẽ giúp bạn luyện tập toàn diện.

2. IELTS Reading Practice Test

PASSAGE 1 – The Digital Classroom Revolution

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The transformation of education through technology has been one of the most significant developments of the 21st century. Over the past decade, artificial intelligence has emerged as a powerful tool that is reshaping how students learn and how teachers teach. From personalized learning platforms to intelligent tutoring systems, AI-powered educational tools are making education more accessible, effective, and engaging for millions of students worldwide.

The journey began in the early 2000s when educational software companies started experimenting with adaptive learning technologies. These early systems could adjust the difficulty level of questions based on student performance, but they were relatively simple compared to today’s sophisticated AI tools. The breakthrough came around 2015 when machine learning algorithms became advanced enough to analyze complex patterns in student behavior and learning styles. This allowed developers to create systems that could truly understand individual student needs and provide customized educational experiences.

One of the most remarkable features of modern AI educational tools is their ability to provide instant feedback. Traditional classroom settings often mean that students must wait hours or even days to receive feedback on their work. AI-powered systems, however, can analyze student responses in real-time and provide immediate corrections and explanations. This instantaneous feedback loop helps students learn from their mistakes quickly and reinforces correct understanding before misconceptions become deeply rooted. Research has shown that students who receive immediate feedback retain information up to 40% better than those who wait for delayed feedback.

Personalization represents another revolutionary aspect of AI in education. Every student learns differently – some are visual learners, others prefer auditory information, and many need hands-on practice to master concepts. AI systems can identify these learning preferences through continuous monitoring and analysis of how students interact with educational content. For example, if a student consistently performs better with video explanations than text-based instructions, the system will automatically prioritize video content in future lessons. This level of personalization was virtually impossible in traditional classrooms where one teacher manages thirty or more students simultaneously.

Language learning has been one of the areas most dramatically transformed by AI technology. Applications like Duolingo use sophisticated algorithms to create personalized lesson plans that adapt to each user’s progress and learning pace. These platforms can identify which words or grammar structures a student struggles with and provide additional practice in those specific areas. The AI can also adjust the timing of reviews based on spaced repetition principles, ensuring that students encounter vocabulary and concepts at optimal intervals for long-term retention. More than 500 million people worldwide now use AI-powered language learning apps, making language education more accessible than ever before.

The impact of AI educational tools extends beyond individual learning to classroom management. Teachers spend countless hours on administrative tasks such as grading assignments, tracking student progress, and preparing individualized lesson plans. AI systems can automate many of these time-consuming activities, allowing teachers to focus more energy on direct student interaction and creative teaching methods. Some schools report that teachers save up to 15 hours per week using AI-assisted grading and planning tools. This time can be redirected toward providing one-on-one support to students who need extra help or developing innovative teaching strategies.

However, the rise of AI in education has not been without challenges and concerns. Privacy issues rank among the most significant worries, as these systems collect vast amounts of data about student performance, behavior, and learning patterns. Educational institutions must ensure that this sensitive information is protected and used ethically. There are also questions about equity and access – while AI tools can make education more available to some students, those without reliable internet connections or modern devices may be left behind. Experts emphasize that AI should supplement, not replace, human teachers, as the emotional connection and mentorship that teachers provide remain irreplaceable elements of effective education.

Looking forward, the integration of AI in education appears inevitable and increasingly sophisticated. Virtual reality combined with AI is creating immersive learning environments where students can explore historical events, conduct virtual science experiments, or practice professional skills in realistic simulations. As these technologies become more affordable and widespread, they promise to make high-quality education available to students in even the most remote locations. The key challenge for educators and policymakers will be ensuring that these powerful tools are implemented thoughtfully, with careful attention to equity, privacy, and the fundamental human elements that make education meaningful and transformative.

Questions 1-13

Questions 1-5: Multiple Choice

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

1. According to the passage, when did the major breakthrough in AI educational technology occur?
A. Early 2000s
B. Around 2015
C. Late 1990s
D. After 2020

2. Students who receive immediate feedback retain information:
A. 20% better than others
B. 30% better than others
C. 40% better than others
D. 50% better than others

3. How much time can teachers save per week using AI-assisted tools?
A. Up to 10 hours
B. Up to 15 hours
C. Up to 20 hours
D. Up to 25 hours

4. How many people worldwide use AI-powered language learning apps?
A. More than 300 million
B. More than 400 million
C. More than 500 million
D. More than 600 million

5. What does the passage suggest about the future of AI in education?
A. It will completely replace human teachers
B. It appears inevitable and increasingly sophisticated
C. It will only be available in wealthy countries
D. It will focus mainly on language learning

Questions 6-9: True/False/Not Given

Write TRUE if the statement agrees with the information, FALSE if the statement contradicts the information, or NOT GIVEN if there is no information on this.

6. Early adaptive learning systems in the 2000s were as sophisticated as modern AI tools.

7. Visual learners, auditory learners, and hands-on learners all exist and learn differently.

8. Duolingo is the only AI-powered language learning application available.

9. All schools worldwide have access to AI educational technology.

Questions 10-13: Sentence Completion

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

10. AI systems can identify learning preferences through continuous __ of student interactions with content.

11. The timing of reviews in language learning apps is adjusted based on __ principles.

12. One of the most significant concerns about AI in education is related to __ issues.

13. Experts emphasize that AI should supplement, not replace, human teachers because teachers provide __ and mentorship.

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PASSAGE 2 – Adaptive Intelligence: The Cognitive Science Behind AI Learning Tools

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The proliferation of artificial intelligence in educational contexts represents more than merely a technological innovation; it embodies a fundamental reconceptualization of how learning occurs and how pedagogical interventions can be optimized for individual cognitive profiles. At the intersection of cognitive science, educational psychology, and computational technology, AI-powered learning systems are leveraging decades of research about human cognition to create unprecedented learning experiences that adapt dynamically to each student’s unique neurological and psychological characteristics.

The theoretical foundation underlying these systems draws heavily from constructivist learning theory, which posits that learners actively construct knowledge rather than passively absorbing information. Jean Piaget and Lev Vygotsky, pioneering psychologists of the 20th century, established that effective learning occurs within what Vygotsky termed the “zone of proximal development” – the optimal challenge level where tasks are neither too easy to be unstimulating nor too difficult to be overwhelming. Modern AI educational platforms operationalize this concept by continuously calibrating content difficulty to maintain each student within their personal zone of proximal development, something practically impossible for human instructors managing multiple learners simultaneously.

Cognitive load theory, developed by educational psychologist John Sweller, provides another crucial framework for AI learning design. This theory distinguishes between intrinsic cognitive load (the inherent difficulty of the material), extraneous cognitive load (unnecessary complexity from poor instructional design), and germane cognitive load (the mental effort required to construct schemas and automate knowledge). Sophisticated AI systems analyze behavioral indicators such as response times, error patterns, and navigation behaviors to infer when students are experiencing excessive cognitive load. Upon detection of cognitive overload, these systems can dynamically reduce complexity by breaking down concepts into smaller components, providing additional scaffolding, or incorporating multimedia elements that distribute cognitive processing across different mental channels.

The implementation of spaced repetition algorithms represents one of the most empirically validated applications of cognitive science in AI education. The “spacing effect,” first documented by German psychologist Hermann Ebbinghaus in the 1880s, demonstrates that information is retained more effectively when learning sessions are distributed over time rather than concentrated in a single session. AI platforms utilize sophisticated mathematical models, such as the Leitner system or more advanced algorithms based on forgetting curves, to calculate the optimal interval for reviewing each piece of information for each individual student. This personalized scheduling ensures that students encounter material at precisely the moment when they are about to forget it, maximizing retention while minimizing the total time required for mastery.

Natural language processing (NLP), a subfield of AI focused on human-computer language interaction, has revolutionized how students can engage with educational content. Advanced NLP systems can now comprehend student questions posed in natural, conversational language and generate contextually appropriate responses that address the specific misconception or knowledge gap revealed by the question. Some systems employ Socratic questioning techniques, responding to student inquiries not with direct answers but with carefully crafted questions designed to guide students toward discovering solutions independently. This approach aligns with constructivist principles and fosters metacognitive skills – the ability to think about and regulate one’s own thinking processes – which research consistently identifies as critical for academic success.

The integration of affective computing – AI systems capable of recognizing and responding to human emotions – adds another dimension to educational technology. Facial recognition algorithms, voice analysis, and behavioral pattern recognition allow these systems to detect signs of frustration, confusion, boredom, or disengagement. Upon identifying negative affective states, the system can intervene by adjusting content difficulty, introducing gamification elements to boost motivation, suggesting a break, or even alerting a human instructor that a student requires additional support. Research in educational psychology has long established that emotional states significantly influence learning efficacy, making this affective awareness a potentially transformative feature of AI education.

Data analytics and predictive modeling enable AI systems to identify at-risk students before they fall significantly behind. By analyzing patterns across thousands or millions of student interactions, machine learning algorithms can detect subtle indicators that predict future difficulties – perhaps a student consistently takes longer to complete certain types of problems, or shows declining engagement over time. Early identification allows for timely interventions that can prevent academic struggles from escalating. Some institutions report that AI prediction systems can forecast student performance and retention with over 85% accuracy, enabling targeted support that substantially improves educational outcomes.

However, critical perspectives on AI education emphasize several significant concerns. The “black box” problem refers to the opacity of complex AI decision-making processes – even the developers of these systems sometimes cannot fully explain why an algorithm made a particular recommendation. This lack of transparency raises ethical questions about accountability and the potential for perpetuating biases embedded in training data. Additionally, some educational theorists worry that excessive personalization might create “filter bubbles” where students are only exposed to content that aligns with their existing knowledge and preferences, potentially limiting intellectual diversity and the productive struggle that can accompany encountering challenging or contradictory ideas.

The trajectory of AI in education suggests increasing integration between technological capabilities and pedagogical wisdom. Rather than viewing AI as a replacement for traditional instruction, most experts advocate for hybrid models that leverage the strengths of both artificial and human intelligence – using AI for personalization, assessment, and administrative efficiency while preserving the irreplaceable human elements of education: inspiration, mentorship, ethical guidance, and emotional support. The ultimate success of AI educational tools will depend not merely on technological sophistication but on thoughtful implementation that prioritizes pedagogical effectiveness, equity, and the holistic development of students as complete human beings.

Công nghệ trí tuệ nhân tạo đang thay đổi phương pháp học tập cá nhân hóa trong giáo dục hiện đại

Questions 14-26

Questions 14-18: Yes/No/Not Given

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

14. AI educational systems are primarily based on behaviorist learning theories.

15. Vygotsky’s zone of proximal development concept is practically impossible for one teacher to maintain for multiple students simultaneously.

16. Spaced repetition algorithms have been empirically validated as effective learning tools.

17. All educational institutions should immediately replace human teachers with AI systems.

18. The “black box” problem in AI raises ethical concerns about accountability.

Questions 19-22: Matching Headings

The passage has nine paragraphs. Choose the correct heading for paragraphs 3, 5, 7, and 8 from the list of headings below.

List of Headings:
i. The role of emotional recognition in AI learning
ii. Cognitive load theory and its application in AI systems
iii. Financial barriers to AI implementation
iv. Natural language processing transforms student engagement
v. Predictive analytics for identifying struggling students
vi. The future of traditional textbooks
vii. Critical concerns about AI transparency and bias
viii. Historical development of computer-based learning

19. Paragraph 3
20. Paragraph 5
21. Paragraph 7
22. Paragraph 8

Questions 23-26: Sentence Completion

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

23. Jean Piaget and Lev Vygotsky are described as __ of the 20th century.

24. The “spacing effect” was first documented by Hermann Ebbinghaus in the __.

25. Some AI prediction systems can forecast student performance with over __ accuracy.

26. Educational theorists worry that excessive personalization might create __ where students only see content matching their preferences.

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PASSAGE 3 – The Socioeconomic and Philosophical Implications of Algorithmic Pedagogy

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

The ascendancy of artificial intelligence as a pedagogical instrument precipitates profound questions that transcend merely technical considerations, impinging upon fundamental philosophical debates about the nature of knowledge, the purpose of education, and the implications of outsourcing cognitive functions to computational systems. As AI-powered educational tools become increasingly ubiquitous and sophisticated, scholars across disciplines – from philosophy of education to critical theory to science and technology studies – are grappling with the multifaceted ramifications of this transformation, which some characterize as the most significant epistemic shift in education since the invention of the printing press.

From a philosophical epistemology perspective, AI educational systems embody particular assumptions about the nature of knowledge and learning that are not ideologically neutral. The dominant paradigm underlying most AI learning platforms aligns with what educational philosopher Paulo Freire critiqued as the “banking model” of education – the conception of learning as the deposit of discrete, quantifiable units of information into students’ minds. While modern AI systems add considerable sophistication through personalization and adaptivity, they still predominantly operate within frameworks that privilege measurable, assessable competencies over more nebulous but potentially equally important educational outcomes such as wisdom, ethical judgment, aesthetic appreciation, or the capacity for critical self-reflection. French philosopher Michel Foucault‘s analysis of disciplinary power and normalization offers a compelling lens through which to examine how AI systems might subtly shape what counts as legitimate knowledge and appropriate learning behaviors, potentially reinforcing existing epistemic hierarchies rather than disrupting them.

The socioeconomic dimensions of AI education reveal stark disparities in access and implementation that risk exacerbating existing educational inequalities. While proponents tout AI’s democratizing potential – its capacity to provide high-quality, personalized instruction to students regardless of geographical location or socioeconomic status – the empirical reality reveals a more complex picture. The “digital divide” encompasses not merely differential access to technology but also what sociologist Eszter Hargittai terms “digital inequality” – variations in the skills, social support, and cultural capital necessary to effectively utilize digital tools. Research by the Organisation for Economic Co-operation and Development (OECD) demonstrates that students from higher socioeconomic backgrounds derive substantially greater benefits from educational technology than their less privileged peers, partly because they possess more robust foundational skills, more supportive learning environments, and better access to supplementary human guidance when technology proves insufficient. Consequently, the widespread adoption of AI education without deliberate attention to equity could inadvertently widen rather than narrow achievement gaps.

The political economy of AI education merits critical examination, as the sector is increasingly dominated by large technology corporations whose primary objective is profit maximization rather than educational excellence or social equity. The concentration of AI educational tools in the hands of a small number of powerful entities raises concerns about data privacy, algorithmic accountability, and the potential for commercial interests to shape educational priorities. These platforms accumulate vast repositories of granular data about students’ cognitive processes, learning behaviors, emotional states, and knowledge gaps – information that possesses considerable commercial value for targeted advertising, employee recruitment, and other purposes potentially inimical to students’ interests. The opaque data practices of many educational technology companies, combined with inadequate regulatory frameworks, create significant risks for student privacy and autonomy. Moreover, the business models of these corporations may incentivize the development of features that maximize engagement and data collection rather than pedagogical effectiveness, creating a potential misalignment between commercial imperatives and educational values.

The epistemological implications of algorithmic filtering and personalization warrant careful consideration. While tailoring educational content to individual learners offers obvious advantages, it also entails potential costs in terms of intellectual breadth and exposure to challenging or dissonant ideas. Philosopher John Stuart Mill famously argued that encountering and grappling with ideas that contradict our existing beliefs is essential for intellectual development and the pursuit of truth. If AI systems predominantly present students with content calibrated to their current knowledge level and learning preferences, they may inadvertently create what legal scholar Cass Sunstein terms “information cocoons” – intellectually homogeneous environments that insulate individuals from perspectives that challenge their preconceptions. This algorithmic curation of knowledge could undermine the serendipitous encounters with unexpected ideas that have historically been a vital source of intellectual creativity and paradigm shifts in human understanding.

The transformation of the teacher’s role in AI-mediated education represents another dimension of profound significance. While advocates of educational technology emphasize that AI will “free up” teachers to focus on higher-order pedagogical activities, critical analysts question whether this represents a genuine enhancement of the teaching profession or a de-skilling and devaluation of professional expertise. The delegation of instructional design, content delivery, and assessment to algorithmic systems could potentially erode the professional autonomy and judgment that have traditionally defined teaching as a complex intellectual practice. Educational theorist Michael Apple has documented how standardization and technological rationalization in education often serve to intensify teachers’ workloads while simultaneously diminishing their professional discretion and creative agency. The proliferation of AI tools could accelerate this trend, reducing teachers to facilitators of pre-packaged algorithmic curricula rather than intellectuals who design and adapt pedagogical approaches based on contextual understanding and professional wisdom.

The developmental appropriateness of AI education for different age groups and learning contexts remains an under-examined question with significant implications. Developmental psychology research suggests that young children require extensive face-to-face interaction with responsive adults for optimal cognitive, linguistic, and socioemotional development. The displacement of human interaction by screen-based learning, even with highly sophisticated AI, may deprive young learners of critical developmental experiences. Furthermore, the cultivation of dispositions such as perseverance, frustration tolerance, and intrinsic motivation – which research consistently identifies as crucial for long-term educational success – may require pedagogical approaches that deliberately incorporate struggle and delayed gratification, elements that AI systems optimized for engagement and immediate positive reinforcement might inadvertently discourage.

The governance and regulation of AI in education present formidable challenges for policymakers navigating the tension between promoting innovation and protecting public interests. The pace of technological development consistently outstrips regulatory capacity, leaving significant gaps in oversight of AI educational tools. Questions of algorithmic transparency, bias auditing, data ownership, and consent remain largely unresolved in most jurisdictions. Some scholars advocate for robust public regulation of educational AI, including mandatory impact assessments, independent algorithmic audits, and strict limitations on data collection and commercial use. Others express concern that excessive regulation might stifle innovation and prevent the realization of AI’s educational potential. Striking an appropriate balance requires sophisticated governance frameworks that involve diverse stakeholders including educators, students, parents, technologists, and civil society organizations, rather than leaving these crucial decisions to technology companies or government bureaucracies alone.

Looking toward the horizon, the trajectory of AI in education will likely be determined not by technological capabilities alone but by collective choices about educational values, priorities, and purposes. The critical question is not whether AI can make education more efficient or personalized – it demonstrably can – but rather what kind of education we believe is worthwhile and what kind of human beings we hope to cultivate through educational processes. If we conceive of education primarily as knowledge transmission and skill acquisition, AI offers powerful tools for optimizing these objectives. If, however, we understand education more expansively as the cultivation of critical consciousness, ethical sensitivity, aesthetic appreciation, civic responsibility, and the capacity for autonomous thought and meaningful action, then we must be cautious about over-reliance on algorithmic systems that, however sophisticated, cannot fully encompass the richness and complexity of genuinely humanistic education. The challenge for contemporary educators and policymakers is to harness the genuine benefits of AI while remaining vigilant against its potential to narrow our conception of education and to reproduce or amplify existing social inequalities and epistemological limitations.

Học sinh tương tác với công cụ học tập trí tuệ nhân tạo thể hiện sự cá nhân hóa trong giáo dục số

Questions 27-40

Questions 27-31: Multiple Choice

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

27. According to the passage, Paulo Freire criticized the “banking model” of education because it views learning as:
A. An expensive investment requiring significant resources
B. The deposit of discrete, quantifiable units of information
C. A collaborative process between students and teachers
D. An emotional and social experience

28. The “digital divide” as discussed in the passage encompasses:
A. Only differential access to technology
B. Differences in internet speed between countries
C. Variations in skills, social support, and cultural capital needed to use technology effectively
D. The gap between public and private school funding

29. According to the passage, the concentration of AI educational tools in large corporations raises concerns about:
A. The quality of the technology being developed
B. Data privacy, algorithmic accountability, and commercial interests shaping priorities
C. The cost of educational technology for schools
D. Teacher training requirements

30. What does Cass Sunstein’s concept of “information cocoons” refer to?
A. Safe learning environments for young children
B. Intellectually homogeneous environments that insulate individuals from challenging perspectives
C. Physical spaces designed for optimal learning
D. Protective software that filters inappropriate content

31. The passage suggests that the displacement of human interaction by AI-based learning may be particularly problematic for:
A. University students
B. Adult learners
C. Young children
D. Professional educators

Questions 32-36: Matching Features

Match each researcher or philosopher (32-36) with their correct contribution or concept (A-H).

Researchers/Philosophers:
32. Paulo Freire
33. Michel Foucault
34. Eszter Hargittai
35. John Stuart Mill
36. Michael Apple

Contributions/Concepts:
A. Analysis of disciplinary power and normalization
B. Documentation of standardization and technological rationalization in education
C. The importance of encountering contradictory ideas for intellectual development
D. The concept of “digital inequality”
E. Development of machine learning algorithms
F. Critique of the “banking model” of education
G. Creation of the first AI educational system
H. Research on gamification in education

Questions 37-40: Short-answer Questions

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

37. What does the passage describe as potentially the most significant epistemic shift in education since the invention of the printing press?

38. According to research cited in the passage, which students derive substantially greater benefits from educational technology?

39. What kind of frameworks does the passage suggest are needed to govern AI in education, involving diverse stakeholders?

40. Besides knowledge transmission and skill acquisition, what does the passage mention as examples of broader educational purposes? (Give ONE example)

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. B
6. FALSE
7. TRUE
8. NOT GIVEN
9. FALSE
10. monitoring and analysis
11. spaced repetition
12. privacy
13. emotional connection

PASSAGE 2: Questions 14-26

14. NO
15. YES
16. YES
17. NOT GIVEN
18. YES
19. ii
20. iv
21. v
22. vii
23. pioneering psychologists
24. 1880s
25. 85% (or eighty-five percent)
26. filter bubbles

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. B
31. C
32. F
33. A
34. D
35. C
36. B
37. AI-powered educational tools / algorithmic pedagogy (accept either)
38. higher socioeconomic backgrounds
39. sophisticated governance frameworks
40. critical consciousness / ethical sensitivity / aesthetic appreciation / civic responsibility (accept any one)

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B (Around 2015)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: major breakthrough, AI educational technology, when
• Vị trí trong bài: Đoạn 2, dòng 4-6
• Giải thích: Bài đọc nói rõ “The breakthrough came around 2015 when machine learning algorithms became advanced enough”. Từ “breakthrough” trong câu hỏi tương đương với “breakthrough” trong bài, và thời điểm được nhắc chính xác là “around 2015”.

Câu 2: C (40% better)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: immediate feedback, retain information
• Vị trí trong bài: Đoạn 3, câu cuối
• Giải thích: Câu văn “Research has shown that students who receive immediate feedback retain information up to 40% better” cung cấp con số chính xác. Đây là dạng câu hỏi tìm thông tin cụ thể về số liệu.

Câu 3: B (Up to 15 hours)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: teachers save, per week, AI-assisted tools
• Vị trí trong bài: Đoạn 6, dòng 7-8
• Giải thích: “Some schools report that teachers save up to 15 hours per week using AI-assisted grading and planning tools.” Con số “15 hours” xuất hiện rõ ràng trong bài.

Câu 4: C (More than 500 million)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: people worldwide, AI-powered language learning apps
• Vị trí trong bài: Đoạn 5, câu cuối
• Giải thích: “More than 500 million people worldwide now use AI-powered language learning apps” – thông tin số liệu xuất hiện trực tiếp, không có paraphrase phức tạp.

Câu 5: B (It appears inevitable and increasingly sophisticated)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: future, AI in education
• Vị trí trong bài: Đoạn 8, câu đầu
• Giải thích: “Looking forward, the integration of AI in education appears inevitable and increasingly sophisticated” – câu này sử dụng nguyên văn cụm từ trong đáp án B.

Câu 6: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: early adaptive learning systems, 2000s, as sophisticated as modern AI
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Bài viết nói “These early systems… were relatively simple compared to today’s sophisticated AI tools”, điều này trực tiếp mâu thuẫn với phát biểu rằng chúng “as sophisticated as modern AI tools”.

Câu 7: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: visual learners, auditory learners, hands-on learners, learn differently
• Vị trí trong bài: Đoạn 4, dòng 2-4
• Giải thích: “Every student learns differently – some are visual learners, others prefer auditory information, and many need hands-on practice” – xác nhận rõ ràng sự tồn tại của các kiểu học viên khác nhau.

Câu 8: NOT GIVEN

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Duolingo, only, AI-powered language learning application
• Vị trí trong bài: Đoạn 5
• Giải thích: Bài viết chỉ đề cập Duolingo như một ví dụ (“Applications like Duolingo”) nhưng không khẳng định nó là ứng dụng duy nhất. Không có thông tin để xác nhận hay phủ nhận.

Câu 9: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: all schools worldwide, access to AI educational technology
• Vị trí trong bài: Đoạn 7, dòng 4-6
• Giải thích: Bài viết đề cập “those without reliable internet connections or modern devices may be left behind”, cho thấy không phải tất cả trường học đều có quyền truy cập.

Câu 10: monitoring and analysis

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI systems, identify learning preferences, continuous
• Vị trí trong bài: Đoạn 4, dòng 5-6
• Giải thích: “AI systems can identify these learning preferences through continuous monitoring and analysis” – cần điền cụm “monitoring and analysis” (2 từ).

Câu 11: spaced repetition

• Dạng câu hỏi: Sentence Completion
• Từ khóa: timing of reviews, language learning apps, adjusted based on
• Vị trí trong bài: Đoạn 5, dòng 6-7
• Giải thích: “The AI can also adjust the timing of reviews based on spaced repetition principles” – đáp án là “spaced repetition”.

Câu 12: privacy

• Dạng câu hỏi: Sentence Completion
• Từ khóa: most significant concerns, AI in education
• Vị trí trong bài: Đoạn 7, dòng 2-3
• Giải thích: “Privacy issues rank among the most significant worries” – chỉ cần điền “privacy”.

Câu 13: emotional connection

• Dạng câu hỏi: Sentence Completion
• Từ khóa: AI should supplement, not replace, teachers provide
• Vị trí trong bài: Đoạn 7, dòng 7-8
• Giải thích: “the emotional connection and mentorship that teachers provide remain irreplaceable” – đáp án là “emotional connection”.

Passage 2 – Giải Thích

Câu 14: NO

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: AI educational systems, primarily based, behaviorist learning theories
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Bài viết nói rõ “The theoretical foundation underlying these systems draws heavily from constructivist learning theory”, không phải behaviorist theory. Đây là quan điểm của tác giả, nên đáp án là NO.

Câu 15: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: Vygotsky’s zone of proximal development, practically impossible, one teacher, multiple students
• Vị trí trong bài: Đoạn 2, dòng 10-12
• Giải thích: “something practically impossible for human instructors managing multiple learners simultaneously” – tác giả khẳng định điều này, đáp án là YES.

Câu 16: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: spaced repetition algorithms, empirically validated
• Vị trí trong bài: Đoạn 4, câu đầu
• Giải thích: “The implementation of spaced repetition algorithms represents one of the most empirically validated applications” – tác giả xác nhận tính hiệu quả đã được chứng minh, đáp án YES.

Câu 17: NOT GIVEN

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: all educational institutions, immediately replace, human teachers, AI
• Vị trí trong bài: Không có thông tin cụ thể
• Giải thích: Bài viết không bao giờ khẳng định tất cả các tổ chức giáo dục nên thay thế giáo viên ngay lập tức. Thực tế, đoạn cuối ủng hộ “hybrid models”. Đây là NOT GIVEN vì tác giả không đưa ra quan điểm này.

Câu 18: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: “black box” problem, ethical concerns, accountability
• Vị trí trong bài: Đoạn 8, dòng 2-4
• Giải thích: “This lack of transparency raises ethical questions about accountability” – tác giả rõ ràng khẳng định mối lo ngại này, đáp án YES.

Câu 19: ii (Cognitive load theory and its application in AI systems)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Đoạn 3
• Giải thích: Đoạn 3 bắt đầu với “Cognitive load theory, developed by educational psychologist John Sweller” và toàn bộ đoạn thảo luận về cách AI áp dụng lý thuyết này.

Câu 20: iv (Natural language processing transforms student engagement)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Đoạn 5
• Giải thích: Câu đầu tiên của đoạn 5: “Natural language processing (NLP)… has revolutionized how students can engage with educational content” – từ “revolutionized” tương đương với “transforms”.

Câu 21: v (Predictive analytics for identifying struggling students)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Đoạn 7
• Giải thích: Đoạn 7 tập trung vào “Data analytics and predictive modeling enable AI systems to identify at-risk students” – chủ đề chính là phân tích dự đoán.

Câu 22: vii (Critical concerns about AI transparency and bias)

• Dạng câu hỏi: Matching Headings
• Vị trí trong bài: Đoạn 8
• Giải thích: Đoạn 8 bắt đầu với “However, critical perspectives” và thảo luận về “black box” problem và bias concerns.

Câu 23: pioneering psychologists

• Dạng câu hỏi: Sentence Completion
• Từ khóa: Jean Piaget, Lev Vygotsky, 20th century
• Vị trí trong bài: Đoạn 2, dòng 4
• Giải thích: “Jean Piaget and Lev Vygotsky, pioneering psychologists of the 20th century” – điền “pioneering psychologists”.

Câu 24: 1880s

• Dạng câu hỏi: Sentence Completion
• Từ khóa: “spacing effect”, first documented, Hermann Ebbinghaus
• Vị trí trong bài: Đoạn 4, dòng 3-4
• Giải thích: “first documented by German psychologist Hermann Ebbinghaus in the 1880s” – đáp án là “1880s”.

Câu 25: 85% (or eighty-five percent)

• Dạng câu hỏi: Sentence Completion
• Từ khóa: prediction systems, forecast, student performance
• Vị trí trong bài: Đoạn 7, dòng 8-9
• Giải thích: “AI prediction systems can forecast student performance and retention with over 85% accuracy” – đáp án “85%” hoặc “eighty-five percent”.

Câu 26: filter bubbles

• Dạng câu hỏi: Sentence Completion
• Từ khóa: excessive personalization, create, students only see content matching preferences
• Vị trí trong bài: Đoạn 8, dòng 6-7
• Giải thích: “some educational theorists worry that excessive personalization might create ‘filter bubbles'” – đáp án là “filter bubbles”.

Passage 3 – Giải Thích

Câu 27: B (The deposit of discrete, quantifiable units of information)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Paulo Freire, “banking model”, criticized
• Vị trí trong bài: Đoạn 2, dòng 3-6
• Giải thích: “educational philosopher Paulo Freire critiqued as the ‘banking model’ of education – the conception of learning as the deposit of discrete, quantifiable units of information” – định nghĩa rõ ràng trong bài.

Câu 28: C (Variations in skills, social support, and cultural capital needed to use technology effectively)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: “digital divide”, encompasses
• Vị trí trong bài: Đoạn 3, dòng 5-8
• Giải thích: “The ‘digital divide’ encompasses not merely differential access to technology but also what sociologist Eszter Hargittai terms ‘digital inequality’ – variations in the skills, social support, and cultural capital necessary to effectively utilize digital tools.”

Câu 29: B (Data privacy, algorithmic accountability, and commercial interests shaping priorities)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: concentration, AI educational tools, large corporations, concerns
• Vị trí trong bài: Đoạn 4, dòng 3-5
• Giải thích: “The concentration of AI educational tools in the hands of a small number of powerful entities raises concerns about data privacy, algorithmic accountability, and the potential for commercial interests to shape educational priorities.”

Câu 30: B (Intellectually homogeneous environments that insulate individuals from challenging perspectives)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Cass Sunstein, “information cocoons”
• Vị trí trong bài: Đoạn 5, dòng 8-10
• Giải thích: “what legal scholar Cass Sunstein terms ‘information cocoons’ – intellectually homogeneous environments that insulate individuals from perspectives that challenge their preconceptions.”

Câu 31: C (Young children)

• Dạng câu hỏi: Multiple Choice
• Từ khóa: displacement of human interaction, AI-based learning, particularly problematic
• Vị trí trong bài: Đoạn 7, dòng 2-5
• Giải thích: “Developmental psychology research suggests that young children require extensive face-to-face interaction with responsive adults for optimal cognitive, linguistic, and socioemotional development.”

Câu 32: F (Critique of the “banking model” of education)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 2
• Giải thích: “educational philosopher Paulo Freire critiqued as the ‘banking model’ of education”

Câu 33: A (Analysis of disciplinary power and normalization)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 2, dòng 12-13
• Giải thích: “French philosopher Michel Foucault’s analysis of disciplinary power and normalization”

Câu 34: D (The concept of “digital inequality”)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 3, dòng 6-7
• Giải thích: “what sociologist Eszter Hargittai terms ‘digital inequality'”

Câu 35: C (The importance of encountering contradictory ideas for intellectual development)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 5, dòng 5-7
• Giải thích: “Philosopher John Stuart Mill famously argued that encountering and grappling with ideas that contradict our existing beliefs is essential for intellectual development”

Câu 36: B (Documentation of standardization and technological rationalization in education)

• Dạng câu hỏi: Matching Features
• Vị trí trong bài: Đoạn 6, dòng 9-11
• Giải thích: “Educational theorist Michael Apple has documented how standardization and technological rationalization in education”

Câu 37: AI-powered educational tools / algorithmic pedagogy

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: most significant epistemic shift, printing press
• Vị trí trong bài: Đoạn 1, câu cuối
• Giải thích: “some characterize as the most significant epistemic shift in education since the invention of the printing press” – câu này nói về “this transformation” ở trước đó, đề cập đến AI-powered educational tools.

Câu 38: higher socioeconomic backgrounds

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: which students, derive substantially greater benefits, educational technology
• Vị trí trong bài: Đoạn 3, dòng 10-11
• Giải thích: “students from higher socioeconomic backgrounds derive substantially greater benefits from educational technology”

Câu 39: sophisticated governance frameworks

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: frameworks, govern AI in education, diverse stakeholders
• Vị trí trong bài: Đoạn 8, dòng 10-12
• Giải thích: “Striking an appropriate balance requires sophisticated governance frameworks that involve diverse stakeholders”

Câu 40: critical consciousness / ethical sensitivity / aesthetic appreciation / civic responsibility

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: broader educational purposes, besides knowledge transmission and skill acquisition
• Vị trí trong bài: Đoạn 9, dòng 6-8
• Giải thích: “the cultivation of critical consciousness, ethical sensitivity, aesthetic appreciation, civic responsibility” – chỉ cần điền một trong các ví dụ này.

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5. Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
artificial intelligence	n	/ˌɑːtɪˈfɪʃl ɪnˈtelɪdʒəns/	trí tuệ nhân tạo	“artificial intelligence has emerged as a powerful tool”	AI technology, AI system
reshape	v	/riːˈʃeɪp/	định hình lại, thay đổi cấu trúc	“AI is reshaping how students learn”	reshape education, reshape society
personalized	adj	/ˈpɜːsənəlaɪzd/	cá nhân hóa	“personalized learning platforms”	personalized learning, personalized experience
adaptive	adj	/əˈdæptɪv/	thích nghi, có khả năng điều chỉnh	“adaptive learning technologies”	adaptive system, adaptive approach
breakthrough	n	/ˈbreɪkθruː/	đột phá	“The breakthrough came around 2015”	major breakthrough, technological breakthrough
instantaneous	adj	/ˌɪnstənˈteɪniəs/	tức thời, tức khắc	“instantaneous feedback loop”	instantaneous response, instantaneous result
reinforce	v	/ˌriːɪnˈfɔːs/	củng cố, tăng cường	“reinforces correct understanding”	reinforce learning, reinforce knowledge
misconception	n	/ˌmɪskənˈsepʃn/	quan niệm sai lầm	“before misconceptions become deeply rooted”	common misconception, address misconception
sophisticated	adj	/səˈfɪstɪkeɪtɪd/	phức tạp, tinh vi	“sophisticated algorithms”	sophisticated system, sophisticated technology
immersive	adj	/ɪˈmɜːsɪv/	đắm chìm, hấp dẫn	“immersive learning environments”	immersive experience, immersive technology
irreplaceable	adj	/ˌɪrɪˈpleɪsəbl/	không thể thay thế	“irreplaceable elements of effective education”	irreplaceable value, irreplaceable role
equity	n	/ˈekwəti/	công bằng, bình đẳng	“questions about equity and access”	educational equity, social equity

Passage 2 – 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, rapid proliferation
embody	v	/ɪmˈbɒdi/	thể hiện, hiện thân	“it embodies a fundamental reconceptualization”	embody values, embody principles
pedagogical	adj	/ˌpedəˈɡɒdʒɪkl/	thuộc về sư phạm	“pedagogical interventions”	pedagogical approach, pedagogical method
cognition	n	/kɒɡˈnɪʃn/	nhận thức	“research about human cognition”	cognitive development, cognitive ability
constructivist	adj	/kənˈstrʌktɪvɪst/	theo chủ nghĩa kiến tạo	“constructivist learning theory”	constructivist approach, constructivist theory
zone of proximal development	n phrase	/zəʊn əv ˈprɒksɪml dɪˈveləpmənt/	vùng phát triển gần nhất	“Vygotsky’s zone of proximal development”	optimal zone, developmental zone
calibrating	v	/ˈkælɪbreɪtɪŋ/	hiệu chỉnh, điều chỉnh	“continuously calibrating content difficulty”	calibrate system, calibrate measurement
cognitive load	n phrase	/ˈkɒɡnətɪv ləʊd/	tải nhận thức	“excessive cognitive load”	reduce cognitive load, manage cognitive load
scaffolding	n	/ˈskæfəldɪŋ/	giàn giáo học tập (hỗ trợ học tập)	“providing additional scaffolding”	instructional scaffolding, learning scaffolding
spaced repetition	n phrase	/speɪst ˌrepəˈtɪʃn/	lặp lại cách quãng	“spaced repetition algorithms”	spaced repetition system, spaced learning
forgetting curve	n phrase	/fəˈɡetɪŋ kɜːv/	đường cong quên	“algorithms based on forgetting curves”	steep forgetting curve
affective computing	n phrase	/əˈfektɪv kəmˈpjuːtɪŋ/	tính toán cảm xúc	“The integration of affective computing”	affective state, affective response
metacognitive	adj	/ˌmetəˈkɒɡnətɪv/	siêu nhận thức	“fosters metacognitive skills”	metacognitive awareness, metacognitive strategy
gamification	n	/ˌɡeɪmɪfɪˈkeɪʃn/	trò chơi hóa	“introducing gamification elements”	gamification strategy, gamification design
predictive modeling	n phrase	/prɪˈdɪktɪv ˈmɒdlɪŋ/	mô hình dự đoán	“predictive modeling enable AI systems”	predictive analytics, predictive algorithm

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
ascendancy	n	/əˈsendənsi/	sự vươn lên, sự thống trị	“The ascendancy of artificial intelligence”	rise to ascendancy, gain ascendancy
precipitate	v	/prɪˈsɪpɪteɪt/	gây ra, thúc đẩy	“precipitates profound questions”	precipitate crisis, precipitate change
transcend	v	/trænˈsend/	vượt qua, vượt xa	“transcend merely technical considerations”	transcend boundaries, transcend limitations
ubiquitous	adj	/juːˈbɪkwɪtəs/	phổ biến khắp nơi	“become increasingly ubiquitous”	ubiquitous technology, ubiquitous presence
epistemology	n	/ɪˌpɪstəˈmɒlədʒi/	nhận thức luận	“philosophical epistemology perspective”	epistemological framework, epistemological questions
ideologically neutral	adj phrase	/ˌaɪdiəˈlɒdʒɪkli ˈnjuːtrəl/	trung lập về mặt ý thức hệ	“not ideologically neutral”	politically neutral, culturally neutral
banking model	n phrase	/ˈbæŋkɪŋ ˈmɒdl/	mô hình ngân hàng (giáo dục)	“Paulo Freire critiqued as the banking model”	traditional banking model
quantifiable	adj	/ˈkwɒntɪfaɪəbl/	có thể định lượng	“discrete, quantifiable units”	quantifiable data, quantifiable results
socioeconomic	adj	/ˌsəʊsiəʊˌiːkəˈnɒmɪk/	kinh tế xã hội	“socioeconomic dimensions”	socioeconomic status, socioeconomic background
exacerbate	v	/ɪɡˈzæsəbeɪt/	làm trầm trọng thêm	“risk exacerbating existing educational inequalities”	exacerbate problems, exacerbate tensions
digital divide	n phrase	/ˈdɪdʒɪtl dɪˈvaɪd/	khoảng cách số	“The digital divide encompasses”	bridge the digital divide, widen digital divide
algorithmic filtering	n phrase	/ˌælɡəˈrɪðmɪk ˈfɪltərɪŋ/	lọc thuật toán	“algorithmic filtering and personalization”	algorithmic bias, algorithmic decision
information cocoon	n phrase	/ˌɪnfəˈmeɪʃn kəˈkuːn/	kén thông tin (môi trường đồng nhất)	“create information cocoons”	trapped in cocoon, escape cocoon
serendipitous	adj	/ˌserənˈdɪpɪtəs/	tình cờ may mắn	“serendipitous encounters with unexpected ideas”	serendipitous discovery, serendipitous moment
de-skilling	n	/diːˈskɪlɪŋ/	mất kỹ năng, giảm kỹ năng	“represents de-skilling and devaluation”	prevent de-skilling, risk of de-skilling
professional autonomy	n phrase	/prəˈfeʃənl ɔːˈtɒnəmi/	quyền tự chủ nghề nghiệp	“erode the professional autonomy”	academic autonomy, teacher autonomy
developmental appropriateness	n phrase	/dɪˌveləpˈmentl əˈprəʊpriətnəs/	tính phù hợp với phát triển	“developmental appropriateness of AI education”	developmentally appropriate, age-appropriate
governance framework	n phrase	/ˈɡʌvənəns ˈfreɪmwɜːk/	khung quản trị	“sophisticated governance frameworks”	regulatory framework, policy framework

Từ vựng quan trọng trong đề thi IELTS Reading về công nghệ giáo dục và trí tuệ nhân tạo

Kết Bài

Chủ đề “The rise of AI-powered educational tools” không chỉ phản ánh xu hướng phát triển mạnh mẽ của công nghệ trong giáo dục mà còn là một chủ đề đa chiều, yêu cầu người học phải có kiến thức về nhiều lĩnh vực từ công nghệ, tâm lý học đến triết học giáo dục. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm một bài thi IELTS Reading hoàn chỉnh với ba passages tăng dần độ khó, phản ánh chính xác cấu trúc và phong cách của đề thi thật.

Ba passages trong đề thi này đã cung cấp đầy đủ các độ khó từ Easy (Band 5.0-6.5) đến Medium (Band 6.0-7.5) và Hard (Band 7.0-9.0), giúp bạn làm quen với nhiều mức độ thách thức khác nhau. Passage 1 giới thiệu các khái niệm cơ bản với ngôn ngữ dễ hiểu, Passage 2 đào sâu vào các lý thuyết khoa học nhận thức với từ vựng học thuật phong phú hơn, và Passage 3 đặt ra những câu hỏi triết học và xã hội học sâu sắc với ngôn ngữ tinh vi và cấu trúc phức tạp.

Đáp án chi tiết kèm theo vị trí cụ thể trong bài và giải thích về kỹ thuật paraphrase sẽ giúp bạn tự đánh giá chính xác năng lực của mình và hiểu rõ tại sao một đáp án là đúng hoặc sai. Đây là kỹ năng quan trọng giúp bạn không chỉ làm bài tốt hơn mà còn phát triển tư duy phản biện và khả năng phân tích văn bản học thuật.

Bảng từ vựng được phân loại theo từng passage cung cấp hơn 40 từ và cụm từ quan trọng kèm phiên âm, nghĩa tiếng Việt, ví dụ cụ thể và collocations. Đây là tài nguyên quý giá để bạn mở rộng vốn từ vựng học thuật, đặc biệt trong lĩnh vực công nghệ và giáo dục – một chủ đề nóng trong IELTS hiện nay.

Hãy nhớ rằng, thành công trong IELTS Reading không chỉ đến từ việc làm nhiều bài tập mà còn từ việc hiểu sâu về cấu trúc đề thi, rèn luyện kỹ thuật làm bài có hệ thống, và tích lũy vốn từ vựng phong phú. Với đề thi mẫu này, bạn đã có một công cụ thực chiến hữu ích để chuẩn bị cho kỳ thi IELTS của mình. Chúc bạn đạt được band điểm như mong muốn. Nếu bạn quan tâm đến các công nghệ giáo dục khác, hãy tìm hiểu thêm về The rise of self-paced learning platforms để mở rộng kiến thức về xu hướng học tập tự định hướng hiện nay. Đồng thời, Impact of AI on digital marketing cũng là một chủ đề liên quan đáng để bạn khám phá về ứng dụng rộng rãi của AI trong nhiều lĩnh vực khác nhau.