Trí tuệ nhân tạo (AI) đang cách mạng hóa ngành giáo dục toàn cầu, từ việc cá nhân hóa trải nghiệm học tập đến tối ưu hóa phương pháp giảng dạy. Chủ đề “How Is AI Being Used To Enhance Educational Outcomes?” xuất hiện ngày càng thường xuyên trong IELTS Reading, phản ánh xu hướng công nghệ hiện đại. Với hơn 20 năm kinh nghiệm giảng dạy IELTS, tôi nhận thấy chủ đề công nghệ giáo dục xuất hiện khoảng 15-20% trong các đề thi thực tế gần đây.
Bài viết này cung cấp một bộ đề thi IELTS Reading hoàn chỉnh với ba passages tăng dần độ khó, từ Easy (Band 5.0-6.5) đến Hard (Band 7.0-9.0). Bạn sẽ được thực hành với 40 câu hỏi đa dạng, bao gồm Multiple Choice, True/False/Not Given, Matching Headings, Summary Completion và nhiều dạng khác. Mỗi câu hỏi đều có đáp án chi tiết kèm giải thích về vị trí thông tin, kỹ thuật paraphrase và chiến lược làm bài. Bên cạnh đó, bạn sẽ học được hơn 40 từ vựng học thuật quan trọng về công nghệ và giáo dục, giúp tăng vốn từ vựng cho cả bài thi Reading và Writing.
Đề thi này phù hợp cho học viên từ band 5.0 trở lên, đặc biệt hữu ích cho những bạn đang nhắm đến band 6.5-8.0.
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 tính 1 điểm, không bị trừ điểm khi sai. Điểm số được chuyển đổi theo thang band từ 0-9.
Phân bổ thời gian khuyến nghị:
- Passage 1: 15-17 phút (độ khó thấp nhất)
- Passage 2: 18-20 phút (độ khó trung bình)
- Passage 3: 23-25 phút (độ khó cao nhất)
- Thời gian dự phòng: 2-3 phút để kiểm tra đáp án
Lưu ý quan trọng: Bạn phải ghi đáp án trực tiếp vào answer sheet trong 60 phút, không có thời gian chép bài riêng như phần Listening.
Các Dạng Câu Hỏi Trong Đề Này
Bộ đề thi này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice – Chọn đáp án đúng từ các phương án cho sẵn
- True/False/Not Given – Xác định thông tin đúng, sai hay không được đề cập
- Matching Headings – Ghép tiêu đề phù hợp với mỗi đoạn văn
- Sentence Completion – Hoàn thành câu với số từ giới hạn
- Summary Completion – Điền từ vào đoạn tóm tắt
- Matching Features – Ghép đặc điểm với các mục trong bài
- Short-answer Questions – Trả lời câu hỏi ngắn với số từ giới hạn
Mỗi dạng câu hỏi yêu cầu kỹ năng đọc hiểu khác nhau, từ scanning (quét thông tin) đến skimming (đọc lướt tìm ý chính) và detailed reading (đọc kỹ chi tiết).
IELTS Reading Practice Test
PASSAGE 1 – The Dawn of Intelligent Tutoring Systems
Độ khó: Easy (Band 5.0-6.5)
Thời gian đề xuất: 15-17 phút
The integration of artificial intelligence (AI) into education has opened up unprecedented opportunities for personalized learning. Over the past decade, educators and technology developers have collaborated to create intelligent tutoring systems (ITS) that adapt to individual student needs, providing customized instruction that was once only possible with one-on-one human tutoring. These systems represent a significant shift from the traditional “one-size-fits-all” approach to education.
Intelligent tutoring systems work by continuously monitoring student performance and adjusting the difficulty level, pace, and teaching methods accordingly. For example, if a student struggles with algebra, the system might provide additional practice problems, offer alternative explanations using visual aids, or break down complex concepts into smaller, more manageable steps. This adaptive learning technology ensures that students are neither bored with material that is too easy nor frustrated with content that is too challenging.
One of the most successful implementations of AI in education has been in language learning platforms. Applications like Duolingo and Babbel use machine learning algorithms to analyze how users interact with lessons, identifying patterns in their mistakes and learning speeds. The software then customizes subsequent lessons to address specific weaknesses while reinforcing areas of strength. Research conducted by MIT in 2021 found that students using AI-powered language apps showed 40% faster improvement compared to those using traditional textbook methods.
Automated grading systems represent another transformative application of AI in education. These systems can evaluate not only multiple-choice tests but also essays and short-answer responses. By using natural language processing (NLP), AI can assess grammar, vocabulary usage, argument structure, and even creativity in student writing. Teachers at Lincoln High School in California reported saving approximately 15 hours per week on grading after implementing an AI assessment tool, allowing them to dedicate more time to direct student interaction and lesson planning.
The benefits of AI extend beyond individual student learning to classroom management and administrative tasks. Predictive analytics tools can identify students who are at risk of falling behind before they actually do, enabling early intervention. By analyzing attendance records, assignment completion rates, test scores, and even the time students spend on different tasks, AI systems can alert teachers to potential problems. A pilot program in Texas schools demonstrated that such early warning systems reduced student dropout rates by 18% over two years.
Despite these advantages, the implementation of AI in education is not without challenges. Digital inequality remains a significant concern, as students from low-income families may lack access to the necessary technology and internet connectivity. Furthermore, there are valid concerns about data privacy and the potential for algorithmic bias. If the data used to train AI systems reflects existing educational inequalities, the technology might inadvertently perpetuate these disparities rather than reduce them.
Teachers and educational policymakers emphasize that AI should complement rather than replace human instruction. The emotional support, motivation, and nuanced understanding that human teachers provide cannot be replicated by algorithms. Professor Sarah Mitchell from Stanford University argues that “the ideal educational environment combines the efficiency and personalization of AI with the empathy and adaptability of skilled teachers.” Her research suggests that hybrid learning models, which integrate both AI tools and traditional teaching methods, produce the best outcomes for students across all age groups.
Looking forward, experts predict that AI will become increasingly sophisticated in understanding not just what students know, but how they learn best. Emotion recognition technology, currently in development, could allow educational software to detect when students are confused, frustrated, or disengaged and adjust accordingly. While such advances promise to make learning more effective, they also raise important ethical questions about surveillance and student autonomy that educators must carefully consider.
Hệ thống dạy học thông minh AI tương tác với học sinh trong lớp học hiện đại
Questions 1-6: Multiple Choice
Choose the correct letter, A, B, C or D.
1. According to the passage, intelligent tutoring systems differ from traditional education by:
A. replacing teachers entirely
B. providing the same instruction to all students
C. adapting to each student’s individual needs
D. focusing only on mathematics subjects
2. When a student has difficulty with a subject, ITS will:
A. move them to a lower grade level
B. provide additional practice and alternative explanations
C. inform their parents immediately
D. remove them from the program
3. Research from MIT in 2021 showed that AI-powered language learning:
A. was 40% more expensive than textbooks
B. took 40% longer than traditional methods
C. resulted in 40% faster improvement
D. was used by 40% of students
4. Automated grading systems using natural language processing can assess:
A. only multiple-choice questions
B. only mathematical equations
C. grammar, vocabulary, and argument structure
D. student attendance records
5. The predictive analytics tools in Texas schools:
A. increased dropout rates by 18%
B. reduced dropout rates by 18%
C. had no effect on dropout rates
D. were rejected by teachers
6. Professor Sarah Mitchell believes that:
A. AI should completely replace human teachers
B. AI is ineffective in education
C. hybrid models combining AI and human teaching work best
D. only traditional teaching methods are effective
Questions 7-10: 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
7. Duolingo and Babbel are examples of AI-powered language learning platforms.
8. Teachers at Lincoln High School saved approximately 20 hours per week using AI grading systems.
9. All students in the United States have equal access to AI educational technology.
10. Emotion recognition technology is already widely used in all schools.
Questions 11-13: Sentence Completion
Complete the sentences below.
Choose NO MORE THAN THREE WORDS from the passage for each answer.
11. One major concern about AI in education is _____ which means some students lack access to necessary technology.
12. If AI training data reflects existing problems, it might _____ rather than solve educational inequalities.
13. Human teachers provide emotional support and _____ that AI cannot replicate.
PASSAGE 2 – AI-Driven Assessment and Feedback Mechanisms
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The educational landscape is experiencing a paradigm shift with the introduction of sophisticated AI-driven assessment tools that fundamentally transform how student learning is measured and supported. Unlike traditional standardized tests that provide a snapshot assessment at a single point in time, these advanced systems enable continuous, formative evaluation that tracks learning progress across multiple dimensions and provides real-time feedback to both students and educators. This evolution represents not merely a technological upgrade but a reconceptualization of the assessment process itself.
Contemporary AI assessment platforms employ multi-modal data analysis, integrating information from diverse sources including written responses, problem-solving approaches, collaborative work patterns, and even metacognitive indicators such as how long students pause before answering or how frequently they revise their work. This holistic approach creates a comprehensive learning profile that extends far beyond what conventional tests can capture. Carnegie Mellon University’s Learning Analytics program has pioneered techniques that analyze thousands of student interactions per hour, identifying subtle patterns that might indicate misunderstanding or mastery of concepts.
The implementation of natural language generation (NLG) technology has revolutionized feedback delivery in educational contexts. Rather than generic comments like “good work” or “needs improvement,” AI systems can now produce detailed, contextualized feedback that addresses specific aspects of student performance. For instance, when evaluating an essay on climate change, an AI system might comment: “Your introduction effectively establishes the topic’s relevance, but the argument in paragraph three would be strengthened by incorporating quantitative evidence rather than relying solely on anecdotal examples.” This level of specificity helps students understand precisely what they need to improve and how to do it.
Stealth assessment, an innovative approach developed at the University of Wisconsin, embeds evaluation seamlessly within learning activities, making the assessment process virtually invisible to students. Rather than taking formal tests, students engage with game-based learning environments where their problem-solving strategies, persistence, and knowledge application are continuously monitored and analyzed. This approach reduces test anxiety while providing more authentic measures of student capabilities. A three-year study involving 15,000 students across seven countries found that stealth assessment identified struggling students an average of six weeks earlier than traditional testing methods, allowing for timely intervention.
The capacity of AI to provide instantaneous feedback addresses one of the most significant limitations of traditional education: the delay between student work and teacher response. Research in cognitive science consistently demonstrates that feedback is most effective when delivered immediately, while the learning experience is still fresh in students’ minds. Automated feedback systems can respond within seconds, explaining not just whether an answer is correct but why, often providing worked examples or alternative solution paths. This immediacy is particularly valuable in subjects like mathematics and computer programming, where understanding one concept is prerequisite to grasping subsequent material.
However, the deployment of AI assessment technologies raises significant pedagogical and ethical considerations. Critics argue that algorithmic evaluation may inadvertently prioritize measurable outcomes over less quantifiable but equally important aspects of learning such as creativity, critical thinking, and intellectual risk-taking. Dr. James Anderson, a education researcher at Oxford University, warns of the potential for “teaching to the algorithm,” where instruction becomes overly focused on what AI systems can measure and reward, potentially narrowing the curriculum and stifling innovation.
The question of bias in AI assessment systems demands serious attention. Machine learning models learn from historical data, and if that data reflects existing socioeconomic, racial, or gender disparities, the AI may perpetuate these inequities. A 2022 investigation by the Educational Testing Service discovered that certain automated essay scoring systems consistently rated essays discussing topics more familiar to affluent students higher than those focusing on experiences common in working-class communities, even when writing quality was equivalent. Algorithmic transparency and regular bias audits are essential to ensure fair assessment practices.
Furthermore, the over-reliance on data-driven insights can lead educators to overlook the importance of professional judgment and contextual understanding. Numbers and patterns identified by AI systems require interpretation within the broader context of each student’s circumstances, learning history, and personal challenges. The most effective implementation of AI assessment combines algorithmic precision with pedagogical expertise, using technology to inform rather than dictate instructional decisions.
Despite these challenges, when implemented thoughtfully, AI-driven assessment can democratize access to high-quality feedback that was previously available only to students with extensive tutoring resources. Students in under-resourced schools or remote areas can receive detailed guidance on their work without waiting days or weeks for teacher responses. The scalability of AI systems means that personalized feedback, once a luxury, can become a standard feature of education for all students regardless of their geographic or economic circumstances.
Công nghệ đánh giá học tập bằng AI phản hồi tức thì cho học sinh
Questions 14-19: Yes/No/Not Given
Do the following statements agree with the views of the writer in the passage?
Write:
- YES if the statement agrees with the views of the writer
- NO if the statement contradicts the views of the writer
- NOT GIVEN if it is impossible to say what the writer thinks about this
14. AI assessment systems represent a complete improvement over traditional testing methods in all aspects.
15. Multi-modal data analysis provides a more comprehensive picture of student learning than conventional tests.
16. Natural language generation technology can only produce generic feedback comments.
17. Stealth assessment is more effective than traditional testing at identifying students who need help.
18. All teachers have sufficient training to effectively use AI assessment tools.
19. AI systems should be used to inform rather than replace teacher judgment in assessment.
Questions 20-23: Matching Headings
Choose the correct heading for paragraphs C, D, F, and G from the list of headings below.
List of Headings:
i. The problem of inequality in AI training data
ii. Benefits of immediate response in learning
iii. How AI generates personalized comments
iv. Hidden evaluation methods in educational games
v. The financial cost of implementing AI systems
vi. Teachers’ resistance to new technology
vii. Concerns about limiting educational goals
viii. International differences in AI adoption
20. Paragraph C
21. Paragraph D
22. Paragraph F
23. Paragraph G
Questions 24-26: Summary Completion
Complete the summary below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.
AI-driven assessment tools can track student learning continuously rather than providing only a 24. _____ assessment at one time. Carnegie Mellon’s program can analyze thousands of student interactions and identify 25. _____ that show whether students understand concepts. However, critics worry that focusing too much on AI might lead to “26. _____,” where education becomes too focused on what algorithms can measure.
PASSAGE 3 – The Neuroscience of AI-Enhanced Learning: Cognitive Optimization and Educational Efficacy
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The confluence of artificial intelligence and neuroscientific research has precipitated a fundamental reconceptualization of how technology can optimize human learning processes. Contemporary AI educational systems increasingly leverage insights from cognitive neuroscience, particularly findings related to memory consolidation, attention mechanisms, and neuroplasticity, to design interventions that align with the brain’s intrinsic learning architecture. This interdisciplinary synthesis represents a significant departure from earlier behaviourist approaches to educational technology, which treated the mind as a “black box” and focused solely on observable inputs and outputs without considering the underlying cognitive mechanisms.
Neuroscience-informed AI systems employ spaced repetition algorithms that capitalize on the spacing effect, a robust phenomenon whereby information is retained more effectively when learning sessions are distributed over time rather than massed together. The SuperMemo algorithm, refined over decades of research, calculates optimal review intervals based on the forgetting curve – the exponential decay of memory traces over time first documented by Hermann Ebbinghaus in the 1880s. Modern implementations enhance this approach by incorporating individual learning velocities and domain-specific retention patterns, creating personalized review schedules that maximize long-term retention while minimizing study time. Neuroimaging studies using functional magnetic resonance imaging (fMRI) have demonstrated that such optimally-spaced learning produces stronger activation patterns in the hippocampus and prefrontal cortex, regions crucially implicated in memory formation and retrieval.
The application of attention analytics represents another frontier in AI-enhanced education. By employing eye-tracking technology, electroencephalography (EEG), and analysis of interaction patterns, these systems can detect when cognitive load exceeds optimal levels or when students are cognitively disengaged. Cognitive Load Theory, developed by John Sweller, posits that working memory has severely limited capacity, and instructional design should avoid extraneous cognitive load that doesn’t contribute to learning. AI systems can dynamically adjust content presentation – simplifying language, adding visual supports, or breaking complex information into smaller chunks – when sensors indicate cognitive overload. Conversely, when engagement metrics suggest insufficient challenge, the system can increase complexity to maintain students within their zone of proximal development, the optimal range theorized by Lev Vygotsky where learning is most efficient.
Neuroplasticity – the brain’s capacity to reorganize neural pathways in response to experience – provides the biological foundation for learning, and AI systems are increasingly designed to facilitate rather than merely accommodate this process. Research by neuroscientists at the Max Planck Institute has revealed that learning experiences producing optimal levels of difficulty – challenging enough to require effortful processing but not so difficult as to induce frustration – trigger the greatest synaptic strengthening and neural network expansion. AI tutoring systems that continuously calibrate task difficulty to maintain this “desirable difficulty” have demonstrated superior outcomes. A longitudinal study published in Nature Neuroscience found that students using such adaptively challenging AI systems showed not only improved performance in the target subject but also enhanced domain-general cognitive abilities, including working memory capacity and executive function, suggesting that optimally designed AI learning experiences may promote broader cognitive development.
The multimodal learning capabilities of advanced AI systems align with neuroscientific findings about how the brain processes and integrates information from different sensory modalities. The dual coding theory, supported by extensive neuroimaging evidence, suggests that information presented simultaneously through verbal and visual channels is more likely to be retained because it creates multiple representational pathways in the brain. AI systems can automatically generate complementary visual representations – diagrams, animations, or interactive simulations – to accompany textual explanations, and vice versa. Moreover, these systems can identify individual differences in perceptual preferences; some students demonstrate stronger retention from visual information, others from auditory or kinesthetic modalities. Personalized multimodal presentation that emphasizes each student’s strength modalities while still exercising weaker ones represents a sophisticated application of neuroscientific principles.
However, the integration of neuroscience with AI in educational contexts is not without epistemological and practical challenges. The reductionist tendency to equate learning with measurable neural changes risks oversimplifying the complex social, emotional, and contextual dimensions of human education. Critical education theorists caution against neuro-determinism – the assumption that brain-based explanations are inherently more valid or complete than psychological, sociological, or pedagogical perspectives. Dr. Eleanor Fitzpatrick, a philosopher of education at Cambridge University, argues that “while neuroscience can inform how we design learning experiences, it cannot determine what is worth learning or what kind of human beings we hope to nurture through education.” The instrumental rationality often embedded in neuroscience-based AI systems may privilege efficiency over other educational values such as intellectual autonomy, critical consciousness, or ethical development.
The ecological validity of neuroscience research poses another significant concern. Most neuroscientific findings derive from highly controlled laboratory settings that bear little resemblance to authentic educational environments. The confounding variables present in real classrooms – social dynamics, emotional states, motivational factors, cultural contexts – are typically absent from neuroimaging studies. Consequently, the direct applicability of such findings to complex educational scenarios remains questionable. Some educational neuroscientists advocate for naturalistic neuroimaging studies conducted in actual learning environments, but such research is technically challenging and expensive, limiting its scalability.
Furthermore, the commercialization of “brain-based learning” has produced a proliferation of educational technologies making unsubstantiated neuroscientific claims. The Organization for Economic Cooperation and Development (OECD) has identified numerous “neuromyths” – misconceptions about the brain that are widely believed by educators and marketed by technology companies. These include oversimplified notions about “learning styles” based on supposed right-brain/left-brain differences, claims about critical periods beyond which certain learning becomes impossible, and exaggerated assertions about brain training games improving general intelligence. The uncritical adoption of neuroscience-branded AI systems without rigorous evaluation of their actual efficacy represents a significant risk to educational quality and resource allocation.
Despite these caveats, the judicious integration of neuroscientific insights with AI technology holds genuine promise for enhancing educational outcomes. The key lies in maintaining epistemic humility about what neuroscience can and cannot tell us about learning, ensuring that technological implementations are rigorously evaluated through randomized controlled trials and longitudinal studies, and recognizing that the most powerful educational approaches will likely combine neuroscience-informed AI with skilled human instruction, supportive social environments, and attention to students’ holistic development. As Professor Michael Chen of the MIT Media Lab observes, “The question is not whether AI or neuroscience can improve education – it’s how we can deploy these tools in ways that genuinely serve all learners while remaining vigilant about potential unintended consequences and inequitable access.”
Giao diện não bộ máy tính trong hệ thống học tập thích ứng AI
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C or D.
27. According to the passage, neuroscience-informed AI systems differ from earlier behaviourist approaches by:
A. focusing only on what can be observed
B. considering the cognitive processes inside the mind
C. using more expensive technology
D. requiring more teacher training
28. The SuperMemo algorithm is based on:
A. the idea that all students learn at the same pace
B. the forgetting curve showing how memory decays over time
C. the principle that information should be learned all at once
D. research conducted only in the last decade
29. According to Cognitive Load Theory:
A. working memory has unlimited capacity
B. all cognitive load is beneficial for learning
C. instructional design should avoid unnecessary cognitive burden
D. complex information should never be simplified
30. The study in Nature Neuroscience found that AI systems with adaptive difficulty:
A. only improved performance in one specific subject
B. had no effect on cognitive abilities
C. improved both subject performance and general cognitive abilities
D. were too challenging for most students
31. Dr. Eleanor Fitzpatrick’s main concern is that:
A. neuroscience is completely irrelevant to education
B. brain-based explanations may oversimplify education’s broader purposes
C. AI systems are too expensive for schools
D. teachers cannot understand neuroscience research
Questions 32-36: Matching Features
Match each researcher or organization (A-H) with the correct statement (Questions 32-36).
Write the correct letter, A-H.
List of Researchers/Organizations:
A. Hermann Ebbinghaus
B. John Sweller
C. Lev Vygotsky
D. Max Planck Institute
E. Eleanor Fitzpatrick
F. OECD
G. Michael Chen
H. Nature Neuroscience (journal)
32. Identified misconceptions about the brain widely believed by educators
33. First documented how memory decays exponentially over time
34. Theorized about the optimal range where learning is most efficient
35. Warned that neuroscience cannot determine what is worth learning
36. Published research showing AI systems improved domain-general cognitive abilities
Questions 37-40: Summary Completion
Complete the summary using the list of words, A-L, below.
Contemporary AI educational systems increasingly use findings from neuroscience to optimize learning. These systems employ 37. _____ algorithms that distribute learning over time, which neuroscience shows is more effective than studying everything at once. AI can also detect when students experience 38. _____, which occurs when working memory capacity is exceeded, and adjust content accordingly. However, critics warn against 39. _____, the belief that brain-based explanations are always superior to other perspectives. Additionally, most neuroscience research lacks 40. _____ because it is conducted in controlled laboratories rather than real educational settings.
Word List:
A. spaced repetition
B. cognitive overload
C. neuro-determinism
D. ecological validity
E. visual processing
F. emotional intelligence
G. memory consolidation
H. cultural diversity
I. financial resources
J. teacher training
K. standardized testing
L. social interaction
Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- C
- B
- C
- C
- B
- C
- TRUE
- FALSE
- FALSE
- FALSE
- digital inequality
- perpetuate these disparities
- nuanced understanding
PASSAGE 2: Questions 14-26
- NO
- YES
- NO
- YES
- NOT GIVEN
- YES
- iii
- iv
- vii
- ii
- snapshot
- subtle patterns
- teaching to the algorithm
PASSAGE 3: Questions 27-40
- B
- B
- C
- C
- B
- F
- A
- C
- E
- H
- A
- B
- C
- D
Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: intelligent tutoring systems, differ from traditional education
- Vị trí trong bài: Đoạn 1, câu 2-3
- Giải thích: Bài văn nói rõ ITS “adapt to individual student needs” (thích ứng với nhu cầu của từng học sinh), khác với cách tiếp cận “one-size-fits-all” (một khuôn mẫu cho tất cả) của giáo dục truyền thống. Đây là paraphrase của đáp án C “adapting to each student’s individual needs”.
Câu 2: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: student struggles, ITS will
- Vị trí trong bài: Đoạn 2, câu 2
- Giải thích: Bài văn cho ví dụ cụ thể: “the system might provide additional practice problems, offer alternative explanations using visual aids, or break down complex concepts”. Đây chính xác là đáp án B được paraphrase lại.
Câu 7: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Duolingo, Babbel, AI-powered language learning platforms
- Vị trí trong bài: Đoạn 3, câu 2
- Giải thích: Bài văn nói rõ “Applications like Duolingo and Babbel use machine learning algorithms” – đây là ví dụ trực tiếp về AI-powered platforms, nên câu này đúng (TRUE).
Câu 8: FALSE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Lincoln High School, 20 hours per week
- Vị trí trong bài: Đoạn 4, câu 3
- Giải thích: Bài văn nói “approximately 15 hours per week”, không phải 20 giờ như câu hỏi nêu. Do đó câu này là FALSE.
Câu 11: digital inequality
- Dạng câu hỏi: Sentence Completion
- Từ khóa: concern, students lack access to technology
- Vị trí trong bài: Đoạn 6, câu 2
- Giải thích: Bài văn nói “Digital inequality remains a significant concern, as students from low-income families may lack access to the necessary technology”. Cụm “digital inequality” chính xác trả lời câu hỏi về vấn đề khiến học sinh thiếu truy cập công nghệ.
Câu 12: perpetuate these disparities
- Dạng câu hỏi: Sentence Completion
- Từ khóa: AI training data reflects existing problems
- Vị trí trong bài: Đoạn 6, câu cuối
- Giải thích: Bài văn cảnh báo “the technology might inadvertently perpetuate these disparities rather than reduce them” khi dữ liệu huấn luyện AI phản ánh bất bình đẳng hiện có.
Passage 2 – Giải Thích
Câu 14: NO
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Các đoạn F, G, H
- Giải thích: Tác giả không cho rằng AI tốt hơn hoàn toàn ở mọi khía cạnh. Các đoạn sau trong bài liệt kê nhiều thách thức và lo ngại về AI assessment như bias, narrowing curriculum, và over-reliance. Do đó câu này mâu thuẫn với quan điểm của tác giả (NO).
Câu 15: YES
- Dạng câu hỏi: Yes/No/Not Given
- Vị trí trong bài: Đoạn B, câu 2-3
- Giải thích: Tác giả nói rõ multi-modal data analysis tạo ra “a comprehensive learning profile that extends far beyond what conventional tests can capture” – đồng ý rằng nó cung cấp bức tranh toàn diện hơn (YES).
Câu 20: iii (Paragraph C)
- Dạng câu hỏi: Matching Headings
- Giải thích: Đoạn C tập trung vào natural language generation (NLG) và cách AI tạo ra “detailed, contextualized feedback” thay vì “generic comments” – tương ứng với heading “How AI generates personalized comments”.
Câu 21: iv (Paragraph D)
- Dạng câu hỏi: Matching Headings
- Giải thích: Đoạn D nói về “stealth assessment” được nhúng trong “game-based learning environments” nơi đánh giá trở nên “virtually invisible” – tương ứng với “Hidden evaluation methods in educational games”.
Câu 24: snapshot
- Dạng câu hỏi: Summary Completion
- Vị trí trong bài: Đoạn A, câu 2
- Giải thích: Bài văn đối chiếu AI systems với “traditional standardized tests that provide a snapshot assessment at a single point in time” – từ “snapshot” là từ cần điền.
Câu 26: teaching to the algorithm
- Dạng câu hỏi: Summary Completion
- Vị trí trong bài: Đoạn F, câu 2
- Giải thích: Dr. Anderson cảnh báo về “teaching to the algorithm” khi giáo dục trở nên quá tập trung vào những gì AI có thể đo lường.
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Vị trí trong bài: Đoạn A, câu cuối
- Giải thích: Bài văn nói rõ neuroscience-informed AI khác với behaviourist approaches vì không coi mind là “black box” và không chỉ tập trung vào “observable inputs and outputs” mà còn xem xét “underlying cognitive mechanisms” – tức là các quá trình nhận thức bên trong (đáp án B).
Câu 28: B
- Dạng câu hỏi: Multiple Choice
- Vị trí trong bài: Đoạn B, câu 2-3
- Giải thích: Bài văn giải thích SuperMemo algorithm “calculates optimal review intervals based on the forgetting curve – the exponential decay of memory traces over time first documented by Hermann Ebbinghaus” – đúng với đáp án B.
Câu 32: F (OECD)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn H, câu 2
- Giải thích: “The Organization for Economic Cooperation and Development (OECD) has identified numerous ‘neuromyths’ – misconceptions about the brain that are widely believed by educators”.
Câu 33: A (Hermann Ebbinghaus)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn B, câu 2
- Giải thích: “the forgetting curve – the exponential decay of memory traces over time first documented by Hermann Ebbinghaus in the 1880s”.
Câu 37: A (spaced repetition)
- Dạng câu hỏi: Summary Completion
- Vị trí trong bài: Đoạn B, câu 1
- Giải thích: Câu tóm tắt nói về thuật toán “distribute learning over time” – đây chính là “spaced repetition algorithms” được đề cập trong bài.
Câu 40: D (ecological validity)
- Dạng câu hỏi: Summary Completion
- Vị trí trong bài: Đoạn G, tiêu đề và nội dung
- Giải thích: Đoạn G thảo luận về “ecological validity” và giải thích rằng hầu hết nghiên cứu neuroscience thiếu tính này vì được thực hiện trong “highly controlled laboratory settings” chứ không phải môi trường giáo dục thực tế.
Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| artificial intelligence | n | /ˌɑːtɪˈfɪʃəl ɪnˈtelɪdʒəns/ | trí tuệ nhân tạo | The integration of artificial intelligence into education has opened up unprecedented opportunities | artificial intelligence technology, artificial intelligence system |
| personalized learning | n | /ˈpɜːsənəlaɪzd ˈlɜːnɪŋ/ | học tập cá nhân hóa | AI enables personalized learning that adapts to each student | personalized learning experience, personalized learning platform |
| intelligent tutoring system | n | /ɪnˈtelɪdʒənt ˈtjuːtərɪŋ ˈsɪstəm/ | hệ thống dạy kèm thông minh | Intelligent tutoring systems adapt to individual student needs | develop intelligent tutoring systems, implement intelligent tutoring systems |
| adaptive learning | n | /əˈdæptɪv ˈlɜːnɪŋ/ | học tập thích ứng | This adaptive learning technology ensures students are properly challenged | adaptive learning technology, adaptive learning approach |
| machine learning algorithm | n | /məˈʃiːn ˈlɜːnɪŋ ˈælɡərɪðəm/ | thuật toán máy học | The apps use machine learning algorithms to analyze user interactions | develop machine learning algorithms, apply machine learning algorithms |
| natural language processing | n | /ˈnætʃrəl ˈlæŋɡwɪdʒ ˈprəʊsesɪŋ/ | xử lý ngôn ngữ tự nhiên | AI uses natural language processing to assess student writing | natural language processing technology, natural language processing capability |
| predictive analytics | n | /prɪˈdɪktɪv ænəˈlɪtɪks/ | phân tích dự đoán | Predictive analytics tools can identify at-risk students early | use predictive analytics, predictive analytics system |
| digital inequality | n | /ˈdɪdʒɪtl ˌɪnɪˈkwɒləti/ | bất bình đẳng số | Digital inequality remains a significant concern in AI education | address digital inequality, reduce digital inequality |
| algorithmic bias | n | /ˌælɡəˈrɪðmɪk ˈbaɪəs/ | thiên kiến thuật toán | There are concerns about algorithmic bias in AI systems | prevent algorithmic bias, detect algorithmic bias |
| hybrid learning model | n | /ˈhaɪbrɪd ˈlɜːnɪŋ ˈmɒdl/ | mô hình học tập kết hợp | Hybrid learning models combine AI tools with traditional teaching | implement hybrid learning models, design hybrid learning models |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| paradigm shift | n | /ˈpærədaɪm ʃɪft/ | sự thay đổi mô thức | Education is experiencing a paradigm shift with AI assessment | undergo a paradigm shift, represent a paradigm shift |
| formative evaluation | n | /ˈfɔːmətɪv ɪˌvæljuˈeɪʃn/ | đánh giá hình thành | AI enables continuous formative evaluation of learning progress | conduct formative evaluation, formative evaluation process |
| multi-modal data analysis | n | /ˌmʌltiˈməʊdl ˈdeɪtə əˈnæləsɪs/ | phân tích dữ liệu đa phương thức | AI platforms employ multi-modal data analysis from diverse sources | perform multi-modal data analysis, multi-modal data analysis technique |
| holistic approach | n | /həˈlɪstɪk əˈprəʊtʃ/ | cách tiếp cận toàn diện | This holistic approach creates a comprehensive learning profile | adopt a holistic approach, holistic approach to education |
| natural language generation | n | /ˈnætʃrəl ˈlæŋɡwɪdʒ ˌdʒenəˈreɪʃn/ | tạo ngôn ngữ tự nhiên | Natural language generation technology revolutionizes feedback delivery | natural language generation system, natural language generation capability |
| contextualized feedback | n | /kənˈtekstʃuəlaɪzd ˈfiːdbæk/ | phản hồi theo ngữ cảnh | AI produces detailed contextualized feedback for each student | provide contextualized feedback, contextualized feedback mechanism |
| stealth assessment | n | /stelθ əˈsesmənt/ | đánh giá ngầm | Stealth assessment embeds evaluation within learning activities | implement stealth assessment, stealth assessment approach |
| cognitive load | n | /ˈkɒɡnətɪv ləʊd/ | tải nhận thức | AI can detect when cognitive load exceeds optimal levels | reduce cognitive load, manage cognitive load |
| algorithmic evaluation | n | /ˌælɡəˈrɪðmɪk ɪˌvæljuˈeɪʃn/ | đánh giá thuật toán | Critics worry that algorithmic evaluation may prioritize measurable outcomes | rely on algorithmic evaluation, algorithmic evaluation system |
| pedagogical expertise | n | /ˌpedəˈɡɒdʒɪkl ˌekspɜːˈtiːz/ | chuyên môn sư phạm | Effective AI implementation combines algorithmic precision with pedagogical expertise | demonstrate pedagogical expertise, require pedagogical expertise |
| bias audit | n | /ˈbaɪəs ˈɔːdɪt/ | kiểm toán thiên kiến | Regular bias audits are essential to ensure fair assessment | conduct bias audits, bias audit process |
| democratize access | v | /dɪˈmɒkrətaɪz ˈækses/ | dân chủ hóa khả năng tiếp cận | AI can democratize access to high-quality feedback | democratize access to education, democratize access to resources |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| confluence | n | /ˈkɒnfluəns/ | sự hợp lưu, sự kết hợp | The confluence of AI and neuroscience has changed education | confluence of factors, confluence of ideas |
| neuroscientific research | n | /ˌnjʊərəʊsaɪənˈtɪfɪk rɪˈsɜːtʃ/ | nghiên cứu thần kinh học | AI systems leverage insights from neuroscientific research | conduct neuroscientific research, neuroscientific research findings |
| memory consolidation | n | /ˈmeməri kənˌsɒlɪˈdeɪʃn/ | củng cố trí nhớ | AI design considers findings about memory consolidation | memory consolidation process, enhance memory consolidation |
| neuroplasticity | n | /ˌnjʊərəʊplæˈstɪsəti/ | tính dẻo thần kinh | Neuroplasticity provides the biological foundation for learning | brain neuroplasticity, neuroplasticity research |
| intrinsic learning architecture | n | /ɪnˈtrɪnsɪk ˈlɜːnɪŋ ˈɑːkɪtektʃə/ | kiến trúc học tập nội tại | AI aligns with the brain’s intrinsic learning architecture | intrinsic learning architecture of the brain |
| spaced repetition algorithm | n | /speɪst ˌrepəˈtɪʃn ˈælɡərɪðəm/ | thuật toán lặp lại giãn cách | Spaced repetition algorithms optimize review intervals | implement spaced repetition algorithms, spaced repetition algorithm effectiveness |
| forgetting curve | n | /fəˈɡetɪŋ kɜːv/ | đường cong lãng quên | The algorithm is based on the forgetting curve concept | forgetting curve theory, study forgetting curve |
| cognitive load theory | n | /ˈkɒɡnətɪv ləʊd ˈθɪəri/ | lý thuyết tải nhận thức | Cognitive Load Theory guides instructional design | apply Cognitive Load Theory, Cognitive Load Theory principles |
| working memory | n | /ˈwɜːkɪŋ ˈmeməri/ | trí nhớ làm việc | Working memory has severely limited capacity | working memory capacity, working memory limitations |
| zone of proximal development | n | /zəʊn əv ˈprɒksɪml dɪˈveləpmənt/ | vùng phát triển gần | Systems maintain students in their zone of proximal development | zone of proximal development theory, work within zone of proximal development |
| synaptic strengthening | n | /sɪˈnæptɪk ˈstreŋθənɪŋ/ | tăng cường synapse | Optimal difficulty triggers synaptic strengthening | synaptic strengthening process, promote synaptic strengthening |
| dual coding theory | n | /ˈdjuːəl ˈkəʊdɪŋ ˈθɪəri/ | lý thuyết mã hóa kép | Dual coding theory supports multimodal learning design | dual coding theory application, dual coding theory principles |
| epistemological | adj | /ɪˌpɪstəməˈlɒdʒɪkl/ | thuộc nhận thức luận | There are epistemological challenges in integrating neuroscience with AI | epistemological assumptions, epistemological concerns |
| neuro-determinism | n | /ˌnjʊərəʊ dɪˈtɜːmɪnɪzəm/ | chủ nghĩa quyết định thần kinh | Critical theorists caution against neuro-determinism | neuro-determinism risks, avoid neuro-determinism |
| ecological validity | n | /ˌiːkəˈlɒdʒɪkl vəˈlɪdəti/ | giá trị sinh thái | Most neuroscience research lacks ecological validity | ecological validity concerns, ensure ecological validity |
| neuromyths | n | /ˈnjʊərəʊmɪθs/ | những huyền thoại thần kinh | OECD identified numerous neuromyths in education | debunk neuromyths, common neuromyths |
| epistemic humility | n | /ɪˈpɪstemɪk hjuːˈmɪləti/ | sự khiêm tốn nhận thức | Integration requires epistemic humility about neuroscience limits | practice epistemic humility, epistemic humility approach |
Kết Bài
Chủ đề “How is AI being used to enhance educational outcomes?” không chỉ phản ánh xu hướng công nghệ hiện đại mà còn đặt ra những câu hỏi quan trọng về tương lai của giáo dục. Qua bộ đề thi IELTS Reading này, bạn đã được tiếp xúc với ba góc nhìn khác nhau về AI trong giáo dục: từ những ứng dụng cơ bản và lợi ích rõ ràng (Passage 1), đến các hệ thống đánh giá phức tạp cùng những thách thức đi kèm (Passage 2), và cuối cùng là sự tích hợp sâu sắc giữa AI với thần kinh học cùng những tranh luận học thuật (Passage 3).
Ba passages này cung cấp đầy đủ các độ khó từ Band 5.0 đến Band 9.0, giúp bạn làm quen với cấu trúc câu, từ vựng và mức độ phức tạp của nội dung trong kỳ thi thật. 40 câu hỏi đa dạng dạng từ Multiple Choice, True/False/Not Given, đến Matching Headings và Summary Completion đã rèn luyện toàn bộ kỹ năng đọc hiểu cần thiết cho IELTS.
Đáp án chi tiết kèm giải thích về vị trí thông tin và kỹ thuật paraphrase giúp bạn hiểu rõ cách thức tìm câu trả lời chính xác, không chỉ cho đề này mà còn áp dụng được cho mọi bài thi Reading khác. Hơn 40 từ vựng quan trọng được trình bày chi tiết sẽ là tài sản quý giá không chỉ cho phần Reading mà còn cho Writing và Speaking.
Hãy nhớ rằng, thành công trong IELTS Reading đến từ việc luyện tập thường xuyên với các đề thi chất lượng cao như thế này. Đừng chỉ làm một lần – hãy quay lại sau một tuần, làm lại và so sánh kết quả để đo lường sự tiến bộ của mình. Chúc bạn đạt band điểm mục tiêu trong kỳ thi IELTS sắp tới!
