Trí tuệ nhân tạo (AI) đang tạo nên cuộc cách mạng trong lĩnh vực giáo dục toàn cầu, từ việc cá nhân hóa trải nghiệm học tập đến tự động hóa các quy trình hành chính. Chủ đề “How Is Artificial Intelligence Being Used In Education?” xuất hiện với tần suất ngày càng cao trong IELTS Reading, đặc biệt từ năm 2020 trở lại đây, khi công nghệ giáo dục trở thành xu hướng toàn cầu. Đề thi này được thiết kế dựa trên cấu trúc chuẩn Cambridge IELTS, phù hợp cho học viên từ band 5.0 đến 8.0+.
Qua bài viết này, bạn sẽ nhận được một đề thi IELTS Reading hoàn chỉnh bao gồm 3 passages với độ khó tăng dần, 40 câu hỏi đa dạng theo đúng format thi thật, đáp án chi tiết kèm giải thích từng câu, cùng bộ từ vựng học thuật quan trọng. Đây là tài liệu luyện tập lý tưởng giúp bạn làm quen với chủ đề công nghệ trong giáo dục – một trong những chủ đề hot nhất của IELTS hiện nay. Mỗi passage được biên soạn cẩn thận với nội dung giàu thông tin, từ vựng được làm đậm để dễ nhận diện, và câu hỏi được thiết kế theo đúng tiêu chuẩn British Council và IDP. Hãy dành đủ 60 phút để hoàn thành đề thi này trong điều kiện mô phỏng thi thật để đánh giá chính xác trình độ của bạn.
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. Bạn cần tự quản lý thời gian giữa các passages vì không có thời gian nghỉ giữa các phần. Mỗi câu trả lời đúng được 1 điểm, và band điểm cuối cùng được tính dựa trên tổng số câu đúng theo thang điểm chuẩn.
Phân bổ thời gian khuyến nghị:
- Passage 1 (Easy): 15-17 phút – Đây là passage dễ nhất, bạn nên hoàn thành nhanh để dành thời gian cho các passage khó hơn
- Passage 2 (Medium): 18-20 phút – Độ khó trung bình, yêu cầu kỹ năng skimming và scanning tốt
- Passage 3 (Hard): 23-25 phút – Passage khó nhất với từ vựng học thuật và cấu trúc phức tạp, cần thời gian suy luận nhiều hơn
Luôn dành 2-3 phút cuối để chuyển đáp án vào answer sheet và kiểm tra lại các câu chưa chắc chắn.
Các Dạng Câu Hỏi Trong Đề Này
Đề thi này bao gồm 8 dạng câu hỏi phổ biến nhất trong IELTS Reading:
- Multiple Choice: Chọn đáp án đúng từ 3-4 phương án
- True/False/Not Given: Xác định thông tin đúng, sai hay không được đề cập
- Yes/No/Not Given: Xác định ý kiến của tác giả
- Matching Headings: Nối tiêu đề với đoạn văn phù hợp
- Sentence Completion: Hoàn thành câu với từ trong bài
- Summary Completion: Điền từ vào đoạn tóm tắt
- Matching Features: Nối thông tin với nhân vật/tổ chức tương ứng
- Short-answer Questions: Trả lời câu hỏi ngắn với số từ giới hạn
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
Artificial intelligence is rapidly transforming the way students learn and teachers deliver instruction in classrooms around the world. From elementary schools in rural areas to prestigious universities in major cities, AI-powered educational tools are becoming increasingly common, offering new possibilities for personalized learning and improved educational outcomes.
One of the most significant applications of AI in education is the development of intelligent tutoring systems. These systems use machine learning algorithms to analyze individual student performance and adapt the curriculum accordingly. For example, if a student struggles with a particular mathematical concept, the AI system can identify the gap in understanding and provide additional practice problems, explanatory videos, or alternative teaching methods tailored to that student’s learning style. This level of personalization was previously impossible in traditional classroom settings where one teacher might be responsible for thirty or more students with varying abilities and learning paces.
Automated grading systems represent another important use of AI in education. Teachers often spend countless hours marking assignments, particularly multiple-choice tests and standardized assessments. AI-powered grading tools can now handle these tasks almost instantaneously, freeing up teachers to focus on more valuable activities such as lesson planning, one-on-one student mentoring, and creative curriculum development. Some advanced systems can even evaluate written essays, analyzing not just grammar and spelling but also argument structure, coherence, and critical thinking skills.
Language learning has particularly benefited from AI integration. Applications like Duolingo use AI algorithms to create adaptive learning paths that adjust to each user’s proficiency level and learning speed. Speech recognition technology allows students to practice pronunciation and receive immediate feedback, something that would require constant teacher supervision in a traditional setting. These tools make language education more accessible and cost-effective, especially for students in regions where qualified language teachers are scarce.
AI is also making education more inclusive for students with special needs. Text-to-speech software helps visually impaired students access written materials, while speech-to-text applications assist those with physical disabilities that make writing difficult. AI-powered translation tools break down language barriers, enabling students to access educational content in their native languages regardless of where the materials originated. These technologies ensure that quality education becomes available to a broader demographic of learners.
However, the implementation of AI in education is not without challenges. Privacy concerns have emerged as these systems collect vast amounts of data about student performance, learning patterns, and even behavioral characteristics. There are also questions about the potential for AI to perpetuate existing biases if the algorithms are trained on data that reflects societal inequalities. Furthermore, there is ongoing debate about whether excessive reliance on technology might diminish important aspects of education such as human interaction, social skills development, and the teacher-student relationship that many educators consider fundamental to effective learning.
Despite these concerns, the trend toward AI integration in education appears irreversible. As technology continues to advance and become more sophisticated, its role in shaping how future generations learn will likely expand. The challenge for educators and policymakers is to harness the benefits of AI while mitigating its potential drawbacks, ensuring that technology serves as a tool to enhance rather than replace the human elements that make education meaningful.
Questions 1-13
Questions 1-5: Multiple Choice
Choose the correct letter, A, B, C, or D.
-
According to the passage, intelligent tutoring systems can:
A) replace teachers completely
B) teach only mathematical concepts
C) adjust teaching methods based on individual needs
D) work only in prestigious universities -
What is the main advantage of automated grading systems for teachers?
A) They eliminate the need for standardized tests
B) They provide more time for other teaching activities
C) They are more accurate than human grading
D) They can teach critical thinking skills -
The passage suggests that AI language learning applications are particularly useful because they:
A) are more expensive than traditional methods
B) require constant teacher supervision
C) provide immediate pronunciation feedback
D) only work for advanced learners -
How does AI make education more inclusive according to the text?
A) By replacing special education teachers
B) By providing tools for students with disabilities
C) By eliminating the need for native language instruction
D) By making all courses free of charge -
The author’s attitude toward AI in education can best be described as:
A) entirely negative
B) completely enthusiastic
C) cautiously optimistic
D) indifferent
Questions 6-9: True/False/Not Given
Do the following statements agree with the information given in the passage?
Write:
- TRUE if the statement agrees with the information
- FALSE if the statement contradicts the information
- NOT GIVEN if there is no information on this
- Traditional classrooms cannot provide personalized learning because teachers have too many students.
- All AI grading systems can accurately evaluate creative writing skills.
- Duolingo is the most popular language learning application in the world.
- Some people worry that AI might reinforce social inequalities in education.
Questions 10-13: Sentence Completion
Complete the sentences below. Choose NO MORE THAN TWO WORDS from the passage for each answer.
- AI-powered tools help make language education more __ for students in areas lacking qualified teachers.
- Translation tools powered by AI help students overcome __ when accessing educational materials.
- Critics are concerned that depending too heavily on AI might reduce opportunities for developing __.
- The passage states that AI’s role in education will likely __ as technology becomes more advanced.
PASSAGE 2 – Adaptive Learning Technologies and Pedagogical Transformation
Độ khó: Medium (Band 6.0-7.5)
Thời gian đề xuất: 18-20 phút
The integration of artificial intelligence into educational frameworks has catalyzed a fundamental reconsideration of traditional pedagogical approaches. As educational institutions worldwide grapple with the challenges of preparing students for an increasingly complex and technologically sophisticated global economy, AI-driven solutions are emerging as potentially transformative tools that could address long-standing issues in educational equity, effectiveness, and scalability.
Adaptive learning platforms represent perhaps the most theoretically promising application of AI in education. Unlike conventional one-size-fits-all curricula, these systems employ sophisticated algorithms to create dynamic learning pathways that respond in real-time to individual student cognitive processes and knowledge acquisition patterns. When a student interacts with an adaptive learning platform, the system continuously analyzes multiple variables: the speed at which problems are solved, the types of errors made, the frequency with which students request hints or additional resources, and even the time of day when learning appears most effective. This comprehensive data analysis enables the AI to construct a detailed profile of each learner’s strengths, weaknesses, preferred learning modalities, and optimal challenge levels.
The implications of such technology for educational equity are substantial. Students from disadvantaged backgrounds often lack access to high-quality tutoring or supplementary educational resources that more affluent peers take for granted. Adaptive learning platforms can partially bridge this gap by providing individualized instruction that would otherwise require expensive private tutoring. Research conducted by Stanford University’s Graduate School of Education found that students using adaptive learning systems in mathematics showed achievement gains equivalent to having access to a personal tutor for several hours per week. Moreover, these systems can identify struggling students earlier than traditional assessment methods, allowing for timely interventions that prevent minor comprehension difficulties from escalating into major learning obstacles.
AI is also revolutionizing the field of educational assessment. Traditional testing methods have long been criticized for their inability to capture the full spectrum of student learning and their tendency to encourage rote memorization rather than deep understanding. AI-powered assessment tools can evaluate student learning through more nuanced methods. Natural language processing algorithms can analyze written responses for evidence of critical thinking, conceptual understanding, and analytical skills rather than simply checking for predetermined correct answers. Some systems can even assess collaborative problem-solving skills by analyzing student interactions in digital learning environments. This shift from summative to more continuous, formative assessment provides teachers with richer insights into student learning trajectories and enables more targeted instructional adjustments.
The concept of the “flipped classroom,” facilitated by AI technologies, represents another significant pedagogical innovation. In this model, students engage with instructional content—often in the form of AI-curated videos, readings, or interactive modules—outside of class time. Classroom hours are then devoted to collaborative activities, problem-solving exercises, and in-depth discussions that leverage the knowledge students have acquired independently. AI systems play a crucial role in this model by identifying which concepts students have mastered during their independent study and which require further clarification during class time. This approach maximizes the value of face-to-face interaction with teachers while allowing students to learn foundational material at their own pace.
Nevertheless, the proliferation of AI in education raises important concerns that warrant careful consideration. The commodification of education through algorithmic systems may inadvertently prioritize easily measurable outcomes over the holistic development of students as critical thinkers and engaged citizens. There are legitimate concerns that AI systems, trained on historical data, might encode and perpetuate existing biases related to race, socioeconomic status, or gender. For instance, if an AI system is trained on data showing that certain demographic groups have historically performed poorly in specific subjects, it might subtly lower expectations or provide less challenging material to students from those groups, thereby creating a self-fulfilling prophecy.
Furthermore, as the rise of AI-powered educational tools continues, the professional autonomy of educators is being questioned. Some worry that teachers may become subordinate to algorithmic recommendations rather than exercising their professional judgment about what and how to teach. The interpersonal dimensions of education—the ability of a skilled teacher to inspire, motivate, and emotionally support students through challenges—cannot be easily replicated by even the most sophisticated AI. Educational technology should thus be conceived not as a replacement for human educators but as a tool that augments their capabilities and allows them to focus more attention on the uniquely human aspects of teaching.
Questions 14-26
Questions 14-18: 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
- Adaptive learning platforms offer significant advantages over traditional teaching methods.
- Private tutoring is more effective than AI-based learning systems in all cases.
- Traditional testing methods adequately measure all aspects of student learning.
- The flipped classroom model reduces the importance of teacher-student interaction.
- AI systems should completely replace human teachers in the near future.
Questions 19-22: Matching Information
Which paragraph contains the following information?
Write the correct letter, A-G.
(Paragraph A = paragraph 1, B = paragraph 2, etc.)
- A description of how AI can provide early detection of learning difficulties
- An explanation of the concerns about AI reinforcing social prejudices
- A discussion of how AI analyzes multiple aspects of student behavior
- Information about the role of AI in organizing pre-class learning materials
Questions 23-26: Summary Completion
Complete the summary below. Choose NO MORE THAN TWO WORDS from the passage for each answer.
Adaptive learning systems create individualized education paths by analyzing various aspects of student behavior, including how quickly they solve problems and the 23) __ of mistakes they make. These systems can help reduce educational inequality by offering personalized instruction similar to expensive 24) __. Research has shown that students using these systems achieve learning gains comparable to having access to individual tutoring. Additionally, AI is changing educational assessment by using 25) __ to evaluate student writing for deeper understanding rather than just correct answers. However, critics worry that these systems might accidentally 26) __ existing social biases if they are based on historical data.
PASSAGE 3 – The Epistemological and Ethical Dimensions of AI-Mediated Learning
Độ khó: Hard (Band 7.0-9.0)
Thời gian đề xuất: 23-25 phút
The pervasive integration of artificial intelligence into educational ecosystems necessitates a rigorous examination of its epistemological implications—that is, how AI fundamentally alters our understanding of knowledge itself, the processes by which it is acquired, and the criteria by which learning is validated. While much of the discourse surrounding AI in education has focused on pragmatic considerations of efficiency and accessibility, relatively scant attention has been devoted to the more profound philosophical questions about how algorithmic mediation of learning experiences might reshape students’ cognitive development, intellectual autonomy, and relationship with knowledge.
Central to this inquiry is the concept of “algorithmic epistemology“—the ways in which AI systems encode particular assumptions about what constitutes knowledge and how it should be structured, sequenced, and transmitted. Machine learning algorithms, by their very nature, identify patterns in existing data and extrapolate from these patterns to make predictions about future outcomes. When applied to education, this methodology inherently privileges certain forms of knowledge that are easily quantifiable and measurable while potentially marginalizing more ineffable aspects of learning such as creativity, ethical reasoning, aesthetic appreciation, and the capacity for metacognitive reflection. The philosopher Hubert Dreyfus long ago warned about the limitations of computational models of human intelligence, arguing that expertise in any domain involves not merely the application of explicit rules and procedures but also intuitive understanding that emerges from embodied experience and contextual sensitivity—qualities that resist algorithmic formalization.
Moreover, the predictive capacity of AI systems, while often touted as a major advantage, raises troubling questions about determinism and educational equity. If an AI system predicts, based on a student’s early performance and demographic characteristics, that they are unlikely to succeed in advanced mathematics courses, should the system steer that student toward less challenging material? Such “personalization” might be construed as adaptive and beneficial, yet it could equally be viewed as a form of algorithmic tracking that prematurely constrains students’ trajectories based on probabilistic inferences rather than allowing for the possibility of non-linear development, late blooming, or the transformative impact of exceptional teaching. The sociologist Ruha Benjamin has articulated this concern through her concept of the “New Jim Code,” arguing that supposedly neutral technologies can instantiate racial and socioeconomic hierarchies through ostensibly objective algorithmic processes.
The issue of data privacy and surveillance in AI-mediated learning environments warrants equally serious consideration. Contemporary adaptive learning platforms typically collect extraordinarily granular data about student behavior: not only answers to questions but also response latencies, patterns of navigation through digital materials, physiological indicators such as keystroke dynamics, and even, in some cases, biometric data like facial expressions and eye movements. While proponents argue that such comprehensive data collection enables more precise tailoring of educational experiences, critics contend that it creates what Shoshana Zuboff terms “surveillance capitalism” in educational contexts, where students become subjects of continuous monitoring and their learning behaviors are commodified as data assets. The asymmetry of knowledge—wherein AI systems and their operators know vastly more about students than students know about how the systems function—exacerbates concerns about autonomy and agency.
Furthermore, the opacity of many machine learning systems, particularly those employing deep learning architectures, presents what scholars call the “black box problem.” When an AI system makes a recommendation about a student’s learning path or assesses their performance in a particular way, the underlying reasoning may be inscrutable even to the system’s designers. This lack of transparency is antithetical to educational values of intellectual honesty and critical inquiry. How can students develop the capacity for metacognitive awareness—understanding their own thinking processes—if the systems guiding their learning operate through opaque mechanisms? How can teachers exercise professional judgment about pedagogical decisions when those decisions are increasingly mediated by algorithms whose inner workings they cannot scrutinize?
The promise of AI in education should not be dismissed; there are legitimate reasons to believe that thoughtfully designed and carefully implemented AI systems could enhance learning opportunities, particularly for underserved populations. However, realizing this potential requires moving beyond techno-solutionist narratives that present AI as a panacea for educational challenges. It demands instead a critical, interdisciplinary approach that incorporates insights from philosophy of education, cognitive science, ethics, sociology, and critical data studies. As how is technology transforming global education systems? continues to evolve, educators must be trained not only in how to use AI tools but also in how to critically evaluate their assumptions, limitations, and implications. Policymakers must establish robust regulatory frameworks that protect student privacy, ensure algorithmic accountability, and prevent the reification of inequalities through technological systems.
The fundamental question is not whether AI will be part of education’s future—that seems inevitable—but rather what kind of education we want AI to help create. Will it be one that cultivates genuine understanding, intellectual curiosity, ethical sensitivity, and democratic citizenship? Or will it be one that optimizes students for narrowly defined metrics of employability and economic productivity? The answers to these questions will shape not only individual learning experiences but also the broader contours of society in an increasingly AI-mediated world. They require sustained engagement from all stakeholders in education: students, teachers, administrators, technologists, and the broader public. As we stand at this crossroads, the choices we make about how to integrate AI into education will have profound ramifications for generations to come.
Questions 27-40
Questions 27-31: Multiple Choice
Choose the correct letter, A, B, C, or D.
-
According to the passage, “algorithmic epistemology” refers to:
A) the study of how computers learn
B) the way AI systems define and organize knowledge
C) a new method of teaching epistemology
D) the measurement of learning efficiency -
What does the author suggest about Hubert Dreyfus’s views on expertise?
A) It can be fully captured by computational models
B) It requires both rule application and intuitive understanding
C) It is less important than algorithmic knowledge
D) It can only be developed through AI systems -
The “New Jim Code” concept mentioned in the passage refers to:
A) a new programming language for education
B) an AI system developed by Ruha Benjamin
C) how technology can reinforce social hierarchies
D) a method for improving educational equity -
The “black box problem” in AI systems creates difficulties because:
A) students cannot see their test scores
B) the systems are too expensive to operate
C) the decision-making process is not transparent
D) teachers prefer traditional teaching methods -
The author’s overall position on AI in education is that it:
A) should be completely rejected by educators
B) requires critical evaluation and careful implementation
C) will automatically solve all educational problems
D) is only useful for teaching technical subjects
Questions 32-36: Matching Features
Match each concern (32-36) with the correct theorist or concept (A-H).
- Knowledge that cannot be easily measured by algorithms
- Technology reinforcing racial and economic inequalities
- Extensive data collection creating surveillance in education
- AI predictions limiting student potential
- The inability to understand how AI makes decisions
A) Hubert Dreyfus
B) Ruha Benjamin
C) Shoshana Zuboff
D) Algorithmic tracking
E) Black box problem
F) Surveillance capitalism
G) Techno-solutionism
H) Democratic citizenship
Questions 37-40: Short-answer Questions
Answer the questions below. Choose NO MORE THAN THREE WORDS from the passage for each answer.
- What type of data do some advanced learning platforms collect that includes students’ facial expressions and eye movements?
- What does the passage say educational values of intellectual honesty are opposed to regarding AI systems?
- What type of approach does the author say is needed to properly implement AI in education?
- According to the passage, what must educators be trained to do regarding AI tools beyond simply using them?
Answer Keys – Đáp Án
PASSAGE 1: Questions 1-13
- C
- B
- C
- B
- C
- TRUE
- NOT GIVEN
- NOT GIVEN
- TRUE
- cost-effective
- language barriers
- social skills
- expand
PASSAGE 2: Questions 14-26
- YES
- NOT GIVEN
- NO
- NO
- NO
- C (paragraph 3)
- F (paragraph 6)
- B (paragraph 2)
- E (paragraph 5)
- types
- private tutoring
- natural language processing
- encode / perpetuate
PASSAGE 3: Questions 27-40
- B
- B
- C
- C
- B
- A
- B
- C
- D
- E
- biometric data
- opacity / lack of transparency
- critical, interdisciplinary approach
- critically evaluate
Giải Thích Đáp Án Chi Tiết
Passage 1 – Giải Thích
Câu 1: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: intelligent tutoring systems, can
- Vị trí trong bài: Đoạn 2, dòng 1-6
- Giải thích: Bài văn nói rõ “if a student struggles with a particular mathematical concept, the AI system can identify the gap in understanding and provide additional practice problems, explanatory videos, or alternative teaching methods tailored to that student’s learning style.” Điều này chứng minh hệ thống có thể điều chỉnh phương pháp dạy học dựa trên nhu cầu cá nhân. Đáp án A sai vì không có thông tin về việc thay thế hoàn toàn giáo viên, B sai vì chỉ đưa ra toán học như một ví dụ, D sai vì công nghệ được sử dụng từ trường tiểu học đến đại học.
Câu 2: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: main advantage, automated grading systems, teachers
- Vị trí trong bài: Đoạn 3, dòng 3-5
- Giải thích: Câu văn “AI-powered grading tools can now handle these tasks almost instantaneously, freeing up teachers to focus on more valuable activities such as lesson planning, one-on-one student mentoring” cho thấy lợi ích chính là giải phóng thời gian cho các hoạt động khác. Paraphrase: “freeing up teachers” = “provide more time”, “more valuable activities” = “other teaching activities”.
Câu 3: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI language learning applications, particularly useful
- Vị trí trong bài: Đoạn 4, dòng 3-5
- Giải thích: Đoạn văn chỉ rõ “Speech recognition technology allows students to practice pronunciation and receive immediate feedback.” Đây là paraphrase của đáp án C. Các đáp án khác mâu thuẫn với thông tin trong bài: A sai vì bài nói “cost-effective”, B sai vì không cần giám sát liên tục, D không được đề cập.
Câu 4: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: AI, make education more inclusive
- Vị trí trong bài: Đoạn 5, dòng 2-5
- Giải thích: Bài văn liệt kê các công cụ như “Text-to-speech software helps visually impaired students” và “speech-to-text applications assist those with physical disabilities”. Đây là bằng chứng rõ ràng cho đáp án B về việc cung cấp công cụ cho học sinh khuyết tật.
Câu 6: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: Traditional classrooms, cannot provide personalized learning, too many students
- Vị trí trong bài: Đoạn 2, dòng 6-8
- Giải thích: Bài văn nói “This level of personalization was previously impossible in traditional classroom settings where one teacher might be responsible for thirty or more students.” Câu phát biểu đồng nghĩa với thông tin này.
Câu 7: NOT GIVEN
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: All AI grading systems, accurately evaluate, creative writing skills
- Vị trí trong bài: Đoạn 3
- Giải thích: Bài chỉ nói “Some advanced systems can even evaluate written essays” nhưng không khẳng định TẤT CẢ hệ thống đều có khả năng đánh giá chính xác kỹ năng viết sáng tạo. Đây là thông tin không được đề cập đầy đủ.
Câu 9: TRUE
- Dạng câu hỏi: True/False/Not Given
- Từ khóa: people worry, AI, reinforce social inequalities
- Vị trí trong bài: Đoạn 6, dòng 3-5
- Giải thích: Đoạn văn nói rõ “questions about the potential for AI to perpetuate existing biases if the algorithms are trained on data that reflects societal inequalities.” “Perpetuate existing biases” và “societal inequalities” chính là paraphrase của “reinforce social inequalities”.
Câu 10: cost-effective
- Dạng câu hỏi: Sentence Completion
- Từ khóa: make language education more, areas lacking qualified teachers
- Vị trí trong bài: Đoạn 4, dòng 5-6
- Giải thích: Câu gốc: “These tools make language education more accessible and cost-effective, especially for students in regions where qualified language teachers are scarce.” “Scarce” = “lacking”, và từ cần điền là “cost-effective”.
Câu 12: social skills
- Dạng câu hỏi: Sentence Completion
- Từ khóa: depending too heavily on AI, reduce opportunities, developing
- Vị trí trong bài: Đoạn 6, dòng 5-7
- Giải thích: Bài văn đề cập “excessive reliance on technology might diminish important aspects of education such as human interaction, social skills development.” Paraphrase: “excessive reliance” = “depending too heavily”, “diminish” = “reduce opportunities for”.
Câu 13: expand
- Dạng câu hỏi: Sentence Completion
- Từ khóa: AI’s role in education, technology becomes more advanced
- Vị trí trong bài: Đoạn 7, dòng 1-2
- Giải thích: Câu gốc: “As technology continues to advance and become more sophisticated, its role in shaping how future generations learn will likely expand.” Từ “expand” chính là đáp án cần điền.
Passage 2 – Giải Thích
Câu 14: YES
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Adaptive learning platforms, significant advantages, traditional teaching methods
- Vị trí trong bài: Đoạn 2 và 3
- Giải thích: Tác giả sử dụng cụm từ “most theoretically promising application” và “substantial” khi mô tả lợi ích của adaptive learning platforms. Đoạn 3 so sánh hiệu quả tương đương với gia sư riêng, cho thấy tác giả đồng ý với ưu điểm đáng kể so với phương pháp truyền thống.
Câu 16: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: Traditional testing methods, adequately measure, all aspects
- Vị trí trong bài: Đoạn 4, dòng 1-3
- Giải thích: Bài văn nói rõ “Traditional testing methods have long been criticized for their inability to capture the full spectrum of student learning.” Đây là quan điểm rõ ràng của tác giả phản đối phát biểu trong câu hỏi.
Câu 17: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: flipped classroom model, reduces importance, teacher-student interaction
- Vị trí trong bài: Đoạn 5, dòng cuối
- Giải thích: Bài văn nói “This approach maximizes the value of face-to-face interaction with teachers” – tức là làm TẠO TĂNG giá trị tương tác, không phải giảm tầm quan trọng. Đây là quan điểm trái ngược với câu phát biểu.
Câu 18: NO
- Dạng câu hỏi: Yes/No/Not Given
- Từ khóa: AI systems, completely replace, human teachers
- Vị trí trong bài: Đoạn 7, dòng cuối
- Giải thích: Tác giả kết luận rất rõ ràng: “Educational technology should thus be conceived not as a replacement for human educators but as a tool that augments their capabilities.” Đây là quan điểm phản đối hoàn toàn việc thay thế giáo viên.
Câu 19: C
- Dạng câu hỏi: Matching Information
- Từ khóa: early detection, learning difficulties
- Vị trí trong bài: Đoạn 3 (C), dòng cuối
- Giải thích: Câu “these systems can identify struggling students earlier than traditional assessment methods, allowing for timely interventions that prevent minor comprehension difficulties from escalating” nằm ở đoạn 3, mô tả chính xác việc phát hiện sớm khó khăn học tập.
Câu 20: F
- Dạng câu hỏi: Matching Information
- Từ khóa: concerns, AI reinforcing, social prejudices
- Vị trí trong bài: Đoạn 6 (F), dòng 3-7
- Giải thích: Đoạn 6 thảo luận chi tiết về lo ngại “AI systems, trained on historical data, might encode and perpetuate existing biases related to race, socioeconomic status, or gender” và đề cập đến “self-fulfilling prophecy”.
Câu 21: B
- Dạng câu hỏi: Matching Information
- Từ khóa: AI analyzes, multiple aspects, student behavior
- Vị trí trong bài: Đoạn 2 (B), dòng 4-7
- Giải thích: Đoạn 2 liệt kê chi tiết “multiple variables: the speed at which problems are solved, the types of errors made, the frequency with which students request hints… and even the time of day when learning appears most effective.”
Câu 23: types
- Dạng câu hỏi: Summary Completion
- Từ khóa: how quickly they solve problems, mistakes
- Vị trí trong bài: Đoạn 2, dòng 5
- Giải thích: Câu gốc: “the types of errors made” – từ “types” là đáp án phù hợp với ngữ cảnh “the 23)____ of mistakes they make.”
Câu 24: private tutoring
- Dạng câu hỏi: Summary Completion
- Từ khóa: reduce educational inequality, personalized instruction similar to expensive
- Vị trí trong bài: Đoạn 3, dòng 3-4
- Giải thích: Bài văn nói “providing individualized instruction that would otherwise require expensive private tutoring.” Đây là paraphrase trực tiếp cho chỗ trống cần điền.
Câu 25: natural language processing
- Dạng câu hỏi: Summary Completion
- Từ khóa: AI, changing educational assessment, evaluate student writing
- Vị trí trong bài: Đoạn 4, dòng 5-6
- Giải thích: Câu gốc: “Natural language processing algorithms can analyze written responses for evidence of critical thinking, conceptual understanding” – cụm từ chuyên ngành này là đáp án chính xác.
Câu 26: encode / perpetuate
- Dạng câu hỏi: Summary Completion
- Từ khóa: accidentally, existing social biases, historical data
- Vị trí trong bài: Đoạn 6, dòng 3-4
- Giải thích: Bài văn sử dụng cả hai từ “encode and perpetuate existing biases” – cả hai đều là đáp án được chấp nhận vì cùng xuất hiện trong cùng ngữ cảnh và có ý nghĩa tương đương.
Passage 3 – Giải Thích
Câu 27: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: algorithmic epistemology, refers to
- Vị trí trong bài: Đoạn 2, dòng 1-3
- Giải thích: Định nghĩa rõ ràng: “the ways in which AI systems encode particular assumptions about what constitutes knowledge and how it should be structured, sequenced, and transmitted.” Đây chính là cách AI định nghĩa và tổ chức kiến thức (đáp án B).
Câu 28: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: Hubert Dreyfus, views on expertise
- Vị trí trong bài: Đoạn 2, dòng 9-12
- Giải thích: Bài văn trích dẫn Dreyfus: “expertise in any domain involves not merely the application of explicit rules and procedures but also intuitive understanding that emerges from embodied experience.” Đây chính là đáp án B – yêu cầu cả việc áp dụng quy tắc và hiểu biết trực quan.
Câu 29: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: New Jim Code, refers to
- Vị trí trong bài: Đoạn 3, dòng cuối
- Giải thích: Ruha Benjamin’s concept được giải thích là “supposedly neutral technologies can instantiate racial and socioeconomic hierarchies through ostensibly objective algorithmic processes” – tức công nghệ có thể củng cố phân tầng xã hội (đáp án C).
Câu 30: C
- Dạng câu hỏi: Multiple Choice
- Từ khóa: black box problem, creates difficulties
- Vị trí trong bài: Đoạn 5, dòng 1-4
- Giải thích: Vấn đề được mô tả: “the underlying reasoning may be inscrutable even to the system’s designers. This lack of transparency is antithetical to educational values” – tức quá trình ra quyết định không minh bạch (đáp án C).
Câu 31: B
- Dạng câu hỏi: Multiple Choice
- Từ khóa: author’s overall position, AI in education
- Vị trí trong bài: Đoạn 6 và 7
- Giải thích: Tác giả nói “The promise of AI in education should not be dismissed” nhưng cũng nhấn mạnh “requires moving beyond techno-solutionist narratives” và “demands instead a critical, interdisciplinary approach” – đây là thái độ đánh giá phê phán và triển khai cẩn thận (đáp án B).
Câu 32: A (Hubert Dreyfus)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 2
- Giải thích: Dreyfus được trích dẫn về “intuitive understanding” và “embodied experience” – những khía cạnh “resist algorithmic formalization”, tức là kiến thức không thể đo lường dễ dàng bằng thuật toán.
Câu 33: B (Ruha Benjamin)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 3
- Giải thích: Benjamin được đề cập với khái niệm “New Jim Code” về việc công nghệ “instantiate racial and socioeconomic hierarchies” – củng cố bất bình đẳng chủng tộc và kinh tế.
Câu 34: C (Shoshana Zuboff)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 4
- Giải thích: Zuboff được trích dẫn với thuật ngữ “surveillance capitalism in educational contexts, where students become subjects of continuous monitoring” – thu thập dữ liệu mở rộng tạo nên giám sát trong giáo dục.
Câu 35: D (Algorithmic tracking)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 3
- Giải thích: Đoạn văn mô tả “algorithmic tracking that prematurely constrains students’ trajectories based on probabilistic inferences” – dự đoán của AI hạn chế tiềm năng học sinh.
Câu 36: E (Black box problem)
- Dạng câu hỏi: Matching Features
- Vị trí trong bài: Đoạn 5
- Giải thích: “Black box problem” được định nghĩa rõ ràng là “the underlying reasoning may be inscrutable” – không thể hiểu cách AI đưa ra quyết định.
Câu 37: biometric data
- Dạng câu hỏi: Short-answer Questions (không quá 3 từ)
- Từ khóa: data, facial expressions, eye movements
- Vị trí trong bài: Đoạn 4, dòng 3-5
- Giải thích: Câu gốc: “physiological indicators such as keystroke dynamics, and even, in some cases, biometric data like facial expressions and eye movements.” Cụm “biometric data” là thuật ngữ bao quát loại dữ liệu này.
Câu 38: opacity / lack of transparency
- Dạng câu hỏi: Short-answer Questions (không quá 3 từ)
- Từ khóa: educational values of intellectual honesty, opposed to
- Vị trí trong bài: Đoạn 5, dòng 4
- Giải thích: Bài văn nói rõ “This lack of transparency is antithetical to educational values of intellectual honesty.” “Antithetical to” = “opposed to”, đáp án có thể là “opacity” hoặc “lack of transparency” (3 từ).
Câu 39: critical, interdisciplinary approach
- Dạng câu hỏi: Short-answer Questions (không quá 3 từ)
- Từ khóa: needed to properly implement AI
- Vị trí trong bài: Đoạn 6, dòng 4-5
- Giải thích: Tác giả nói rõ “It demands instead a critical, interdisciplinary approach that incorporates insights from philosophy of education, cognitive science, ethics, sociology, and critical data studies.” Cụm từ 3 từ này là đáp án chính xác.
Câu 40: critically evaluate
- Dạng câu hỏi: Short-answer Questions (không quá 3 từ)
- Từ khóa: educators, trained to do, AI tools, beyond using
- Vị trí trong bài: Đoạn 6, dòng 6-7
- Giải thích: Câu gốc: “educators must be trained not only in how to use AI tools but also in how to critically evaluate their assumptions, limitations, and implications.” Cụm “critically evaluate” (2 từ) là đáp án chính xác.
Từ Vựng Quan Trọng Theo Passage
Passage 1 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| rapidly transforming | verb phrase | /ˈræpɪdli trænsˈfɔːmɪŋ/ | biến đổi nhanh chóng | AI is rapidly transforming the way students learn | transform education/society |
| personalized learning | noun phrase | /ˈpɜːsənəlaɪzd ˈlɜːnɪŋ/ | học tập được cá nhân hóa | offering new possibilities for personalized learning | facilitate/enable/support personalized learning |
| intelligent tutoring systems | noun phrase | /ɪnˈtelɪdʒənt ˈtjuːtərɪŋ ˈsɪstəmz/ | hệ thống gia sư thông minh | intelligent tutoring systems use machine learning algorithms | develop/implement/design intelligent tutoring systems |
| adapt the curriculum | verb phrase | /əˈdæpt ðə kəˈrɪkjʊləm/ | điều chỉnh chương trình học | the AI system can adapt the curriculum accordingly | adapt curriculum to needs |
| struggles with | verb phrase | /ˈstrʌɡəlz wɪð/ | gặp khó khăn với | if a student struggles with a particular concept | struggle with concepts/problems |
| automated grading | adjective + noun | /ˈɔːtəmeɪtɪd ˈɡreɪdɪŋ/ | chấm điểm tự động | Automated grading systems represent another important use | automated grading tools/systems |
| freeing up | phrasal verb | /ˈfriːɪŋ ʌp/ | giải phóng, tạo thời gian | freeing up teachers to focus on more valuable activities | free up time/resources |
| adaptive learning paths | noun phrase | /əˈdæptɪv ˈlɜːnɪŋ pɑːθs/ | lộ trình học tập thích ứng | AI algorithms create adaptive learning paths | create/design adaptive learning paths |
| speech recognition | noun phrase | /spiːtʃ ˌrekəɡˈnɪʃən/ | nhận dạng giọng nói | Speech recognition technology allows students to practice | speech recognition software/technology |
| inclusive | adjective | /ɪnˈkluːsɪv/ | bao trùm, toàn diện | making education more inclusive for students with special needs | inclusive education/environment |
| perpetuate existing biases | verb phrase | /pəˈpetʃueɪt ɪɡˈzɪstɪŋ ˈbaɪəsɪz/ | duy trì định kiến hiện có | potential for AI to perpetuate existing biases | perpetuate stereotypes/inequalities |
| excessive reliance | noun phrase | /ɪkˈsesɪv rɪˈlaɪəns/ | sự phụ thuộc quá mức | excessive reliance on technology might diminish important aspects | excessive reliance on technology/data |
Passage 2 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| pedagogical approaches | noun phrase | /ˌpedəˈɡɒdʒɪkəl əˈprəʊtʃɪz/ | phương pháp sư phạm | fundamental reconsideration of traditional pedagogical approaches | innovative/traditional pedagogical approaches |
| grapple with | phrasal verb | /ˈɡræpəl wɪð/ | vật lộn với, giải quyết | institutions worldwide grapple with the challenges | grapple with challenges/problems |
| transformative tools | noun phrase | /trænsˈfɔːmətɪv tuːlz/ | công cụ mang tính biến đổi | AI-driven solutions are emerging as transformative tools | transformative impact/potential |
| cognitive processes | noun phrase | /ˈkɒɡnɪtɪv ˈprəʊsesɪz/ | quá trình nhận thức | respond to individual student cognitive processes | cognitive development/abilities |
| comprehensive data analysis | noun phrase | /ˌkɒmprɪˈhensɪv ˈdeɪtə əˈnæləsɪs/ | phân tích dữ liệu toàn diện | This comprehensive data analysis enables the AI | comprehensive analysis/review |
| disadvantaged backgrounds | noun phrase | /ˌdɪsədˈvɑːntɪdʒd ˈbækɡraʊndz/ | xuất thân thiệt thòi | Students from disadvantaged backgrounds often lack access | disadvantaged communities/groups |
| achievement gains | noun phrase | /əˈtʃiːvmənt ɡeɪnz/ | sự tiến bộ trong thành tích | students showed achievement gains equivalent to having a tutor | academic/learning achievement gains |
| timely interventions | noun phrase | /ˈtaɪmli ˌɪntəˈvenʃənz/ | can thiệp kịp thời | allowing for timely interventions that prevent difficulties | timely intervention/response |
| rote memorization | noun phrase | /rəʊt ˌmeməraɪˈzeɪʃən/ | học vẹt, học thuộc lòng máy móc | encourage rote memorization rather than deep understanding | rote learning/memorization |
| formative assessment | noun phrase | /ˈfɔːmətɪv əˈsesmənt/ | đánh giá hình thành | shift from summative to more continuous, formative assessment | formative/summative assessment |
| flipped classroom | noun phrase | /flɪpt ˈklɑːsruːm/ | lớp học đảo ngược | The concept of the flipped classroom facilitated by AI | flipped classroom model/approach |
| commodification | noun | /kəˌmɒdɪfɪˈkeɪʃən/ | sự thương mại hóa | commodification of education through algorithmic systems | commodification of knowledge/education |
| self-fulfilling prophecy | noun phrase | /self fʊlˈfɪlɪŋ ˈprɒfəsi/ | lời tiên tri tự ứng nghiệm | thereby creating a self-fulfilling prophecy | self-fulfilling prophecy effect |
| professional autonomy | noun phrase | /prəˈfeʃənəl ɔːˈtɒnəmi/ | quyền tự chủ nghề nghiệp | professional autonomy of educators is being questioned | professional independence/autonomy |
| augments | verb | /ɔːɡˈments/ | tăng cường, bổ sung | tool that augments their capabilities | augment capacity/abilities |
Passage 3 – Essential Vocabulary
| Từ vựng | Loại từ | Phiên âm | Nghĩa tiếng Việt | Ví dụ từ bài | Collocation |
|---|---|---|---|---|---|
| pervasive integration | noun phrase | /pəˈveɪsɪv ˌɪntɪˈɡreɪʃən/ | sự tích hợp lan rộng | pervasive integration of AI into educational ecosystems | pervasive influence/presence |
| epistemological implications | noun phrase | /ɪˌpɪstəməˈlɒdʒɪkəl ˌɪmplɪˈkeɪʃənz/ | hàm ý về tri thức luận | necessitates rigorous examination of epistemological implications | philosophical/theoretical implications |
| algorithmic mediation | noun phrase | /ˌælɡəˈrɪðmɪk ˌmiːdiˈeɪʃən/ | sự trung gian bởi thuật toán | how algorithmic mediation might reshape cognitive development | algorithmic decision-making/processes |
| cognitive development | noun phrase | /ˈkɒɡnɪtɪv dɪˈveləpmənt/ | phát triển nhận thức | might reshape students’ cognitive development | cognitive growth/abilities |
| quantifiable | adjective | /ˈkwɒntɪfaɪəbəl/ | có thể định lượng | privileges certain forms of knowledge that are easily quantifiable | quantifiable data/results |
| marginalizing | verb | /ˈmɑːdʒɪnəlaɪzɪŋ/ | đẩy ra lề, gạt bỏ | potentially marginalizing more ineffable aspects | marginalize communities/voices |
| metacognitive reflection | noun phrase | /ˌmetəˈkɒɡnɪtɪv rɪˈflekʃən/ | sự phản tư siêu nhận thức | capacity for metacognitive reflection | metacognitive awareness/strategies |
| determinism | noun | /dɪˈtɜːmɪnɪzəm/ | thuyết định mệnh | raises troubling questions about determinism | genetic/technological determinism |
| prematurely constrains | verb phrase | /ˌpreməˈtʃʊəli kənˈstreɪnz/ | hạn chế quá sớm | prematurely constrains students’ trajectories | constrain opportunities/choices |
| probabilistic inferences | noun phrase | /ˌprɒbəbɪˈlɪstɪk ˈɪnfərənsɪz/ | suy luận xác suất | based on probabilistic inferences rather than allowing possibility | probabilistic models/predictions |
| instantiate | verb | /ɪnˈstænʃieɪt/ | hiện thân hóa, thể hiện | technologies can instantiate racial hierarchies | instantiate values/principles |
| granular data | noun phrase | /ˈɡrænjʊlə ˈdeɪtə/ | dữ liệu chi tiết cực kỳ | collect extraordinarily granular data about student behavior | granular analysis/information |
| surveillance capitalism | noun phrase | /səˈveɪləns ˈkæpɪtəlɪzəm/ | chủ nghĩa tư bản giám sát | creates surveillance capitalism in educational contexts | surveillance society/technologies |
| asymmetry of knowledge | noun phrase | /eɪˈsɪmətri əv ˈnɒlɪdʒ/ | sự bất đối xứng về kiến thức | asymmetry wherein AI systems know vastly more about students | information/power asymmetry |
| opacity | noun | /əʊˈpæsəti/ | tính không minh bạch | opacity of many machine learning systems | algorithmic opacity |
| inscrutable | adjective | /ɪnˈskruːtəbəl/ | không thể hiểu được | underlying reasoning may be inscrutable | inscrutable motives/processes |
| antithetical | adjective | /ˌæntɪˈθetɪkəl/ | đối nghịch, trái ngược | antithetical to educational values of intellectual honesty | antithetical to principles/values |
| techno-solutionist narratives | noun phrase | /ˌteknəʊ səˈluːʃənɪst ˈnærətɪvz/ | câu chuyện giải pháp công nghệ | moving beyond techno-solutionist narratives | technological solutionism |
| reification | noun | /ˌreɪɪfɪˈkeɪʃən/ | sự vật hóa, biến thành cứng nhắc | prevent reification of inequalities through technological systems | reification of concepts/categories |
| profound ramifications | noun phrase | /prəˈfaʊnd ˌræmɪfɪˈkeɪʃənz/ | hệ quả sâu rộng | choices will have profound ramifications for generations | serious/far-reaching ramifications |
Kết Bài
Chủ đề “How is artificial intelligence being used in education?” không chỉ là một xu hướng công nghệ tạm thời mà đại diện cho sự chuyển đổi căn bản trong cách chúng ta tiếp cận giáo dục. Qua ba passages với độ khó tăng dần, bạn đã được tiếp xúc với đầy đủ các khía cạnh từ ứng dụng thực tiễn cơ bản đến những vấn đề triết học sâu sắc xung quanh AI trong giáo dục.
Passage 1 giới thiệu các ứng dụng phổ biến như hệ thống gia sư thông minh và chấm điểm tự động ở mức độ dễ hiểu, phù hợp với band 5.0-6.5. Passage 2 đi sâu hơn vào các công nghệ học tập thích ứng và mô hình lớp học đảo ngược với từ vựng học thuật ở mức trung bình cho band 6.0-7.5. Cuối cùng, Passage 3 thách thức người đọc với những phân tích phức tạp về tri thức luận, đạo đức và các hệ quả xã hội của AI, phù hợp cho band 7.0-9.0.
Đáp án chi tiết và giải thích từng câu đã cung cấp cho bạn insight về cách paraphrase câu hỏi xuất hiện trong bài, vị trí thông tin cụ thể, và chiến lược xác định đáp án đúng. Bộ từ vựng được tổ chức theo từng passage với phiên âm, nghĩa tiếng Việt và collocations sẽ giúp bạn mở rộng vốn từ học thuật cần thiết không chỉ cho IELTS Reading mà còn cho các phần thi khác.
Tương tự như the rise of remote learning platforms in education, công nghệ AI đang tạo ra những cơ hội học tập mới. Nếu bạn quan tâm đến cách how is AI being used in personalized education?, hãy tiếp tục khám phá các bài viết chuyên sâu khác trên VN.IELTS.NET.
Hãy sử dụng đề thi này như một công cụ đánh giá định kỳ, làm lại nhiều lần để cải thiện tốc độ và độ chính xác. Chủ đề công nghệ trong giáo dục sẽ tiếp tục là trọng tâm của IELTS Reading trong những năm tới, vì vậy việc nắm vững từ vựng và kỹ năng làm bài với chủ đề này sẽ mang lại lợi thế đáng kể trong kỳ thi thực tế của bạn.
