Đề 9Study soạn tốt nghiệp THPT môn Tiếng Anh năm 2025 - Mã đề 10

Read the following text and mark the letter A, B, C or D on your answer sheet to indicate the option that best fits each of the numbered blanks from 1 to 6.

Green Startups and Youth Entrepreneurship

In many countries, young entrepreneurs are increasingly turning to green startups as a way to combine environmental impact with business opportunity. These enterprises rely heavily on collaboration, often bringing together people [1] ______ they’ve met through school programs, youth conferences, or online innovation challenges. However, only ventures with clearly defined goals tend to [2] ______ long-term investor interest.

One marker of potential is the team’s ability to scale without compromising their values. Projects that originate from student initiatives or university incubators often [3] ______ from access to mentorship, peer collaboration, and basic funding. Yet what truly differentiates strong candidates is how they respond when early outcomes fall short of expectations. A pattern of [4] ______ attempts at planning and execution often signals deeper issues in leadership or adaptability. This includes rethinking supply chains, leveraging digital tools, and anticipating future demands—skills that require both creativity and discipline. As climate-conscious business models grow in popularity, [5] ______ scalable ideas are beginning to attract widespread attention. The movement is no longer confined to niche circles but has started to [6] ______ broader attention worldwide.

Read the following text and mark the letter A, B, C or D on your answer sheet to indicate the option that best fits each of the numbered blanks from 1 to 6.

Green Startups and Youth Entrepreneurship

In many countries, young entrepreneurs are increasingly turning to green startups as a way to combine environmental impact with business opportunity. These enterprises rely heavily on collaboration, often bringing together people [1] ______ they’ve met through school programs, youth conferences, or online innovation challenges. However, only ventures with clearly defined goals tend to [2] ______ long-term investor interest.

One marker of potential is the team’s ability to scale without compromising their values. Projects that originate from student initiatives or university incubators often [3] ______ from access to mentorship, peer collaboration, and basic funding. Yet what truly differentiates strong candidates is how they respond when early outcomes fall short of expectations. A pattern of [4] ______ attempts at planning and execution often signals deeper issues in leadership or adaptability. This includes rethinking supply chains, leveraging digital tools, and anticipating future demands—skills that require both creativity and discipline. As climate-conscious business models grow in popularity, [5] ______ scalable ideas are beginning to attract widespread attention. The movement is no longer confined to niche circles but has started to [6] ______ broader attention worldwide.

Read the following text and mark the letter A, B, C or D on your answer sheet to indicate the option that best fits each of the numbered blanks from 1 to 6.

Green Startups and Youth Entrepreneurship

In many countries, young entrepreneurs are increasingly turning to green startups as a way to combine environmental impact with business opportunity. These enterprises rely heavily on collaboration, often bringing together people [1] ______ they’ve met through school programs, youth conferences, or online innovation challenges. However, only ventures with clearly defined goals tend to [2] ______ long-term investor interest.

One marker of potential is the team’s ability to scale without compromising their values. Projects that originate from student initiatives or university incubators often [3] ______ from access to mentorship, peer collaboration, and basic funding. Yet what truly differentiates strong candidates is how they respond when early outcomes fall short of expectations. A pattern of [4] ______ attempts at planning and execution often signals deeper issues in leadership or adaptability. This includes rethinking supply chains, leveraging digital tools, and anticipating future demands—skills that require both creativity and discipline. As climate-conscious business models grow in popularity, [5] ______ scalable ideas are beginning to attract widespread attention. The movement is no longer confined to niche circles but has started to [6] ______ broader attention worldwide.

Read the following text and mark the letter A, B, C or D on your answer sheet to indicate the option that best fits each of the numbered blanks from 1 to 6.

Green Startups and Youth Entrepreneurship

In many countries, young entrepreneurs are increasingly turning to green startups as a way to combine environmental impact with business opportunity. These enterprises rely heavily on collaboration, often bringing together people [1] ______ they’ve met through school programs, youth conferences, or online innovation challenges. However, only ventures with clearly defined goals tend to [2] ______ long-term investor interest.

One marker of potential is the team’s ability to scale without compromising their values. Projects that originate from student initiatives or university incubators often [3] ______ from access to mentorship, peer collaboration, and basic funding. Yet what truly differentiates strong candidates is how they respond when early outcomes fall short of expectations. A pattern of [4] ______ attempts at planning and execution often signals deeper issues in leadership or adaptability. This includes rethinking supply chains, leveraging digital tools, and anticipating future demands—skills that require both creativity and discipline. As climate-conscious business models grow in popularity, [5] ______ scalable ideas are beginning to attract widespread attention. The movement is no longer confined to niche circles but has started to [6] ______ broader attention worldwide.

Read the following text and mark the letter A, B, C or D on your answer sheet to indicate the option that best fits each of the numbered blanks from 1 to 6.

Green Startups and Youth Entrepreneurship

In many countries, young entrepreneurs are increasingly turning to green startups as a way to combine environmental impact with business opportunity. These enterprises rely heavily on collaboration, often bringing together people [1] ______ they’ve met through school programs, youth conferences, or online innovation challenges. However, only ventures with clearly defined goals tend to [2] ______ long-term investor interest.

One marker of potential is the team’s ability to scale without compromising their values. Projects that originate from student initiatives or university incubators often [3] ______ from access to mentorship, peer collaboration, and basic funding. Yet what truly differentiates strong candidates is how they respond when early outcomes fall short of expectations. A pattern of [4] ______ attempts at planning and execution often signals deeper issues in leadership or adaptability. This includes rethinking supply chains, leveraging digital tools, and anticipating future demands—skills that require both creativity and discipline. As climate-conscious business models grow in popularity, [5] ______ scalable ideas are beginning to attract widespread attention. The movement is no longer confined to niche circles but has started to [6] ______ broader attention worldwide.

Read the following text and mark the letter A, B, C or D on your answer sheet to indicate the option that best fits each of the numbered blanks from 1 to 6.

Green Startups and Youth Entrepreneurship

In many countries, young entrepreneurs are increasingly turning to green startups as a way to combine environmental impact with business opportunity. These enterprises rely heavily on collaboration, often bringing together people [1] ______ they’ve met through school programs, youth conferences, or online innovation challenges. However, only ventures with clearly defined goals tend to [2] ______ long-term investor interest.

One marker of potential is the team’s ability to scale without compromising their values. Projects that originate from student initiatives or university incubators often [3] ______ from access to mentorship, peer collaboration, and basic funding. Yet what truly differentiates strong candidates is how they respond when early outcomes fall short of expectations. A pattern of [4] ______ attempts at planning and execution often signals deeper issues in leadership or adaptability. This includes rethinking supply chains, leveraging digital tools, and anticipating future demands—skills that require both creativity and discipline. As climate-conscious business models grow in popularity, [5] ______ scalable ideas are beginning to attract widespread attention. The movement is no longer confined to niche circles but has started to [6] ______ broader attention worldwide.

Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 7 to 14.

AI-powered language learning tools are increasingly embedded in both classroom instruction and independent study, offering personalised pathways that respond to learner performance. These systems employ core adaptive mechanisms such as natural language processing, speech recognition, real-time learner analytics, and adaptive sequencing to adjust vocabulary drills, grammar explanations, and pronunciation feedback. In parallel, they provide engagement-enhancing features including gesture-based interaction, voice-triggered input, mobile-compatible platforms, and browser-based extensions. The proliferation of these tools—driven by easier mobile access and lightweight browser design—has made them available to a far wider audience. To sustain motivation, many integrate gamified elements such as performance streaks, unlockable content, and adaptive quizzes—design choices intended to generate measurable gains in time on task.

Proponents contend that AI-assisted learning addresses gaps in conventional language education by enabling targeted, form-focused practice with immediate feedback loops. Machine learning models can diagnose recurring errors in pronunciation, syntax, or usage and deliver remediation through micro-drills, rewriting tasks, or input enhancement. Some systems paraphrase or restructure learner output to model more native-like expressions. Critics, however, highlight the risk of algorithmic opacity: users are seldom informed of the rationale behind correction priorities, and training data may encode subtle linguistic or cultural biases. Feedback—though rapid—can appear impersonal, rigid, or demotivating when it contradicts classroom instruction or lacks contextual nuance.

Educational ministries across Southeast Asia are integrating AI into hybrid teaching models and national language programmes. Government-funded pilots of AI-based writing assessment have demonstrated notable efficiencies in scoring and rubric alignment. In these trials, ministries, teacher associations, and technical partners each contributed to refining the system’s accuracy. They also show that combining algorithmic suggestions with teacher moderation allows instructors to override or annotate automated recommendations, balancing scalability with pedagogical soundness. Nevertheless, concerns around transparency, accountability, and governance of learner data must be addressed to maintain inclusivity and instructional integrity.

Policy reviews show that most participating ministries reported sustained improvements in marking efficiency and reduced teacher workload after adopting AI–human moderation systems. This compromise preserves the speed of automation while retaining educators’ judgement. Its success depends on continued professional training and on regulations that safeguard learner privacy while clarifying the permissible scope of algorithmic decision-making. By embedding human expertise within robust governance structures, policymakers aim to create a scalable yet accountable model that serves diverse learner populations and sustains public trust.

Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 7 to 14.

AI-powered language learning tools are increasingly embedded in both classroom instruction and independent study, offering personalised pathways that respond to learner performance. These systems employ core adaptive mechanisms such as natural language processing, speech recognition, real-time learner analytics, and adaptive sequencing to adjust vocabulary drills, grammar explanations, and pronunciation feedback. In parallel, they provide engagement-enhancing features including gesture-based interaction, voice-triggered input, mobile-compatible platforms, and browser-based extensions. The proliferation of these tools—driven by easier mobile access and lightweight browser design—has made them available to a far wider audience. To sustain motivation, many integrate gamified elements such as performance streaks, unlockable content, and adaptive quizzes—design choices intended to generate measurable gains in time on task.

Proponents contend that AI-assisted learning addresses gaps in conventional language education by enabling targeted, form-focused practice with immediate feedback loops. Machine learning models can diagnose recurring errors in pronunciation, syntax, or usage and deliver remediation through micro-drills, rewriting tasks, or input enhancement. Some systems paraphrase or restructure learner output to model more native-like expressions. Critics, however, highlight the risk of algorithmic opacity: users are seldom informed of the rationale behind correction priorities, and training data may encode subtle linguistic or cultural biases. Feedback—though rapid—can appear impersonal, rigid, or demotivating when it contradicts classroom instruction or lacks contextual nuance.

Educational ministries across Southeast Asia are integrating AI into hybrid teaching models and national language programmes. Government-funded pilots of AI-based writing assessment have demonstrated notable efficiencies in scoring and rubric alignment. In these trials, ministries, teacher associations, and technical partners each contributed to refining the system’s accuracy. They also show that combining algorithmic suggestions with teacher moderation allows instructors to override or annotate automated recommendations, balancing scalability with pedagogical soundness. Nevertheless, concerns around transparency, accountability, and governance of learner data must be addressed to maintain inclusivity and instructional integrity.

Policy reviews show that most participating ministries reported sustained improvements in marking efficiency and reduced teacher workload after adopting AI–human moderation systems. This compromise preserves the speed of automation while retaining educators’ judgement. Its success depends on continued professional training and on regulations that safeguard learner privacy while clarifying the permissible scope of algorithmic decision-making. By embedding human expertise within robust governance structures, policymakers aim to create a scalable yet accountable model that serves diverse learner populations and sustains public trust.

Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 7 to 14.

AI-powered language learning tools are increasingly embedded in both classroom instruction and independent study, offering personalised pathways that respond to learner performance. These systems employ core adaptive mechanisms such as natural language processing, speech recognition, real-time learner analytics, and adaptive sequencing to adjust vocabulary drills, grammar explanations, and pronunciation feedback. In parallel, they provide engagement-enhancing features including gesture-based interaction, voice-triggered input, mobile-compatible platforms, and browser-based extensions. The proliferation of these tools—driven by easier mobile access and lightweight browser design—has made them available to a far wider audience. To sustain motivation, many integrate gamified elements such as performance streaks, unlockable content, and adaptive quizzes—design choices intended to generate measurable gains in time on task.

Proponents contend that AI-assisted learning addresses gaps in conventional language education by enabling targeted, form-focused practice with immediate feedback loops. Machine learning models can diagnose recurring errors in pronunciation, syntax, or usage and deliver remediation through micro-drills, rewriting tasks, or input enhancement. Some systems paraphrase or restructure learner output to model more native-like expressions. Critics, however, highlight the risk of algorithmic opacity: users are seldom informed of the rationale behind correction priorities, and training data may encode subtle linguistic or cultural biases. Feedback—though rapid—can appear impersonal, rigid, or demotivating when it contradicts classroom instruction or lacks contextual nuance.

Educational ministries across Southeast Asia are integrating AI into hybrid teaching models and national language programmes. Government-funded pilots of AI-based writing assessment have demonstrated notable efficiencies in scoring and rubric alignment. In these trials, ministries, teacher associations, and technical partners each contributed to refining the system’s accuracy. They also show that combining algorithmic suggestions with teacher moderation allows instructors to override or annotate automated recommendations, balancing scalability with pedagogical soundness. Nevertheless, concerns around transparency, accountability, and governance of learner data must be addressed to maintain inclusivity and instructional integrity.

Policy reviews show that most participating ministries reported sustained improvements in marking efficiency and reduced teacher workload after adopting AI–human moderation systems. This compromise preserves the speed of automation while retaining educators’ judgement. Its success depends on continued professional training and on regulations that safeguard learner privacy while clarifying the permissible scope of algorithmic decision-making. By embedding human expertise within robust governance structures, policymakers aim to create a scalable yet accountable model that serves diverse learner populations and sustains public trust.

Read the passage and mark the letter A, B, C or D on your answer sheet to indicate the best answer to each of the following questions from 7 to 14.

AI-powered language learning tools are increasingly embedded in both classroom instruction and independent study, offering personalised pathways that respond to learner performance. These systems employ core adaptive mechanisms such as natural language processing, speech recognition, real-time learner analytics, and adaptive sequencing to adjust vocabulary drills, grammar explanations, and pronunciation feedback. In parallel, they provide engagement-enhancing features including gesture-based interaction, voice-triggered input, mobile-compatible platforms, and browser-based extensions. The proliferation of these tools—driven by easier mobile access and lightweight browser design—has made them available to a far wider audience. To sustain motivation, many integrate gamified elements such as performance streaks, unlockable content, and adaptive quizzes—design choices intended to generate measurable gains in time on task.

Proponents contend that AI-assisted learning addresses gaps in conventional language education by enabling targeted, form-focused practice with immediate feedback loops. Machine learning models can diagnose recurring errors in pronunciation, syntax, or usage and deliver remediation through micro-drills, rewriting tasks, or input enhancement. Some systems paraphrase or restructure learner output to model more native-like expressions. Critics, however, highlight the risk of algorithmic opacity: users are seldom informed of the rationale behind correction priorities, and training data may encode subtle linguistic or cultural biases. Feedback—though rapid—can appear impersonal, rigid, or demotivating when it contradicts classroom instruction or lacks contextual nuance.

Educational ministries across Southeast Asia are integrating AI into hybrid teaching models and national language programmes. Government-funded pilots of AI-based writing assessment have demonstrated notable efficiencies in scoring and rubric alignment. In these trials, ministries, teacher associations, and technical partners each contributed to refining the system’s accuracy. They also show that combining algorithmic suggestions with teacher moderation allows instructors to override or annotate automated recommendations, balancing scalability with pedagogical soundness. Nevertheless, concerns around transparency, accountability, and governance of learner data must be addressed to maintain inclusivity and instructional integrity.

Policy reviews show that most participating ministries reported sustained improvements in marking efficiency and reduced teacher workload after adopting AI–human moderation systems. This compromise preserves the speed of automation while retaining educators’ judgement. Its success depends on continued professional training and on regulations that safeguard learner privacy while clarifying the permissible scope of algorithmic decision-making. By embedding human expertise within robust governance structures, policymakers aim to create a scalable yet accountable model that serves diverse learner populations and sustains public trust.