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01 · Anchor passage
A teacher used an AI summary as a draft but checked every claim against the original article.
/ə ˈtit͡ʃɚ just ən eɪ aɪ ˈsʌmɚi æz ə dɹæft bʌt t͡ʃɛkt ˈɛvɹi kleɪm əˈɡɛnst ðə ɚˈɪd͡ʒənəl ˈɑɹtɪkəl./
一位老师使用人工智能摘要作为草稿，但对照原始文章检查了每项主张。
A teacher used an — 保留结构，替换真实细节。At first, this looked like a small decision about artificial intelligence, but its result soon became visible.
/æt fɚst, ðɪs lʊkt laɪk ə smɔl dɪˈsɪʒən əˈbaʊt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns, bʌt ɪts ɹɪˈzʌlt sun bɪˈkeɪm ˈvɪzɪbəl./
起初，这看起来像是一个关于人工智能的小决定，但其结果很快就变得可见。
result — 保留结构，替换真实细节。The case raises a broader question: how automated systems create value and new responsibilities.
/ðə keɪs ˈɹeɪzɪz ə ˈbɹɔdɚ ˈkwɛst͡ʃən: haʊ ˈɔtoʊˌmeɪtɪd ˈsɪstəmz kɹiˈeɪt ˈvælju ænd nju ɹiˌspɑnsɪˈbɪlətiz./
该案提出了一个更广泛的问题：自动化系统如何创造价值和新的责任。
question — 保留结构，替换真实细节。A natural English sentence states the subject and action directly instead of copying Chinese word order.
/ə ˈnæt͡ʃɚəl ˈɪŋɡlɪʃ ˈsɛntəns steɪts ðə ˈsʌbd͡ʒɪkt ænd ˈækʃən dɪˈɹɛktli ɪnˈstɛd əv ˈkɑpiɪŋ t͡ʃaɪˈniz wɚd ˈɔɹdɚ./
自然的英语句子直接陈述主语和动作，而不是照搬中文词序。
instead of — 保留结构，替换真实细节。The example draws attention to a learning algorithm, which can shape how people respond to artificial intelligence.
/ðə ɪɡˈzæmpəl dɹɔz əˈtɛnʃən tə ə ˈlɚnɪŋ ˈælɡɚˌɪðəm, wɪt͡ʃ kən ʃeɪp haʊ ˈpipəl ɹɪˈspɑnd tə ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns./
这个例子提请注意一种学习算法，它可以决定人们如何响应人工智能。
learning algorithm — 保留结构，替换真实细节。A closer look at a workplace automation reveals a practical side of artificial intelligence that general opinions often miss.
/ə ˈkloʊsɚ lʊk æt ə ˈwɚkˌpleɪs ɔtoʊˈmeɪʃən ɹɪˈvilz ə ˈpɹæktɪkəl saɪd əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ðæt ˈd͡ʒɛnɚəl əˈpɪnjənz ˈɔftən mɪs./
仔细观察工作场所自动化，可以发现一般观点经常忽视的人工智能实用的一面。
workplace automation — 保留结构，替换真实细节。The case shows why a substantial effect matters when people make decisions about artificial intelligence.
/ðə keɪs ʃoʊz waɪ ə sʌbˈstænʃəl ɪˈfɛkt ˈmætɚz wɛn ˈpipəl meɪk dɪˈsɪʒənz əˈbaʊt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns./
案件表明，当人们做出关于人工智能的决定时，实质性效果为何重要。
substantial — 保留结构，替换真实细节。People usually notice the value of artificial intelligence only after a real event changes their routine.
/ˈpipəl ˈjuʒəli ˈnoʊtɪs ðə ˈvælju əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ˈoʊnli ˈæftɚ ə ɹil ɪˈvɛnt ˈt͡ʃeɪnd͡ʒɪz ðɛɹ ɹuˈtin./
人们通常只有在真实事件改变他们的日常生活之后才会注意到人工智能的价值。
usually — 保留结构，替换真实细节。When discussing artificial intelligence, people should explain why a clear explanation matters.
/wɛn dɪˈskʌsɪŋ ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns, ˈpipəl ʃʊd ɪkˈspleɪn waɪ ə klɪɹ ˌɛkspləˈneɪʃən ˈmætɚz./
在讨论人工智能时，人们应该解释清楚的解释为何重要。
clear — 保留结构，替换真实细节。A useful question about artificial intelligence focuses on what happened, why it matters, and what should happen next.
/ə ˈjusfəl ˈkwɛst͡ʃən əˈbaʊt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ˈfoʊkəsɪz ɔn wʌt ˈhæpənd, waɪ ɪt ˈmætɚz, ænd wʌt ʃʊd ˈhæpən nɛkst./
关于人工智能的一个有用的问题集中在发生了什么、为什么重要以及接下来应该发生什么。
question — 保留结构，替换真实细节。A concrete example of artificial intelligence makes the central idea easier to understand and evaluate.
/ə ˈkɑŋkɹit ɪɡˈzæmpəl əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns meɪks ðə ˈsɛntɹəl aɪˈdiə ˈiziɚ tə ˌʌndɚˈstænd ænd ɪˈvæljuˌeɪt./
人工智能的具体例子使中心思想更容易理解和评估。
example — 保留结构，替换真实细节。The meaning of artificial intelligence becomes clearer when people connect it to a real experience.
/ðə ˈminɪŋ əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns bɪˈkʌmz ˈklɪɹɚ wɛn ˈpipəl kəˈnɛkt ɪt tə ə ɹil ɪkˈspɪɹiəns./
当人们将人工智能与真实的经验联系起来时，人工智能的意义就变得更加明确。
meaning — 保留结构，替换真实细节。Personal experience is a useful starting point, but evidence about artificial intelligence must also include other groups and conditions.
/ˈpɚsənəl ɪkˈspɪɹiəns ɪz ə ˈjusfəl ˈstɑɹtɪŋ pɔɪnt, bʌt ˈɛvədəns əˈbaʊt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns mʌst ˈɔlsoʊ ɪŋˈklud ˈʌðɚ ɡɹups ænd kənˈdɪʃənz./
个人经验是一个有用的起点，但有关人工智能的证据还必须包括其他群体和条件。
personal experience — 保留结构，替换真实细节。A single example can show that a response is possible; it cannot prove that the response will work everywhere.
/ə ˈsɪŋɡəl ɪɡˈzæmpəl kən ʃoʊ ðæt ə ɹɪˈspɑns ɪz ˈpɑsəbəl; ɪt kæˈnɑt pɹuv ðæt ðə ɹɪˈspɑns wɪl wɚk ˈɛvɹiˌwɛɹ./
一个例子就可以表明响应是可能的；它不能证明这种应对措施在任何地方都有效。
example — 保留结构，替换真实细节。For that reason, responsible decisions define a goal, observe the result, and change direction when the evidence is weak.
/fɚ ðæt ˈɹizən, ɹiˈspɑnsɪbəl dɪˈsɪʒənz dɪˈfaɪn ə ɡoʊl, əbˈzɚv ðə ɹɪˈzʌlt, ænd t͡ʃeɪnd͡ʒ dɪˈɹɛkʃən wɛn ðə ˈɛvədəns ɪz wik./
因此，负责任的决策会定义目标，观察结果，并在证据薄弱时改变方向。
reason — 保留结构，替换真实细节。People can describe an experience when a real situation involving artificial intelligence requires action.
/ˈpipəl kən dɪˈskɹaɪb ən ɪkˈspɪɹiəns wɛn ə ɹil ˌsɪt͡ʃuˈeɪʃən ɪnˈvɑlvɪŋ ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ɹiˈkwaɪɚz ˈækʃən./
当涉及人工智能的真实情况需要采取行动时，人们可以描述一种体验。
describe an experience — 保留结构，替换真实细节。People can compare two responses to artificial intelligence only when they use the same criteria.
/ˈpipəl kən kəmˈpɛɹ tu ɹɪˈspɑnsɪz tə ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ˈoʊnli wɛn ðeɪ juz ðə seɪm kɹaɪˈtɪɹiə./
人们只有使用相同的标准才能比较两种对人工智能的反应。
compare — 保留结构，替换真实细节。A convincing view of artificial intelligence gives a reason and then tests it against a concrete example.
/ə kənˈvɪnsɪŋ vju əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ɡɪvz ə ˈɹizən ænd ðɛn tɛsts ɪt əˈɡɛnst ə ˈkɑŋkɹit ɪɡˈzæmpəl./
对人工智能的令人信服的观点给出了一个理由，然后用一个具体的例子来检验它。
example — 保留结构，替换真实细节。The result matters because it shows whether a response to artificial intelligence worked in practice.
/ðə ɹɪˈzʌlt ˈmætɚz bɪˈkʌz ɪt ʃoʊz ˈwɛðɚ ə ɹɪˈspɑns tə ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns wɚkt ɪn ˈpɹæktɪs./
结果很重要，因为它表明对人工智能的反应在实践中是否有效。
result — 保留结构，替换真实细节。Personal experience can introduce artificial intelligence, but a wider judgement also needs evidence.
/ˈpɚsənəl ɪkˈspɪɹiəns kən ˌɪntɹoʊˈdus ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns, bʌt ə ˈwaɪdɚ ˈd͡ʒʌd͡ʒmənt ˈɔlsoʊ nidz ˈɛvədəns./
个人经验可以引入人工智能，但更广泛的判断也需要证据。
personal experience — 保留结构，替换真实细节。The effect of artificial intelligence may vary according to a person's needs, resources, and circumstances.
/ðə ɪˈfɛkt əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns meɪ ˈvɛɹi əˈkɔɹdɪŋ tə ə ˈpɚsənz nidz, ˈɹisɔɹsɪz, ænd ˈsɚkəmˌstænsɪz./
人工智能的效果可能因人的需求，资源和情况而异。
effect — 保留结构，替换真实细节。However, an early success can hide a cost that appears later.
/ˌhaʊˈɛvɚ, ən ˈɚli səkˈsɛs kən haɪd ə kɔst ðæt əˈpɪɹz ˈleɪtɚ./
然而，早期的成功可能会掩盖后来出现的成本。
however — 保留结构，替换真实细节。This matters because the decision changes what people can do in ordinary life.
/ðɪs ˈmætɚz bɪˈkʌz ðə dɪˈsɪʒən ˈt͡ʃeɪnd͡ʒɪz wʌt ˈpipəl kən du ɪn ˈɔɹdɪˌnɛɹi laɪf./
这很重要，因为决定会改变人们在普通生活中所能做的事情。
because — 保留结构，替换真实细节。Although one response may help, no single solution will suit every person.
/ɔlˈðoʊ wʌn ɹɪˈspɑns meɪ hɛlp, noʊ ˈsɪŋɡəl səˈluʃən wɪl sut ˈɛvɹi ˈpɚsən./
尽管一种回应可能会有所帮助，但没有一种解决方案适合所有人。
although — 保留结构，替换真实细节。The most practical lesson is that artificial intelligence should be judged through real effects rather than attractive slogans.
/ðə moʊst ˈpɹæktɪkəl ˈlɛsən ɪz ðæt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ʃʊd bi d͡ʒʌd͡ʒd θɹu ɹil ɪˈfɛkts ˈɹæðɚ ðən əˈtɹæktɪv ˈsloʊɡənz./
最实际的教训是人工智能应该通过实际效果而不是有吸引力的口号来判断。
rather than — 保留结构，替换真实细节。Good decisions connect a clear purpose with evidence, human experience, and the willingness to revise a weak approach.
/ɡʊd dɪˈsɪʒənz kəˈnɛkt ə klɪɹ ˈpɚpəs wɪθ ˈɛvədəns, ˈhjumən ɪkˈspɪɹiəns, ænd ðə ˈwɪlɪŋnəs tə ɹɪˈvaɪz ə wik əˈpɹoʊt͡ʃ./
好的决定会把明确目标、事实证据、人的真实经验，以及修正薄弱方案的意愿联系起来。
clear — 保留结构，替换真实细节。When those parts stay connected, the topic becomes easier to discuss in daily conversation and in an IELTS answer.
/wɛn ðoʊz pɑɹts steɪ kəˈnɛktɪd, ðə ˈtɑpɪk bɪˈkʌmz ˈiziɚ tə dɪˈskʌs ɪn ˈdeɪli ˌkɑnvɚˈseɪʃən ænd ɪn ən aɪ ɛl ti ɛs ˈænsɚ./
当这些部分保持连贯时，无论日常交流还是 IELTS 回答，这个主题都会更容易展开。
When those parts stay — 保留结构，替换真实细节。A teacher used an AI summary as a draft but checked every claim against the original article.
/ə ˈtit͡ʃɚ just ən eɪ aɪ ˈsʌmɚi æz ə dɹæft bʌt t͡ʃɛkt ˈɛvɹi kleɪm əˈɡɛnst ðə ɚˈɪd͡ʒənəl ˈɑɹtɪkəl./
At first, this looked like a small decision about artificial intelligence, but its result soon became visible.
/æt fɚst, ðɪs lʊkt laɪk ə smɔl dɪˈsɪʒən əˈbaʊt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns, bʌt ɪts ɹɪˈzʌlt sun bɪˈkeɪm ˈvɪzɪbəl./
The case raises a broader question: how automated systems create value and new responsibilities.
/ðə keɪs ˈɹeɪzɪz ə ˈbɹɔdɚ ˈkwɛst͡ʃən: haʊ ˈɔtoʊˌmeɪtɪd ˈsɪstəmz kɹiˈeɪt ˈvælju ænd nju ɹiˌspɑnsɪˈbɪlətiz./
A natural English sentence states the subject and action directly instead of copying Chinese word order.
/ə ˈnæt͡ʃɚəl ˈɪŋɡlɪʃ ˈsɛntəns steɪts ðə ˈsʌbd͡ʒɪkt ænd ˈækʃən dɪˈɹɛktli ɪnˈstɛd əv ˈkɑpiɪŋ t͡ʃaɪˈniz wɚd ˈɔɹdɚ./
The example draws attention to a learning algorithm, which can shape how people respond to artificial intelligence.
/ðə ɪɡˈzæmpəl dɹɔz əˈtɛnʃən tə ə ˈlɚnɪŋ ˈælɡɚˌɪðəm, wɪt͡ʃ kən ʃeɪp haʊ ˈpipəl ɹɪˈspɑnd tə ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns./
A closer look at a workplace automation reveals a practical side of artificial intelligence that general opinions often miss.
/ə ˈkloʊsɚ lʊk æt ə ˈwɚkˌpleɪs ɔtoʊˈmeɪʃən ɹɪˈvilz ə ˈpɹæktɪkəl saɪd əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ðæt ˈd͡ʒɛnɚəl əˈpɪnjənz ˈɔftən mɪs./
The case shows why a substantial effect matters when people make decisions about artificial intelligence.
/ðə keɪs ʃoʊz waɪ ə sʌbˈstænʃəl ɪˈfɛkt ˈmætɚz wɛn ˈpipəl meɪk dɪˈsɪʒənz əˈbaʊt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns./
People usually notice the value of artificial intelligence only after a real event changes their routine.
/ˈpipəl ˈjuʒəli ˈnoʊtɪs ðə ˈvælju əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ˈoʊnli ˈæftɚ ə ɹil ɪˈvɛnt ˈt͡ʃeɪnd͡ʒɪz ðɛɹ ɹuˈtin./
When discussing artificial intelligence, people should explain why a clear explanation matters.
/wɛn dɪˈskʌsɪŋ ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns, ˈpipəl ʃʊd ɪkˈspleɪn waɪ ə klɪɹ ˌɛkspləˈneɪʃən ˈmætɚz./
A useful question about artificial intelligence focuses on what happened, why it matters, and what should happen next.
/ə ˈjusfəl ˈkwɛst͡ʃən əˈbaʊt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ˈfoʊkəsɪz ɔn wʌt ˈhæpənd, waɪ ɪt ˈmætɚz, ænd wʌt ʃʊd ˈhæpən nɛkst./
A concrete example of artificial intelligence makes the central idea easier to understand and evaluate.
/ə ˈkɑŋkɹit ɪɡˈzæmpəl əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns meɪks ðə ˈsɛntɹəl aɪˈdiə ˈiziɚ tə ˌʌndɚˈstænd ænd ɪˈvæljuˌeɪt./
The meaning of artificial intelligence becomes clearer when people connect it to a real experience.
/ðə ˈminɪŋ əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns bɪˈkʌmz ˈklɪɹɚ wɛn ˈpipəl kəˈnɛkt ɪt tə ə ɹil ɪkˈspɪɹiəns./
Personal experience is a useful starting point, but evidence about artificial intelligence must also include other groups and conditions.
/ˈpɚsənəl ɪkˈspɪɹiəns ɪz ə ˈjusfəl ˈstɑɹtɪŋ pɔɪnt, bʌt ˈɛvədəns əˈbaʊt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns mʌst ˈɔlsoʊ ɪŋˈklud ˈʌðɚ ɡɹups ænd kənˈdɪʃənz./
A single example can show that a response is possible; it cannot prove that the response will work everywhere.
/ə ˈsɪŋɡəl ɪɡˈzæmpəl kən ʃoʊ ðæt ə ɹɪˈspɑns ɪz ˈpɑsəbəl; ɪt kæˈnɑt pɹuv ðæt ðə ɹɪˈspɑns wɪl wɚk ˈɛvɹiˌwɛɹ./
For that reason, responsible decisions define a goal, observe the result, and change direction when the evidence is weak.
/fɚ ðæt ˈɹizən, ɹiˈspɑnsɪbəl dɪˈsɪʒənz dɪˈfaɪn ə ɡoʊl, əbˈzɚv ðə ɹɪˈzʌlt, ænd t͡ʃeɪnd͡ʒ dɪˈɹɛkʃən wɛn ðə ˈɛvədəns ɪz wik./
People can describe an experience when a real situation involving artificial intelligence requires action.
/ˈpipəl kən dɪˈskɹaɪb ən ɪkˈspɪɹiəns wɛn ə ɹil ˌsɪt͡ʃuˈeɪʃən ɪnˈvɑlvɪŋ ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ɹiˈkwaɪɚz ˈækʃən./
People can compare two responses to artificial intelligence only when they use the same criteria.
/ˈpipəl kən kəmˈpɛɹ tu ɹɪˈspɑnsɪz tə ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ˈoʊnli wɛn ðeɪ juz ðə seɪm kɹaɪˈtɪɹiə./
A convincing view of artificial intelligence gives a reason and then tests it against a concrete example.
/ə kənˈvɪnsɪŋ vju əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ɡɪvz ə ˈɹizən ænd ðɛn tɛsts ɪt əˈɡɛnst ə ˈkɑŋkɹit ɪɡˈzæmpəl./
The result matters because it shows whether a response to artificial intelligence worked in practice.
/ðə ɹɪˈzʌlt ˈmætɚz bɪˈkʌz ɪt ʃoʊz ˈwɛðɚ ə ɹɪˈspɑns tə ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns wɚkt ɪn ˈpɹæktɪs./
Personal experience can introduce artificial intelligence, but a wider judgement also needs evidence.
/ˈpɚsənəl ɪkˈspɪɹiəns kən ˌɪntɹoʊˈdus ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns, bʌt ə ˈwaɪdɚ ˈd͡ʒʌd͡ʒmənt ˈɔlsoʊ nidz ˈɛvədəns./
The effect of artificial intelligence may vary according to a person's needs, resources, and circumstances.
/ðə ɪˈfɛkt əv ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns meɪ ˈvɛɹi əˈkɔɹdɪŋ tə ə ˈpɚsənz nidz, ˈɹisɔɹsɪz, ænd ˈsɚkəmˌstænsɪz./
However, an early success can hide a cost that appears later.
/ˌhaʊˈɛvɚ, ən ˈɚli səkˈsɛs kən haɪd ə kɔst ðæt əˈpɪɹz ˈleɪtɚ./
This matters because the decision changes what people can do in ordinary life.
/ðɪs ˈmætɚz bɪˈkʌz ðə dɪˈsɪʒən ˈt͡ʃeɪnd͡ʒɪz wʌt ˈpipəl kən du ɪn ˈɔɹdɪˌnɛɹi laɪf./
Although one response may help, no single solution will suit every person.
/ɔlˈðoʊ wʌn ɹɪˈspɑns meɪ hɛlp, noʊ ˈsɪŋɡəl səˈluʃən wɪl sut ˈɛvɹi ˈpɚsən./
The most practical lesson is that artificial intelligence should be judged through real effects rather than attractive slogans.
/ðə moʊst ˈpɹæktɪkəl ˈlɛsən ɪz ðæt ˌɑɹtəˈfɪʃəl ɪnˈtɛlɪd͡ʒəns ʃʊd bi d͡ʒʌd͡ʒd θɹu ɹil ɪˈfɛkts ˈɹæðɚ ðən əˈtɹæktɪv ˈsloʊɡənz./
Good decisions connect a clear purpose with evidence, human experience, and the willingness to revise a weak approach.
/ɡʊd dɪˈsɪʒənz kəˈnɛkt ə klɪɹ ˈpɚpəs wɪθ ˈɛvədəns, ˈhjumən ɪkˈspɪɹiəns, ænd ðə ˈwɪlɪŋnəs tə ɹɪˈvaɪz ə wik əˈpɹoʊt͡ʃ./
When those parts stay connected, the topic becomes easier to discuss in daily conversation and in an IELTS answer.
/wɛn ðoʊz pɑɹts steɪ kəˈnɛktɪd, ðə ˈtɑpɪk bɪˈkʌmz ˈiziɚ tə dɪˈskʌs ɪn ˈdeɪli ˌkɑnvɚˈseɪʃən ænd ɪn ən aɪ ɛl ti ɛs ˈænsɚ./
02 · Vocabulary
n./phrase · 算法
a key idea used when discussing artificial intelligence
learning algorithm
n./phrase · 自动化
a key idea used when discussing artificial intelligence
workplace automation
adj. · 大量的；实质的
large in amount or importance
a substantial difference
adv. · 通常
in the way that happens most often
usually study
adj. · 清楚的
easy to hear or understand
a clear answer
n. · 问题
a sentence that asks for information
ask a question
n. · 例子
something that shows what you mean
give an example
n. · 意思
the idea expressed by a word
guess the meaning
v. · 描述
to say what someone or something is like
describe an experience
v. · 比较
to examine how things are similar or different
compare two options
n. · 原因
a cause or explanation
give a reason
n. · 结果
something that happens because of an action
as a result
n./v. · 经历；体验
knowledge gained by doing something
personal experience
n. · 影响；结果
a change caused by something
have an effect on
adv. · 然而
used to introduce a contrasting idea
however, some people
conj. · 因为
for the reason that
because of
conj. · 尽管
despite the fact that
although it is
03 · Chunks
learning algorithm算法；保留结构，替换一个真实细节。workplace automation自动化；保留结构，替换一个真实细节。a substantial difference大量的；实质的；保留结构，替换一个真实细节。usually study通常；保留结构，替换一个真实细节。a clear answer清楚的；保留结构，替换一个真实细节。ask a question问题；保留结构，替换一个真实细节。give an example例子；保留结构，替换一个真实细节。guess the meaning意思；保留结构，替换一个真实细节。04 · Grammar
这个结构负责把事实、原因、条件或立场组织成一个边界清楚的复杂句。
结构：main clause + logical relationship + supporting clause
原文证据：A natural English sentence states the subject and action directly instead of copying Chinese word order.
常见错误：先保证两个分句都完整，再追求句式复杂度；不要用连接词拼接残句。
你的替换：保留结构，把主题换成自己的工作、生活或学习经历。
05 · Pronunciation
练法：慢读单词 → 读完整搭配 → 回到原句；每一步只录一遍，听完再修正。
06 · Spiral review
Day 215、Day 213、Day 209、Day 202、Day 186
07 · Speak with AI
你是我的一对一英语口语教练。今天是 Day 216，主题是 artificial intelligence，程度目标是 B2。
先让我用自己的话复述今天的文章；一次只问一个问题，并等待我回答。
如果我卡住，只给 2–4 个关键词或一个句首，不要直接给完整答案。
请引导我使用这些词：algorithm, automation, substantial, usually, clear, question, example, meaning, describe。
我的回答至少包含：一个事实、一个原因、一个具体例子、一个结果或判断。
每轮结束后只纠正最影响理解的 1 个语法问题和 1 个发音问题，然后让我重新回答同一个问题。
最后让我脱稿回答：How is artificial intelligence connected to your real life, and what have you learned from it?
结束时用中文列出：做得好的证据、需要修正的两点、三个可复用句型、明天应复习的内容。08 · Imitation
必须回收
algorithm, automation, substantial, usually, clear, question, example, meaning, describe09 · Three-hour path