跟读练习: Explaining how AI is killing the PhD in less than 10 minutes - 通过视频学习英语口语
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The PhD, or Doctorate of Philosophy, is one of the most privileged and honorable degrees obtained today.
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About 2 -3 % of adults actually have a doctorate degree, according to U .S.
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Census data.
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While there are many types of PhDs that one can earn,
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the degree is well known for its long length of research and robust processes within the field of higher education.
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Additionally, a PhD focuses on theory and using future research from other studies to guide the next.
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Most individuals earn PhDs because they have goals to work in higher paying jobs that have large impacts on society.
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While there are multiple other reasons why individuals go for their doctor degrees, some jobs include leadership, industry research, and higher education at universities.
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But one of the biggest misconceptions about earning a PhD is that you're finished once when you get it.
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However, earning the degree simply signifies that you are now ready for the next big step, which in some cases means more research.
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Eventually, those with PhDs may have the opportunity to get their studies published in a journal article, and the more prestigious the journal article,
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the greater the reward in some cases, meaning money and status within the academia world.
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While going for a PhD is hard, and post -doctorate life can also be a challenge, why not use AI to make things easier?
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Well, after all, ever since the launch of ChatGBT, generative AI has the ability to find, synthesize, and even write the most robust components of research.
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AI has the potential to eliminate hours upon hours of grunt work that consume a researcher's time,
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which can be a lot for a doctoral candidate or somebody who tries to go for their PhD.
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But sometimes the cure can be worse than the disease,
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because before we explain why AI can potentially harm the process of earning a PhD in ethical future work,
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we need to explore the history of the dissertation process and how writers conducted research.
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Before computers, dissertations were a manual labor of intellect.
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Advisors were the central gatekeepers, and their approval determined the structured tone and even the acceptability of your ideas.
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The work was deeply theoretical, focused on philosophy, frameworks, and peer knowledge.
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Practical application was rare, and access to information was limited to what could be found in physical libraries or archives.
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Research meant travel, note cards, and long hours in dim reading rooms.
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This was an era where the dissertation was a momentum and not just a project.
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Once when the 1990s arrived, the dissertation changed rapidly.
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The arrival of computers revolutionized everything.
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Word processors like
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Microsoft Word replaced typewriters, making revision and formatting far much easier.
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Digital databases such as JSTOR, ProQuest, and EBSCO opened the doors to global research at the click of a button.
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Standardized formatting became non -negotiable and templates for APA, MLA, and Chicago style ensured every dissertation looked and felt uniform.
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Committees became more specialized, demanding stronger methodological rigor, and new approaches like mixed methods began merging qualitative and quantitative traditions.
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The dissertation was no longer just theoretical, it was evolving into something measurable, structured, and data -driven.
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By the 2010s, the computer had completely changed the dissertation forever, and submissions were electronic, archived in PDF format for online access.
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Collaborations became global, and students worked across time zones using tools like Mendeley, Google Docs, and Zotero.
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Research grew interdisciplinary, weaving together education, technology, psychology, and even business.
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Universities began emphasizing real -world impact.
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In fact, open access publishing and institutional repositories brought dissertations into public view, signaling a move toward democratized knowledge.
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The dissertation process has gone from a tedious process of handling library cards to a much more simple,
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stay at home process if you owned a computer.
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However, just when the dissertation process felt like it had changed so rapidly, another wave of change was about to hit.
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AI tools like ChatGBT, Elicit, and Cite began assisting students in organizing, summarizing, and synthesizing vast amounts of literature.
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What once took months of reading could now be mapped in hours.
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But this power came with responsibility.
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Universities introduced AI use declarations requiring transparency about how these tools were used.
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Ethical guidelines emphasized maintaining authorship, integrity, and providing that the ideas were still the students' own.
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Data -driven dissertations became the norm.
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Machine learning, analytics, and digital ethnography redefined what counts as research.
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At the same time, the students' voice became more important than ever.
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Institutions began expecting reflexivity, the ability to demonstrate authenticity, originality, and scholarly judgment.
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even with AI support.
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Peer review was also affected.
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AI -assisted editors like Grammarly, Trinka, and ChatGPT joined human advisors in collaborative feedback loops.
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Some universities even began accepting multi -model submissions, interactive dashboards, digital appendices, and visual models alongside the written text.
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Research is showing that businesses are requiring their workers to learn more about AI.
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Otherwise, workers are going to get left behind.
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And if that's not enough, we need to talk about the big elephant in the room.
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Plagiarism.
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AI has made research faster, but not necessarily better.
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Some doctoral students are beginning to rely too heavy on artificial intelligence to write their papers, summarize literature, or even generate arguments that they barely understand.
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When this happens, the line between researcher and algorithm begins to blur.
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If a machine does the synthesis, where does the scholarly's originality come from?
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Well, universities are struggling to find that balance.
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Jared Henderson created a video titled
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Everyone is Cheating, Even the Professors, which is about the rising trend of AI usage in academia.
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The video analyzes how AI creates more opportunities for students and professors to cheat by using AI.
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Henderson cites that universities are struggling to innovate meaningful policy that combat AI.
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Instead, he cites that Ohio State has drafted a policy that attempts to coexist with AI because AI is here to stay.
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So, where's the balance?
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AI tools can misrepresent sources, fabricate citations, and subtly shift the meaning of ideas.
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They can make writing look more polished than the student's true comprehension allows, and, in doing so, they create a new kind of inequality.
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Those who know how to use AI strategically appear more capable than those who don't, even if their understanding is the same.
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There's also a deeper, quieter issue, which is the loss of intellectual struggle.
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For generations, the challenge of writing a dissertation was what shaped a scholar's identity.
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It was through the long, messy process of writing, revising, and defending ideas that true expertise was formed.
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All those hours late in the library looking up research
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and being the detective has since been turned into writing a prompt through an AI site and getting everything you ever wanted.
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If AI shortcuts that process, what happens to the depth of our thinking?
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Are we producing scholars or simply managers of machine -generated ideas?
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That's the question universities, advisors, and students are now forced to confront.
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PhD students' reliance on AI for writing papers raises ethical concerns and may undermine critical thinking and analytical skills.
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A balanced use of AI is recommended to maintain academic integrity and intellectual rigor.
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Additionally, AI has very mixed feelings amongst professors.
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Some professors fear that their advisees may be relying too much on AI.
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In the case of some universities, comprehensible exams that require weeks' worth of writing have been permanently canceled due to the rise of AI.
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So, where do we go from here?
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AI is rewriting the rules of research, and with it, the very soul of the dissertation.
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For centuries, earning a PhD meant proving your endurance, your originality, and your ability to push the boundaries of human knowledge,
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which is true to this day still.
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But in 2025, we're standing at a crossroads where speed, convenience, and technology challenge what it means to think deeply.
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Artificial intelligence has given scholars the ability to analyze faster, write clearer, and publish sooner.
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But it also tempts us to skip the struggle that once defined academic excellence.
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Now isn't whether AI belongs in research, it's how far we're willing to let it go.
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Will the next generation of schools use AI to expand the human mind, or allow it to quietly replace the hard work that makes discovery meaningful?
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Because in the end, a dissertation is about using one's own voice to examine the gaps within the literature.
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It's an understatement to say that this conversation is just getting started.
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If you want to see where AI is taking the future of academia, subscribe for more content.
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本课的词汇与口语要点
这节口语课以视频“Explaining how AI is killing the PhD in less than 10 minutes”为素材。 视频中反复出现的词有：dissertation, Phd, universities, students, process。 这段视频共有 97 个句子、1426 个单词可供跟读。 讲话部分时长为 8:40。 说话人语速自然，每分钟约 165 个词，接近日常对话。 只有 74% 的单词属于英语最常用的 3,000 词，词汇难度较高。
视频中的重点词汇
视频中 15 个值得学习的单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| dissertation 名词 | /ˌdɪsəˈteɪʃən/ | 論文 /论文 |
| earn 动词 | /ɝn/ | 掙 /挣, 賺 /赚 |
| format 名词 | /ˈfɔːɹ.mæt/ | 格式, 開本 /开本 |
| ethical 形容词 | /ˈɛθɪkəl/ | 道德的 |
| advisor 名词 | /ædˈvaɪ.zə(ɹ)/ | 顧問 /顾问 |
| academia 名词 | /ˌæk.əˈdi.mi.ə/ | 學術界 /学术界, 學術圈 /学术圈 |
| deeply 副词 | /ˈdi(ː)pli/ | 深, 深深 |
| assist 动词 | /əˈsɪst/ | 幫 /帮, 幫助 /帮助 |
| rapidly 副词 | /ˈɹæpɪdli/ | 迅速地, 快速地 |
| intellectual 形容词 | /ˌɪntəˈlɛkt͡ʃʊəl/ | 智力的, 思想的 |
| rely 动词 | /ɹɪˈlaɪ/ | 依賴 /依赖, 依靠 |
| artificial 形容词 | /ˌɑː.tɪˈfɪʃ.əl/ | 人造的 |
| integrity 名词 | /ɪnˈtɛɡɹəti/ | 正直 |
| additionally 副词 | 並且 /并且, 而且 | |
| meaningful 形容词 | /ˈmiː.nɪŋ.fl̩/ | 有意義 /有意义 |
视频中出现的短语动词
| 单词 | 发音 | 释义 |
|---|---|---|
| leave behind 动词 | 遺棄 /遗弃 | |
| look up 动词 | /ˌlʊk ˈʌp/ | 仰, 上看 |
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | could be found · be mapped · were used |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | has gone · has made · has drafted |
| 定语从句 who / which + 从句 — 补充说明人或事物 | somebody who tries · Professors, which is · Those who know |
需要注意的发音
说话人用了 5 次缩略和弱读形式，例如 we're, don't, isn't。请按听到的简短形式来说。
- “th” 音: ethical /ˈɛθɪkəl/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, algorithm /ˈælɡəɹɪðm̩/, synthesis /ˈsɪnθəsɪs/, authenticity /ɑθɛnˈtɪsɪti/
- “sh” 和 “zh” 音: dissertation /ˌdɪsəˈteɪʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, publish /ˈpʌblɪʃ/, submission /səbˈmɪʃən/, potentially /pəˈtɛnʃ(ə)li/
- 长单词——注意重音位置: dissertation /ˌdɪsəˈteɪʃən/, academia /ˌæk.əˈdi.mi.ə/, intellectual /ˌɪntəˈlɛkt͡ʃʊəl/, artificial /ˌɑː.tɪˈfɪʃ.əl/, integrity /ɪnˈtɛɡɹəti/
中文母语者容易读错的音：
- /v/ — 不要读成 /w/，上齿轻触下唇: advisor /ædˈvaɪ.zə(ɹ)/, archive /ˈɑɹˌkaɪv/, vast /væst/, discovery /dɪˈskʌv.ɹi/, arrival /əˈɹaɪ.vl̩/
- 词尾辅音 — 要读清楚，后面不要加元音: format /ˈfɔːɹ.mæt/, assist /əˈsɪst/, generate /ˈd͡ʒɛn.ə.ɹeɪt/, publish /ˈpʌblɪʃ/, archive /ˈɑɹˌkaɪv/
- /r/ 和 /l/ 的区别: rapidly /ˈɹæpɪdli/, rely /ɹɪˈlaɪ/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, doctoral /ˈdɒktəɹəl/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 dissertation, earn, format 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。