シャドーイング練習: Explaining how AI is killing the PhD in less than 10 minutes - 動画で英語スピーキングを学ぶ
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97 文
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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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このレッスンの語彙とスピーキングのポイント
このスピーキングレッスンは、動画「Explaining how AI is killing the PhD in less than 10 minutes」を教材にしています。 繰り返し出てくる語は次のとおりです：dissertation, Phd, universities, students, process。 この動画には、シャドーイング用の文が97文、単語が1426語あります。 音声の長さは8:40です。 話す速さは1分あたり約165語で、日常会話に近い自然なペースです。 英語の頻出3,000語に含まれる単語は74%だけなので、語彙は難しめです。
この動画の重要語彙
動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。
| 単語 | 発音 | 意味 |
|---|---|---|
| dissertation 名詞 | /ˌdɪsəˈteɪʃən/ | 学位論文, 博士論文 |
| earn 動詞 | /ɝn/ | 稼ぐ, 得る |
| format 名詞 | /ˈfɔːɹ.mæt/ | 書式, フォーマット |
| ethical 形容詞 | /ˈɛθɪkəl/ | 倫理的な |
| advisor 名詞 | /ædˈvaɪ.zə(ɹ)/ | 顧問, 相談役 |
| deeply 副詞 | /ˈdi(ː)pli/ | 深く |
| assist 動詞 | /əˈsɪst/ | 手伝う |
| intellectual 形容詞 | /ˌɪntəˈlɛkt͡ʃʊəl/ | 知的な |
| rely 動詞 | /ɹɪˈlaɪ/ | 頼る, あてにする |
| artificial 形容詞 | /ˌɑː.tɪˈfɪʃ.əl/ | 人工の |
| generate 動詞 | /ˈd͡ʒɛn.ə.ɹeɪt/ | 生む, 生み出す |
| meaningful 形容詞 | /ˈmiː.nɪŋ.fl̩/ | 有意義な |
| peer 名詞 | /pɪɹ/ | 同輩 |
| submission 名詞 | /səbˈmɪʃən/ | 服従, 恭順 |
| theoretical 形容詞 | /ˌθi.əˈɹɛt.ɪ.kəl/ | 理論的な |
動画に出てくる句動詞
| 単語 | 発音 | 意味 |
|---|---|---|
| leave behind 動詞 | 置き忘れる | |
| look up 動詞 | /ˌlʊk ˈʌp/ | 見上げる |
この動画の文法
話し手がよく使っている文型を、動画の実際の表現とともに紹介します。
| 文型 | 動画での表現 |
|---|---|
| 受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる | could be found · be mapped · were used |
| 現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している | has gone · has made · has drafted |
| 関係詞節 who / which + 節 — 人や物について情報を加える | somebody who tries · Professors, which is · Those who know |
注意したい発音
話し手はwe're, don't, isn'tなど、短縮形や弱形を5回使っています。聞こえたとおりの短い形で発音しましょう。
- 「th」の音: ethical /ˈɛθɪkəl/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, algorithm /ˈælɡəɹɪðm̩/, synthesis /ˈsɪnθəsɪs/, authenticity /ɑθɛnˈtɪsɪti/
- 「sh」と「zh」の音: dissertation /ˌdɪsəˈteɪʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, publish /ˈpʌblɪʃ/, submission /səbˈmɪʃən/, potentially /pəˈtɛnʃ(ə)li/
- 長い単語（アクセントの位置に注意）: dissertation /ˌdɪsəˈteɪʃən/, academia /ˌæk.əˈdi.mi.ə/, intellectual /ˌɪntəˈlɛkt͡ʃʊəl/, artificial /ˌɑː.tɪˈfɪʃ.əl/, integrity /ɪnˈtɛɡɹəti/
日本語話者が苦手な音：
- /r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: rapidly /ˈɹæpɪdli/, rely /ɹɪˈlaɪ/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, doctoral /ˈdɒktəɹəl/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/
- /v/ — /b/ にならないように、上の歯を下唇に当てる: advisor /ædˈvaɪ.zə(ɹ)/, archive /ˈɑɹˌkaɪv/, vast /væst/, discovery /dɪˈskʌv.ɹi/, arrival /əˈɹaɪ.vl̩/
- /f/ — 「フ」ではなく、上の歯と下唇で出す: format /ˈfɔːɹ.mæt/, artificial /ˌɑː.tɪˈfɪʃ.əl/, meaningful /ˈmiː.nɪŋ.fl̩/, emphasize /ˈɛm.fə.saɪz/, defend /dɪˈfɪnd/
この動画での練習方法
- まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
- まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
- 自分の声を録音して元の音声と比べます。dissertation, earn, formatなどの単語に特に注意しましょう。
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。