쉐도잉 연습: Explaining how AI is killing the PhD in less than 10 minutes - 영상으로 영어 말하기 배우기
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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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이 레슨의 어휘와 말하기 포인트
이 말하기 레슨은 영상 “Explaining how AI is killing the PhD in less than 10 minutes”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: dissertation, Phd, universities, students, process. 이 영상에는 섀도잉할 문장 97개와 단어 1426개가 있습니다. 말하는 구간의 길이는 8:40입니다. 화자는 분당 약 165단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 74%뿐이라 어휘가 어려운 편입니다.
이 영상의 핵심 어휘
영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.
| 단어 | 발음 | 뜻 |
|---|---|---|
| earn 동사 | /ɝn/ | 벌다 |
| format 명사 | /ˈfɔːɹ.mæt/ | 포맷 |
| ethical 형용사 | /ˈɛθɪkəl/ | 윤리적, 륜리적 |
| advisor 명사 | /ædˈvaɪ.zə(ɹ)/ | 고문 |
| academia 명사 | /ˌæk.əˈdi.mi.ə/ | 학계 |
| deeply 부사 | /ˈdi(ː)pli/ | 깊이 |
| assist 동사 | /əˈsɪst/ | 돕다 |
| rapidly 부사 | /ˈɹæpɪdli/ | 급속(急速)히 |
| rely 동사 | /ɹɪˈlaɪ/ | 의지(依支)하다 |
| artificial 형용사 | /ˌɑː.tɪˈfɪʃ.əl/ | 인공, 인조 |
| meaningful 형용사 | /ˈmiː.nɪŋ.fl̩/ | 유의미(有意味)하다 |
| peer 명사 | /pɪɹ/ | 동배 |
| publish 동사 | /ˈpʌblɪʃ/ | 출판하다 |
| submission 명사 | /səbˈmɪʃən/ | 복종, 굴복 |
| archive 명사 | /ˈɑɹˌkaɪv/ | 아카이브, 보관소 |
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | could be found · be mapped · were used |
| 현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때 | has gone · has made · has drafted |
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | somebody who tries · Professors, which is · Those who know |
주의할 발음
화자는 we're, don't, isn't 같은 축약형과 약화된 형태를 5번 사용합니다. 들리는 대로 짧게 발음하세요.
- “th” 소리: ethical /ˈɛθɪkəl/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, algorithm /ˈælɡəɹɪðm̩/, synthesis /ˈsɪnθəsɪs/, authenticity /ɑθɛnˈtɪsɪti/
- “sh”와 “zh” 소리: dissertation /ˌdɪsəˈteɪʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, publish /ˈpʌblɪʃ/, submission /səbˈmɪʃən/, potentially /pəˈtɛnʃ(ə)li/
- 긴 단어 — 강세 위치에 주의: dissertation /ˌdɪsəˈteɪʃən/, academia /ˌæk.əˈdi.mi.ə/, intellectual /ˌɪntəˈlɛkt͡ʃʊəl/, artificial /ˌɑː.tɪˈfɪʃ.əl/, integrity /ɪnˈtɛɡɹəti/
한국어 화자가 어려워하는 소리:
- /f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: format /ˈfɔːɹ.mæt/, artificial /ˌɑː.tɪˈfɪʃ.əl/, meaningful /ˈmiː.nɪŋ.fl̩/, emphasize /ˈɛm.fə.saɪz/, defend /dɪˈfɪnd/
- /v/ — ㅂ(/b/)과 구별하기: advisor /ædˈvaɪ.zə(ɹ)/, archive /ˈɑɹˌkaɪv/, vast /væst/, discovery /dɪˈskʌv.ɹi/, arrival /əˈɹaɪ.vl̩/
- /z/ — ㅈ이 아니라 성대를 울리는 /s/: advisor /ædˈvaɪ.zə(ɹ)/, analyze /ˈæn.əˌlaɪz/, emphasize /ˈɛm.fə.saɪz/, exam /ɪɡˈzæm/, examine /ɪɡˈzæmɪn/
이 영상으로 연습하는 방법
- 먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
- 0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
- 자신의 목소리를 녹음해 원본과 비교하고, earn, format, ethical 같은 단어에 특히 주의합니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.