[태그:] AI Literacy

  • Parenting in the AI Era: Five Abilities Children Need Before a Good University

    Parenting in the AI Era: Five Abilities Children Need Before a Good University

    When an era arrives in which AI can study on a child’s behalf, what should parents leave their children with? In an interview with Jisik Inside, Professor Jo Byeok raises this question quite directly. If parents hold only to good universities, high scores, and more private education, they may fall one step behind the changes of the AI era.

    The point is not “let’s stop studying.” Basic knowledge is still necessary. But using all of a child’s time to chase correct answers is becoming increasingly risky. In an age when AI can find correct answers quickly, the ability to ask questions, build relationships, and interpret one’s own experiences becomes more important.

    Scene from an interview with Professor Jo Byeok
    Source: Screenshot from the Jisik Inside YouTube video

    Why a good-university strategy is no longer enough

    For Korean parents, the strategy for a child’s success has long been simple: get good grades, enter a good university, and secure a stable job. Professor Jo Byeok says this strategy was quite powerful in the past, but it may not work the same way in the AI era.

    The video includes a striking analogy. In a family photograph taken about 100 years ago, three brothers are in the same place at the same time, yet they look as if they are living in completely different eras. One is holding on to old symbols of success, while another has moved toward the education of a new age. The scene asks today’s parents the same question: Is the “good path” we are holding on to really the path our children will live on?

    Scene explaining changing times and educational choices
    Source: Screenshot from the Jisik Inside YouTube video

    1. Character is not etiquette; it is a capability in the AI era

    The first message in the video is that “character is also a capability.” Here, character does not simply mean being kind and polite. Professor Jo describes character as a uniquely human quality: communication, empathy, collaboration, and resilience, all human strengths that AI has difficulty replacing.

    In the past, when knowledge and skills came first, character was sometimes treated as an extra. But as AI increasingly handles knowledge processing and the search for correct answers, the situation changes. People still have to work with people and solve ambiguous problems together. That is why character is no longer merely “nice to have,” but a core competency that helps a child endure over the long term.

    2. The ability to ask questions is deeper than prompt technique

    As the AI era begins, many people say that we need to “ask good questions.” The ability to write good prompts is certainly necessary. But the questioning ability discussed in the video is broader than that. It is closer to a child taking ownership of their own learning.

    Children naturally ask many questions. But as they grow older, the number of questions decreases. That ability is suppressed by phrases such as “Don’t think about useless things; just study,” “Hurry up and do your homework,” and “Solve this problem first.” What parents need to do is not give children more answers, but restore an atmosphere in which it is safe to ask questions.

    Scene explaining questions and uniquely human abilities
    Source: Screenshot from the Jisik Inside YouTube video

    3. Future literacy is not the ability to predict the future

    Professor Jo does not describe “future literacy” as the ability to forecast the future. Instead, he describes it as the ability to create the future one wants to live in. This distinction is important.

    AI may be better at prediction. Reading data and patterns and calculating possible scenarios are AI strengths. But deciding what future we want, what life we will choose, and what relationships we will protect is the human role. That is why parents should help children become not “children who guess the right answer,” but “children who design their own future.”

    4. Unique matters more than best

    Admissions competition keeps children comparing themselves with others. The standards become who scored higher, who entered a better school, and who got ahead faster. But in the AI era, competitiveness does not have to come only from beating others.

    Professor Jo emphasizes “unique” over “best.” When a child has their own experiences, interests, questions, failures, and stories of recovery, they can become competitive without constantly competing. A person who quickly gives the same correct answer as everyone else will be compared with AI. But a person who sees problems from their own perspective and contributes through collaboration with others is not easily replaced.

    Scene explaining future literacy and questions
    Source: Screenshot from the Jisik Inside YouTube video

    5. A parent’s first question should be about feelings, not scores

    The final advice in the video is the most practical. When a child comes home from school, parents who ask “What did you learn today?” or “What score did you get?” may be moving against the AI era. A parent who instead asks “Did you have fun at school today?” connects with the child’s emotions.

    This does not mean giving up on study. It means restoring the child’s vitality and relationships first. The parent-child relationship is not a project that disappears once college admissions are over. It is a lifelong relationship. When that relationship feels safe, children ask more questions, explore farther, and stand up again even after failure.

    Interview scene explaining empathetic questions from parents
    Source: Screenshot from the Jisik Inside YouTube video

    Five questions parents can change today

    Parenting in the AI era is not something that has to wait for sweeping institutional reform. Parents can begin by changing the questions they use at home.

    1. Instead of “What score did you get today?” ask “What was the most interesting moment today?”
    2. Instead of “Why don’t you even know that?” ask “Where did it start to feel confusing?”
    3. Instead of “That dream is unrealistic,” ask “What experience would help you get closer to that dream?”
    4. Instead of “Everyone else is doing it, so why aren’t you?” ask “What would you like to try in your own way?”
    5. Instead of “Hurry up and say the correct answer,” ask “What would happen if we changed it into a different question?”

    When the question changes, the way a child brings out their own thoughts also begins to change little by little. It may feel awkward at first. As the video says, it is not so much difficult as unfamiliar.

    Recommended reading

    FAQ

    Will school study become less important in the AI era?

    Basic knowledge is still important. However, spending all available time only on score competition is risky. On top of basic learning, children also need to develop questioning ability, AI literacy, empathy, collaboration, and resilience.

    Does the character Professor Jo talks about mean only being nice?

    No. In the video, character is closer to a uniquely human quality. It includes abilities that AI has difficulty replacing, such as communication, empathy, collaboration, and resilience.

    How can parents support a child’s questions?

    Rather than giving the correct answer immediately, first listen to what kind of thinking led to the child’s question. If you create an atmosphere that welcomes questions without judging them, children can gradually regain ownership of their own learning.

    Is it wrong to aim for a good university?

    The goal itself is not wrong. The problem is treating a good university as the only strategy for success. In the AI era, what matters more than the name of the university is what questions a child can create, how they interpret their experiences, and what contribution they can make.

    References

    Original Korean article: Parenting and education in the AI era with Jo Byeok

    Image source: Captured images used in this article are stills from the original YouTube video. They are used for review, commentary, and educational explanation, and copyright remains with the original rights holders and the channel.

  • Metacognition in the AI Era: How to Check Your Thinking Before Trusting Smart Answers

    Metacognition in the AI Era: How to Check Your Thinking Before Trusting Smart Answers

    A bright illustration of a person looking at their own thinking from a step back in front of an AI screen and notebook
    Metacognition is the power to step outside your thoughts and look again at the state of your thinking.

    Before leaving work, you ask ChatGPT to draft a report. The answer comes quickly. The sentences look plausible. But something bothers you.

    “Is this right?”

    In the past, the ability to find answers mattered. Now it is different. Answers appear too easily. The problem is noticing whether I truly understand the answer, whether I can trust it, and whether I have adapted it to my situation.

    The needed ability here is metacognition. Simply put, metacognition is “knowing what I know and what I do not know.” It may sound like the secret of good students, but today it is becoming a basic capability for office workers, creators, educators, and AI users.

    ## Metacognition is the ability to look at thinking once more

    Metacognition sounds like a difficult psychology term, but in daily life it is familiar.

    When solving a problem, you may realize, “I thought I knew this concept, but I cannot explain it.” In a meeting, you may pause and ask, “Am I stating a fact or a guess?” While writing, you may notice, “The sentences are smooth, but the logic is empty.”

    All of these moments connect to metacognition. The core is stepping back. Do not remain trapped inside thought; look again at the state of your thinking.

    Metacognition is therefore not simple self-reflection. More precisely, it is a technique for adjusting judgment. It distinguishes what you know from what you do not know, checks the gap between confidence and evidence, and changes strategy when necessary.

    ## Why metacognition matters again now

    Metacognition is an old concept, but it has become important again because generative AI is changing our thinking process.

    In a 2025 CHI paper, researchers from Microsoft Research and Carnegie Mellon analyzed 936 generative-AI use cases from 319 knowledge workers. A notable result appeared: the more users trusted AI, the less critical thinking they tended to perform; the more confident they were in their own task, the more critical thinking they tended to perform.

    It would be too simple to read this as “AI makes people think less.” The more important message is that people who use AI well neither reject AI answers unconditionally nor accept them unconditionally. They verify answers, integrate them into their own context, and keep final responsibility.

    UNESCO also released AI competency frameworks for students and teachers in 2024. These frameworks treat AI not only as tool-use skill but as human-centered judgment and responsible use. Education is shifting from “Can you use AI?” to “Can you check your thinking with AI?”

    A bright illustration with a polished AI answer beside a missing puzzle piece and magnifying glass
    Plausible answers can help understanding, but they can also create the illusion of understanding.

    ## The illusion that grows as AI becomes smarter

    The biggest danger in the AI era is not only wrong answers. A subtler danger is the illusion that “I understood.”

    When you read text organized by AI, your head feels clearer. The summary is neat and examples are included. But when you try to explain it to someone, you may be unable to speak.

    At that moment, you may possess information without understanding it.

    Recent arXiv studies discuss similar concerns. AI can raise the level of individual creative output, but group-level diversity of ideas may decline. Long reasoning traces or explanations from LLMs can increase user confidence, but do not always improve actual task performance.

    Some of these papers are still preprints, so they should be read carefully. Still, the direction is clear: AI explanations can help understanding, but they can also create the feeling of understanding.

    That is why metacognition is needed. Do not ask only “Is the answer good?” Ask “To what level do I understand this answer?”

    A bright checklist illustration with icons for eyes, evidence checking, opposing views, pausing, and experiments
    Good questions lead us to check the evidence and gaps in our own judgment instead of simply trusting AI answers.

    ## Five questions that build metacognition

    Metacognition is not a matter of innate intelligence. It is closer to a habit. These five questions alone can improve the quality of thinking.

    ### 1. What am I mistaking as knowledge right now?

    The first thing to check is illusion. Familiar words feel known, but familiarity and understanding are different.

    A good method is one-sentence explanation. After reading a concept, explain it in one sentence as if to an elementary-school student. If you get stuck, it is not yet your knowledge.

    AI answers are the same. Do not copy them as-is; ask, “How would I say this in my own words?”

    ### 2. Does my confidence come from evidence or atmosphere?

    People trust content more easily when sentences are smooth. AI answers are especially like this. A confident tone, organized lists, and expert terms quickly create trust.

    Metacognition asks where confidence comes from. Is my certainty based on data, experience, authoritative sources, or merely plausible sentences?

    For work reports, sources must be checked. For investment, policy, and health topics, this matters even more.

    ### 3. Could opposing evidence change my judgment?

    When metacognition is weak, people protect their own thoughts. When it is strong, people test them.

    The same attitude is needed with AI. Ask, “What are the objections to this claim?” “Under what conditions could this conclusion be wrong?” and “How could this be interpreted from another perspective?” The quality of the answer changes.

    The point is not to add objections formally. Your judgment must be able to change in practice.

    ### 4. Am I looking for an answer, or do I want to stop thinking?

    The busier we are, the more we want answers. More precisely, we want to end thinking. AI satisfies this desire very well.

    The problem is that fast closure is dangerous for important judgments. Hiring, strategy, curriculum design, writing, and business planning do not end with one right answer. They contain context, purpose, and stakeholders.

    The metacognitive question is simple: “Do I need a conclusion now, or do I need exploration?” Distinguishing those moments is important.

    ### 5. Can I verify this with the next action?

    Good thinking becomes verifiable action. Metacognition is weak if it remains only internal reflection.

    If you wrote something, have one person read it. If you made a lecture plan, test it with a five-minute explanation. If AI recommended a strategy, try a small experiment first.

    When you move from “it seems right” to “let’s check it small,” thinking becomes real capability.

    A bright workflow illustration moving from drafting to AI review, source checking, and final judgment
    A good AI-use routine includes verification, reconstruction, and final judgment, not only fast answers.

    ## A metacognitive routine for work and learning

    Metacognition does not require grand training. Put it into the day as a short routine.

    Before starting work, write three things: what I know, what I do not know, and what I need to check. Before a meeting, write your assumptions. After a meeting, leave one line about what changed in your thinking.

    When using AI, the routine should be clearer:

    – First, write a short draft of your own.
    – Ask AI to improve it.
    – Separate facts, interpretations, and suggestions in the AI answer.
    – Mark parts that need sources.
    – Rewrite the final sentence with your own judgment.

    The order matters. If you hand everything to AI from the beginning, your own standard disappears. If you make your own draft first, AI becomes a checker rather than a replacement.

    ## Metacognition is a human speed in the AI era

    AI is fast. So we feel we must become faster. But not every thought should become faster.

    Important work needs slow zones: time to pause, doubt, explain again, and verify through small experiments.

    Metacognition protects that slow zone. It is not lazy hesitation; it is an intentional pause for better judgment.

    People who use AI well in the future will not only know many prompts. More important will be the ability to see the state of one’s own thinking: what I know, what I do not know, when to trust AI, and when to check again.

    That is metacognition. Today it is becoming central not only to study methods but also to how we work and learn.

    ## Further reading

    – [Human Value in the AI Era](https://www.thinknote.co.kr/ai-era-human-value/)
    – [What Will Winners Prepare in the AI Era?](https://www.thinknote.co.kr/ai-era-winner-preparation/)
    – [Creative Thinking Has Become More Important in the AI Era](https://www.thinknote.co.kr/creative-thinking-kim-jung-woon/)

    ## FAQ

    ### What is metacognition?

    Metacognition is the ability to notice what you know and do not know and adjust learning or judgment strategies accordingly. In simple terms, it is the ability to look at your own thinking once more.

    ### Does high metacognition help study?

    Generally, yes. People with strong metacognition find what they do not know quickly and can change learning methods. Knowing where to check is more important than simply studying longer.

    ### Why is metacognition important in the AI era?

    AI quickly gives plausible answers. Users may therefore think they understand things they do not understand. Metacognition helps verify AI answers and judge them again in one’s own context.

    ### How can metacognition be trained?

    The easiest method is a questioning habit: What do I know? What do I not know? What is the evidence? What is the opposing evidence? How can I test this in a small way?

    ### Does using AI weaken metacognition?

    Not always. If AI is used only as an answer provider, thinking may shrink. But if it is used for draft review, objections, source checking, and experiment design, it can strengthen metacognition.

    ## References

    – [Microsoft Research, The Impact of Generative AI on Critical Thinking, CHI 2025](https://www.microsoft.com/en-us/research/publication/the-impact-of-generative-ai-on-critical-thinking-self-reported-reductions-in-cognitive-effort-and-confidence-effects-from-a-survey-of-knowledge-workers/)
    – [UNESCO Digital Library, AI competency framework for students, 2024](https://unesdoc.unesco.org/ark:/48223/pf0000391105)
    – [UNESCO Digital Library, AI competency framework for teachers, 2024](https://unesdoc.unesco.org/ark:/48223/pf0000391104)
    – [arXiv, Individual Gain, Collective Loss](https://arxiv.org/abs/2606.05532)
    – [arXiv, Explaining Too Much?](https://arxiv.org/abs/2605.25856)
    – [arXiv, Guided Sensemaking](https://arxiv.org/abs/2606.02260)

    [Original Korean article](https://www.thinknote.co.kr/metacognition-ai-thinking-checklist/)

  • In the AI Era, What You Need to Learn Before Prompts Is Your Own Language

    In the AI Era, What You Need to Learn Before Prompts Is Your Own Language

    Where does the difference come from between people who use AI well and those who do not? Many answer, “Do they know good prompts?” In reality, the issue is different. The core is not a few prompt sentences, but whether I can say what I want with context and criteria included. The Ildangbaek video “Human intelligence expressed delicately through language: the beginning of AI prompt engineering” shows this point well. The starting point is the book *AI Language Lessons for Intellectual Conversation*, but the conversation is less a book introduction than a question about language sense in the AI era. For Korean users, the question is even more important: when talking with AI in a language rich in omission and nuance, what must we make clearer? ## The AI-use gap comes from “language resolution,” not tool operation
    A person structuring thoughts in a notebook beside an AI chat screen while preparing a prompt
    A good prompt begins with organizing thoughts and criteria before sentence technique.
    Many people first say to AI: “Organize this for me.” “Do not make it too long.” “Do not sound stiff.” “Do not draw an image; just show the prompt.” Between people, this often works because we read surrounding context, facial expressions, previous conversations, organizational culture, and tone. AI, however, guesses context the user did not provide. When the guess is right, it is convenient; when it is wrong, the result goes off track. Prompt guides from OpenAI and Anthropic commonly emphasize clear instructions, enough context, and the desired output format. A good prompt is not a magic sentence. It is a sentence that reduces what AI must guess. The key difference appears here. People who use AI well do not simply write longer questions. They structure context: purpose, reader, constraints, examples, preferences rather than bans, output format, and verification criteria. ## Why Korean is harder for AI
    A workshop scene arranging blank cards and notes to explain Korean context and nuance
    Korean omissions and nuance require clearer context explanation for AI.
    One of the video’s most interesting points is the high-context nature of Korean. Korean often omits subjects and objects. A single particle changes focus. Honorifics may be processed on the surface, but sarcasm and irony are different issues. For example, sentences equivalent to “Cheolsu went to school” with different Korean particles may look similar but have different focus. “It is okay” may mean acceptance or refusal. Mixed emotions such as relief and regret vary by context. Humans read these differences from the situation. AI mostly receives text. Korean users therefore need to provide more context. “Do it appropriately” is convenient for humans but information-poor for AI. This also appears in translation. Anthropic’s interpretability research suggests large language models can connect multilingual inputs to shared internal concept spaces. But that does not mean Korean nuance is perfectly preserved. Emotion, omission, irony, and speaker intention can be lost when moving between languages. ## Prompt engineering is not “asking well”; it is operating a system
    A meeting room reviewing AI-based workflow and quality-control procedures
    In organizations, prompt engineering goes beyond asking questions and becomes quality and operations design.
    The video distinguishes prompts from prompt engineering. Everyday users can ask AI conversationally. But business systems, customer service, automation, and content pipelines are different. Prompt engineering is not simply “the skill of asking beautifully.” It examines how models differ in response tendencies, analyzes why wrong answers appear, designs structures that reduce cost, connects multi-step work reliably, and controls consistency. Writing may require creativity, but customer notices or legal and policy guidance should not vary every time. In such cases, generation settings like temperature, example-based output, verification steps, and retry conditions are needed. Prompt engineering is therefore both a language skill and an operations skill. It starts from an individual’s questioning habits, but in organizations it expands into quality and cost management. ## “Do this” is stronger than “Do not do that”
    A work scene converting vague request cards into specific instruction cards
    For AI, it is more stable to give desired direction and criteria than only prohibitions.
    One practical tip repeated in the video is to use positive statements rather than negatives. “Use everyday words” is better than “Do not use technical terms.” “Write within three sentences per paragraph” is better than “Do not make it long.” “Write as a short explanatory passage” is clearer than “Do not use a list.” AI does not always handle negative instructions reliably. In image, video, or multimodal models, negative words can blur the desired result. Even in text models, “do not” can place the banned element at the center of context. | Common request | Better request | |—|—| | Do not write too difficultly | Use everyday words a middle-school student can understand | | Do not make it long | Explain only three key points within 600 characters | | Do not sound like AI | Mix short and long sentences and reduce repeated expressions | | Organize it for me | Organize it in the order of background, key issue, and action items | | Do not include subjective opinions | Separate confirmed facts from interpretation | This difference looks small, but the result changes greatly. Reducing the space for AI to guess reduces revision time. ## The productivity debate is about what you delegate, not how much you use AI
    A person reviewing an AI-generated draft with a checklist and field context
    AI productivity depends on the ability to divide what AI should handle from what people must judge.
    Opinions differ on whether AI truly improves productivity. Some research already observes concrete effects. NBER’s “Generative AI at Work” found that generative AI tools increased average productivity in customer support and especially helped less-experienced workers. The ILO’s analysis of generative AI and jobs, by contrast, suggests many jobs are more likely to have some tasks automated or assisted than to be fully replaced. This matches the video’s conclusion: AI does not simply eliminate all work; it redistributes components of work. The question is not “How much do you use AI?” It is whether you can decide what to delegate and what humans should judge. Simple summarization, drafting, format conversion, and repetitive replies are easy to delegate. Reading customer anxiety, judging field context, and cautiously confirming unspoken needs still remain heavily human. ## Five prompt principles for Korean users ### 1. Restore omitted subjects and objects Before writing “organize it,” state what should be organized, for whom, and for what purpose. Omission is natural in Korean conversation, but it becomes a blank for AI. ### 2. Turn negative statements into positive ones Say “Use a friendly but not exaggerated tone” rather than “Do not sound stiff.” Goals are more stable than bans. ### 3. Decide the output format first A table, list, paragraph, report, blog post, email, and presentation script are different outputs. Without a format, AI gives an average answer. ### 4. Separate context and criteria Give background as background, requirements as requirements, and verification criteria as verification criteria. If everything is mixed into one sentence, AI may miss priorities. ### 5. Do not try to finish in one turn Good AI use is closer to multi-turn collaboration than a single command. Receive a draft, strengthen criteria, revise again, and verify at the end. ## A prompt is ultimately a conversation habit, not just a technique UNESCO’s AI competency framework treats AI-era capability not as mere tool operation but as human-centered thinking, ethics, critical judgment, and practical use. Prompts are similar. They are not shortcuts to memorize. Talking with AI is a process of making your own thinking clearer. If you do not know what you want, AI does not know either. If you do not give criteria, AI produces an average. If you omit context, AI guesses. That is why the video’s core is deeper than “write better prompts.” Competitiveness in the AI era belongs not to people who know many technical tricks, but to people who can examine their own language and design context. In that sense, future AI literacy may be a language issue before it is a coding issue, especially for Korean users. The words we naturally omitted, the atmosphere we relied on, and the “do it appropriately” we handed over must all become sentences again in front of AI. ## Further reading – [Metacognition in the AI Era](https://www.thinknote.co.kr/metacognition-ai-thinking-checklist/) – [In the AI Agent Era, How Knowledge Workers Must Change](https://www.thinknote.co.kr/ai-agent-valuable-education/) – [Six Habits of People Who Get Smarter the More They Use AI](https://www.thinknote.co.kr/ai-smarter-use-six-habits/) ## References – [Ildangbaek video](https://youtu.be/OcmSp1Cn5rg?si=LQk_SQmJmDuqaETH) – [OpenAI, Prompt engineering](https://platform.openai.com/docs/guides/prompt-engineering) – [Anthropic, Prompt engineering overview](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) – [Anthropic, Tracing the thoughts of a large language model](https://www.anthropic.com/research/tracing-thoughts-language-model) – [Brynjolfsson, Li, and Raymond, Generative AI at Work](https://www.nber.org/papers/w31161) – [International Labour Organization, Generative AI and Jobs](https://www.ilo.org/publications/generative-ai-and-jobs-global-analysis-potential-effects-job-quantity-and-quality) – [UNESCO, AI competency framework for teachers](https://www.unesco.org/en/articles/ai-competency-framework-teachers) ## FAQ ### Are Korean prompts disadvantaged compared with English prompts? Not always. But Korean relies heavily on omission, particles, honorifics, and context, so AI often has to guess intention. When writing in Korean, it is better to state situation and criteria more clearly. ### Do I have to learn prompt engineering? Everyday users do not need grand engineering. But if you use AI for work, you need the basic habit of giving purpose, context, output format, and verification criteria. ### Why does “do not do this” often fail with AI? Negative statements place the prohibited object inside the context. Some models do not reliably reflect the ban. It is usually better to specify the desired behavior in positive terms. ### Can AI writing be made to feel human? To some degree. Adjusting sentence length, repeated expressions, omissions, rhythm, and concrete situations can reduce a mechanical feel. But AI does not possess real experience or judgment. ### What should humans do in the AI era? Humans must interpret context, set criteria, and make final judgments. Customer emotion, field situations, organizational tacit knowledge, and ethical judgment remain difficult to standardize fully. [Original Korean article](https://www.thinknote.co.kr/ai-korean-prompt-literacy/)

    Image source: Captured images used in this article are stills from the original YouTube video. They are used for review, commentary, and educational explanation, and copyright remains with the original rights holders and the channel.

  • In the AI Era, What You Need to Learn Before Prompts Is Your Own Language

    In the AI Era, What You Need to Learn Before Prompts Is Your Own Language

    The difference between people who use AI well and those who do not—where does it come from? People often answer, “It depends on whether you know good prompts.” In reality, it is a little different. The core issue is not a handful of prompt sentences. It is whether I can say what I want while also including the context and criteria behind it.

    The Ildangbaek video “Human Intelligence Expressed Delicately Through Language! The Beginning of AI Prompt Engineering” illustrates this point well. The video begins with the book AI Language Lessons for Intellectual Conversation, but rather than being a simple book introduction, it is closer to a conversation that asks what kind of language sense we need in the AI era. For Korean-language users in particular, there is an even more important question: when we talk with AI in a language like Korean, where omissions and nuance are common, what do we need to say more clearly?

    The AI Usage Gap Comes Less from “How to Use the Tool” Than from the “Resolution of Language”

    A person preparing a prompt by structuring ideas in a notebook beside an AI chat screen
    A good prompt begins not with sentence technique, but with organizing your thoughts and criteria.

    When many people first use AI, they say things like this:

    “Just organize this for me.” “Don’t make it too long.” “Don’t use a stiff tone.” “Don’t draw an image—just show me the prompt.”

    Between people, this level of instruction usually works to some extent. That is because we read the surrounding situation, facial expressions, prior conversations, organizational culture, and tone of voice together. But AI guesses the context the user has not provided. If the guess is right, it feels convenient. If it is wrong, the result becomes completely off target.

    The prompt guides from OpenAI and Anthropic both emphasize “clear instructions, sufficient context. The desired output format.” Ultimately, a good prompt is not a magic sentence. It is a sentence that reduces the parts AI has to guess.

    This is where an important difference appears. People who use AI well are not necessarily people who write longer questions. They are people who structure context. They provide purpose, audience, constraints, examples, preferences rather than only prohibitions, output format, and validation criteria together.

    Why Korean Is a More Difficult Language for AI

    A workshop scene with blank cards and notes arranged to explain Korean context and nuance
    Korean’s omissions and nuances require clearer explanations of context when working with AI.

    One of the most interesting points in the video is the high-context nature of Korean. Korean frequently omits subjects and objects. A single particle can change the focus of a sentence. Honorifics may be handled reasonably well at the surface level. Sarcasm and irony are entirely different matters.

    For example, “Cheolsu-neun went to school” and “Cheolsu-ga went to school” may look similar, but their focus is different. “It’s okay” can mean that something is truly okay, or it can mean refusal. “Siwon-seopseop-hada”—a Korean expression that combines feeling refreshed or relieved with feeling sad or regretful—has a different ratio of relief to regret depending on the situation.

    People read these differences through the situation. AI mostly receives them as text. That is why Korean users need to provide AI with more context. “Take care of it” is convenient, but from AI’s point of view it is an instruction with too little information.

    This issue also appears in translation. Anthropic’s interpretability research shows that large language models can connect inputs from multiple languages to a shared internal conceptual space. But that does not mean Korean nuance is perfectly preserved. In the movement between languages, emotion, omission, irony, and the speaker’s intent can be lost.

    Prompt Engineering Is Not “Asking Good Questions”; It Is Managing a System

    A meeting-room scene reviewing an AI-based workflow and quality control process
    In organizations, prompt engineering goes beyond asking better questions and becomes a matter of quality and operational design.

    The video distinguishes between prompts and prompt engineering. Everyday users can simply ask questions as if they were talking with AI. But the story changes in work systems, customer service, automation, and content production pipelines.

    Prompt engineering is not simply “the skill of asking pretty questions.” It involves looking at how answer tendencies differ from model to model. It analyzes why wrong answers emerged. It designs structures that reduce cost. It connects multi-step tasks reliably. It controls the consistency of results.

    For example, writing requires creativity, but customer guidance copy or legal and policy guidance becomes problematic if it changes every time. In these cases, generation settings such as temperature, example-based output, validation steps, and retry conditions are needed.

    In other words, prompt engineering is a language skill and, at the same time, an operational skill. It begins with an individual’s way of asking questions. In organizations it expands into quality management and cost management.

    “Do It This Way” Is Stronger Than “Don’t Do That”

    A work scene in which vague request cards are organized and converted into specific instruction cards
    For AI, it is more stable to specify the desired direction and criteria than to state only prohibitions.

    One practical tip repeated in the video is to use positive statements rather than negative ones. “Use everyday words” is better than “Don’t use technical terms.” “Keep each paragraph to three sentences or fewer” is better than “Don’t write too much.” “Write it as a short explanatory passage” is clearer than “Don’t write it as a list.”

    AI does not always process a user’s negative phrasing reliably. In image, video, and multimodal models in particular, negative words can blur the desired result. Even in text models, saying “don’t do this” can sometimes place the prohibited element at the center of the context.

    At work, it is better to change requests like this:

    Common requestBetter request
    Don’t write it too difficult.Write it in everyday language that a middle school student can understand.
    Don’t make it long.Explain only the three core points within 600 Korean characters.
    Don’t make it sound like AI.Mix short and long sentences, and reduce repeated expressions.
    Just organize it for me.Organize it in the order of background, key issues, and action items.
    Don’t include subjective opinions.Separate verified facts from interpretation.

    This difference may look small, but the results change significantly. When you reduce the room AI has to guess, you reduce the time you spend revising.

    The Core of the AI Productivity Debate Is Not “How Much You Used It” but “What You Delegated”

    A scene reviewing an AI-generated draft with a human checklist and field context
    AI productivity depends on the ability to decide what to delegate and what humans should judge.

    Opinions differ on whether AI actually increases productivity. Still, some studies have already observed concrete effects. The NBER paper “Generative AI at Work” found. Generative AI tools increased average productivity in customer support work, with especially large effects for less experienced employees.

    By contrast, the ILO’s analysis of generative AI and jobs suggests. Many occupations are more likely to see some tasks automated or supported than to be completely replaced. This perspective also connects with the video’s conclusion. AI does not necessarily eliminate all work; rather, it redivides the components of work.

    The question is not “Do you use AI a lot?” It is the ability to decide what to delegate and what humans should judge. Simple summaries, drafts, format conversions, and repeated responses are easy to delegate to AI. But reading a customer’s anxious feelings, judging field context. Carefully confirming unspoken needs are still largely human responsibilities.

    Five Prompt Principles for Korean-Language Users

    1. Restore the Omitted Subject and Object

    Before writing “Organize this,” write what should be organized, for whom, and for what purpose. In Korean conversation, omission is natural, but for AI it becomes a blank space.

    2. Turn Negative Sentences into Positive Sentences

    Instead of saying “Don’t write in a stiff way,” say “Write in a friendly but not exaggerated tone.” Giving a goal is more stable than giving only a prohibition.

    3. Decide the Output Format First

    A table, list, paragraph, report, blog post, email, and presentation script are all different outputs. If you do not set the format, AI produces an average answer.

    4. Provide Context and Criteria Separately

    Separate the background as background, requirements as requirements, and validation criteria as validation criteria. If you mix everything into one sentence, AI can also miss the relative importance.

    5. Do Not Try to Finish Everything in One Turn

    Good AI use is closer to multi-turn collaboration than to a single turn. Receive a draft, strengthen the criteria, revise it again, and validate it at the end. This is not a command; it is collaboration.

    In the End, Prompts Are Not a Technique but a Habit of Conversation

    UNESCO’s AI competency framework sees the abilities needed in the AI era not as simple tool usage. As human-centered thinking, ethics, critical judgment, and practical application. Prompts are the same. They are not something to memorize like keyboard shortcuts.

    Talking with AI is a process of making my own thinking clearer. If I do not know what I want, AI does not know either. If I do not provide criteria, AI produces an average value. If I omit context, AI guesses.

    That is why the core of the video goes deeper than “Let’s write better prompts.” Competitiveness in the AI era comes not to people who know a lot of techniques. To people who can examine their own language and design context.

    To put it a little strongly, future AI literacy may be a language issue before it is a coding issue. This is especially true for Korean-language users. The words we naturally omitted, the things we passed over through atmosphere. The tasks we handed off by saying “take care of it” must all become sentences again in front of AI.

    Recommended Reading

    References

    • Ildangbaek, “Human Intelligence Expressed Delicately Through Language! The Beginning of AI Prompt Engineering,” YouTube, View source
    • OpenAI, “Prompt engineering,” View source
    • Anthropic, “Prompt engineering overview,” View source
    • Anthropic, “Tracing the thoughts of a large language model,” View source
    • Erik Brynjolfsson, Danielle Li, Lindsey R. Raymond, “Generative AI at Work,” NBER Working Paper No. 31161, View source
    • International Labour Organization, “Generative AI and Jobs,” View source
    • UNESCO, “AI competency framework for teachers,” View source

    FAQ

    Are Korean Prompts at a Disadvantage Compared with English Prompts?

    It is not accurate to say they are always at a disadvantage. However, Korean relies heavily on omission, particles, honorifics, and context. AI often has to guess the user’s intent. That is why, when writing in Korean, it is better to state the situation and criteria more clearly.

    Do I Really Need to Learn Prompt Engineering?

    Everyday users do not need to learn grand, formal engineering. But if you use AI for work, you do need the basic habit of providing purpose, context, output format, and validation criteria.

    Why Does Telling AI “Don’t Do That” Often Fail?

    Negative sentences place the prohibited object inside the context. Some models do not reliably reflect the intention behind the prohibition. That is why it is better to specify the desired behavior positively rather than saying only “don’t.”

    Can AI-Written Text Be Made to Sound Human?

    To some extent, yes. Adjusting sentence length, repeated expressions, subject and object omission, inversion, rhythm. Concrete situations can reduce the mechanical feeling. However, AI does not actually possess real experience or judgment on your behalf.

    What Work Should Humans Take On in the AI Era?

    Humans should interpret context, set criteria, and make final judgments. Areas that are difficult to fully standardize in words—such as customer emotions, field situations, an organization’s tacit knowledge. Ethical judgment—still depend heavily on human roles.

    Original Korean article: https://www.thinknote.co.kr/ai-korean-prompt-literacy/

    Image source: Captured images used in this article are stills from the original YouTube video. They are used for review, commentary, and educational explanation, and copyright remains with the original rights holders and the channel.

  • Listening to the Universe with Radio Telescopes

    Listening to the Universe with Radio Telescopes

    This English version of the article is a fuller translation and adaptation of the original Korean article, “AI 취업 공포가 던진 질문: 신입 채용 시장에서 무엇을 준비해야 할까”, for global readers. The article delves into the anxiety surrounding the job market due to the impact of Artificial Intelligence (AI) on employment, particularly for new graduates. It explores the changing landscape of job requirements, the need for adaptability, and the skills necessary to thrive in an AI-driven economy.

    AI job market anxiety for graduates
    AI job market anxiety for graduates.

    Original Korean article: AI 취업 공포가 던진 질문: 신입 채용 시장에서 무엇을 준비해야 할까

    Background of Growing AI Job Market Anxiety

    The article begins by citing a report from KBS News on May 29, 2026, which highlights the challenges faced by graduates from prestigious universities in the United States in securing jobs in the tech industry. This trend is not limited to the US, as it also affects students, job seekers, and educators in Korea, raising questions about the skills required to succeed in the job market.

    The shift in the job market is attributed to the increasing use of AI, which has led to structural changes, reduced hiring, and cost-cutting measures in the tech industry. While having a degree in computer science was once a strong signal for securing a job in the tech industry, the landscape has changed, and the ability to work with AI has become a crucial factor.

    entry level hiring in the AI era
    entry level hiring in the AI era.

    Change in Entry Barriers Rather Than Replacement

    According to Goldman Sachs, generative AI could impact around 300 million jobs worldwide. However, this does not necessarily mean that all these jobs will disappear. Instead, many jobs will undergo changes, with some tasks being automated, and new ones emerging. The challenge lies in the fact that new graduates lack a proven track record, making it essential for them to demonstrate their ability to work with AI tools and produce results quickly.

    The article emphasizes that the focus should be on the change in entry barriers rather than replacement. While experienced professionals can rely on their existing performance and domain knowledge, new graduates need to demonstrate their ability to work with AI tools and produce results quickly.

    AI skills and career preparation
    AI skills and career preparation.

    Combination of Skills Rather Than a Single Major

    A student featured in a video mentions that they are double-majoring in computer science and accounting to connect technology with real-world business problems. This approach highlights the importance of combining skills and knowledge from different fields to succeed in the AI-driven economy.

    The article suggests that having a single major is no longer sufficient; instead, the ability to combine skills and knowledge from different fields, such as accounting, manufacturing, education, healthcare, and public administration, is becoming increasingly important. The focus should be on understanding real-world problems and being able to structure them using AI.

    college education and AI literacy
    college education and AI literacy.

    Social Issue 1: Youth Anxiety is Not Just a Personal Problem

    The article argues that viewing AI job market anxiety as a personal problem due to a lack of effort is misguided. The promise of a university degree leading to a stable job is weakening, and young people are being asked to acquire more skills and qualifications while companies demand more productivity with fewer employees.

    This creates a social issue, as university education is still focused on imparting knowledge in a specific major, while the job market requires skills such as project execution and AI utilization. Shifting the burden solely to individuals will only exacerbate anxiety.

    new graduate portfolio strategy
    new graduate portfolio strategy.

    Social Issue 2: AI Gap Becomes an Employment Gap

    The article highlights that the difference between those who can use AI tools effectively and those who cannot will result in a productivity gap. This gap can widen due to disparities in access to education, practice environments, and mentorship.

    Therefore, AI education should go beyond just coding skills and include the ability to break down questions, verify data, critically revise results, and design automation that fits the work context.

    Social Issue 3: Focusing Only on Disappearing Jobs Misses New Opportunities

    The article notes that while AI may lead to job displacement in some areas, it also creates new opportunities in fields such as data centers, semiconductors, power, cooling, security, networks, education, consulting, and regulatory compliance.

    Instead of focusing solely on whether to join an AI company, individuals should consider what new bottlenecks are emerging in their industry due to AI and position themselves to address these challenges.

    5 Skills for Individuals to Prepare

    The article outlines five essential skills for individuals to prepare for the AI-driven job market:

    • AI tool utilization: applying tools such as search, summary, coding, documentation, and data cleaning to real-world tasks
    • Domain understanding: connecting major knowledge to real-world problems
    • Verification ability: checking AI results for errors, biases, and sources
    • Work design ability: dividing repetitive tasks between AI and human roles
    • Communication ability: explaining AI-generated outputs in the organization’s language

    What Universities and Organizations Need to Change

    Universities should not view AI utilization solely as a means of preventing academic misconduct. Instead, they should teach students how to use AI in their major courses, how to verify results, and how to take responsibility for their outputs.

    Companies and public organizations should also change their approach to hiring and education. Rather than simply asking if a candidate has experience with AI, they should provide real-world data and ask them to define problems, design prompts, verify results, and write reports.

    Conclusion: Transition Strategy Over Fear

    The article concludes that while AI job market anxiety is real, it is essential to focus on developing a transition strategy rather than simply being fearful. The key question should be “What problems can I solve better with AI?” rather than “Will AI take my job?”

    What young people need is not just a collection of specs, but a practical portfolio that demonstrates their ability to connect their major with AI and real-world problems. Universities and organizations also have a clear role to play in redesigning their approach to education and work.

    Related Reading

    Continue with these related Thinknote English articles in the Digital Transformation cluster.

    FAQ

    What is this article about?

    This article explains a digital transformation, platform, market-structure, or technology-adoption topic with Korea-specific context and global implications.

    How should I use this guide?

    Use it to understand market signals and strategic patterns. Combine it with current market data before making business or investment decisions.

    Where can I read the original Korean article?

    The original Korean article is available here: AI Job Market Anxiety: What New Graduates Should Prepare For.

  • How to Prepare for the AI Era: Literacy, Judgment, and Human Value

    How to Prepare for the AI Era: Literacy, Judgment, and Human Value

    This English version is a fuller translation and adaptation of the original Korean article, “AI 시대의 승자는 무엇을 준비할까? 세바시 강연 6편에서 뽑은 핵심,” for global readers. The article explores the essential skills and mindset required to thrive in the AI era, based on a collection of lectures by six experts. As AI becomes a fundamental tool for work, study, and creativity, the true difference lies in the ability to read the changing flow, redefine problems, and create value that resonates with people.

    prepare for the AI era
    prepare for the AI era.

    Original Korean article: AI 시대의 승자는 무엇을 준비할까? 세바시 강연 6편에서 뽑은 핵심

    Winners in the AI Era Read the Structure of Change

    According to Jang Dong-seon, change is not just about the emergence of new products, but about altering people’s behavior, relationships, and social systems. The true power of change lies in its ability to transform these fundamental aspects of human society. In the context of AI, it’s essential to look beyond the surface level of new tools and technologies and understand the underlying structure of change.

    Direction of Change is More Important than Tool Names

    The names of AI tools are constantly changing, and what’s trendy today may become a basic function tomorrow. Instead of asking “which tool should I learn,” it’s more important to ask: What behavior does this technology make easier? Why do people choose this technology? What assumptions in my work are being challenged? What new expectations will customers, colleagues, and organizations have as a result of this change? Winners in the AI era focus on understanding the structure of change rather than just following new features.

    AI literacy and future scenarios
    AI literacy and future scenarios.

    In an Uncertain Future, Multiple Scenarios are Necessary

    Seo Yong-seok describes the current era as one of “super uncertainty,” characterized by climate crises, geopolitical conflicts, technological shocks, and economic changes. In such an environment, making definitive predictions about the future can be hazardous. Instead, it’s essential to develop the ability to imagine multiple possible futures and prepare for various scenarios.

    Future Literacy is the Ability to Reduce Shock

    Future literacy is not about predicting the future accurately but about being able to imagine multiple possible futures and prepare for them. This ability is crucial for individuals and organizations to navigate the complexities of the AI era. By developing future literacy, we can reduce the shock of unexpected events and create a more resilient and adaptable mindset.

    human relationships in the AI era
    human relationships in the AI era.

    AI Proximity Increases the Importance of Human Relationship Safety Nets

    Kim Sang-gyun highlights the potential for people to become emotionally dependent on AI characters and conversational technologies. As AI becomes more natural and responsive, we may start to see it as a relationship partner rather than just a machine. However, this can lead to a weakening of human relationships if we rely too heavily on AI for emotional support.

    AI Utilization Ability Includes Boundary Sense

    While AI can be useful for providing comfort, advice, and conversation, it’s essential to maintain a sense of boundaries and not rely solely on AI for emotional support. In the workplace, AI can assist with tasks, but human judgment, responsibility, and trust-building are still essential. A strong safety net in the AI era requires a combination of technological proficiency, boundary sense, and human relationships.

    problem solving with AI tools
    problem solving with AI tools.

    Literacy is the Basic Fitness for the AI Era

    Lee Jung-mo emphasizes that literacy is not just about reading texts but about understanding information, connecting contexts, and evaluating the validity of explanations. In an era where AI can generate answers quickly, literacy is more crucial than ever. It’s essential to develop the ability to critically evaluate AI-generated content and ask questions like: What is the basis for this answer? Are there any missing conditions? Are there alternative interpretations?

    Answer-Receiving Ability is Less Important than Answer-Judging Ability

    AI can produce plausible sentences rapidly, but that doesn’t mean they are always accurate or relevant. It’s essential to develop the ability to judge answers critically, considering factors like context, assumptions, and potential biases. By doing so, we can use AI-generated content as a starting point for further inquiry and exploration.

    AI era checklist for work and learning
    AI era checklist for work and learning.

    AI is a Problem-Solving Tool, Not a Technology for Show

    Jo Yong-min cautions against adopting AI as a trendy technology without a clear understanding of its purpose. True utilization of AI begins when we accurately identify the problems we want to solve. It’s essential to define problems clearly, break them down into smaller parts, and distinguish between tasks that AI can handle and those that require human judgment.

    Good AI Utilization Starts with Problem Definition

    Instead of asking “should we use AI,” it’s more important to ask “what problem do we want to solve with AI?” By focusing on problem definition, we can use AI as a tool to enhance productivity and creativity, rather than just as a means to showcase technology.

    Ultimately, Human-Selected Value is the Survival Strategy

    Choi Jae-bung emphasizes that while AI can accelerate production and reduce costs, the ultimate value lies in being chosen by people. Whether it’s a product, service, or idea, its value is determined by the people who use it, interact with it, and recommend it to others. In the AI era, it’s essential to develop the ability to understand human problems, design better experiences, and build trust.

    Subscriptions and Likes are Not Just Simple Buttons

    Subscriptions and likes are digital signals of human selection. People invest time in things that are helpful, enjoyable, trustworthy, and meaningful to them. Companies and individuals who fail to receive these signals may struggle to survive, even with advanced AI capabilities. Therefore, preparation for the AI era requires a combination of technological proficiency, human understanding, and trust-building abilities.

    Practical Checklist for Winners in the AI Era

    To prepare for the AI era, it’s essential to start with small, practical steps. Here’s a checklist to get you started: Measure the time saved by using AI for one task per week, review AI-generated content for accuracy and context, distinguish between repetitive and judgment-based tasks, record customer or colleague pain points, and manage human relationships, trust, and communication alongside AI utilization. Remember, the key is not just about knowing AI but about using it to solve problems, create value, and build meaningful relationships.

    Related Reading

    Continue with these related Thinknote English articles in the Digital Transformation cluster.

    FAQ

    What is this article about?

    This article explains a digital transformation, platform, market-structure, or technology-adoption topic with Korea-specific context and global implications.

    How should I use this guide?

    Use it to understand market signals and strategic patterns. Combine it with current market data before making business or investment decisions.

    Where can I read the original Korean article?

    The original Korean article is available here: How to Prepare for the AI Era: Literacy, Judgment, and Human Value.