As of 2025, AI is no longer a special technology but part of daily life. Even middle and high school students use ChatGPT for homework and Midjourney for drawing. However, they fall silent when faced with the question "How does it work?"
YouTube videos are superficial, and original papers are blocked by language barriers. What's more important is understanding not just how to use AI, but how AI was born and has evolved. To become a future AI researcher, it's essential to grasp the theoretical foundation and historical context that form the basis of current technology.
AI Papers is an innovative class from AI Camp that bridges this gap. It's Korea's only paper-based AI education program where students systematically learn actual published AI papers in Korean, together with current university special professors. It supports middle and high school students with intermediate Korean proficiency to understand AI's development process and core principles, building the foundation to become future AI researchers.
AI Papers has now established itself as an essential prerequisite course for students preparing to enter AI-related departments. This is because major Korean universities have begun to consider paper reading ability and understanding of AI development history as important evaluation factors when selecting new students for AI departments.
Recent interviews for AI-related departments at universities frequently include questions like "Explain the historical development process of AI" and "Explain why deep learning has emerged at this point in time." AI Papers graduates can provide in-depth, paper-based answers to such questions, giving them differentiated competitive advantages in admissions.
The core of the program is carefully selected Korean AI papers, systematic curriculum, and direct guidance from current faculty. Rather than simple knowledge transfer, we explore together the journey of how AI has evolved.
From the Turing Test to large language models, understand the historical context of why AI developed in certain directions at specific times. Through our self-developed AI history workbook, understand the technical limitations and breakthroughs of each era, gaining insights into what problems future AI researchers need to solve.
Explore core papers that became milestones in AI development in chronological order. Analyze why specific technologies emerged at certain times and how previous research limitations were overcome, understanding the continuity of research and moments of innovation.
Learn about AI's winter and revival history through the first paper. By understanding why AI faced multiple crises and how they were overcome, develop long-term perspective as a future researcher.
Through the second paper, explore the major paradigm shift from rule-based to statistics-based, and then to deep learning. Understand what conceptual shifts researchers made at each turning point and why they were revolutionary.
Through the third paper, analyze the technical foundation of the current AI boom. Comprehensively understand why deep learning has grown explosively recently and how the three elements of data, computing power, and algorithms aligned.
Based on AI development history, derive problems that need to be solved in the future. Establish and present your own hypothesis about what the current limitations of AI are and where the next breakthrough will come from. This becomes core material for university admission personal statements and interviews.
AI Papers doesn't stop at theoretical learning. Participants experience actual research culture.
Create your own AI history map connecting major papers and events. Through this process, you'll clearly understand the cause-and-effect relationships of technological development and build the foundation for systematic answers in admission interviews.
Predict the future based on AI's past and present. Answer questions like "What would cause the next AI crisis?" and "Where will the next paradigm shift occur?" to develop researcher-like thinking skills.
Understand ethical issues raised during AI development in their historical context. Learn from past mistakes and internalize the sense of responsibility that future researchers should have.
The hidden gem of AI Papers is the community.
Design a concrete roadmap to become an AI researcher. Receive practical career counseling on which undergraduate department to choose, when to go to graduate school, whether studying abroad is essential, and more.
Draw AI's future together. Share each other's research interests, discuss topics to research together in the future, and meet future research colleagues.
Meet seniors currently researching AI in graduate school. Get a preview of the vivid life of researchers through lab life, paper writing process, conference participation experiences, and more.
Create your own notes organizing core technologies, researchers, and breakthroughs by era. This becomes a treasure trove for writing admission personal statements and preparing for interviews.
Constantly ask questions like "Why did that technology emerge at that time?" and "Why was it impossible before?" This way of thinking is the true attitude of a researcher.
When looking at ChatGPT, think "Where did this originate from?" The ability to understand current technology in its historical context is a great advantage in admissions.
While studying AI development history, look for "problems that haven't been solved yet." This becomes the core of your university application motivation and future research plans.
Systematically organize what you've learned and your insights. Essays like "Future Prospects Through AI Development History" become powerful differentiating factors in admissions.
AI Papers is not just one class from AI Camp. It's an essential first step to become a future AI researcher.
What you gain here goes beyond knowledge:
When you create new breakthroughs in AI in the future, the historical insights learned in AI Papers will become the compass for innovation. Only those who know how AI has evolved can know where AI should go.
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