The original article is written in Korean by myself Link. I put the translation generated by Claude. I polished some word choice which generative AI cannot translate properly.

I will try to rewrite this article in the future within December.

Since ChatGPT emerged three years ago, GenAI has made remarkable progress and has deeply influenced at least university education. I believe fundamental changes in the traditional education system are necessary, but there doesn’t seem to be much discussion about what direction we should take. In this essay, I want to organize my thoughts on the direction university-level education should pursue.

Let’s imagine writing a novel based on the emotion of fear. I tried it with Gemini. I entered the prompt: “I want to write a horror novel. What suggestions would you make for initial settings or a draft?” Gemini replied that the main character should be an ordinary person readers can empathize with, and when they face an abnormal horror situation, readers should also feel fear. Additionally, I should give the main character a fatal weakness or flaw. Finally, I must define the object of fear, and the object of fear (monster, ghost, curse, etc.), but I should not be described in detail.

The first answer AI produces from a prompt is, as expected, average and predictably structured. However, works that transcend their era—novels that people still read 200 years later—don’t fit the structure Gemini suggested. Around 1817, at Byron’s villa, while Percy Shelley and Claire Clairmont were trying to break the monotony of rainy days, the idea came up to write horror stories. Mary struggled to write a novel because of a lack of inspiration, but eventually wrote one after an interesting dream. It was called “Walking Dream,” now known as Frankenstein. Though written 200 years ago, it’s the first science fiction novel, and rather than being analyzed merely as fantasy, it’s a novel beloved to this day for raising many questions, adapted into plays and, recently, into films.

While we could speak of this novel’s originality in many ways, taking the theme of “horror” and extracting “the primal emotions of pain and loneliness when a new life form created by science and technology is abandoned at birth, struggles in that abandonment, tries to assimilate into society but isn’t accepted because of its appearance”. This is the discovery of subject matter that transcends average knowledge and thinking. I believe this became a great novel that continues to raise many questions today, thanks to Mary Shelley’s life journey.

Current mathematics education at universities has a structure of introducing definitions, proving them, advancing through examples, checking one’s knowledge, and moving forward by solving practice problems as assignments. Incidentally, this educational composition became established about 70 years ago; in the past, students read without practice problems, understanding between the lines of the text and creating their own examples. However, with the universalization of university education and the development of engineering, the need to solve concrete practical problems emerged, and I understand that this led to the establishment of current teaching methods. However, the birth of generative AI is shaking the foundation of this long-standing educational system. The practice-problem method of evaluating students turns out to be ineffective and it becomes problematic. In other words, the foundation of the educational system, knowledge verification and proficiency evaluation through homework, has been shaken.

So how should we solve this problem? We must now always assume AI usage. Therefore, I think attempting to block it entirely is an idea that goes against the times. While there are negative effects, education’s goal should be to maximize positive impacts and enable future generations to develop better ideas. GenAI is a new era’s search tool. Documents written in French or Russian are now more accessible, and when preparing classes, I can quickly research unfamiliar historical contexts, so I can deliver lectures that are more engaging for students. When writing papers and studying other papers, GenAI helps roughly grasp core ideas and kindly explains parts that are so basic I’m afraid to ask about, filling knowledge gaps. Despite these advantages, insisting only on past systems is no different from arguing that we should return to calculator-era exams because computers ruin students.

How was Mary Shelley able to write such a novel? Mary had intellectual soil that allowed access to much knowledge, studying literature and science by reading many books in her library under her open-minded father and mother, and she could write a novel inspired by Galvanism, which was popular in the late 18th century. With Galvani’s successful experiment of moving dead animal muscles using electricity, she could conceive the idea of creating life with electricity for Frankenstein’s birth. Additionally, she could write such a novel thanks to her husband, Percy Shelley’s, support. In 1800, women’s status was significantly lower than it is now, making it difficult to publish novels under one’s own name. Having someone who can support you gives great strength in taking on new challenges.

So how should university education proceed? We need to care more about students and stop viewing them merely as objects of evaluation. To do this, we need an era that pays more attention to students, especially those with a passion for learning, by focusing on how students can acquire new perspectives and understand more effectively. We must remind students who simply use AI to get answers and grades that they’re wasting their time. If they use knowledge not taught in class, they should be severely punished, but we shouldn’t discourage the attitude of trying to learn new knowledge by using AI to write their answers more perfectly.

However, as a practical matter, in the case of basic mathematics courses, it’s true that if these courses are shaken, there will be great difficulty in digesting other science and engineering classes. For this reason, for basic courses only, it may be necessary to change the system to periodic problem-solving assignments, build basic stamina through handwriting, and evaluate through quizzes. Additionally, during that process, AI supports repetitive learning, and instructors use the saved time to provide customized guidance to where students are stuck. This is the direction university education should head in the next 10 years. Now we need to focus on how we can teach those who could ask a daring question.

Since students in American universities are absent so often, I don’t know how to solve this. However, universities must now pay more attention not to the simple transmission of knowledge, but to the role of laying intellectual groundwork so that new imagination can unfold.