Cautious Collaboration: AI Partnerships Built on Foundations of Solid Learning
Cautious Collaboration: AI Partnerships Built on Foundations of Solid Learning
Sungjune Park
As a professor primarily teaching programming to beginners, he always stressed the fundamentals of coding and logic. Yet, when AI tools began to emerge—integrated into platforms like Google Colab—Dr. Park found himself at a crossroads, intrigued by the potential of these systems and the challenges they could bring.
His course was designed for novices, and he had not planned on heavily incorporating advanced technology into the curriculum. However, with the seamless availability of AI-powered code generation tools, he realized that denying his students access to such features would be impractical.
“If AI is available, why not let them explore it?”
he mused, understanding that the tool could serve as both a boon and a barrier to genuine learning. This reflective mindset was evident in his early experiments, where he introduced AI-generated examples and coding challenges to his students.
In practice, Dr. Park began using AI to craft multiple versions of programming problems. He found that when given clear instructions, the AI could churn out accurate and efficient code—often better than what he could quickly devise himself. This not only saved him time but also opened up new avenues for creating diverse learning materials. Yet, with this efficiency came an unexpected dilemma. While roughly 20% of his students leveraged the AI tool to truly enhance their learning, the majority used it merely to get quick answers. Rather than engaging deeply with the problem-solving process, many opted for a shortcut that risked undermining their fundamental understanding of programming.
One memorable incident encapsulated his concerns. In an assignment meant to challenge students to troubleshoot and fix a piece of faulty code, several submissions—though technically correct in execution—revealed a troubling trend: many students were simply parroting the AI’s output without grasping the underlying concepts. Dr. Park believes students need to learn the basic concepts before they can obtain the full benefits from using AI.
Beyond classroom applications, Dr. Park’s exploration of AI led him to ponder its broader impact on his field. He foresaw a future where AI would not only enhance productivity but also potentially reduce the demand for traditional programming roles.
“If AI can generate efficient code on command, then perhaps our roles as programmers and educators will need to evolve,”
he observed.
Moreover, Dr. Park was impressed by the ancillary benefits of AI. During a recent Zoom meeting, he marveled at how the system effortlessly generated a detailed summary of the discussion, accurately attributing comments to the right participants. This capability underscored AI’s promise in streamlining administrative tasks and enhancing communication. However, it also led him to question the future of teaching: if AI can summarize lectures and generate accurate materials, what unique value does a human instructor offer? Despite these concerns, he noted that many students still preferred his personal guidance over an impersonal machine-generated response.
Ultimately, Dr. Sungjune Park’s journey with AI has been one of cautious optimism mixed with thoughtful skepticism. While he embraces the efficiencies and novel opportunities AI presents—both in the classroom and in professional practice—he remains vigilant about its potential to undermine deep learning.
As he continues to explore this evolving landscape, Dr. Park is committed to finding a balanced approach that harnesses AI’s power while ensuring that students build a robust, foundational understanding of their discipline.