Artificial Intelligence is transforming nearly every area of modern life, and education is no exception. From research assistance to content generation and data analysis, AI tools are becoming embedded in everyday workflows. Yet while some universities attempt to resist this shift by banning tools such as ChatGPT and similar platforms, others are beginning to acknowledge that these technologies represent a structural shift rather than a temporary trend.
Over the past few years, technological progress has significantly reshaped how we think, generate ideas, and process information. Without consciously noticing it, many people have adapted to a new productivity paradigm defined by speed, automation, and augmented intelligence. In professional environments, AI is already assisting with coding, marketing, legal drafting, design, and strategic analysis. Expecting students to operate in a completely AI-free environment may no longer reflect the reality of the modern workplace they are preparing to enter.
By contrast, the traditional higher education model was built centuries ago. It functions within established hierarchies, formal roles, accreditation systems, and institutional competition for prestige. Students are still evaluated through essays, exams, and research projects designed in a pre-digital era. While these systems were once effective measures of skill and knowledge, they were not designed for a world in which cognitive tools can amplify human output.
At the same time, internal academic policies often limit students’ ability to freely experiment with emerging digital tools, even when those tools could enhance research, refine ideas, or accelerate creative exploration. Although assignments and projects may appear to encourage independent thinking, there are instances where unconventional approaches, especially AI-assisted ones, are met with skepticism.
According to Artur Zhdan, founder of the AI writing platform GPTinf, this resistance reflects deeper structural bottlenecks within education.
“Colleges are constrained by bureaucratic bottlenecks that have prevented meaningful change for decades, if not centuries. These bottlenecks must be overcome; otherwise, improvements in learning efficiency will be too slow to keep pace with the demands of the economy.
When professors insist that students not use AI, they ignore the inevitable: the world is changing rapidly — faster than the education system can adapt.”
Zhdan also compares the current state of higher education to Baumol’s cost disease. This economic theory explains why labor-intensive sectors such as education become more expensive over time. Unlike manufacturing, where productivity gains lower costs, education relies heavily on human labor. As wages rise across the economy, universities must increase tuition and operational costs even when productivity remains relatively stable.
“Baumol’s cost disease has made education unreasonably expensive, as it relies heavily on highly paid human labor. As fewer humans are required in the educational process, costs could decrease significantly. AI has the potential to democratize education in ways that are currently difficult to foresee.”
From this perspective, banning AI does not solve the structural problem. It delays adaptation while costs continue to rise. If AI can assist with research synthesis, feedback, content refinement, and tutoring, it may help reduce inefficiencies embedded in the system. The long-term impact could extend beyond cost reduction to broader accessibility.
Zhdan’s position comes from personal experience. While applying to universities, he used AI tools to support research, structure arguments, and refine drafts. However, he noticed that AI-generated output often appeared mechanical and easily identifiable. That realization led him to explore methods for improving the quality and naturalness of AI-assisted writing, which ultimately influenced the creation of GPTinf.
Yet he argues that the broader disruption of AI in education goes far beyond writing tools:
“The real disruption is not automation itself, but the collapse of skill scarcity. When technical execution becomes abundant, differentiation must come from taste, strategic thinking, and domain insight.
AI reduces the marginal cost of experimentation. As a result, more ideas will be tested, more prototypes will be built, and more failures will occur – but the pace of innovation will accelerate.”
In this environment, the role of education may need to evolve. If AI lowers technical barriers, universities may need to focus more on judgment, critical thinking, interdisciplinary reasoning, and ethics. Mechanical execution becomes less valuable when tools can replicate it instantly. Human differentiation shifts toward interpretation, creativity, and strategic insight.
The topic of the use of AI tools in educational and university settings continues to generate debate among experts, students, and the general public. Concerns around plagiarism, dependency, and academic integrity are legitimate. However, history suggests that banning transformative technologies rarely prevents adoption. It often delays adaptation and widens the gap between institutions and reality.
It is increasingly evident that AI technologies are reshaping the knowledge economy faster than institutions can meaningfully react. In this context, resisting AI may not safeguard educational standards. Instead, it risks widening the gap between institutional learning models and the evolving demands of the global economy. The central question is no longer whether AI should be integrated into education but how education systems can evolve quickly enough to remain relevant in a world that is changing.
