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The Cracks in Today's Academic System

By Jaehyun Lee — May 23, 2025

The academic system, once a driver of rigor and innovation, now faces deep structural strains. Incentives like “publish or perish,” overloaded peer review, and funding-driven research have eroded quality and credibility. Human costs, credential devaluation, and the rise of AI-generated content further accelerate decline. These pressures fuel replication failures, predatory publishing, and distorted metrics. Reform—emphasizing quality over quantity, transparent peer review, and nonprofit publishing—is essential to safeguard the integrity and relevance of academia.

Dangers of AI – Why It Is Different Than Any Other Technological Reform

By Guest Writer Tae Won Park (Pseudonym) — June 24, 2025

This article examines the dual nature of artificial intelligence (AI) as both an enabler of unprecedented productivity and a source of profound risks. Drawing on own early AI adoption experiences, it highlights how AI democratizes complex tasks and lowers barriers for non-technical users, yet simultaneously fuels concerns about job displacement, reliability issues, and declining opportunities for skill development. Broader societal risks—including soaring energy consumption, deepfakes, and unhealthy social dependencies—further distinguish AI from past technological shifts. The central concern is whether societies can adapt quickly enough to AI's accelerating pace of change without leaving large populations behind. The article argues that effective training, clear usage standards, and strong regulatory guardrails are essential to ensure that AI's benefits are realized while minimizing harm.

How to Best Utilize Bioinformatics in Your Research

By Jaehyun Lee — July 23, 2025

Bioinformatics has rapidly evolved from the sequencing of the human genome to today's advanced single-cell, spatial, and multimodal analyses. This article explores how bioinformatics can empower researchers with granular, big data insights that accelerate hypothesis generation and complement wet-lab studies. At the same time, it highlights common misconceptions and limitations—such as the risk of false positives, the importance of data quality, and the need for experimental validation. Ultimately, bioinformatics should be seen as a powerful tool for generating and refining research questions, rather than as a substitute for experimental proof.

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