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The Return of ‘Research Ecosystem’ as Policy Language

The government has once again placed the “Basic Research Ecosystem” at the forefront of its agenda. The 「Plan to Nurture the Basic Research Ecosystem」, announced jointly by relevant ministries in December 2025, sets a goal to leapfrog South Korea into the ranks of the “World’s Top 5 Basic Research Powerhouses” by 2030. Led by the Ministry of Science and ICT and the Ministry of Education, the policy targets universities, research institutes, and the researchers performing basic research within them. This plan is noteworthy because it presents a blueprint that focuses more on how to manage basic research rather than simply how much to fund it.

The government diagnoses that while quantitative investment in basic research has expanded, performance relative to investment has stagnated. Korea still lags behind major advanced nations in the number of “Top 1%” researchers and world-class research institutions. This recognition aligns with recent controversies over R&D budget adjustments that sparked concerns about hindering academic diversity and weakening research stability. The new plan addresses these issues by reorganizing the structure across four pillars: Investment Systems, Researchers, Research Institutions, and Research Foundations.

Why the ‘Benefit Rate’ Has Become the Central Metric

The most frequently recurring concept in this plan is the ‘Benefit Rate’ (Grant Success Rate). The government announced it will shift the criteria for managing basic research projects from simple budget size or the number of projects to the benefit rate—the percentage of total researchers actually performing basic research. Representative goals include securing a 30% benefit rate for all faculty, 50% for full-time faculty, and 70% for junior faculty by 2030.

This management approach is persuasive in terms of expanding the base of basic research. It aims to mitigate the concentration of resources on a small elite group and ensure that more researchers have access to fundamental research opportunities. Simultaneously, this metric shifts the unit of policy management from “individual projects” to the “participation ratio of the entire group.” This can be interpreted as redefining basic research as a policy area that must maintain a certain participation structure before qualitative outcomes are even measured.

Extending Research Duration and the Conditions for Long-term Study

The government stated it will extend the duration of individual research projects from the current 1–3 years to 3–5 years. By providing linked support for up to two follow-up studies on the same topic, researchers can now sustain a single project for up to 11 years. This is an institutional attempt to enable “Deep-Dive” long-term research.

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Extension of research duration is viewed positively as it frees researchers from the burden of repetitive applications and evaluations, allowing them to focus on their work. Given the nature of basic research, where results are difficult to produce in the short term, the predictability of the research period is directly linked to the quality of the work. However, for this to truly guarantee long-term exploration, evaluation methods must move away from short-term performance metrics.

The Strategic Shift: Introduction of Block Funding

The most structural change in this plan is the introduction of ‘Performance-based Block Funding’ to strengthen university research competitiveness. Under this system, the government provides a lump sum of research funds to the university as a unit, encouraging the institution to invest in its foundation—such as hiring full-time researchers, support staff, and expanding facilities and equipment.

Block funding is described as a system that enhances university autonomy. The goal is to move away from individual project-based support and allow universities to build infrastructure according to their own strategies. However, this also transfers the responsibility for research performance to the university level. The moment the condition “performance-based” is attached, block funding becomes a system that couples autonomy with administrative accountability.

Integrating Basic Research with AI

The government plans to establish 40 Basic Research AI Centers across universities and train 2,000 researchers capable of combining basic research with AI by 2030. Enhancing the research environment through AI is a recurring theme in science and technology policy.

If AI utilization expands in basic research, it will likely change the methodology of research itself. Studies utilizing large-scale data analysis or simulations can produce results different from traditional approaches. At the same time, AI can be used as a tool for research evaluation and administrative efficiency. The challenge lies in how these two objectives—research innovation and administrative efficiency—will be balanced in the field.

Conclusion: From ‘How to Support’ to ‘How to Manage’

The 「Plan to Nurture the Basic Research Ecosystem」 is significant in that it attempts to manage basic research as a single system rather than as isolated projects. The benefit rate, long-term research, block funding, and AI centers are all components of this system.

For this “ecosystem” to actually function, the relationships between these elements must be clear. Whether researchers can sustain their work in a predictable environment, whether universities can balance autonomy with responsibility, and whether policy goals translate into academic diversity rather than just numerical management will be the true criteria for success. The question of how to support basic research is now moving toward the question of what structure will be used to manage it.

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