This study aims to develop an AI-based analysis system that aligns with the international trend of AI legislation, including the EU's AI Act, while also addressing the analytical needs of the public sector. The focus is on providing timely and objective information to policymakers and specialized researchers by exploring advanced analytical methodologies. As the complexity and volume of data rapidly increase in the modern policy environment, these methods have become essential for governments to obtain the objective information needed for critical decision-making. To achieve this, the study integrates machine learning, natural language processing (NLP), and Large Language Models (LLM) to create a system capable of meeting the analytical demands of government entities. The target dataset consists of “quantum” field data collected from South Korea's National R&D Information System (NTIS). Machine learning was applied to this data to assess the validity of the analysis, while BERTopic, a natural language analysis package, was used for text analysis. With the introduction of LLMs, the extracted information from machine learning and natural language analysis was not merely listed but also connected in meaningful ways to provide policy insights. This approach enhanced the transparency and reliability of AI analysis, minimizing potential errors or distortions in the data analysis process. In conclusion, this study emphasizes the development of a system that enables rapid and accurate information provision while maintaining compatibility with international AI regulations such as the AI Act. The use of LLMs, in particular, contributed to enhancing the system’s capabilities for deeper and more multifaceted analysis.

1. 서 론
2. 선행연구
3. 연구방법론
    3.1 분석 데이터
    3.2 지도 기반 기계학습 적용 방법
    3.3 비지도 기반 기계학습 적용 방법
    3.4 자연어 분석
    3.5 LLM 적용 방법
4. 분석 결과
5. 결 론
Acknowledgement
References

• Kisu Park(AICONX) | 박기수
• Kihyun Hong(AICONX) | 홍기현
• Jung Sun Lim(Korea Institute of Science and Technology Information) | 임정선 (한국과학기술정보연구원) Corresponding author