Purpose: This study aims to develop and evaluate A4, a generative AI agent (Adaptive Aptitude Assessment by AI for Nursing Students), designed to support undergraduate nursing students in matching their personal traits with suitable clinical roles. Method: An exploratory research design was used with 71 nursing students and 65 clinical nurses. Developed using ChatGPT-4, the A4 agent classified nursing roles, identified required personal traits, and generated adaptive test items. Perceived person–job fit was measured via survey, and the data were analyzed using descriptive statistics and independent t-tests. Results: Among students, 80.3% agreed that the A4 results represented their characteristics, compared with 69.2% of nurses. Nursing students had higher perceived aptitude–fit scores (2.99 ± 0.62) than nurses (2.74 ± 0.69), indicating a significant difference (t = 2.19, p = .030). Conclusion: The findings indicate that the generative AI-based adaptive assessment, which incorporated clinical scenarios, was perceived by nursing students and clinical nurses as a tool that represents personal traits. This suggests its potential to support personalized career guidance in nursing education and serve as a supplementary tool for clinical practicum orientation.

최근 게임분야에서 수준 높은 인공지능 에이전트의 구현은 많은 주목을 받고 있다. 그 중 Monte-Carlo Tree Search(MCTS)는 완전 정보를 가진 게임에서 무작위 탐색을 통해 최적의 해를 구할 수 있는 알고리즘으로, 수식으로 표현되지 않는 경우에 근사치를 계산하는 용도로 적합하다. 하스스톤과 같은 Trading Card Game(TCG) 장르의 게임은 상대방의 카드와 플레이 를 예측할 수 없기 때문에 불완전 정보를 가지고 있다. 본 논문에서는 불완전 정보 카드 게임 에서 인공지능 에이전트를 생성하기 위해 MCTS 알고리즘을 응용하는 방법을 제안하고, 현재 서비스되는 하스스톤 게임에 적용하여 봄으로써 MCTS 알고리즘의 실용성을 검증한다.