챗봇 어플리케이션 내부 상관관계 분석을 통한 AI 품질 프로세스 인자 연구
A Study on the AI Quality Process Factors Through Internal Correlation Analysis Of Chatbot Applications
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
본 연구는 개발 완료된 사용자 측면의 AI 챗봇 서비스의 품질 및 프로세스에 영향을 미치는 인자를 파악하여 인공지능 챗봇을 활용한 어플리케이션 서비스의 지속적 만족도를 높이고자 하였다. 개발된 챗봇 agent 개발자 및 연계 서비스 이용자의 응답 데이터를 통해 만족도와 품질 항목 관련한 인자를 분석하고 인공지능챗봇의 만족도와 함께 상관관계 분석을 실시 하였다. 연구 결과에 따라 인공지능 챗봇의 만족도는 응답 데이터에 인자의 상관관계를 분석하여 프로세스를 확인할 수 있다. 이러한 상관관계의 인자 요소를 통해 AI 챗봇의 특징과 함께 품질 프로세스를 확인할 수 있다. 이러한 상관관계 분석을 통해 인공지능 자체가 가지는 학습 능력을 통한 지속적 품질 개선 기반이 필요함을 확인하였다. 이 연구 결과로 agent 개발자 및 이용자 간의 인자를 분석하였고, 지속적 강화 학습을 통한 AI 품질 프로세스를 도출하고 이를 지속적 개선 운영하는데의미를 가진다.
Abstract
This study identified factors that affect the quality and process of AI chatbot service from the user's perspective after it has been developed to increase the continuous satisfaction of application services using AI chatbot. The factors related to satisfaction and quality items were analyzed through the response data of the developed chatbot agent developer and the users of the linked service, and correlation analysis was conducted with the satisfaction of the AI chatbot. According to the research results, the satisfaction of AI chatbot can be checked by analyzing the correlation of factors in the response data. These correlation factors can be used to identify the quality process along with the characteristics of the AI chatbot. Through this correlation analysis, we confirmed the need for a foundation for continuous quality improvement through the learning ability of AI. itself. As a result of this study we analyzed the factors between chatbot agent developers and users, and derived an AI quality process through continuous reinforcement learning, which has implications for continuous improvement operations.
참고문헌 (13)
- WI. Kim, "An Effective Adaptive Dialogue Strategy Using Reinforcement Loaming", Journal of KIISE, 35(1), pp.33~40 (2008)
- CS. Jung, "A Study on the Method of Implementing an AI Chatbot to Respond to the POST COVID-19 Untact Era", Journal of Information Technology Services, 19(4), pp.31~47 (2020)[DOI]
- CG. Yang, "A Study on the Satisfaction and Dissatisfaction in AI Chatbot", Asia-Pacific Journal of Business Venturing and Entrepreneurship, 17(2), pp.167~177 (2022)[DOI]
- ES. Kim, "A Selection Technique of Source Project in Heterogeneous Defect Prediction based on Correlation Coefficients", Journal of JOK, 48(8), pp.920~927 (2021)[DOI]
- BN. Choe, "Correlation Analysis Between Casual and Outcome Factors for Developing Land Information System", Journal of Korean Society for Geospatial Information Science, 23(6), pp.1~8 (2015)
- YH. Park, "Recommendation System Based on Correlation Analysis of User Behavior Data in Online Shopping Mall Environment", Journal of KTCCS, 13(1), pp.10~20 (2024)
- IS. Yoon, "Research on Correlating Data Loading with User Experience", Journal of JPEE, 16(2), pp.185~193 (2024)
- MJ. Seo, "Consumers’ Negative Responses to the Communication Failure of Chatbots in Online Fashion Shopping Malls", 24(2), pp.183~194 (2022)[DOI]
- BH. Kim, "Research on Correlating Data Loading with User Experience", Journal of APJCRI, 10(1), pp.99~108 (2024)[DOI]
- YR. Wang, "A study on the correlation between immersion for Intention of Continuous Use of TikTok users", Journal of JCD, 86, pp.175~186 (2024)
- HJ. Park, "A Study on the Correlation between Visual Reality Factors in the Metaverse Platform-Centering on the neurological visual perception structure-", Journal of KISD, 19(1), pp.263~278 (2024)
- Lee, Don-Hee, "A Study on S", W Development Process Improvement based on CMMI's Quantitative Project Management, 2018, pp.18(3), 101~110
- KS, Cho, "The Quality Performance Management of CMMI in the Era of Industry 4.0", Journal of the Korean society for Quality Management, 47(1), pp.17~32 (2019)[DOI]