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Generative AI–Driven Real-Time Coding Education: Investigating the Effects of Latency, Security, and Traffic on Learning Outcomes
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- 발행기관
- 국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
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- 간행물
- International Journal of Internet, Broadcasting and Communication 바로가기
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- 통권
- Vol.17 No.1 (2025.02)바로가기
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- 페이지
- pp.406-413
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A465054
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- This paper investigates how network latency, security protocols, and traffic loads collectively influence both learning outcomes and user satisfaction in a generative AI–driven real-time coding education environment. An experiment with 80 undergraduate participants was conducted, assigning each to specific combinations of latency (low, mid, high), security (low, mid, high), and traffic (low, mid, high) via a partial Latin square approach. Quantitative measures (e.g., task completion rate, response times, error rate) and qualitative data (e.g., interviews, cognitive load surveys) were collected. Statistical analyses (ANOVA, effect sizes) reveal that high latency and stringent security significantly increased response delays (p < .01), thereby reducing learning performance and satisfaction, while heavy traffic (100+ concurrent users) further exacerbated error rates and degraded user experience. These findings highlight the importance of balancing security requirements with system responsiveness to fully exploit the benefits of generative AI–based coding support.
목차
Abstract
1. INTRODUCTION
2. SCENARIO DEFINITION
2.1 Learner Profile and Tasks
3. SYSTEM ARCHITECTURE
3.1 Overall Structure
3.2 Security Levels
3.3 Latency and Traffic Control
4. RESEARCH METHODS
4.1 Participants and Experimental Design
4.2 Experimental Procedure
4.3Measured Variables
5. RESULTS AND DISCUSSION
5.1 Statistical Analysis Overview
5.2 Quantitative Findings
5.3 Qualitative Insights
5.4 Additional Visual Illustrations of Generative AI Outputs
5.5 Task Completion Across Latency × Security
6. FURTHER VISUALIZATIONS AND DATA TABLES
6.1 Response Time and Error Rate Distributions
6.2 Descriptive Statistics
6.3 Feedback Summary
7. CONCLUSION
7.1 Implications
7.2 Limitations and Future Work
References
저자
간행물 정보
발행기관
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- 발행기관명
- 국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
- 설립연도
- 2000
- 분야
- 공학>전자/정보통신공학
- 소개
- 인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.
간행물
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- 간행물명
- International Journal of Internet, Broadcasting and Communication
- 간기
- 계간
- pISSN
- 2288-4920
- eISSN
- 2288-4939
- 수록기간
- 2009~2025
- 십진분류
- KDC 326 DDC 380
이 권호 내 다른 논문 / International Journal of Internet, Broadcasting and Communication Vol.17 No.1
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