Earticle

초록

영어

This study aims to investigate generative AI (GAI) services, which are leading innovations in various domains and significantly affecting education with a range of positive and negative effects. Specifically, this study seeks to empirically examine the academic performance of graduate students who regularly use generative AI services such as ChatGPT (Generative Pre-trained Transformer). To establish the research framework and hypotheses, this study incorporates concepts from social cognitive theory and the concept of dependency. Also, this study introduces graduate students' personal and socio-environmental factors alongside the dependency variables of habitual and addictive behavior of GAI and their impact on learning and task performance. This study uses a structural methodology in order to test the hypotheses and a mixed approach that includes Artificial Neural Networks (ANN) to predict the most critical variables. We plan to collect data from 500 graduate students who actively utilize generative AI services to achieve this. Based on the outcomes of this research, this study tries to offer academic and practical implications for educational institutions and administration.

목차

Abstract
Introduction
Literature Review
Generative AI
Social Cognitive Theory
Dependency behavior
Academic Performance
Research Model and Methods
Research Model
Measurement items
Data Collection and Analysis
Future Study & Conclusion
References

키워드

저자

• Seoyoun Lee [ School of Management and Economics Beijing Institute of Technology ]
• Younghoon Chang [ Nottingham University Business School China University of Nottingham Ningbo China ]
• Haejung Yun [ College of Science and Industry Convergence Ewha Womans University ]
• Qiuju Yin [ School of Management and Economics Beijing Institute of Technology ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658