Earticle

초록

영어

Artificial intelligence is increasingly used in language-learning services, yet it remains unclear whether learners evaluate identical educational explanations differently when they are labelled as AI-generated. This study examined the effect of AI labelling on the perceived quality of Korean language explanations by integrating Technology Acceptance Model constructs with source evaluation. A quantitative A/B survey was conducted with 270 Korean language learners, randomly assigned to an AI-labelled condition or a no-label control condition. Participants evaluated identical Korean language explanations using measures of perceived ease of use, perceived usefulness, and perceived quality. Reliability analysis, exploratory factor analysis, MANOVA, hierarchical regression, and robustness checks were conducted. Perceived ease of use and perceived usefulness positively predicted perceived quality. The AI label did not directly reduce perceived quality, but it weakened the relationship between perceived usefulness and perceived quality. Language proficiency was not a significant moderator.

목차

Abstract
Introduction
Theoretical Background and Hypotheses
Method
Results
Discussion and Implications
Limitations and Future Research
Conclusion
References

키워드

저자

• Martin Kis [ Department of Glocal Business Administration, Graduate School, Busan University of Foreign Studies ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2026

• 십진분류

  KDC 325 DDC 658