Earticle

초록

영어

This study examines how AI tools can be integrated into college education to promote active and efficient learning for students in a Quantitative Business Analysis course. The main focus of the course is to teach statistics and data processing with R to a class of 50 first-year International Business Administration students. With the variety of topics and assignments, it is a challenge to provide personalized feedback for a class of this size. To resolve these issues, AI tools are used along with traditional lab sessions and supplementary video lectures. A mid-semester survey was conducted to evaluate students' experiences with this new methodology. Its effectiveness in improving their skills and comprehension, and their preferences of future AI integration were examined to determine the optimal level of AI integration to improve outcomes in the course. The survey shows that students evaluate AI and video lectures as highly effective in learning R coding and completing assignments. However, many still prefer to retain in-person interactions such as lab sessions. We need to find an optimized mixture that combines traditional teaching methods with AI tools to improve students’ satisfaction and their learning outcomes. It is worth noting students without prior coding experience showed almost the same responses regarding AI-assisted course with students with prior experience. This proves AI-integrated method can satisfy both groups. Only small differences between two groups were observed in students’ confidence and their support for further AI integration. This issue can be resolved with additional help for no experience group such as orientation sessions at the beginning of the semester. The originality of this study lies in its empirical evaluation of AI as a new educational tool that can make personalized learning possible. This new method allows students, even in large classes, to progress at their own pace and skill level. This research can contribute to finding a new educational framework adaptable to diverse learning contexts.

목차

Abstract
1. Introduction
2. Literature
3. Methodology of AI Assistance in R Education
4. Survey and Its Findings
5. Conclusion
References
 Survey Questions

키워드

저자

• Hyojung Koo [ Teaching Assistant Professor, International Business Administration, Dankook University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005