THE IMPACT OF AI AND PEER FEEDBACK ON RESEARCH WRITING SKILLS: A STUDY USING THE CGSCHOLAR PLATFORM AMONG KAZAKHSTANI SCHOLARS

This research studies the impact of AI and Peer feedback on the academic writing development of Kazakhstani scholars using the CGScholar platform − the product of cutting-edge research and development into collaborative learning, big data, and artificial intelligence developed by educators and computer scientists at the University of Illinois Urbana-Champaign (UIUC). The study aimed to find out how familiarity with AI tools and peer feedback processes affects participants’ openness to incorporating feedback into their academic writing. The study involved 36 Bolashak scholars enrolled in a scientific internship focused on education at the University of UIUC. A survey with 15 questions with multiple-choice, Likert scale, and open-ended questions was employed to collect a data. The survey was conducted via Google Forms in both English and Russian to ensure linguistic accessibility. Demographic information such as age, gender, and first language were collected to provide a nuanced understanding of the data. The analysis revealed a moderate positive correlation between familiarity with AI tools and openness to making changes based on feedback, and a strong positive correlation between research writing experience and expectations of peer feedback, especially in the area of research methodology. These results show that participants are open minded to AI-assisted feedback, however they still highly appreciate peer input, especially regarding methodological guidance. This study demonstrates the potential benefits of integrating AI tools with traditional feedback mechanisms to improve research writing quality in academic settings. Further research is recommended to evaluate the long-term impact of AI and peer feedback on academic writing skills, particularly through longitudinal studies that assess skill retention over multiple feedback cycles. Additionally, expanding the study to include a more diverse academic audience will provide deeper insights into how feedback mechanisms function across different research cultures and disciplines.