Raigul Zheldibayeva

Raigul Zheldibayeva, Ana Karina de Oliveira Nascimento, Vania Castro et al.

This multiple-case study examined the potential of a Generative AI (GenAI) tool, CyberScholar, to support K-12 students' writing across disciplines. This tool integrates teacher-provided rubrics, materials, and exemplars through Retrieval-Augmented Generation (RAG), producing criterion-specific formative feedback and ratings. The study involved 143 students and five teachers in grades 7 through 11 across five U.S. middle and high schools. Data sources included classroom observations, student post-surveys (n = 79), student focus group interviews (n = 18), and teacher surveys (n = 5). Qualitative analysis followed two cycles of coding to identify patterns within and across cases. Findings indicate that students valued CyberScholar's immediate, rubric-based feedback and noticed improvements in their writing as they revised, using it to refine organization, elaboration, and style. They also highlighted the tool's interactive, iterative qualities, which fostered revision and reduced reliance on teacher feedback. However, participants noted inconsistencies in the automated rating system and occasional misalignment with assignment expectations. Teachers reported that CyberScholar saved time on feedback and supported more targeted, higher-order instructional practices. The study underscores the promise of rubric-grounded GenAI formative feedback for developing writing skills, while emphasizing the need for human oversight, calibration of automated ratings, and attention to contextual factors shaping adoption.

Raigul Zheldibayeva

This research studies the impact of AI and peer feedback on the academic writing development of Kazakhstani scholars using the CGScholar platform - a product of research into collaborative learning, big data, and artificial intelligence developed by educators and computer scientists at the University of Illinois at Urbana-Champaign (UIUC). The study aimed to find out how familiarity with AI tools and peer feedback processes impacts participants' openness to incorporating feedback into their academic writing. The study involved 36 scholars enrolled in a scientific internship focused on education at UIUC. A survey with 15 multiple-choice questions, a Likert scale, and open-ended questions was used to collect 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 was collected to provide a detailed 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.

Vania Castro, Ana Karina de Oliveira Nascimento, Raigul Zheldibayeva et al.

This paper focuses on the piloting of CyberScholar, a Generative AI assistant tool that aims to provide formative feedback on writing in K-12 contexts. Specifically, this study explores how students worked with CyberScholar in diverse subject areas, including English Language Arts, Social Studies, and Modern World History classes in Grades 7, 8, 10, and 11 in three schools in the Midwest and one in the Northwest of the United States. This paper focuses on CyberScholar's potential to support K-12 students' writing in diverse subject areas requiring written assignments. Data were collected through implementation observations, surveys, and interviews by participating 121 students and 4 teachers. Thematic qualitative analysis revealed that the feedback tool was perceived as a valuable tool for supporting student writing through detailed feedback, enhanced interactivity, and alignment with rubric criteria. Students appreciated the tool's guidance in refining their writing. For the students, the assistant tool suggests restructuring feedback as a dynamic, dialogic process rather than a static evaluation, a shift that aligns with the cyber-social learning idea, self-regulation, and metacognition. For the teaching side, the findings indicate a shift in teachers' roles, from serving primarily as evaluators to guiding AI feedback processes that foster better student writing and critical thinking.