Bill Cope

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.

Gabriela C. Zapata, Bill Cope, Mary Kalantzis et al.

This study investigates the use of generative AI to support formative assessment through machine generated reviews of peer reviews in graduate online courses in a public university in the United States. Drawing on Systemic Functional Linguistics and Appraisal Theory, we analyzed 120 metareviews to explore how generative AI feedback constructs meaning across ideational, interpersonal, and textual dimensions. The findings suggest that generative AI can approximate key rhetorical and relational features of effective human feedback, offering directive clarity while also maintaining a supportive stance. The reviews analyzed demonstrated a balance of praise and constructive critique, alignment with rubric expectations, and structured staging that foregrounded student agency. By modeling these qualities, AI metafeedback has the potential to scaffold feedback literacy and enhance leaner engagement with peer review.

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.

Anastasia Olga, Tzirides, Akash Saini et al.

The launch of ChatGPT in November 2022 precipitated a panic among some educators while prompting qualified enthusiasm from others. Under the umbrella term Generative AI, ChatGPT is an example of a range of technologies for the delivery of computer-generated text, image, and other digitized media. This paper examines the implications for education of one generative AI technology, chatbots responding from large language models, or C-LLM. It reports on an application of a C-LLM to AI review and assessment of complex student work. In a concluding discussion, the paper explores the intrinsic limits of generative AI, bound as it is to language corpora and their textual representation through binary notation. Within these limits, we suggest the range of emerging and potential applications of Generative AI in education.