AI-ENABLED PEER REVIEW IN VISUAL EDUCATION - Rawat, Kalpana; Hashmi , Sadaf; Khurana, Shivam; Kashid, Shweta; Ghumman, Sukhman; Sahoo, Tapasmini (ShodhKosh: Journal of Visual and Performing Arts, 2025)
It may change the sphere of visual education that provides one of the most significant processes of learning comment and criticism with the introduction of artificial intelligence (AI) into the peer review system. The study examines how, why, and what impacts an AI-enabled peer review system can be designed and utilized in the art, design, and visual communication domains. The use of traditional peer review in visual education is useful in pedagogy but it is a process that is subjective, lacks in quality and time compression. To do away with such issues, this paper suggests a hybrid system that fulfills human knowledge and feedback systems run by AI. The research uses mixed methods method in order to explore the perception of students and the validity of their remarks. The core component of the system is composed of support vectors machine (SVM), GPT-neox and convolutional neural networks (CNN). The models take into consideration the visual results and the written criticisms to make the input relevant to the situation and simple to comprehend. Transparency, explainability, and the human-AI contact interface are all strained to ensure that the feedback is useful and acceptable in accordance with the best practices in the field of education. At the implementation stage, the AI-based system is implemented into the current school processes and the usefulness and effectiveness of the AI-based system in the classroom are experimented. The evidence shows that AI-assisted peer review can be used to produce more consistent reviews and facilitate deliberative learning in the process of reducing the anxiety of teachers and, at the same time, having no adverse effect on the creativity of students.