AI-ENABLED PEER REVIEW IN VISUAL EDUCATION
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6665Abstract [English]
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.
References
Audras, D., Na, X., Isgar, C., and Tang, Y. (2022). Virtual Teaching Assistants: A Survey of a Novel Teaching Technology. International Journal of Chinese Education, 11, Article 2212585X221121674. https://doi.org/10.1177/2212585X221121674 DOI: https://doi.org/10.1177/2212585X221121674
Crompton, H., and Song, D. (2021). The Potential of Artificial Intelligence in Higher Education. Revista Virtual Universidad Católica del Norte, 62, 1–4. https://doi.org/10.35575/rvucn.n62a1 DOI: https://doi.org/10.35575/rvucn.n62a1
Demiray, B. Z., Sit, M., and Demir, İ. (2021). DEM Super-Resolution with EfficientNetV2. arXiv (preprint). https://doi.org/10.1007/s42979-020-00442-2 DOI: https://doi.org/10.1007/s42979-020-00442-2
Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., and Baah, P. K. (2022). The Impact of a Virtual Teaching Assistant (chatbot) on Students’ Learning in Ghanaian Higher Education. International Journal of Educational Technology in Higher Education, 19, Article 57. https://doi.org/10.1186/s41239-022-00362-6 DOI: https://doi.org/10.1186/s41239-022-00362-6
Ewing, G. J., Mantilla, R., Krajewski, W. F., and Demir, İ. (2022). Interactive Hydrological Modelling and Simulation on Client-Side web Systems: An Educational Case Study. Journal of Hydroinformatics, 24, 1194–1206. https://doi.org/10.2166/hydro.2022.061 DOI: https://doi.org/10.2166/hydro.2022.061
Gautam, A., Sit, M., and Demir, İ. (2022). Realistic River Image Synthesis Using Deep Generative Adversarial Networks. Frontiers in Water, 4, Article 784441. https://doi.org/10.3389/frwa.2022.784441 DOI: https://doi.org/10.3389/frwa.2022.784441
Huang, J., Saleh, S., and Liu, Y. (2021). A Review on Artificial Intelligence in Education. Academic Journal of Interdisciplinary Studies, 10, 206. https://doi.org/10.36941/ajis-2021-0077 DOI: https://doi.org/10.36941/ajis-2021-0077
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023). ChatGPT for good? On Opportunities and Challenges of Large Language Models for Education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274 DOI: https://doi.org/10.1016/j.lindif.2023.102274
Lateef, S. A., Ansari, M. A., Ansari, M., and Manwatkar, T. (2025, May). AI Image Generator Using Open Source for Text to Image. International Journal of Electrical and Electronics and Computer Science (IJEECS), 14(1), 217–220.
Lee, H. (2023). The Rise of ChatGPT: Exploring its Potential in Medical Education. Anatomical Sciences Education, 17, 926–931. https://doi.org/10.1002/ase.2270 DOI: https://doi.org/10.1002/ase.2270
Li, Z., and Demir, İ. (2023). U-Net-Based Semantic Classification for Flood Extent Extraction Using SAR Imagery and GEE Platform: A Case Study for 2019 Central US Flooding. Science of the Total Environment, 869, Article 161757. https://doi.org/10.1016/j.scitotenv.2023.161757 DOI: https://doi.org/10.1016/j.scitotenv.2023.161757
Perkins, M. (2023). Academic Integrity Considerations of AI Large Language Models in the Post-Pandemic Era: ChatGPT and Beyond. Journal of University Teaching and Learning Practice, 20, Article 7. https://doi.org/10.53761/1.20.02.07 DOI: https://doi.org/10.53761/1.20.02.07
Ramirez, C. E., Sermet, Y., and Demir, İ. (2023). HydroLang Markup Language: Community-Driven Web Components for Hydrological Analyses. Journal of Hydroinformatics, 25, 1171–1187. https://doi.org/10.2166/hydro.2023.149 DOI: https://doi.org/10.2166/hydro.2023.149
Ramirez, C. E., Sermet, Y., Molkenthin, F., and Demir, İ. (2022). HydroLang: An Open-Source Web-Based Programming Framework for Hydrological Sciences. Environmental Modelling and Software, 157, Article 105525. https://doi.org/10.1016/j.envsoft.2022.105525 DOI: https://doi.org/10.1016/j.envsoft.2022.105525
Sajja, R., Sermet, Y., Cwiertny, D. M., and Demir, İ. (2023). Platform-Independent and Curriculum-Oriented Intelligent Assistant for Higher Education. International Journal of Educational Technology in Higher Education, 20, Article 42. https://doi.org/10.1186/s41239-023-00412-7 DOI: https://doi.org/10.1186/s41239-023-00412-7
Sit, M., Langel, R. J., Thompson, D. A., Cwiertny, D. M., and Demir, İ. (2021). Web-Based Data Analytics Framework for Well Forecasting and Groundwater Quality. Science of the Total Environment, 761, Article 144121. https://doi.org/10.1016/j.scitotenv.2020.144121 DOI: https://doi.org/10.1016/j.scitotenv.2020.144121
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Kalpana Rawat, Dr. Sadaf Hashmi , Shivam Khurana, Dr. Shweta Kashid, Sukhman Ghumman, Dr. Tapasmini Sahoo
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
