CHATGPT AS A CO-TEACHER IN ART EDUCATION

Authors

  • Dr. Manoj Kumar Pathak Associate Professor, Department of English, Arka Jain University, Jamshedpur, Jharkhand, India
  • Jenifer Patel Assistant Professor, Department of Fashion Design, Parul Institute of Design, Parul University, Vadodara, Gujarat, India
  • Aseem Aneja Centre of Research Impact and Outcome, Chitkara University, Rajpura 140417, Punjab, India
  • Manivannan Karunakaran Professor and Head, Department of Information Science and Engineering, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India
  • Achala Dwivedi Assistant Professor, School of Sciences, Noida International University, 203201, India
  • Mary Praveena J Department of Computer Science and Engineering, Aarupadai Veedu Institute of Technology, Vinayaka Mission’s Research Foundation (Deemed to be University), Tamil Nadu, India
  • Tushar Jadhav Department of E and TC Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037 India

DOI:

https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6873

Keywords:

Chatgpt in Education, Art Pedagogy, AI-Assisted Creativity, Co-Teaching Models, Digital Art Learning, Human–AI Collaboration

Abstract [English]

The fast pace of integrating artificial intelligence in the educational setting has created new learning possibilities especially in creative subjects like art education. In this paper, it is suggested to consider ChatGPT as a Co-Teacher to demonstrate how generating AI may aid instructional procedures, increase student engagement, and facilitate differentiated learning opportunities. Being a smart assistant, ChatGPT aids in ideation, explanations of techniques, exploration of concepts and formative critique, hence replacing human art educators, rather than consuming them. The paper reviews the abilities of ChatGPT in a variety of creative settings, including painting, digital illustration, printmaking, 3D modeling, sculpting, and art history, and how AI-guided prompts, descriptions, and judgments can support the creative efforts of students and broaden their visual thinking. An organized Teacher-AI Collaboration Model is provided that describes the workflow of the art lessons that incorporate the points of ChatGPT intervention including: brainstorming, procedural guidance, assessment, and reflective practice. The model illustrates the advantages of the personalized coaching of learners, alternative perspectives, and trial and error through a series of case studies. Meanwhile, the paper touches on the most important issues concerning originality, authorship, and over-trust in algorithmic suggestions. Ethical concerns such as biases in datasets, cultural representation, and human creativity maintenance are taken care of to make the implementation responsible.

References

Borji, A. (2023). Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2 (arXiv preprint). arXiv.

Giannini, T., and Bowen, J. P. (2023). Generative Art and Computational Imagination: Integrating Poetry and Art. In Proceedings of EVA London 2023 (211–219). https://doi.org/10.14236/ewic/EVA2023.37

Hall, J., and Schofield, D. (2025). The Value of Creativity: Human Produced Art vs. AI-Generated Art. Art and Design Review, 13, 65–88. https://doi.org/10.4236/adr.2025.131005

Horton, C. B., Jr., White, M. W., and Iyengar, S. S. (2023). Bias Against AI Art Can Enhance Perceptions of Human Creativity. Scientific Reports, 13, 19001. https://doi.org/10.1038/s41598-023-45202-3

Kannen, N., Ahmad, A., Andreetto, M., Prabhakaran, V., Prabhu, U., Dieng, A. B., and Bhattacharyya, P. (2024). Beyond Aesthetics: Cultural Competence in Text-to-Image Models (arXiv:2407.06863). arXiv.

Liu, B., Wang, L., Lyu, C., Zhang, Y., Su, J., and Shi, S. (2024). On the Cultural Gap in Text-to-Image Generation. In Frontiers in Artificial Intelligence and Applications (Vol. 392, 930–937). https://doi.org/10.3233/FAIA240581

Marcus, G., Davis, E., and Aaronson, S. (2022). A Very Preliminary Analysis of DALL-E 2 (arXiv:2204.13807). arXiv.

Oppenlaender, J., Linder, R., and Silvennoinen, J. (2023). Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering (arXiv:2303.13534). arXiv. https://doi.org/10.1080/10447318.2024.2431761

Prunkl, C. (2024). Human Autonomy at Risk? An Analysis of the Challenges from AI. Minds and Machines, 34, 26. https://doi.org/10.1007/s11023-024-09665-1

Rhem, J. A. (2023). Ethical Use of Data in AI Applications. IntechOpen.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022). High-Resolution Image Synthesis With Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR52688.2022.01042

Santoni de Sio, F. (2024). Artificial Intelligence and the Future of Work: Mapping the Ethical Issues. Journal of Ethics, 28, 407–427. https://doi.org/10.1007/s10892-024-09493-6

Watiktinnakorn, C., Seesai, J., and Kerdvibulvech, C. (2023). Blurring the Lines: How AI is Redefining Artistic Ownership and Copyright. Discover Artificial Intelligence, 3, 3. https://doi.org/10.1007/s44163-023-00088-y

Wei, M., Feng, Y., Chen, C., Luo, P., Zuo, C., and Meng, L. (2024). Unveiling Public Perception of AI Ethics: An Exploration on Wikipedia Data. EPJ Data Science, 13, 26. https://doi.org/10.1140/epjds/s13688-024-00462-5

Westermann, C., and Gupta, T. (2023). Turning Queries into Questions: For a Plurality of Perspectives in the Age of AI and Other Frameworks with Limited (Mind)sets. Technoetic Arts: A Journal of Speculative Research, 21, 3–13. https://doi.org/10.1386/tear_00106_2

Downloads

Published

2025-12-25

How to Cite

Pathak, M. K., Patel, J., Aneja, A., Karunakaran, M., Dwivedi, A., Praveena J, M., & Jadhav, T. (2025). CHATGPT AS A CO-TEACHER IN ART EDUCATION. ShodhKosh: Journal of Visual and Performing Arts, 6(4s), 442–451. https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6873