AI-DRIVEN PRINT ADVERTISING IN EDUCATIONAL INSTITUTIONS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i1s.2025.6648Keywords:
Artificial Intelligence, Print Advertising, Educational Institutions, Predictive Analytics, Augmented RealityAbstract [English]
The use of Artificial Intelligence (AI) in the print advertisement is an extreme step in the landscape of the educational system in which the educational institution is keen to enhance their reach, communication, and branding abilities. The traditional application of print ad, which was once said to be the one-size-fits-all ad with no dynamism, is becoming a dynamic application where the idea of AI-controlled data is applied and automated. This paper explores the application of AI technologies to automatize the print campaigns of educational industry, such as predictive analytics, machine learning, and augmented reality (AR). It investigates how the AI assists institutions in locating the target audiences more precisely, tailor the content, and improve the result of the campaign with the assistance of the data-driven feedback systems. The introduction of such features as QR codes, AR games and AI-made design patterns helps print media to cross its traditional boundaries and establish an interactive and measurable communication. The study is a mixed-method strategy that balances both quantitative data of institutional campaigns and qualitative data of the marketing professionals. The results indicate that AI-enhanced print advertising does not only help in raising student engagement and enrollment rates but also, institutional branding through the integration of traditional and digital media platforms. The suggested theoretical framework is built on the basis of the Technology Acceptance Model and Diffusion of Innovations theory that presented the interdependence between the technological adoption, user perception and the marketing results.
References
Al-Kfairy, M., Mustafa, D., Kshetri, N., Insiew, M., and Alfandi, O. (2024). Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective. Informatics, 11(3), Article 58. https://doi.org/10.3390/informatics11030058 DOI: https://doi.org/10.3390/informatics11030058
Anvekar, S. V., Gurav, P. S., and Oza, K. S. (2025). Sales Forecasting Prediction Using Machine Learning. International Journal of Electrical Engineering and Computer Science (IJEECS), 14(1), 15–19.
Argan, M., Dinç, H., Kaya, S., and Argan, M. T. (2022). Artificial Intelligence (AI) in Advertising. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal. https://doi.org/10.14201/adcaij.28331 DOI: https://doi.org/10.14201/adcaij.28331
De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K.-U., and von Wangenheim, F. (2020). Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing, 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007 DOI: https://doi.org/10.1016/j.intmar.2020.04.007
Enache, M. C. (2020). AI for Advertising. Annals of “Dunarea de Jos” University of Galati, Fascicle I: Economics and Applied Informatics, 26, 28–32. https://doi.org/10.35219/eai1584040978 DOI: https://doi.org/10.35219/eai1584040978
Haleem, A., Javaid, M., Qadri, M. A., Singh, R. P., and Suman, R. (2022). Artificial Intelligence (AI) Applications for Marketing: A Literature-Based Study. International Journal of Intelligent Networks, 3, 119–132. https://doi.org/10.1016/j.ijin.2022.08.005 DOI: https://doi.org/10.1016/j.ijin.2022.08.005
Hatwar, L. R., Pohane, R. B., Bhoyar, S., and Padole, S. P. (2025). Mathematical Modeling on Decay of Radioactive Material Affects Cancer Treatment. International Journal of Research and Development in Management Review, 14(1), 180–182. https://doi.org/10.65521/ijrdmr.v14i1.501 DOI: https://doi.org/10.65521/ijrdmr.v14i1.501
Huh, J., Nelson, M. R., and Russell, C. A. (2023). ChatGPT, AI Advertising, and Advertising Research and Education. Journal of Advertising, 52(4), 477–482. https://doi.org/10.1080/00913367.2023.2227013 DOI: https://doi.org/10.1080/00913367.2023.2227013
Järvi, K., Almpanopoulou, A., and Ritala, P. (2018). Organization of Knowledge Ecosystems: Prefigurative and Partial Forms. Research Policy, 47(8), 1523–1537. https://doi.org/10.1016/j.respol.2018.05.007 DOI: https://doi.org/10.1016/j.respol.2018.05.007
Kandoth, S., and Shekhar, S. K. (2024). Scientometric Visualization of Data on Artificial Intelligence and Marketing: Analysis of Trends and Themes. Science Talks, 9, Article 100309. https://doi.org/10.1016/j.sctalk.2024.100309 DOI: https://doi.org/10.1016/j.sctalk.2024.100309
Rahman, W. F. W. A., Che Fauzi, A. A., Wan Husain, W. S., Che Hassan, S. H., Nik Kamaruzaman, N. U. S., and Wan Abdul Aziz, W. N. H. (2020). The Usage of Artificial Intelligence in Marketing Automation: Potentials and Pitfalls. Journal of Mathematical and Computational Science, 6, 1–8.
Sorenson, O., and Fleming, L. (2020). Science as a Vocation: Knowledge Ecosystems and the Organization of Science. Research Policy, 49, Article 103994.
Stancu, A., and Panait, M. (2025). Marketing Strategy Metamorphosis Under the Impact of Artificial Intelligence Services. Systems, 13(4), Article 227. https://doi.org/10.3390/systems13040227 DOI: https://doi.org/10.3390/systems13040227
Verma, S., Sharma, R., Deb, S., and Maitra, D. (2021). Artificial Intelligence in Marketing: Systematic Review and Future Research Direction. International Journal of Information Management Data Insights, 1, Article 100002. https://doi.org/10.1016/j.jjimei.2020.100002 DOI: https://doi.org/10.1016/j.jjimei.2020.100002
Vodă, A. I., Bortoș, S., and Şoitu, D. T. (2023). Knowledge ecosystem: A Sustainable Theoretical Approach. European Journal of Sustainable Development, 12(2), 47. https://doi.org/10.14207/ejsd.2023.v12n2p47 DOI: https://doi.org/10.14207/ejsd.2023.v12n2p47
Wisetsri, W. (2021). Systematic Analysis and Future Research Directions in Artificial Intelligence for Marketing. Turkish Computer and Mathematics Education Journal, 12(11), 43–55. https://doi.org/10.17762/turcomat.v12i11.5825 DOI: https://doi.org/10.17762/turcomat.v12i11.5825
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Mr. Nikhil Kakade deshmukh, Dr. Ramchandra Vasant Mahadik, Varun Ojha, Vaibhav Kaushik, G.T.Harikrishna Murthy, Om Rakash
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.
