HARNESSING GENERATIVE AI FOR ENHANCED USER EXPERIENCES: INTEGRATION STRATEGIES WITH MODERN WEB TECHNOLOGIES

Authors

  • Sudeesh Goriparthi Senior software engineer, software architecture, Walmart, Dallas, USA. Author

Keywords:

Senior software engineer, software architecture, Walmart, Dallas

Abstract

The rapid development of artificial intelligence has disrupted the process of creating content and websites. AI has demonstrated significant potential in the creation of content-based websites that are customized, which is one of the most popular fields. Personalization is an essential component of a user experience that is uniquely personalized to the individual, and it plays a significant role in both the engagement of customers and the expansion of businesses. We intend to explore the contribution that generative artificial intelligence makes to the creation of tailored content-based websites through the use of this research paper. We will also investigate the benefits and drawbacks of implementation while analyzing the consequences that it has on user satisfaction and commercial success. The methodologies in terms of how they deal with the gathering of data, preprocessing, training of models, production of models, and evaluation. According to the findings of the study, generative artificial intelligence can create websites that are tailored to the preferences and requirements of users. The purpose of this study is to investigate the applications, disadvantages, and potential future applications of generative artificial intelligence in personalized web development by analyzing the available literature and case studies. The methodologies in terms of how they deal with the gathering of data, preprocessing, training of models, production of models, and evaluation. According to the findings of the study, generative artificial intelligence can create websites that are tailored to the preferences and requirements of users.

References

Joseph Babcock; Raghav Bali, Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models, Packt Publishing, 2021.

Joseph Babcock; Raghav Bali, Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models , Packt Publishing, 2021.

D. Vaz, D. R. Matos, M. L. Pardal and M. Correia, "Automatic Generation of Distributed Algorithms with Generative AI," 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S), Porto, Portugal, 2023, pp. 127-131, doi: 10.1109/DSNS58398.2023.00037.

D. De Silva, N. Mills, M. El-Ayoubi, M. Manic and D. Alahakoon, "ChatGPT and Generative AI Guidelines for Addressing Academic Integrity and Augmenting Pre-Existing Chatbots," 2023 IEEE International Conference on Industrial Technology (ICIT), Orlando, FL, USA, 2023, pp. 1-6, doi: 10.1109/ICIT58465.2023.10143123.

F. Rasyad, H. A. Kongguasa, N. C. Onggususilo, Anderies, A. Kurniawan and A. A. S. Gunawan, "A Systematic Literature Review of Generative Adversarial Network Potential In AI Artwork," 2023 International Conference on Computer Science, Information Technology and Engineering (ICCoSITE), Jakarta, Indonesia, 2023, pp. 853-857, doi: 10.1109/ICCoSITE57641.2023.10127706.

M. Wong, Y. -S. Ong, A. Gupta, K. K. Bali and C. Chen, "Prompt Evolution for Generative AI: A Classifier-Guided Approach," 2023 IEEE Conference on Artificial Intelligence (CAI), Santa Clara, CA, USA, 2023, pp. 226-229, doi: 10.1109/CAI54212.2023.00105.

V. Bilgram and F. Laarmann, "Accelerating Innovation With Generative AI: AI-Augmented Digital Prototyping and Innovation Methods," in IEEE Engineering Management Review, vol. 51, no. 2, pp. 18-25, 1 Secondquarter,june 2023, doi: 10.1109/EMR.2023.3272799.

Han, A., & Cai, Z. (2023, June). Design implications of generative AI systems for visual storytelling for young learners. In Proceedings of the 22nd Annual ACM Interaction Design and Children Conference (pp. 470-474).

Nur, N., Goh, S. J., Patel, J., & Mizrahi, M. (2024). NAVIGATING THE ETHICAL LANDSCAPE OF AI INTEGRATION IN EDUCATIONAL SETTINGS. In INTED2024 Proceedings (pp. 7654-7663). IATED.

Wach, K., Duong, C. D., Ejdys, J., Kazlauskaitė, R., Korzynski, P., Mazurek, G., ... & Ziemba, E. (2023). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT. Entrepreneurial Business and Economics Review, 11(2), 7-30.

Griffiths, D., Frías-Martínez, E., Tlili, A., & Burgos, D. (2024). A Cybernetic Perspective on Generative AI in Education: From Transmission to Coordination.

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Published

2024-05-24

How to Cite

Sudeesh Goriparthi. (2024). HARNESSING GENERATIVE AI FOR ENHANCED USER EXPERIENCES: INTEGRATION STRATEGIES WITH MODERN WEB TECHNOLOGIES. INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT (IJAIRD), 2(1), 176-188. https://iaeme-library.com/index.php/IJAIRD/article/view/IJAIRD_02_01_015