Leveraging AI-Driven UI Frameworks for Seamless User Experiences in Daily Life Applications
Gopi Chand Vegineni
Independent researcher, Principal UI/UX Developer, Enrollment and Eligibility team, Maryland, United States
Download PDF
http://doi.org/10.37648/ijiest.v11i01.006
Abstract
Modern application interfaces depend heavily on UI frameworks to create smooth and natural user experiences in digital environments. The emergence of AI technology is transforming modern UI frameworks so they can provide intelligent and personalized user experiences that adapt to user needs. The research study examines AI-driven UI frameworks which improve everyday application experiences through usability optimization as well as interaction automation and content personalization. AI-driven UIs use machine learning and natural language processing together with predictive analytics to dynamically adapt to user preferences while enhancing workflow efficiency and accessibility.
Keywords: Artificial Intelligence; User Interface; Machine Learning; UI Automation; Human-Computer Interaction
- Attaluri, V. (2022). Securing SSH access to EC2 instances with privileged access management (PAM). Multidisciplinary International Journal, 8, 252–260.
- Big data analytics for policy making. (2017). Report: A study prepared for the European Commission DG INFORMATICS (DG DIGIT). European Commission.
- Castillo, C., Imran, M., Lucas, J., Meier, P., & Vieweg, S. (n.d.). AIDR: Artificial intelligence for disaster response.
- Data science meets public policy. (n.d.). The University of Chicago Magazine. https://mag.uchicago.edu/university-news/data-science-meets-public-policy
- IBM Research. (2017, April). Machine learning models for drug discovery. IBM Research Blog. https://www.ibm.com/blogs/research/2017/04/machine-learning-models-drug-discovery/
- Maroju, P. K. (2021). Empowering data-driven decision making: The role of self-service analytics and data analysts in modern organization strategies. International Journal of Innovations in Applied Science and Engineering, 7.
- Mudunuri, L. N. R. (2024a). Artificial intelligence (AI) powered matchmaker: Finding your ideal vendor every time. FMDB Transactions on Sustainable Intelligent Networks, 1(1), 27–39.
- Mudunuri, L. N. R. (2024b). Maximizing every square foot: AI creates the perfect warehouse flow. FMDB Transactions on Sustainable Computing Systems, 2(2), 64–73.
- Mudunuri, L. N. R. (2024c). Utilizing AI for cost optimization in maintenance supply management within the oil industry. International Journal of Innovations in Applied Sciences & Engineering, 10(1), 10–18.
- Mudunuri, L. N. R., Maroju, P. K., & Aragani, V. M. (2025, January 9). Leveraging NLP-driven sentiment analysis for enhancing decision-making in supply chain management. In 2025 Fifth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) (pp. 1–6).
- Palakurti, N. R., Attaluri, V. C., Hullurappa, M., Batchu, R., Mudunuri, L. N. R., & Vemulapalli, G. (2025, April). Identity access management for network devices: Enhancing security in modern IT infrastructure.
- Panyaram, S. (2023a). Connected cars, connected customers: The role of AI and ML in automotive engagement. International Transactions in Artificial Intelligence, 7(7), 1–15.
- Panyaram, S. (2023b). Digital transformation of EV battery cell manufacturing leveraging AI for supply chain and logistics optimization, 18(1), 78–87.
- Panyaram, S. (2024). Automation and robotics: Key trends in smart warehouse ecosystems. International Numeric Journal of Machine Learning and Robots, 8(8), 1–13.
- Program for Monitoring Emerging Diseases (ProMED). (n.d.). About ProMED. http://www.promedmail.org/aboutus/
- Pulivarthy, P. (2022, December 9). AWS data lakes, machine learning, and AI-driven insights for efficiency, quality, and innovation transforming semiconductor manufacturing. International Journal for Multidisciplinary Research (IJFMR), 4(6).
- Pulivarthy, P. (2024a). Optimizing large-scale distributed data systems using intelligent load balancing algorithms. AVE Trends in Intelligent Computing Systems, 1(4), 219–230.
- Pulivarthy, P. (2024b). Semiconductor industry innovations: Database management in the era of wafer manufacturing. FMDB Transactions on Sustainable Intelligent Networks, 1(1), 15–26.
- Puvvada, R. K. (2025a, March). SAP S/4HANA Cloud: Driving digital transformation across industries. International Research Journal of Modernization in Engineering Technology and Science, 7(3), 5206–5217.
- Puvvada, R. K. (2025b, March). The impact of SAP S/4HANA Finance on modern business processes: A comprehensive analysis. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(2), 817–825.
- Sharma, V., Kumar, A., Panat, L., Karajkhede, G., & Lele, A. (n.d.). Malaria outbreak prediction model using machine learning.
- Vegineni, G. C. (2024a). Designing secure and user-friendly interfaces for child support systems: Enhancing fraud detection and data integrity. AIJMR, 2(3).
- Vegineni, G. C. (2024b). Exploring anomalies in dark web activities for automated threat identification. FMDB Transactions on Sustainable Computing Systems, 2(4), 189–200.
- Vinoski, S. (2008). REST eye for the SOA guy. IEEE Internet Computing, 12(1), 82–84. https://doi.org/10.1109/MIC.2008.11
- Zur Muehlen, M., Nickerson, J. V., & Deyoung, K. (2003). Enterprise architecture for workflow management systems. IEEE Computer Society.