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@article{188486,
        author = {Gaurav Tijare and Prof. Vishal Nayakwadi and Aarya Shendaware and Nimisha Tirpude and Abhijeet Uphade},
        title = {From Static to Smart : Enhancing UI/UX Adaptation with Double Q- learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1841-1848},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188486},
        abstract = {Currently, the majority of applications simply have a single interface that cannot be changed regardless of how individuals use the application. The common method, Q-Learning, can give things a strange or annoying fix, which RL can fix, as it will make things smarter and customized, yet it is overly optimistic. To solve this we implemented Double Q-Learning (DQL) to create an interface that actually changes. We have two AI evaluators, one suggests a change, and the other one verifies whether it is possible. This makes the interface continuously develop and prevents poor decisions. With users in real time; when they click on something, the duration of interaction with a feature, which feature to visit, our framework will continue to make minor adjustments to the layout, content, and priorities of features. We realized that this is the best way to make software significantly easier to interact with, to maintain user engagement and increase customer satisfaction. It performs excellently with websites, phones, and also assistive technology.},
        keywords = {Double Q-Learning (DQL), Reinforcement Learning (RL), Human-Computer Interaction (HCI), Personality, Adaptive User Interface (AUI), User Experience (UX); Software engineering, intelligent systems, dynamic response of UI, and bias of overestimation.},
        month = {December},
        }