Accessavior: AI-Driven Real-Time Adaptive Web Interfaces for Intelligent User Experiences

Year : 2026 | Volume : 16 | Issue : 02 | Page : 14 22
By

Nupur Choudhary,

Surendra Kumar Yadav,

Shruti Mathur,

  1. Student, Department of Computer Science and Engineering JECRC University, Rajasthan, India
  2. Professor, Department of Computer Science and Engineering JECRC University, Rajasthan, India
  3. Assistant Professor, Department of Computer Science and Engineering JECRC University, Rajasthan, India

Abstract

As the web has become more diverse, many traditional, one-size-fits-all web accessibility solutions that must be configured manually have proven inadequate in meeting the needs of each user. This paper introduces “Accessavior”, which is an intelligent, behavioural pattern-driven accessibility framework that can automatically customize the personalization of web interfaces based on the interaction patterns of users. The proposed system will continuously track cursor behaviours such as cursor movements, keystroke characteristics, dwell time, scrolling behaviour and navigation loops to detect motor, visual and cognitive accessibility needs. An ensemble machine learning method based on the combination of Random Forest, Support Vector Machine and K-Means clustering is used to classify user needs and to compute adaptive interface changes. The framework dynamically changes the WEBSO elements according to the calculation of the accessibility of the element based on the derived accessibility profile, by changing font size, contrast, size of buttons, simplification of navigation feature and setting up assistance for typing. This work is validated with a test on 2,400 annotated user sessions showing an average adaptation latency of 97ms, an accuracy of 93.2%, precision of 91.6%, recall of 90.9%, and an F1 score of 0.913. Additionally, the people are pleased with the adaptive framework (with positive feedback ratings through various user classes) and from an efficiency point of view, the adaptive framework improves the task completion by 25%. The findings suggest that use of behavioral analytics alongside ensemble learning can facilitate generation of accurate, low-latency and user-centric accessibility adaptations that can lead to the development of intelligent web interfaces which provide improved usability, inclusiveness and overall user experience in real-time environments.

Keywords: Behavioral analytics, web accessibility, machine learning, adaptive interfaces, explainable AI

[This article belongs to Current Trends in Information Technology ]

How to cite this article: Nupur Choudhary, Surendra Kumar Yadav, Shruti Mathur. Accessavior: AI-Driven Real-Time Adaptive Web Interfaces for Intelligent User Experiences. Current Trends in Information Technology. 2026; 16(02):14-22.
How to cite this URL: Nupur Choudhary, Surendra Kumar Yadav, Shruti Mathur. Accessavior: AI-Driven Real-Time Adaptive Web Interfaces for Intelligent User Experiences. Current Trends in Information Technology. 2026; 16(02):14-22. Available from: https://journals.stmjournals.com/ctit/article=2026/view=258940

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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 03/02/2026
Accepted 26/03/2026
Published 10/04/2026
Publication Time 66 Days

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