Jinat Ara, and Cecilia Sik-Lanyi

Towards developing a framework for automated accessibility evaluation of web content from expert perspectives

The current set of web accessibility evaluation tools requires a certain specification of information that requires user or expert perspectives. To improve the correctness and effectiveness of the evaluated result, expert perspectives can lead to great success, especially for the information that requires great effort, knowledge, and broadening research to set their determinator. Also, from the literature, not much effort is being observed to develop solutions for web accessibility evaluation addressing expert perspectives. Besides, the correctness of the evaluation report also depends on the used methods and technologies. Thus, consideration of advanced techniques might improve the performance of the assessment report. Therefore, in this paper, we aim to propose a framework to evaluate the accessibility of web content considering several evaluation criteria from expert perspectives considering several advanced techniques specifically Artificial Intelligence (AI) techniques. The proposed framework includes fifteen criteria that we obtained from consulting web experts and researchers that have a great effect on assessing the accessibility from the user's point of view. The proposed methodology evaluates accessibility following three phases: (a) identification of evaluation criteria from expert perspectives, (b) execution of the web accessibility evaluation process involving different evaluation algorithms incorporating different AI techniques, and (c) validate the framework through experimental and user-centric study to follow-up its computational ability. The proposed method is dynamic in nature and can be applied to different plattforms to evaulate multiple web pages.

Reference:

DOI:  10.36244/ICJ.2025.5.3

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Please cite this paper the following way:

Jinat Ara, and Cecilia Sik-Lanyi, "Towards developing a framework for automated accessibility evaluation of web content from expert perspectives", Infocommunications Journal, Special Issue on AI Transformation, 2025, pp. 16-23, https://doi.org/10.36244/ICJ.2025.5.3