The main objective of this article is to analyze the possibilities of using artificial intelligence (AI) system mechanisms in evaluating and selecting the best websites from the end-user perspective. After preliminary considerations related to recent experiments on measuring and selecting the best solutions for websites, the authors presented the basic elements of the concept of a system for evaluating practical aspects of the representation of web systems with their main functions. To achieve the purpose of the article, conceptual modeling was used based on: literature, practical experience of the authors, and the identification of model factors obtained from previous studies. This article partially solves the research gap that exists in the field of practical frameworks for evaluating websites using AI-assisted tools. The most important achievement of this article is the creation of an automated framework for evaluating websites supported by AI, which could be used by both scientists and business people for research. The next steps of the research should focus on the construction of a real AI-assisted system, its testing, and verification, and on expanding the spectrum of evaluation criteria and new methods for evaluating websites.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

AI-Powered Automated Website Evaluation Tool Framework

  • Witold Chmielarz,
  • Anna Sołtysik-Piorunkiewicz

摘要

The main objective of this article is to analyze the possibilities of using artificial intelligence (AI) system mechanisms in evaluating and selecting the best websites from the end-user perspective. After preliminary considerations related to recent experiments on measuring and selecting the best solutions for websites, the authors presented the basic elements of the concept of a system for evaluating practical aspects of the representation of web systems with their main functions. To achieve the purpose of the article, conceptual modeling was used based on: literature, practical experience of the authors, and the identification of model factors obtained from previous studies. This article partially solves the research gap that exists in the field of practical frameworks for evaluating websites using AI-assisted tools. The most important achievement of this article is the creation of an automated framework for evaluating websites supported by AI, which could be used by both scientists and business people for research. The next steps of the research should focus on the construction of a real AI-assisted system, its testing, and verification, and on expanding the spectrum of evaluation criteria and new methods for evaluating websites.