Rúbricas automatizadas para evaluar buenas prácticas en HTML

This paper presents an automated tool to assess the structural quality of web pages, based on the implementation of standard rubrics using the Flask framework. The tool analyzes essential tags such as <title>, <meta>, <h1>, <img alt> and <p>, assigning scores based on their presence and correct implementation. This solution was applied to 120 pages developed by university students, allowing to identify common patterns in the implementation of web standards, as well as areas for improvement, especially in accessibility and SEO. The results demonstrate the effectiveness of the system to provide fast and objective feedback, optimizing teaching and assessment processes in educational contexts. Finally, possible improvements are discussed, such as the integration of artificial intelligence for more detailed analysis and personalized recommendations.