Desarrollo de un módulo de retroalimentación automática para el aula virtual utilizando inteligencia artificial

Abstract

The project develops an automatic feedback module using artificial intelligence for ESPOL's Aula Virtual educational platform. Its primary goal is to enhance the evaluation of Python programming assignments in the Data Science and Artificial Intelligence program. The initiative addresses the increasing need for personalized and timely feedback, optimizing teaching resources through AI integration. The development utilized LTI standards for Canvas integration, machine learning techniques to analyze code syntax, logic, and efficiency, and natural language processing to generate understandable feedback. Tests with faculty and students ensured the precision and effectiveness of the feedback. Results showed a significant reduction in evaluation time, with 87% of users satisfied with feedback clarity and 100% of students reporting meaningful learning improvements. The system enhances the educational experience by providing detailed, personalized, and real-time feedback. It is concluded that the solution not only reduces teaching workloads but also fosters deep and effective learning, positioning ESPOL as a leader in AI integration in education. Keywords: Machine Learning, Educational Systems, Automated Assessment.