This proposal investigates the potential and applications of AI-based chatbots, focusing specifically on their integration within Moodle, a widely used educational platform. The primary aim is to enhance the teaching and learning process by personalizing interactions to meet individual student needs, providing immediate feedback, and addressing frequently asked questions. This integration aims to improve both accessibility and learning efficiency in Moodle, making educational support more effective and readily available.

The aim is to characterize the interaction between the student and the AI chatbot in order to identify the type of use employed by a student within an online course. That is, to understand if these tools, when integrated into a learning management system, fulfill the original purpose of supporting students in their learning processes within a particular subject.

Firstly, it is essential to identify and define the specific challenges within educational platforms and conduct a comprehensive analysis of current AI chatbot implementations in education. Next, select the necessary technological tools, such as the OpenAI API, and define metrics to evaluate the effectiveness and impact of the proposed AI chatbot solution. These metrics should include user satisfaction, response accuracy, and improvements in learning outcomes, among others. Following this, develop and configure a prototype AI chatbot using the OpenAI API, and then integrate it into Moodle. This process involves designing the chatbot's architecture, developing its user interface, and ensuring seamless interaction with Moodle's existing functionalities.

The proposed AI chatbot architecture for Moodle includes several critical components: a user interface that students interact with directly, such as a chat window integrated into Moodle's interface, designed to be user-friendly and accessible; a chatbot engine that processes student questions and generates responses, comprising a Natural Language Processing (NLP) module to understand questions and interpret intent, and a Machine Learning engine that personalizes responses based on individual performance and behavior patterns; a knowledge base containing comprehensive information used by the chatbot to generate responses, including course content, school policies, and procedures; and finally, integration with Moodle that ensures seamless connectivity between the chatbot and the platform, including user authentication, data extraction from student profiles, and integration with Moodle functions such as assignments and forums.

The research underscores the transformative impact of AI in education, demonstrating that AI chatbots can significantly enhance the learning experience by providing personalized, immediate support tailored to individual needs. By leveraging the capabilities of the existents API for chatbots, the developed chatbot aims to offer intuitive, efficient assistance, improving both educational outcomes and user satisfaction. The study addresses various challenges associated with integrating AI technologies in educational platforms, such as quality control, data privacy, and balancing technological reliance with human interaction. Overall, this implementation promises to make Moodle a more dynamic and responsive learning environment, benefiting both students and educators by streamlining educational processes and enriching the overall educational experience.

Keywords: Chabot, moodle, e-learning, learning analytics.