Enhancing socio-pedagogical engagement in Moodle through AI-driven personalized learning

Abstract

In the rapidly evolving landscape of digital education, platforms like Moodle have become integral to facilitating learning and fostering student engagement. However, challenges persist in maintaining socio-pedagogical engagement and providing personalized support that caters to the diverse needs of learners. This study introduces an AI-driven system designed to enhance socio-pedagogical engagement within Moodle by delivering personalized messages and recommendations tailored to individual student profiles. Leveraging advanced Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques, the proposed system analyses student interactions, performance metrics, and engagement patterns to generate customized content and communication strategies. The methodology involves the development of AI models trained on a combination of simulated datasets that mirror potential real-world scenarios within educational settings. These models are integrated into the Moodle platform to facilitate dynamic adaptation of learning materials and communication based on individual learner needs. Preliminary evaluations conducted through scenario-based testing indicate a significant potential for the AI-driven system to improve student engagement and learning outcomes. The system demonstrates the ability to identify disengagement early and respond with tailored interventions, thereby fostering a more inclusive and responsive educational environment. Moreover, the personalized recommendations have shown promise in addressing varied learning paces and styles, contributing to a more equitable learning experience. Despite the encouraging preliminary findings, the study acknowledges the limitations posed using fictional data and the need for validation through real-world implementation. Future research will focus on deploying the system within actual educational contexts to assess its efficacy comprehensively. Ethical considerations, particularly concerning data privacy and the handling of sensitive student information, will be paramount in these subsequent phases. This research contributes to the burgeoning discourse on the integration of AI in education, highlighting its potential to revolutionize socio-pedagogical engagement. By tailoring educational experiences to individual learner profiles, AI-driven systems like the one proposed can bridge gaps in engagement and support, paving the way for more personalized and effective digital education. The findings underscore the transformative potential of AI in enhancing educational equity and inclusivity, aligning with the contemporary shift towards learner-centered educational paradigms.