AI- DRIVEN CONVERSATIONAL BOTS FOR PERSONALIZED E- LEARNING

Abstract

Personalized e-learning customizes educational content to align with individual learners' needs, preferences, and learning paces, thereby enhancing engagement and effectiveness. Historically, educators have envisioned adaptive learning environments that dynamically respond to each student's progress, aiming to optimize educational outcomes. Prior to the integration of artificial intelligence, methods such as differentiated instruction, modular course designs, and computer-based training programs were employed to achieve these goals. However, these approaches often lacked the capacity to provide real-time, personalized feedback and adapt to the unique learning paths of each student. The development of AI-driven conversational bots addresses these limitations by offering scalable, interactive, and personalized tutoring experiences. Challenges in traditional systems include the inability to provide immediate, tailored feedback, lack of engagement, and difficulty in accommodating diverse learning styles within a standardized curriculum. The proposed system utilizes machine learning algorithms to create conversational agents capable of understanding and responding to individual learner inputs, providing customized guidance, and adapting content delivery to optimize learning outcomes. By analysing student interactions, these AI-driven bots can identify knowledge gaps, adjust instructional strategies in real-time, and offer a more engaging and effective personalized e-learning experience.