Explainable AI-Integrated and GAN-Enabled Dynamic Knowledge Component Prediction System (DKPS) Using Hybrid ML Model

The progressive advancements in education due to the advent of transformative technologies has led to the emergence of customized/personalized learning systems that dynamically adapts to an individual learner’s preferences in real-time mode. The learning route and style of every learner is unique and their understanding varies with the complexity of core components. This paper presents a hybrid approach that integrates generative adversarial networks (GANs), feedback-driven personalization, explainable artificial intelligence (XAI) to enhance knowledge component (KC) prediction and to improve learner outcomes as well as to attain progress in learning. By using these technologies, this proposed system addresses the challenges, namely, adapting educational content to an individual’s requirements, creating high-quality content based on a learner’s profile, and implementing transparency in decision-making. The proposed framework starts with a powerful feedback mechanism to capture both explicit and implicit signals from learners, including performance parameters viz., time spent on tasks, and satisfaction ratings. By analysing these signals, the system vigorously adapts to each learner’s needs and preferences, ensuring personalized and efficient learning. This hybrid model dynamic knowledge component prediction system (DKPS) exhibits a 35% refinement in content relevance and learner engagement, compared to the conventional methods. Using generative adversarial networks (GANs) for content creation, the time required to produce high-quality learning materials is reduced by 40%. The proposed technique has further scope for enhancement by incorporating multimedia content, such as videos and concept-based infographics, to give learners a more extensive understanding of concepts.