###### Abstract  
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. 11 202  
subject of computer science, covering topics such as programming basics, data structures, algorithms, and logic building. Each topic is broken down into modular concepts aligned with standard school curricula, making the system suitable for structured learning environments such as classrooms and online tutoring platforms. To create personalized pathways, the system uses a knowledge graph, where each concept is represented as a node and prerequisite relationships are represented as edges. This structure enables the system to deliver content in a logical and adaptive sequence, rather than randomly [3]. Students undergo an initial diagnostic assessment, and based on their performance, they are guided through a customized learning path that addresses knowledge gaps while reinforcing foundational concepts [4]. The system combines both machine learning and deep learning models to enhance prediction and personalization. XGBoost, a powerful ensemble ML algorithm, is used to identify which knowledge components a learner is likely to struggle with, based on historical data. In parallel, gated recurrent units (GRUs), a type of recurrent neural network (RNN), are employed to model temporal learning patterns and track how learners improve over time [5]. This hybrid approach allows the system to adapt content not only to what learners know but also to how they learn [6]. To generate personalized learning materials, the system employs generative adversarial networks (GANs), which have been effectively used in education for tasks such as generating practice questions, visual learning aids, and even datasets for training models [7; 8; 9; 10]. The generated content is aligned with the learner's current level and target concepts, providing meaningful practice and reinforcing mastery. Feedback is another critical element of learning. However, many learners report dissatisfaction with automated feedback in online learning environments. Prior work analyzing over 17,000 instructor comments revealed 11 types of feedback that impact learning in competency-based models [11].