###### Abstract
To improve transparency and learner confidence, our system incorporates explainable AI (XAI) tools like Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), which allow users to understand why certain modules are recommended and how predictions are made [12; 13]. Despite significant advancements, current systems still face limitations. Many do not dynamically update content based on real-time student feedback, lack transparency in their decision-making processes, and treat all learner behaviours equally--without distinguishing between explicit signals (what students know) and implicit signals (how they learn) [14; 15]. The proposed system addresses these limitations by integrating predictive modelling, dynamic content generation, and interpretable AI into one unified platform. Separating explicit and implicit signals makes learner modelling clearer and more actionable [16; 17; 18; 19]. The convergence of generative AI and adaptive learning is evolving rapidly, presenting both significant opportunities and notable challenges [20]. While numerous researchers have explored adaptive e-learning systems focusing on individual criteria--such as learning styles, cognitive preferences, knowledge levels, or behavioral traits--only a limited number of studies have integrated multiple learning factors for personalization [21]. Recommendation models often utilize a hybrid of collaborative and content-based filtering techniques to produce top-N learning suggestions based on user ratings [22]. Research on learning paths offers valuable insights into student behavior during the learning process, as each learner possesses a distinct knowledge structure shaped by their prior experiences and capabilities [23]. Adaptive educational systems are designed to personalize content delivery and learning pathways, helping to reduce learner disorientation and cognitive overload, thereby enhancing overall learning efficiency [24]. Individual learning preferences, influenced by personality and environmental factors, vary widely; for instance, some students grasp concepts better through verbal instructions, while others benefit more from hands-onexperience [25]. Delivering learning materials and activities tailored to these preferences significantly improves learner satisfaction, enhances academic performance, and increases time efficiency [26; 27]. Personalized assessments further contribute to student motivation and engagement by allowing learners to demonstrate their true competencies [28]. Explainable AI methods tailored for generative AI (GenXAI) provide transparency into AI decisions, offering interpretability at both the individual prediction level and the model-wide level. Traditionally, such explanations have served to enhance trust, support decision-making, and facilitate model debugging [29]. Evolutionary algorithms have been applied to the learning path generation problem by framing it as an optimization task that maximizes the compatibility between the learner's profile and the proposed path [30]. Generative AI, unlike conventional AI models focused on classification or regression, is capable of producing new data outputs such as images, audio, or text by leveraging machine learning and deep learning methodologies [30]. Since the launch of ChatGPT in November 2022, public interest in the application of generative AI within education has grown substantially, prompting ongoing investigation into its educational impact and benefits [31]. AI-powered intelligent assistant (AIIA) systems utilize advanced natural language processing and AI technologies to create dynamic, interactive learning environments [32]. However, as generative AI tools are still emerging in the context of higher education, substantial gaps exist in the literature concerning effective implementation strategies and best practices for adoption [33; 34].