## 2 Related Work  
Adaptive learning provides various forms of personalization, such as customized interfaces [37], tailored learning content [38], and individualized learning paths [39]. When the goal of research is to analyse how learners interact with different types of learning materials, personalized learning content can be a useful choice [1]. The KC approach helps identify many features of educational data. The phenomenon called the curse of dimensionality has weaker explanatory power for the resulting models if relationships between concepts are analysed without selecting key features [40]. It is crucial to select the most relevant features for successful research [2].  
The needs and characteristics of a learner's learning style (LSs) are based on how well they classified and gathered information for the success of adaptive learning systems. To create adaptive, intelligent learning environments, these data were used [41]. Questionnaires to determine students' LSs have remarkable drawbacks [42]. First of all, filling out questionnaires takes time [42]. The results may be inaccurate, since students might not fully understand their learning preferences, leading to inconsistent answers [42]. Lastly, LSs can change over time, but questionnaires provide only static results [42].  
AI techniques have been used to automatically detect LSs to address these issues [41; 42; 43] providing methods that are more effective than questionnaires and that can adapt to changes in students' learning behaviours [42]. Optimizing their learning process and upgrading the overall e-learning experience by using machine learning (ML) algorithms, these approaches automatically map students' behaviour to specific LSs [42; 44].  
The extensive feedback during training, offering more insights than a single value, is provided by such advanced systems as XAI-GAN, which uses explainable AI (XAI) [45]. There is an increasing need for models' decisions to be understandable by users, stakeholders, and decision-makers as AI models grow in complexity. Explainability is essential for scientific coherence and trust in AI systems [45]. The federated learning method is another promising development based on co-training and GANs. It supports/allows each client to independently design and train their own model without sharing its structure or parameters with others. In experiments, this method exceeds the existing ones by 42% in test accuracy, even when model architectures and data distributions varied significantly [14]. Based on previous work that used a single dataset to predict learning paths, this system takes a dynamic approach to target module prediction. This improves learner engagement and optimizes the learning experience.  
Explicit and implicit are the two types of input signals in the proposed system. Models like random forest, logistic regression, and neural networks are suited for the structured signals (i.e., explicit signals). Models like recurrent neural networks (RNNs), long short-term memory (LSTM), or bidirectional encoder representations from transformers (BERT) are better suited for implicit signals involving sequential data, such as learning trends over time. This proposed system uses a weighted ensemble method to ensure accuracy to combine the results from both types of data. An XAI layer is added to improve transparency and interpretability. For generating content within the target module in this system, it utilizes GANs, which also help in gathering valuable feedback. The detailed review of personalised learning path prediction using different learner characteristics and numbers of parameters to implement it dynamically is shown in Table 2.