## 3 Materials and Methodology
### Explainable AI (XAI) Integration in the Proposed System (DKPS)  
To help teachers and learners understand why the system made certain decisions, another method called SHAP is used. SHAP is like a tool that explains what influenced the system's choice. For instance, if the system decides that a student needs easier content, SHAP can show the reasons behind it--maybe the student had low quiz scores or spent  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Iteration** & **Feedback Collected** & **Action Taken** & **Result** \\ \hline \multirow{3}{*}{5} & Satisfaction ratings (1–5) & \multirow{3}{*}{Retrained model with updated feature weights.} & Prediction accuracy improved by 8\%. \\  & inconsistent across modules & & \\  & (explicit) & & \\ \hline \multirow{3}{*}{6} & Learners skipping certain & Made sure learner solved foundational & Coverage of learning \\  & modules (implicit) & priorities to progress consistently. & paths improved by 10\%. \\ \hline \multirow{3}{*}{7} & “Lack of examples in content” & \multirow{3}{*}{Added GAN-generated examples dynamically.} & Learner engagement \\  & —yes (explicit) & & \\ \hline \multirow{3}{*}{8} & High engagement but low quiz & Suggested review modules before advancing. & Knowledge retention \\  & scores (implicit) & & improved by 18\%. \\ \hline \multirow{3}{*}{9} & Positive feedback on personalized & \multirow{3}{*}{Reinforced current recommendation logic.} & System stability and \\  & paths—yes (explicit) & & \\ \hline \multirow{3}{*}{10} & Learner satisfaction consistently & \multirow{3}{*}{Scaled system for new users.} & Model readiness for \\  & high (explicit + implicit) & & \\ \hline \hline \end{tabular}
\end{table}
Table 15: Cont.  
too much time on earlier lessons. This kind of explanation helps build trust and makes the AI more transparent.  
The transparency and trust gained by providing clear, understandable justifications for decisions made across all modules is enhanced by the XAI in DKPS. Learner's engagement levels, quiz performance, and interaction patterns interface to identify a learner's preferences, strengths, and areas for improvement as explained by XAI. Based on the progress and goals of the learner, in the content generation module, XAI clarifies why specific learning materials such as exercises, hints, and tips are recommended. To meet curriculum objectives and the learner's proficiency, the learning pathway and KG nodes are adjusted by the post-test score, GAN-generated content, and feedback generated from the learner and this benefits the ability of XAI to provide detailed transparency and interpretability.  
Relationships between concepts, skills, and modules are mapped, and XAI provides explanations on how this KG informs content sequencing and prerequisite identification. For example, XAI can reveal how the knowledge graph determined the need to reinforce foundational topics like stack operations if a learner struggles with the topic \"advanced recursion\". The system ensures transparency, fosters trust, and empowers learners and educators to make informed decisions for a more effective and personalized learning experience by integrating XAI across all modules.  
The system also uses a weighted ensemble method to make better predictions. Instead of using just one AI model to make decisions, it combines several models and takes a vote. However, it does not treat all models the same; more accurate models receive more importance. This way, the system can make smarter and more reliable choices for each student.  
All of these AI tools work together to keep improving the learning experience. As students interact with the system by doing quizzes, rewatching lessons, or spending more time on certain topics, the system learns from this and adjusts future content. Over time, this creates a smooth and personalized learning path that fits each student's pace and progress.