## 3 Methodology
### AI-Driven Adaptive Feedback System
#### 3.3.1 System Architecture and Interface Design  
The AI-based adaptive feedback model follows the equation:  
\[F_{adaptive}=\alpha P+\beta T+\gamma E\]  
where:  
* \(F_{adaptive}\) = AI-generated feedback score. * \(P\) = Performance score based on correctness. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Demographic Variable** &
\begin{tabular}{c} **Experimental Group** \\ **(\(n\) = 350)** \\ \end{tabular} & **Control Group (\(n\) = 350)** & **Total (N = 700)** \\ \hline Gender & 52\% Male, 48\% Female & 50\% Male, 50\% Female & 51\% Male, 49\% Female \\ Age Range (Mean \(\pm\) SD) & 18–24 (20.8 \(\pm\) 2.1) & 18–24 (21.2 \(\pm\) 2.3) & 18–24 (21.0 \(\pm\) 2.2) \\ Field of Study & 60\% STEM, 40\% Language & 58\% STEM, 42\% Language & 59\% STEM, 41\% Language \\ Prior AI Learning Experience & 42\% Yes, 58\% No & 40\% Yes, 60\% No & 41\% Yes, 59\% No \\ Average GPA (Mean \(\pm\) SD) & 2.85 \(\pm\) 0.6 & 2.92 \(\pm\) 0.7 & 2.89 \(\pm\) 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Participant demographics. * \(T\) = Response time per question. * \(E\) = Engagement level (frequency of AI interactions). * \(\alpha,\ \beta\) and \(\gamma\) = Learning weights optimized via machine learning. The system consisted of ten main interfaces that supported better student learning results as well as instructional choices. Student progress information and AI-generated insights about students alongside access to essential system functionalities were available on the Main Dashboard as seen in Figure 2. The interface showed a performance graph via engagement charts, quick insights, and recent feedback logs to help students track their academic achievements. The system provided users with a structured side menu to move between different modules with ease. The AI Feedback System, which has openAI API integrated at backend to respond to student queries in Figure 3 enabled users to submit their work for instant feedback that categorized mistakes and offered structured recommendation solutions. Students used the text-based input panel to prompt open and multiple choice questions and to provide written and audio responses while the AI analysis panel presented systematic explanations that suggested improvement steps. The system implements adjustable feedback protocols, that use students' past performance patterns for delivering immediate individualized productive feedback. Educational staff accessed the Instructor Analytics Dashboard (Figure 4), which featured complete tools for performance checking and student engagement measurement  
Figure 3: AI-driven feedback system query and analysis. Figure 2: Main dashboard of AI-driven feedback system. together with predictive analytics capabilities. Through this interface, instructors accessed real-time student scoring data in addition to progress reports and automatic AI difficulty assessments that helped them determine which students needed assistance. The engagement analytics module displayed topic understanding through bar chart visualizations and AI-generated instructional recommendation graphs alongside time-on-task performance charts. Instructional staff gained the ability to modify feedback from AI through this interface while also enabling them to select desired feedback strengths and generate analytical reports for educational research purposes. This module offered personalized recommendations by using AI to deliver learning experiences depending on each student's current proficiency level and their conceptual mastery as well as their engagement activities (Figure 5). The skill strength indicator subdivided learners into beginner, intermediate, and advanced groups to deliver specific learning paths. Programmed suggestions from the AI system offered students personalized exercises combined with Supplementary Materials together with instructional video content to boost their performance. Figure 4: Instructor analytics and monitoring. Figure 5: AI learning path and personalized recommendations.