## 3 Methodology
### Participants  
A randomized selection of 700 undergraduate students from three different departments at BIC was conducted. The sample was balanced across STEM and Language Education disciplines as mentioned in Table 3. Within the STEM disciplines, the study  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Focus** & **Conceptual Mastery Improvement** & **Engagement Improvement** & **Retention Improvement** & **Comparison to Current Study** \\ \hline \multirow{3}{*}{[16]} & AI tutors in higher education & Improved understanding (no \%) & Enhanced (qualitative) & Not measured & We add cognitive load reduction (22\%) and broader applicability (STEM + Language). \\ \hline \multirow{3}{*}{Current Study (2025)} & AI feedback & \multirow{3}{*}{28\% improvement classrooms} & \multirow{3}{*}{35\% increase} & \multirow{3}{*}{85\% retention} & Builds on prior work with quantified outcomes, learning analytics, and diverse student needs. \\ \cline{1-1}  & AI feedback in mixed-ability ability classrooms & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Experimental Group** & **Control Group** \\ \hline Feedback Type & AI-Driven Adaptive Feedback & Traditional Instructor Feedback \\ \hline Number of Participants & 350 & 350 \\ \hline Learning Duration & 20 Weeks & 20 Weeks \\ \hline Assessment Type & AI-based adaptive quizzes, real-time feedback & Instructor-led feedback on quizzes and assignments \\ \hline Engagement Metrics & AI-logged interactions, error corrections, adaptive suggestions & Attendance, participation logs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Research design overview.  
focused on topics such as introductory programming concepts (e.g., variables, loops, and functions in Computer Science), algebraic problem-solving (e.g., linear equations and quadratic functions in Mathematics), and fundamental mechanics (e.g., [PERSON]'s laws and kinematics in Physics). For Language Education, the themes included English composition skills (e.g., essay structure and argumentative writing), grammar proficiency (e.g., syntax and tense usage), and vocabulary development (e.g., context-based word acquisition). These topics were selected to represent foundational areas where conceptual learning gaps are prevalent, allowing AI-driven adaptive feedback to target specific challenges in mixed-ability settings.  
Participants were included based on voluntary participation, ensuring ethical adherence and minimizing selection bias.  
Students were selected through a transparent, randomized process from a pool of approximately 1200 undergraduates enrolled in STEM (Computer Science, Mathematics, Physics) and Language Education (English) courses at Beaconhouse International College. Participation was voluntary, and advertised via university announcements, with 700 students consenting after an initial ethics briefing. Random assignment to the experimental (\(n\) = 350, AI-driven feedback) and control (\(n\) = 350, traditional feedback) groups was conducted using a computer-generated randomization algorithm, ensuring balanced representation across gender (51% male, 49% female), age (mean 21.0 \(\pm\) 2.2), and prior academic performance (mean GPA 2.89 \(\pm\) 0.65). This approach minimized selection bias and supported the quasi-experimental design's integrity.