Result: Optimizing feedback mechanisms in higher education quality assessment through an AI-driven framework.

In higher education, efficient feedback systems are essential for preserving and improving educational quality. However, higher education institutions continuously struggle to recover quality assessment processes by incorporating student feedback and academic data, also in delays, bias, and inadequacies in gaining valuable insights are common problems with traditional feedback methods. The primary concern lies in the manual processing of large, unstructured feedback datasets, leading to unnoticed issues and delayed interventions. To address this problem, the research goal is to design an Artificial intelligence (AI)-driven framework to automate, improve, and optimize feedback analysis for educational quality assessment. The data was collected through learning management system (LMS) activity logs, including student responses, participation rates, and academic performance records. Z-score normalization and missing value imputation were used as data preparation approaches to manage missing values and scale features consistently. Term Frequency-Inverse Document Frequency (TF-IDF) was used to process text data to extract features efficiently. The research employs an Adaptive Butterfly-Attention Mechanism Generative Adversarial Network (AB-Att-GAN). The AB and Attention module uses a GAN design to produce refined feedback patterns, focusing on significant feedback features across scales. This method helps interpret student concerns, aligns teaching practices dynamically, and allows early intervention strategies, ensuring quality and relevance. Experimental outcomes using Python showed that the AB-Att-GAN framework attains superior performance when compared to conventional deep learning (DL) models, achieving 98.5% accuracy. Additionally, the technology provides tailored feedback loops and forecast insights for early academic intervention. The AB-Att-GAN architecture displays a greater capacity for evolving high quality, real-time educational feedback systems and promoting continuous institutional development. [ABSTRACT FROM AUTHOR]