AI-powered feedback analytics engine for optimizing vocational education quality assessment

In the evolving landscape of vocational education, enhancing quality requires accurate, timely, and data-driven evaluation mechanisms that transcend traditional assessment methods. This research presents an AI-powered Feedback Analytics Engine designed to optimize the quality assessment of vocational education by systematically collecting, processing, and analyzing feedback from diverse educational sources. The system aggregates data from student surveys, instructor evaluations, learning management system logs, and classroom observations, enabling a holistic view of the teaching and learning environment. Textual data is preprocessed using natural language techniques (NLP), such as cleaning and tokenization. To address data imbalance challenges, the Synthetic Minority Over-sampling Technique (SMOTE) is employed, ensuring equal representation across various performance levels and enhancing model generalizability. Feature extraction is performed using Word2Vec, which captures semantic relationships within the feedback to generate rich vector representations. A Modified Migrating Birds Optimizer-driven Attention-based Recurrent neural network (MMB-Att-RNN) model is used to evaluate vocational education quality, which enables collaborative, distributed training across multiple institutions while preserving data privacy. An integrated attention mechanism helps the model focus on the most relevant features within the feedback, thereby improving prediction accuracy and interpretability. The proposed engine demonstrates superior performance, significantly outperforming traditional models with an accuracy of 98.42%. The results appear in the form of an interactive dashboard that enables educators and administrators to track the effectiveness of their instruction, areas of improvement, and evidence-based decision making. The AI-powered framework converts raw feedback into relevant intelligence and prepares a new standard of constant quality improvement in vocational education.