AI-Driven Analysis of Teacher Job Satisfaction in Higher Education using BiCNN and BiRNN Models

Individual and institutional achievement depend on academic staff job satisfaction. An educator's job satisfaction boosts their happiness and motivation. HEIs are key information hubs, with faculty members shaping the institution's vision and success. Academic staff skill and dedication determine an educational system's success. This study examines higher education professors' job satisfaction using statistical and deep learning methods. The most important features are selected using the ANOVA F-test and Chi-Square filters. A CNN evaluates spatial correlations in raw student survey data, while a BiRNN captures temporal dependencies of generated features. Integrated attention enhances feature extraction and model robustness to temporal shifts. Teacher work satisfaction is better predicted by the CNN-BiRNN model. The model can identify essential faculty satisfaction factors with 99% accuracy, 95% recall, 97% precision, and 94.8% F1-score. CNN and BiRNN with an attention mechanism improve feature extraction, resulting in reliable and efficient faculty job satisfaction projections. These findings show that sophisticated machine learning methods may assess and improve worker satisfaction in higher education, creating a more efficient and motivated academic workforce.