The revolutionary influence of powerful generative models, such as Chat Generative Pre-trained Transformer (ChatGPT) and mid-journey is causing significant changes in the higher education setting. These models examine the future of education and its potential to completely transform the way to teach and learn and have far-reaching implications. In this paper, investigation of ChatGPT’s impact on education using Deep Spectral-Spatial Residual Attention Networks (DSSRAN-CGPT) is proposed. Initially, the input data is amassed from ChatGPT sentiment analysis dataset. The input data is pre-processed using Continuous-discrete Derivative-free Extended Kalman Filter (CDFEKF) to remove the unwanted data from the collected input data; then the pre-processed data are fed to feature extraction using Synchro-Transient-Extraction Transform (STET) to extract the statistical features, like energy, standard deviation, variance and kurtosis. Then the extracted features are supplied to the DSSRAN for effectively classifying the positive sentiment, negative sentiment and neutral sentiment. Generally, DSSRAN does not adapt any optimization approaches to determine the optimal parameters to ensure accurate classification of ChatGPT in education. Hence, Parrot Optimization Algorithm (POA) is proposed to optimize the DSSRAN, which accurately classifies the ChatGPT in education. The performance metrics, like accuracy, precision, recall, specificity, F1-score, computation time and Receiver Operating Characteristic curve (ROC) are analyzed. The performance of the proposed DSSRAN-CGPT approach attains 97% accuracy; 92% higher precision and 75% lower computation time when compared to the existing methods, like exploring the impact of ChatGPT in education: A web mining including machine learning approach (CGPT-ELA), unlocking the opportunities through ChatGPT Tool towards improving the education system (CGPT-AES) and ChatGPT in education: A discourse analysis of Worries and Concerns on Social-Media (CGPT-WCSM).

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