University Assistance System Using Generative Artificial Intelligence

This paper presented a University Assistance system powered by Generative Artificial Intelligence using GANs to provide intelligent, personalized, and scalable support services across academic and administrative university functions. The proposed system addresses long-standing challenges including information overload, limited human resources, delayed responses, and the need for scalable academic support solutions adaptable to diverse institutional contexts. The GAN-based model achieves 94% accuracy with precision of 93%, recall of 92%, and F1-score of 92.5%, significantly outperforming traditional SVM, Naive Bayes, LSTM, and Random Forest baselines. The system provides 24/7 student support, automates routine administrative tasks, and delivers personalized learning experiences through adversarial training on institutional data. User satisfaction scores of 4.3/5.0 confirm practical effectiveness beyond technical performance metrics. By combining GANs with conversational AI and retrieval-augmented generation, the system demonstrates superior capability in handling complex academic query patterns and generating meaningful, factually grounded assistance outputs. The modular architecture ensures scalability, security, and adaptability, making the solution suitable for universities of varying sizes and operational complexities seeking digital transformation through responsible AI adoption.

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