Generative Adversarial Network (Gans) For Realistic Digital Human Creation in Academia

Abstract

Generative Adversarial Networks (GANs) have become a transformative technology for providing and synthesizing highly realistic digital human representations, with significant implication for academia. This paper investigates the use of advanced GAN architecture/models such as StyleGAN, DALL.E and DeepVideo for generating digital professors capable of delivering lectures, interacting with students and supporting educational activities. We evaluate the technical feasibility, ethical implications, and instructional value of deploying Ai-based educators. Our finding suggests that while GANs offer and enhance promising opportunities for personalized and scalable learning experiences, their application demands thoughtful management to mitigate biases, promote fairness and ensure transparency.