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Dr. Arti Bhatt is associated with the Department of Mass Communication at Shri Guru Ram Rai University, Uttarakhand, India. Her research interests include digital media, journalism, communication technologies, and Generative AI applications.
Generative Artificial Intelligence (GenAI) is transforming the media, communication, and creative sectors by facilitating intelligent content generation, tailored user experiences, automated production processes, and improved audience interaction. Generative AI, driven by sophisticated foundation models, large language models (LLMs), generative adversarial networks (GANs), diffusion models, and multimodal AI systems, facilitates the rapid and innovative production of text, images, audio, video, music, animation, and interactive digital experiences. Media organisations, publishers, advertising agencies, broadcasters, filmmakers, game developers, and digital artists are utilising Generative AI to enhance content creation, refine communication strategies, decrease operational expenses, and promote innovation. This chapter examines the principles, technologies, applications, problems, ethical considerations, and future trajectories of Generative AI within media, communication, and creative sectors, emphasising its revolutionary impact on the future of digital creativity and content ecosystems.
The swift integration of Artificial Intelligence (AI), Internet of Things (IoT), Learning Management Systems (LMS), Machine Learning (ML), and Big Data Analytics has revolutionised higher education by facilitating data-informed instruction, learning, and academic choices. Educational establishments consistently provide substantial amounts of data concerning student attendance, evaluations, learning patterns, online engagement, classroom involvement, and academic performance. Artificial Intelligence has arisen as a formidable technology for anticipating student achievement by recognising learning trends, projecting academic results, identifying pupils at risk, and facilitating tailored educational interventions. AI-powered prediction frameworks aid educators, administrators, and policymakers in augmenting academic achievement, diminishing dropout rates, refining curriculum development, and maximising institutional efficacy. This chapter delineates the principles, framework, machine learning methodologies, applications, obstacles, and prospective research avenues for AI-driven student performance forecasting in tertiary education.