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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.
The use of multimodal artificial intelligence (AI) into intelligent education is revolutionising traditional pedagogical methods through the facilitation of adaptive teaching, insightful evaluation, and customised learning experiences. Conventional educational technologies frequently depend on restricted data sources, such test scores, attendance logs, or interactions within learning management systems, which may insufficiently reflect the varied learning requirements, cognitive involvement, and behavioural tendencies of individual learners. This section explores the principles, frameworks, and approaches of multimodal AI for intelligent education and individualised learning. It examines natural language processing, computer vision, speech recognition, learning analytics, transformer architectures, multimodal integration, knowledge tracing, and adaptive recommendation systems. The section further examines issues with data privacy, algorithmic prejudice, accessibility, interpretability, computational demands, and the ethical utilisation of student data. The chapter culminates by suggesting a learner-centric multimodal AI framework that integrates academic achievement, learning behaviours, interaction tendencies, and educational inclinations to foster inclusive, adaptable, and efficient learning, while maintaining instructor supervision and student independence.