Abstract
Generative AI has been gaining popularity in 2023 and it is causing a disruption of various standards in the education system. While the pros and particularly the cons of this technology have been extensively debated, this chapter aims to explore the positive aspects of Generative AI—instead of advocating for a ban. This chapter will first provide an overview of the historical context and evolution of AI. It will be followed by a discussion of different types of Generative AI and the principles of co-creation with it. The uses of Generative AI in education will be described, focusing on the key stakeholders such as educators, students, educational administrators, and schools or institutions. Next, the chapter will explore the Generative AI application across different fields as well as various subjects in education. Several use cases, practices, and their associated benefits will be presented. Finally, the future and the implications of Generative AI will be discussed.
TopA Brief Introduction To Aigc
Generative AI primarily refers to the use of machine learning techniques by which an algorithm learns from existing data, such as images, audio, and text and then generates content that is similar to the original data (Li, 2023; Pavlik, 2023). Whereas the concept of Artificial Intelligence Generated Content (AIGC) goes further and involves the creation of text, images, video, music, and code using AI models as the creative agents. This process includes generating specific content through the utilization of significant cues and fusion generation based on multimodal content — this is possible by training more advanced generative models on larger datasets (Cao et al., 2023; Li, 2023). Learning optimization in AIGC can be achieved using various techniques such as Generative Adversarial Networks (GANs) (Goodfellow et al., 2020), Diffusion Models (Ho et al., 2020), Neural Radiance Fields (NeRF) (Mildenhall et al., 2021), and Natural Language Processing models like Transformer (Vaswani, 2017).
Key Terms in this Chapter
Natural Language Processing: It represents the intersection of artificial intelligence and linguistics, involving computational techniques called machine learning, which are used to train machines the ability to interpret, analyze, and synthesize human language.
Artificial Intelligence Generated Content (AIGC): A novel content production method utilizes Generative AI techniques for generating content. It involves the recognition of various types of semantic information from multimodal sources.
Multimodal AIGC: It involves the transformation of content from one modality to another by utilizing multiple connections and interactions across different data modalities. This means that it can convert content from one form, like text, to another form, such as images or audio, through the integration of data from various sources.
Co-Creation: A collaborative approach that incorporates input from both humans and Generative AI, this dynamic partnership leverages the strengths of human experts and the power of AI to produce desirable content.
Unimodal AIGC: It describes a type of AI content generation where the input and output belong to the same modality such as text, image, video, or audio. In other words, it generates predictions or content within the same medium as the provided input.
Prompt Engineering: Collaborative skills with Generative AI involve producing desired outcomes based on human-provided input.
AI Literacy: The knowledge, competnecies and skills to effectively utilze AI technology for task performance.
Generative AI: It is a form of artificial intelligence that utilizes algorithmic techniques to generate various types of content, including text, audio, images, and videos. The model is trained using deep learning techniques with the provided training data.