This site is maintained by LaGuardia’s Center for Teaching and Learning (CTL) and highlights the work faculty do around the use of AI in their teaching. Read about AI projects developed by your colleagues, connect with faculty and staff to collaborate on a project or learn about their experiences, and find resources shared and recommended by LaGuardia faculty and staff.
What is Generative AI?
ChatGPT explains:
“Generative AI is a type of artificial intelligence that can create new content, such as text, images, music, code, and even videos, by learning patterns from large datasets. Unlike traditional AI, which focuses on analyzing data and making decisions, generative AI produces original outputs that mimic human creativity. Generative AI learns by analyzing vast amounts of data, such as books, academic papers, images, and code. After training, it can generate new content based on user input, such as a prompt or request. Users can refine the AI-generated output by providing feedback or adjusting their prompts to achieve more precise results. Some widely used generative AI tools include ChatGPT, Gemini, and Claude for text and writing tasks. For image creation, tools like DALL·E, MidJourney, and Stable Diffusion can generate artwork and graphics. In programming, AI-powered tools such as GitHub Copilot and Code Llama assist in writing and debugging code. Additionally, platforms like Suno AI and Riffusion enable AI-generated music and audio creation. Generative AI has the potential to enhance learning, improve productivity, and encourage creativity for both students and educators. By integrating AI into education, students can streamline their studies, while faculty can optimize teaching and research efforts. However, it is crucial to use AI responsibly, ensuring academic integrity and ethical application. Understanding how to work with AI effectively allows users to leverage its strengths while maintaining originality and critical thinking in their work.“
Cloude.ai explains:
“Generative AI refers to artificial intelligence systems designed to create new content that resembles human-made output. These systems use deep learning models, particularly large language models and diffusion models, trained on vast datasets to identify patterns and generate novel text, images, audio, code, or other media that wasn’t explicitly programmed. Unlike traditional AI that focuses on analysis or prediction, generative AI can produce creative works, hold conversations, generate realistic images, compose music, and even write functional code—all by predicting what should come next based on its training data and the input it receives. The technology has rapidly advanced in recent years with models like GPT-4, Claude, DALL-E, and Stable Diffusion, transforming industries by automating content creation and enabling new forms of human-AI collaboration.”
