Generative AI is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio, and synthetic data. In higher education, generative AI has been explored for various use cases, such as writing essays, creating lesson plans, producing images, and creating personalized assignments for students. However, early implementations of generative AI have limitations, and there are concerns about the quality and credibility of the generated content. Educators are grappling with how to handle AI in their classrooms, and some have developed AI survival toolkits to guide faculty on how to talk to students about it. Despite the challenges, generative AI holds promise to elevate instructional choices, educational experiences, and student learning.*
*Developed using PerplexityAI. (2023). Perplexity [Large language model]. https://www.perplexity.ai using the inquiry "summarize in a paragraph the present state of generative ai in higher ed in a kind tone"
A language model is a type of machine learning model designed to understand and generate human language based on a probability distribution over text corpora. In recent years, significant scaling improvements have been achieved by increasing model sizes (from a few million parameters to hundreds of billions) and incorporating larger text corpora (from a few gigabytes, e.g., English Wikipedia dataset, to hundreds of gigabytes). These advancements have empowered pre-trained large language models (LLMs) to demonstrate remarkable proficiency across a wide array of downstream LLM-integrated applications (LLMA), such as chatbot, coding assistant, and machine translation.
- Lin, Z. et al Malla: Demystifying Real-world Large Language Model Integrated Malicious Services (2024) https://arxiv.org/abs/2401.03315
