Introducing Generative AI

The emergence of Artificial Intelligence (AI) and large language models (LLMs) has sparked widespread discussion across higher education. While some view AI as a disruptive technology that raises concerns about academic integrity, privacy, and ethics, others see it as a powerful tool that can enhance teaching, learning, research, and administrative work when used thoughtfully and responsibly. (Educause, 2025).  

Bowen and Watson (2024) suggest that AI may have an even greater impact than previous technological advances such as the internet. While the Internet and World Wide Web transformed our relationship with knowledge by expanding access to information, “AI is going to change our relationship with thinking,” ultimately “challenging ideas about creativity and originality” (Bowen & Watson, 2024, p.2). 

As AI continues to evolve, understanding its capabilities, limitations, and responsible use is becoming an essential skill for faculty, staff, and students in higher education.

Artificial intelligence (AI) is the ability of computer systems to perform tasks that typically require human intelligence, such as recognizing speech and images, understanding language, identifying patterns, and making predictions. AI is already part of many everyday tools, including voice assistants (Siri and Alexa), navigation apps, recommendation systems, and spam filters. In higher education, AI powers tools such as Grammarly, Gradescope, Microsoft Copilot, and Semantic Scholar to support writing, grading, research, and productivity.  

Generative Artificial Intelligence (GenAI) is a type of AI that creates new content in response to user prompts. It can generate text, images, audio, video, computer code, presentations, and more. Many GenAI tools are powered by large language models (LLMs), which are trained to recognize language patterns and generate human-like responses. While GenAI can support brainstorming, drafting, summarizing, and creating content, users should always review AI-generated content for accuracy, bias, and appropriateness.  

Learn more: Introduction to generative AI and agents (Microsoft Learn)– A free, self-paced course that introduces the core concepts of generative AI. 

Many generative AI tools are powered by large language models (LLMs) that are trained on vast amounts of text from books, articles, websites, and other publicly available or licensed sources. During training, LLMs learn patterns, relationships, and structures within language rather than memorizing facts or understanding information as humans do.  

When you enter a prompt or ask a question, the model predicts the most likely sequence of words to generate a response. Although the output often sounds natural and conversational, the AI does not think, reason, or “know” information in the same way people do. Instead, it generates responses based on patterns learned during training.   

Because of this, AI-generated content may contain inaccuracies, fabricated information or citations (hallucinations), outdated information, or bias. Generative AI should be used as a tool to support, not replace, human expertise and judgment. Always review AI-generated content, verify important information using reliable sources, evaluate it for bias and appropriateness, and revise the output before using or sharing it.