Understanding Artificial Intelligence
Artificial intelligence has moved from a specialized research topic to an everyday presence in higher education and professional life. Today's AI systems—most notably generative tools capable of producing text, images, and code—have become widely accessible, integrating into everything from classroom instruction and academic research to administrative operations and student services. This rapid adoption brings real opportunities: streamlined workflows, personalized learning support, and new avenues for research and creativity. At the same time, it raises important questions around academic integrity, data privacy, equity of access, and the skills students will need for an AI-influenced workforce. Importantly, all AI in current use—including the most advanced generative tools—remains "narrow" AI: highly capable within specific tasks, but without the general reasoning or understanding a human possesses. Understanding both the capabilities and the limitations of these tools is essential for using them thoughtfully and responsibly across our community.
Common Types of AI
- Machine Learning (ML): Systems that improve at a task by learning patterns from data, rather than following hand-written rules. This is the engine behind most modern AI, including everything below.
- Generative AI: Tools like ChatGPT, Claude, or image generators that create new content — text, images, code, audio — based on a prompt. This is the category most relevant to recent conversations about coursework, writing, and academic integrity.
- Natural Language Processing (NLP): AI that understands and works with human language — used in chatbots, translation tools, and writing assistants.
- Computer Vision: AI that interprets images or video — used in things like campus security systems, accessibility tools (e.g., image descriptions), or lab research.
- Predictive Analytics: AI that forecasts outcomes from data — used in areas like enrollment forecasting, retention risk modeling, or budgeting.
- Recommendation Systems: AI that suggests content or resources based on patterns — similar to what powers Netflix or Amazon, sometimes used in library systems or learning platforms.
Most AI tools people interact with on campus — whether it's a chatbot, a writing assistant, a research tool, or an advising system — often blend several of these categories (e.g., a chatbot combines NLP and generative AI). Understanding these distinctions helps cut through hype and clarifies what these tools can and can't actually do.
Why AI Literacy Matters
As AI tools become a routine part of academic and professional life, understanding how they work is quickly becoming as important as knowing how to use a search engine or spreadsheet. AI literacy isn't about becoming a technical expert—it's about knowing enough to use these tools effectively, evaluate their output critically, and recognize their limitations and biases. For students, this means engaging with AI in ways that support genuine learning rather than bypass it. For faculty and staff, it means making informed decisions about when and how to incorporate AI into teaching, research, and administrative work. Building this shared literacy across our community helps ensure that we adopt these tools thoughtfully, ethically, and in ways that strengthen rather than undermine our core educational mission.