AI In Higher Ed: What It Is, What It Isn’t, And Why It Matters

For many in higher education, AI feels like both an opportunity and a challenge: students are already experimenting with tools like ChatGPT, while faculty are being asked to make sense of what it all means for assignments, assessment, and academic integrity.

This post provides a practical overview of what AI is (and isn’t), how generative AI works, how it’s showing up on campuses, and what myths and misconceptions to be aware of.

Quick Summary

The real opportunity in higher education isn’t avoiding AI: it’s helping students learn to use it responsibly, ethically, and with the critical perspective they’ll need beyond the classroom.

This post offers a foundation in AI basics: not to replace your teaching or lower academic rigor (quite the opposite, actually), but to give you a clearer picture of the tools that are rapidly becoming part of higher education - and how you can use them.

What Is AI?

AI is not one single technology or tool: it’s a broad set of approaches that have developed over decades. Understanding the difference between “behind-the-scenes” AI (like spam filters) and the newer, generative systems (like ChatGPT) will help clarify what’s really new - and what’s simply evolving.

At its most basic, artificial intelligence refers to computer systems that can perform tasks that usually require human intelligence: things like recognizing language, making predictions, or identifying patterns.

There are two main types of AI technology: 

• Traditional or Narrow AI: These are behind-the-scenes systems designed to do one job very well. Examples include plagiarism checkers, spam filters, predictive text, or adaptive learning platforms (and includes tools like Grammarly, email filters, GPS, search engines, and Netflix recommendations, among many others). These applications typically run quietly in the background of everyday teaching and life - and we’ve been using them for years.
• Generative AI: This is the type of AI that’s getting all the attention now. Tools like ChatGPT, Claude, Gemini, or DALL·E generate new content, such as text, images, code, or video. Large Language Models (LLMs), the technology behind tools like ChatGPT and Claude, are trained on massive amounts of text data to predict and generate human-like language. Think of it like an incredibly advanced version of autocomplete: instead of just finishing a word or sentence, it can produce essays, explanations, or dialogues that sound convincingly human. These LLMs don’t “know” information; instead, they predict what comes next in a sequence based on patterns in training data. The result often looks human-like, but can also be inaccurate, biased, or overly simplistic. Understanding this helps demonstrate why generative AI outputs can be both impressive and unreliable - and why human oversight remains essential.

How Generative AI Is Showing Up in Higher Education

Faculty and students are already encountering generative AI in many ways:

• Student Use: Brainstorming ideas, rephrasing writing, generating outlines, or checking explanations of difficult concepts. Some use it responsibly; others may try to submit AI outputs as their own work.
• Faculty Use: Drafting quiz questions, creating case studies, generating discussion prompts, or rephrasing assignment instructions. For many, AI saves time or offers fresh perspectives to adapt.
• Institutional Use: Universities are exploring AI for tutoring systems, student support chatbots, accessibility tools (captioning, summarization), and even administrative workflows.

Like calculators in math education or databases in research, AI is quickly becoming part of the academic environment. The challenge is shaping its use - our own and that of our students - so that it supports learning rather than undermining it.

Common Myths and Misconceptions About AI

Because AI is still relatively new to many in higher education, it’s surrounded by hype, fear, and misinformation. These myths can fuel anxiety or lead to unrealistic expectations. Clearing them up helps us set a more balanced foundation - and realistic expectations for what AI can, and cannot, do in teaching and learning.

Myth 1: AI “thinks” like a human
Reality: AI doesn’t think or understand: instead, it predicts patterns. Students may assume outputs are authoritative, but they’re not.

Myth 2: AI is always accurate
Reality: Generative AI can produce “hallucinations”: plausible but false information, including fabricated sources. It’s important to remember that LLMs are not databases - and that human oversight and verification are always necessary.

Myth 3: AI is brand new
Reality: AI has been embedded in education technology for decades (spellcheck, plagiarism detectors, adaptive quizzes). What’s new is the generative capability and direct access students now have.

Myth 4: AI will replace faculty or student creativity
Reality: AI can generate drafts or ideas, but it lacks judgment, context, and lived experience. The human role shifts from creator-only to creator + critic + curator.

Myth 5: The only response is to ban or heavily restrict AI
Reality: Bans are difficult to enforce and may ignore the reality that students need AI literacy for their professional futures. Instead, faculty should guide students on responsible and critical use.

FAQs: Faculty Questions About AI

Here are a few of the most common questions faculty ask as they start thinking about AI in their teaching:

Q: Should I let students use AI in my class?
There’s no one-size-fits-all answer. The key is aligning AI use with your learning goals. For example, if the goal is practicing foundational writing, you might restrict AI. If the goal is critique and analysis, you might encourage students to evaluate AI outputs. Regardless of what level of AI use you allow in your course(s), you should be upfront about your AI policy with your students.

Q: How do I address AI in my syllabus?
Clarity is key. Outline when AI is allowed, when it’s not, and how students should document their use. Even a brief statement can provide guardrails for transparency and help students stay aligned with course requirements. Need help drafting your course AI policy? We have created this resource to help you do just that. 

Q: How do I grade work that involves AI?
Use rubrics that balance both content and process. In addition to accuracy and originality, add a criterion for how transparently and appropriately AI was used (e.g., Did the student document prompts? Did they refine or critique AI output?). This way, you’re grading not just the final product but also the student’s judgment and engagement.

Q: Is AI secure and ethical to use?
AI tools raise real questions about privacy, bias, and equity. Faculty should check what data is collected, whether access is free or subscription-based, and if all students can participate equally. Since institutional policies are still developing, the most important step is to be transparent: talk with your students about what AI use is (and is not) acceptable in your course and why, and best practices when using AI tools.

Q: What are best practices for using AI tools like ChatGPT or other LLMs?
When using AI tools in your teaching or course design, a few simple practices can help you and your students stay safe, ethical, and effective:

Protect privacy → Don’t enter personal data, student records, or unpublished research.

Be careful with prompts → Give enough detail to be useful, but keep sensitive information out.

Verify outputs → Double-check for accuracy, bias, or oversimplification.

Keep human oversight central → Use AI as a draft partner or brainstorm tool, but rely on your own expertise and judgment for final decisions.

Moving Forward with AI in Higher Ed

AI is here to stay, and higher education has a unique role to play in shaping how it’s understood and used. You don’t need to be an AI expert to start making thoughtful choices: you just need a clear sense of your learning goals, expectations, and teaching philosophy.

By framing AI as a tool to support teaching and learning rather than replace it, faculty can help students build the skills they’ll need for a world where AI is part of everyday life. The opportunity isn’t just managing risks: it’s preparing students to think critically, act ethically, and contribute meaningfully in an AI-driven future.