“I Don’t Know Where to Start with AI”
An Honest Guide for Higher Ed Professionals
If you’ve ever typed those words into a search bar — or just thought them while watching a colleague demo some AI tool — this post was written for you.
But before we get into the practical how-to, let’s be honest about the landscape you’re navigating.
Generative AI is not a neutral development in higher education. It is arriving fast, with significant implications for how we teach, how we work, how students learn, and what institutions are expected to provide and protect. Questions about academic integrity, equity, data privacy, labor, and the future of expertise are not overreactions — they are legitimate, important, and still being worked out. The decision about whether and how to engage with AI is genuinely complex, and the professionals who are approaching it carefully deserve respect, not dismissal.
We will address those bigger questions directly in a separate post. They warrant the space.
What we can say here, plainly, is this: generative AI is not likely to disappear. It is already embedded in the tools many institutions use, in the workflows students are developing, and in the expectations taking shape around professional practice. And for all its complications, it does have real potential to reduce the time spent on repetitive, low-lift tasks — creating more space for the work that actually requires human judgment, relationships, and expertise.
This post is specifically for higher education professionals who have already decided they want to start using AI — and are simply not sure where to begin. If you’re still weighing whether to engage at all, that’s a completely valid place to be, and we’ll speak to that question elsewhere. This is a practical starting point for those who are ready.
So… if you’re ready to begin, here’s what you need to know.
First: You’re Not Behind
There’s a particular kind of pressure that comes with a technology that moves this fast. You walk into a department meeting and someone is talking about prompts. You read a newsletter and it’s full of tools you’ve never heard of. There’s an implicit message in a lot of AI coverage that professionals who haven’t started yet are simply behind.
That framing flattens something that deserves more nuance. Some of our collegues haven’t started because they’re genuinely uncertain about whether they should — and that uncertainty reflects serious thinking, not avoidance. Others have started cautiously and deliberately, which is exactly the right approach for tools that carry real professional and ethical implications.
If you’re reading this, you’ve made a decision to engage. That’s worth acknowledging. And the good news is that the learning curve is shorter than most people expect.
And here is the truth… Your colleadues who seem most fluent with AI tools are rarely years ahead. Most of what they know came from a few months of deliberate practice. Starting intentionally — with a clear sense of what you’re trying to accomplish and why — is worth more than starting early.
What Generative AI Actually Is (Without the Hype)
Generative AI refers to tools that can produce text, images, summaries, or other content based on input you give them. You may have heard of ChatGPT, Claude, Google Gemini, or tools like Adobe Firefly. These are all generative AI tools, and they work by responding to what you write — called a prompt.
You may have also started hearing the term “agentic AI” — and it’s worth understanding the difference before we go any further, because they describe two very different kinds of tools.
Generative AI responds to what you ask. You give it a prompt, it gives you something back — a draft, a summary, an image, a list of ideas. The conversation is the work. You stay in the driver’s seat at every step.
Agentic AI acts on your behalf. It can browse the web, execute tasks, make decisions, and take sequences of actions with minimal input from you. It’s a newer and more complex category — powerful, but with different implications for oversight, accuracy, and professional responsibility.
This post — and the workshop it’s written to support — focuses entirely on generative AI. That’s the category most relevant to everyday higher ed work right now: writing, summarizing, brainstorming, communicating, and creating. Agentic AI is a fascinating and fast-moving space, but it’s a conversation for another day.
Think of it less like a search engine and more like a very fast, very widely-read thinking partner that can help you:
- Draft and refine written content
- Brainstorm ideas when you’re stuck
- Summarize long documents or readings
- Create outlines, agendas, or templates
- Generate images for presentations or communications
- Get a second opinion on the clarity of your writing
It is not magic, and it is not infallible. AI tools make mistakes, sometimes confidently. They reflect the biases present in their training data. They require your judgment and your expertise to be useful. But in the hands of a thoughtful professional, they can meaningfully reduce the time spent on low-lift tasks and open up space for the work that actually requires you.
The Question Nobody Asks Out Loud
“What if I use it wrong?”
This is the fear underneath most AI hesitation in higher education, and it’s worth naming directly. Faculty worry about modeling something inappropriate for students. Staff worry about data privacy or institutional policy. Everyone worries about producing something that sounds like it was written by a robot.
These are legitimate concerns, not signs of technophobia. And they’re exactly the kinds of questions that good AI training addresses head-on.
The short version:
- Most institutions are still developing AI policies, which means now is actually a good time to build informed opinions and practices
- Free tiers of most AI tools don’t require you to upload sensitive data
- The best use of AI preserves your voice, your judgment, and your professional standards — it doesn’t replace them
- Learning to evaluate AI output critically is itself a valuable professional skill
A Low-Stakes Way to Begin Right Now
1Before you register for anything, buy anything, or read another article about AI, try this:
Open ChatGPT (free at chat.openai.com) or Claude (free at claude.ai). Type the following prompt:
“I’m a [your role] at a [type of institution]. I need to write a brief announcement about [something you’re working on]. Can you give me a draft to work from?”
Read what it produces. Notice what it gets right and what it gets wrong. Edit it. Make it sound like you.
That’s it. You’ve started.
The output doesn’t have to be perfect. The point is to experience the tool as a starting point, not a final answer. You’ll immediately begin to develop intuition about what kinds of prompts produce useful results and what kinds of refinements make the output actually yours.
What Builds Confidence: Structure and Practice
Experimenting on your own is a fine beginning. But most professionals find that their AI use stays superficial without some structured guidance — not because they aren’t intelligent, but because the tools have more depth than casual use reveals.
Here’s what actually builds fluency:
- Learning a handful of prompting frameworks that reliably produce better results
- Seeing real examples from your specific professional context, not generic demos
- Having space to practice with guidance available when you’re stuck
- Understanding the responsible-use considerations that matter for your role
The difference between someone who dabbles with AI and someone who genuinely integrates it into their work is usually not intelligence or technical skill. It’s structured practice with real feedback.
A Few Things Worth Knowing Before You Dive In
Some reassurances that tend to come up when higher ed professionals start exploring:
You don’t need to be technical.
None of the major AI tools require coding, technical setup, or specialized knowledge. If you can type a question, you can use these tools.
Free versions are genuinely useful.
ChatGPT, Claude, and Google’s tools all have free tiers that are more than sufficient for professional exploration and most everyday tasks.
Your expertise is still the point.
AI is most useful when the person using it brings domain knowledge, professional judgment, and ethical awareness. Your years of experience in higher education are not made irrelevant by these tools — they’re what makes your AI use better than average.
You’re allowed to take your time.
There is no timeline you’re racing against. Building thoughtful, sustainable AI practices is worth more than rushing to adopt every new tool.
Tools Worth Trying: A Starting Point by Task
The best way to begin is to match a tool to something you actually need to do this week. Below are the tools most widely used by higher education professionals, organized by the kinds of tasks they handle best. All have free tiers that are more than sufficient for everyday professional use.
For drafting, editing, and brainstorming
- ChatGPT (chat.openai.com) — The most widely used general-purpose AI assistant. Strong across a broad range of writing, ideation, and communication tasks. A reliable first tool for most professionals.
- Claude (claude.ai) — Particularly strong for writing tasks where tone and nuance matter. Tends to produce output that sounds more natural and less generically AI-produced. Well-suited to longer documents and thoughtful professional communications.
- Google Gemini (gemini.google.com) — Built into Google Workspace, making it a natural fit for professionals already working in Docs, Gmail, and Slides. Useful for quick drafts and edits without leaving your existing workflow.
For synthesizing documents and research
- Google NotebookLM (notebooklm.google.com) — Upload your own documents — PDFs, reports, policy files, readings — and ask questions about them. NotebookLM only references what you provide, which means it cannot fabricate information from outside your source set. Exceptionally useful for making sense of dense institutional material or preparing for meetings and presentations.
For writing feedback and clarity
- Grammarly (grammarly.com) — Works inside your browser and most writing environments to provide real-time grammar, clarity, and tone suggestions. Low friction and immediately useful. A good tool to have running in the background as you write.
- ChatGPT or Claude — Either tool can serve as a writing feedback partner when prompted specifically. Ask for structural feedback, tone assessment, or a check on whether your argument is clear and complete. The more specific your request, the more useful the response.
For visual communication and design
- Adobe Express (express.adobe.com) — A web-based design tool with AI-powered image generation, professional templates, and one-click background removal. No design experience required. Well-suited to event materials, presentation graphics, and institutional communications.
- Canva (canva.com) — A widely used design platform with strong collaborative features and a large template library. AI-assisted design tools have expanded significantly and are now capable of producing polished results for most professional use cases.
For organizing work and managing information
- Notion AI (notion.com) — Combines note-taking, project management, and AI assistance in a single workspace. Useful for professionals who want AI drafting and summarization tools integrated with where they already keep their work, rather than in a separate application.
A Practical Starting Point
You don’t need to try all of these tools. You need to try one — the one that connects to something you actually have to do this week.
Pick a task that is real and low-stakes: a draft you need to write, a document you need to summarize, a communication you’ve been putting off. Open the tool. Give it a specific, contextual prompt. Read what it produces. Edit it. Make it yours.
That first interaction will teach you more than any overview can. You’ll start to develop a sense for what these tools are actually useful for, where they fall short, and how your own judgment fits into the process. That intuition is what makes AI use genuinely professional rather than merely experimental — and it only comes from doing the work.
The goal is not to master AI. It’s to build a working relationship with a tool or two that reduces friction in your professional life — so you have more capacity for the work that actually requires you.
As you build that practice, keep your professional values close. Use these tools in ways that reflect your judgment, your integrity, and your commitment to the people and institutions you serve. That’s what responsible AI use looks like in practice — not a policy document, but a professional standard you hold yourself to, one task at a time.
