Introduction to Generative AI for Educators: Practice!

The best way to learn what GenAI is capable of, and where it falls short, is by experimenting with the tools.  

Using GenAI tools begins with a “prompt”. This is the information you give to the tool to get it to generate what you want. Prompting is mostly about experience – it takes practice to learn what works well and what doesn’t work.  

Ethan Mollick differentiates two paths to prompting: conversational prompting and structured prompting. 

With conversational prompting, talk to the AI to ask for what you want or might need and see what happens. For most people, today, a conversational approach is enough to help you with your work. 

For some uses, at least for now, a more formal structured approach has value. Structured prompting is about getting the AI tool to do a single task well in a way that is repeatable and adaptable. It usually takes experimentation and effort to make a prompt work somewhat consistently. 

Structured prompts allow you to take what you learned and apply it to different contexts. For example, Microsoft has collected a set of education prompts, which can be adapted or experimented with. 

Regardless of which approach you use, it’s good practice to tell the tool:   

• Who it is – this gives the AI the right context to start from (e.g., you’re an experienced instructor teaching a second-year Economics course) 
• Context for its task - the more context you give it, the more effective it can be (e.g., include points about information you want it to include 
• What you want it to do – include the format of the response, or the number of examples
• What you don’t want it to do (if relevant)   
• Examples or steps – this helps it learn what you want and helps it think step-by-step, which means it will do a better job.  
• End with a question like “what questions do you have before you begin” or “what else do you need to know before you start” for even further clarification of the task  

Learning how to prompt is just part of the equation – push back and interact with AI to improve the response (e.g., ask to expand on a particular point, add an additional point, or change an example). Ultimately, AI is just giving suggestions for us to build upon. We can give feedback to make the response better, take and adapt or combine ideas, or discard what doesn’t work. This is where you use your own knowledge to evaluate and improve the result and untap the real potential of using AI.