Effective learning objectives are critical to setting training programs up for success. Writing learning objectives can be difficult and time consuming, and learning professionals often experience writer’s block in trying to craft learning objectives that go beyond the instructional designer-deride “learn” and “understand” type objectives, and can struggle to come up with effective, action-oriented and observable objectives for their programs.

This article will review a five-step process for using generative artificial intelligence (AI) tools to write effective learning objectives for your training programs.

Step 1: Give AI the parameters of your objectives.

As alluded to above, learning objectives that tout what a participant will “learn” or “understand” post training tend to be ineffective. Training isn’t effective if it doesn’t translate to on-the-job behavior change. Your learning objectives should mirror the ways in which you’re measuring success (more on that later) and thus, they need to be observable and behavior based.

Make sure the AI bot understands what an effective learning objective means. You can use established frameworks, like the SMART (specific, measurable, achievable, realistic and time bound) goal framework, to establish parameters for your AI engine to work within. At the very least, you should help it understand that your learning objectives should be measurable and related to a demonstrated behavior.

Step 2: Provide AI with an explanation of your training program’s goals and desired outcomes.

Next, the AI tool will need to know what the goal of your training program is. Often, this is the business challenge you’re looking to solve through training. You can explain it in a conversational way, and the tool will translate that into learning objectives using the parameters you set in step one. You may even have some learning objectives already that need to be rewritten into a more effective structure.

You can give as much or as little detail as you want here, but keep in mind that AI only has as much context as you provide it with. If you want personalized, AI-generated learning objectives, you’ll need to provide specific information about your unique needs.

Step 3: Ask for objectives.

Once the AI tool understands the parameters of learning objectives and the problem your training program is looking to solve, it’s time to ask for some learning objectives. Always ask for more objectives than you need. You’ll narrow them down in the next step. At this point, your AI prompt may look something like this (using a time management training for project managers as an example):

Prompt: This time management training program is designed to address a common challenge faced by project managers: Limited time and competing priorities often lead to missed deadlines, stressed teams and project delays.

To ensure participants gain practical skills and can demonstrate their newfound knowledge, I’m looking for 10 learning objectives focused on measurable and demonstrable behaviors.

Structure each objective using the following format:

• By the end of this training, participants will be able to: (Specific action verb) + (Desired outcome related to time management).

For example:

• By the end of this training, participants will be able to estimate project tasks with greater accuracy (desired outcome) to ensure realistic timelines (related to time management).

Please provide 10 learning objectives that cover a range of time management skills crucial for successful project managers.

Note: I asked it to write this prompt for me (a little utilized but wonderfully effective use for AI when you’re not sure exactly how to phrase your prompt).

This prompt provides a structure and a general overview of the training program’s goals. It also provides an example. It helps to provide examples in your AI prompts, as they give the engine the best clarity into what you’re looking for and how you’d like your output to look.

This prompt did just what it was meant to do and returned 10 potential learning objectives, with a little back and forth to get rid of “(desired outcome)” and “(related to time management)” which the bot had added to every objective, per my prompt. Never forget how literal a generative AI tool can be!

The output was:

By the end of this training program, participants will be able to:

1. Apply a prioritization matrix to categorize project tasks.
2. Develop a project schedule using critical path analysis.
3. Delegate tasks to team members with specific deadlines and accountability measures.
4. Create a risk mitigation plan that outlines potential delays and proactive solutions.
5. Conduct effective communication meetings with stakeholders, clearly outlining deadlines and project progress.
6. Configure and utilize project management software to track tasks, deadlines, and resource allocation.
7. Differentiate between urgent tasks requiring immediate attention and important tasks driving project goals.
8. Implement the Pomodoro Technique to manage work intervals and minimize distractions.
9. Identify personal time-wasting habits through time tracking tools.
10. Create a personalized time management plan.

You’ll want to go through at this point and revise based on what makes sense for your context. You may ask the AI tool to rewrite or rephrase things or add additional specific objectives. Get them as close as you can to your desired list of objectives before moving to the next step, bringing them to subject matter experts (SMEs) and stakeholders for feedback.

Step 4: Work with SMEs and stakeholders to revise your learning objectives.

Once you’ve been through the list and edited it based on what makes sense given your unique context, bring the objectives to your SMEs and other stakeholders to determine:

• Which of these objectives is most in line with the problem we are trying to solve?
• Which of these objectives can be combined or eliminated?
• Are there any training goals missing from this list?
• Do any of these objectives need to be further customized to our unique context?
• Can we add specific measurables in, and if so, what should those numbers be?

With the help of your SMEs and other stakeholders, you can refine your AI output to the learning objectives that work best for your specific program within your unique context.

Step 5: Ask AI for measurement strategies.

This is where things get interesting. Just as you asked the AI tool to create your learning objectives, you can also get it to help with related measurement strategies. Let’s take the first learning objective it provided as an example.

Using this simple prompt, the AI tool is able to give us plenty of ideas to work with, including a scenario-based pre- and post-test:

Given the learning objective “Apply a prioritization matrix to categorize project tasks,” how could that be measured to assess the program’s success?

The AI output was:

Before and after the training, present participants with a realistic project scenario with a list of tasks. Ask them to categorize the tasks using a prioritization matrix (e.g., Eisenhower Matrix). Compare their pre- and post-training performance to see if they can effectively apply the matrix to prioritize tasks.

This was just one of four ideas it provided, along with recommending that I combine approaches for the best data. As you get deeper into your measurement strategy, the AI tool can help you create rubrics to guide manager or peer raters, create assessment questions based on your unique objectives, help you create custom scenarios for scenario-based assessments and more!

One thing to keep in mind when it comes to that simple prompt is that I was within the same “conversation” (i.e., the same AI instance) as I had been for my previous prompts, so the bot had a “memory” of what we were talking about. You may not be able to get away with something so simple if you start over with a wiped AI instance and may need to “remind” the engine of your previous parameters.

Conclusion

Writing learning objectives can be tedious, but effective learning objectives follow a clear structure that makes them a great candidate for AI assistance. Using these steps, you can cut down on the time you spend brainstorming and crafting effective learning objectives.