Giving Learners Guidance on Using AI
How learning designers and facilitators can help students harness AI for learning — ethically and effectively
Image description: An open laptop on a wooden table, surrounded by books on both sides. On the black laptop screen, a dream-like rainbow colored tree is growing, with its branches extending outside the laptop.
Image generated with AI.
I’m currently enrolled in the first cohort of Building an Inclusive Quality Assurance Rubric offered by Eduflow Academy. It’s my first time being a learner since the public release of LLM AI tools like ChatGPT, so I wanted to highlight how course facilitator Bela Gaytán incorporated these tools into the course.
When I reached our first submission, I saw Bela had included a note about using AI. My first instinct was that this was going to be a warning against plagiarism. I was so wrong.
Instead of warning us away from AI, Gaytán suggested practical ways that we might leverage an AI large language model (LLM) to further our learning.
Keep reading for Gaytán’s, and my own, ideas about how learning designers and facilitators can help position students to harness the power and potential of AI.
Provide learners prompts for effective ideation and drafting
Image description: Close-up on a person’s hand holding a stylus pen above a laptop. They are using the stylus to check off tasks and documents as they complete them.
In the directions for the first written submission, Gaytán suggested we might generate a draft using ChatGPT or other large language model (LLM) and even provided us with a series of iterative prompts to do so.
Let’s be honest: some learners might be tempted to have AI draft their work for the sake of getting the course done quickly, without regard for quality, depth, or mastery.
Gaytán’s recommendation cleverly acknowledges that, validating AI as a tool that can be helpful when used well. Rather than being reactive in addressing poor quality AI-generated work after it has already been submitted, the facilitator can be proactive by ensuring that if students leverage AI tools, they will do so effectively.
As a secondary benefit, in providing learners with specific prompts that she wrote, Gaytán also models what effective LLM prompting looks like—potentially setting students up to craft more effective prompts themselves in the future, even without direct instruction.
Suggest learners use AI for initial feedback
Image description: In the foreground, a blue chat box floats above a laptop, indicating an ongoing process on the device. The chat box is labeled Chat AI and depicts a back-and-forth conversation between a chatbot and a user. In the background, the user’s hands are poised above the keyboard to continue the conversation.
Gaytán also noted that we might use AI to get initial feedback on our work, prior to submission.
To do this, learners run their own drafts through Chat GPT or other LLM, entering a description of the task learners were asked to perform (and a rubric with assessment criteria, if available).
With all of this information as a knowledge base, the LLM can analyze to what extent the learner has fulfilled the requirements of the learning task and deliver that as nearly immediate, written feedback to the learner.
In effect, this makes AI learners’ workshopping partner, prompting them to practice and improve prior to the final assessment.
AI-generated feedback on early learner work also positions AI as a partner to the facilitator. Research demonstrates that feedback is more useful to learners when it’s delivered closer to the learning event.
But depending on the facilitator’s number of learners and the nature of the feedback required, timely feedback is not always possible. This method is more scalable for the facilitator, improving the learner experience and freeing the facilitator up for other necessary work.
Explicitly instruct learners on AI ethics, uses, and limitations
Image description: A teenage boy sits on a gray couch next to a humanoid robot, representing AI. They are each holding a textbook opening in their lap. The boy looks up attentively at the AI robot as it gestures and points to the book, explaining something. The boy has dark hair and is wearing a brown hoodie, while the robot is taller, with a white frame and black joints. Around them are other objects indicating learning, like an orange notebook, a tablet, and a VR headset.
Depending upon learners’ prior knowledge of and experience with AI tools, instructors may want to take one step back and ground learners in the basics of AI, including:
a definition of AI
AI’s uses
AI’s limitations
ethical considerations
I would suggest that this kind of foundational instruction is especially important for research-heavy courses. Without explicit guidance, learners may fall prey to the misconception that LLMs are comparable to search engines/databases, when in fact they’re quite capable of hallucinating all kinds of nonsense.
Instructors should consider at least consider providing an introduction to AI as an optional resource, available to those who need it.
Final thoughts
Given that moving forward, many learners are likely to take their learning to AI tools anyway, it’s smart to provide them with guidance about how to do so productively and effectively.
Showing learners how to harness the power of AI increases the likelihood that they’ll use AI tools in a way that furthers learning outcomes.