Written by Bradley Denison, Director, College of the Mainland
Working with AI, especially when it comes to prompting LLMs, can feel overwhelming at first—there’s so much potential, but also a lot to learn. That’s where scaffolding comes in. Just like in education, scaffolding can help guide you through the learning process by providing structured support early on, then gradually removing that support as you become more proficient.
Here’s how the lessons of scaffolding can translate into effective AI interactions:
1. Start with Simple Prompts (Chunking Information)
In the same way scaffolding encourages breaking down complex information into smaller chunks, learning how to use AI effectively starts with simple, clear prompts.
For someone new to AI, begin with straightforward requests. For example, instead of asking the AI to write a complex research paper in one go, start by asking it to outline the key components of the topic you’re interested in. This builds a foundation and shows how to structure queries for more targeted results.
As you get comfortable with the system’s responses, you can move to more layered or specific prompts, much like how learners progress in scaffolding-based instruction.
Lesson: Break tasks down into smaller steps. Start by asking the AI simple questions to understand how it processes language, and then build up to more complex tasks. This gradual approach will help you learn how to craft effective prompts over time.
2. Use Models and Examples (Guided Practice)
One of the core elements of scaffolding is using models or examples to help learners understand a task. When working with AI, the same principle applies. If you’re unsure how to structure your prompts, use sample questions or example interactions as a guide.
For instance, if you’re teaching someone how to use AI to generate a blog post, you could start by providing a model prompt: “Can you write a blog post introduction about scaffolding in education, using examples from real-world teaching practices?” Once the learner sees the response, they can tweak or refine the prompt to fit their specific needs.
Lesson: Use AI-generated examples to better understand how it processes prompts and tailor your requests based on the outputs. By studying these examples, you learn how to refine your prompts to achieve more accurate and detailed responses.
3. Gradual Release of Responsibility
In scaffolding, learners start with more support and gradually move toward independence. When working with AI, this might look like beginning with very explicit prompts that leave little room for interpretation, and then gradually loosening those constraints as you become more comfortable with how the AI responds.
At first, you might spell out every detail of your request – “List five key benefits of scaffolding in education, include citations, and provide a real-world example from a classroom environment“. As you get more proficient, you can simplify your prompts, knowing that the AI will understand your intent and fill in the details. Eventually, you’ll be able to use very concise prompts, confident that the AI will interpret them correctly based on context.
Lesson: Start with detailed prompts, then refine. As you grow more comfortable, you can reduce the amount of guidance you give the AI, trusting it to provide increasingly sophisticated responses based on less explicit information.
4. Encourage Experimentation (Exploration and Play)
Scaffolding doesn’t just guide learners through a predefined path; it encourages them to explore and experiment within a supported environment. The same can be true when learning to work with AI. Encourage people to try out different types of prompts, explore various angles of a topic, and see how the AI reacts.
For example, if you’re working on an instructional design project, ask the AI to generate ideas, then follow up with a prompt that requests it to evaluate those ideas or refine them. Trying different approaches helps users learn how flexible AI can be, and experimenting will improve the quality of future interactions.
Lesson: Experiment with your prompts. Don’t be afraid to try out different phrasings, change the scope of your queries, or follow up on an initial response with a deeper question. This trial-and-error approach will help you better understand the AI’s capabilities.
5. Provide Timely Feedback (Refining Outputs)
Just as scaffolding requires timely feedback to keep learners on track, working with AI involves refining and adjusting outputs based on the responses you receive. After receiving a response, it’s important to review the output and provide follow-up prompts that either clarify or redirect the AI’s focus.
For instance, if the AI provides a response that’s too broad or off-topic, you can guide it back by offering more context or asking for a more specific example. This back-and-forth process is part of the learning journey with AI.
Lesson: Refine your prompts in response to AI outputs. If the response isn’t quite right, don’t be discouraged—use it as feedback to adjust your query and get closer to the desired result.
Scaffolding Applied to AI Instruction
When teaching someone how to work effectively with AI, you can apply the scaffolding framework in much the same way as you would with other subjects:
1. Begin with Basic Prompts: Introduce the learner to simple questions or tasks. For example, start with asking the AI to explain a simple concept in clear terms, then build on that by asking for examples, comparisons, or deeper explanations.
2. Provide Models and Examples: Show learners examples of well-structured prompts and the responses they generate. Discuss why certain prompts work better than others, giving learners a model to follow when crafting their own questions.
3. Guide Their Practice: Allow learners to craft their own prompts but provide real-time feedback and suggestions. Guide them to adjust their phrasing or scope if needed, much like how you’d coach a student through a difficult problem.
4. Gradually Remove Support: As learners become more comfortable with crafting prompts, reduce the amount of guidance you provide. Encourage them to try more open-ended or experimental prompts, trusting their ability to refine them based on the AI’s feedback.
5. Encourage Reflection and Exploration: Encourage learners to reflect on the AI’s responses, question the assumptions in their prompts, and explore different paths of inquiry. This not only builds their skill in interacting with AI but also nurtures critical thinking.
Just as scaffolding in education helps learners build knowledge and independence over time, it can help anyone learning to work with AI develop the skills and confidence to craft effective prompts and leverage AI to its fullest potential. By starting with structured support, using models and examples, and encouraging experimentation, you can gradually become a more proficient AI user—someone who doesn’t just know how to ask questions, but how to ask the right questions.
Scaffolding, in this sense, isn’t just about teaching AI literacy; it’s about empowering learners to engage with AI in a meaningful, creative way. And that’s a skill that will only become more valuable as technology continues to evolve.
