AI Fluency: Prompting Basics

What is Prompting?

Every interaction with an AI system begins with a prompt, whether it is a simple request like “summarize this article” or a complex task such as “create a lesson plan for an introductory biology class.” The quality of the AI’s response depends largely on the clarity and detail of the prompt you provide.

For instructors using generative AI, prompting is less about programming and more about communication. It involves framing your request so the AI understands the context, audience, and purpose of your task. A strong prompt gives the AI direction, sets expectations for the output, and mirrors the kind of critical thinking you ask of your students.

Learning to prompt effectively helps educators guide AI toward useful results rather than generic answers. It turns AI into a collaborative thinking partner that can help refine ideas, create examples, and support instructional planning while keeping the instructor in control of the process.

Types of Prompts

Prompts can take many forms, and each serves a different purpose depending on what you want the AI tool to do. Some prompts are used to gather information, while others are meant to generate new ideas or guide deeper reflection. Understanding the different types of prompts helps you select the right one for your teaching goal.

Prompts can be grouped in two helpful ways. You can think about them by purpose and you can think about them by interaction pattern. There is no single best type. Choose the option that fits your goal and your workflow.

By purpose

• Informational prompts ask the AI to explain, summarize, or define something.
  Example: Explain Bloom’s Taxonomy in simple terms for undergraduate students.
• Creative prompts ask the AI to generate new ideas, examples, or materials.
  Example: Create three short discussion questions that connect sustainability concepts to real-world examples.
• Analytical prompts ask the AI to compare, critique, or evaluate information.
  Example: Compare two strategies for assessing group projects in large classes.
• Practical or task-based prompts ask the AI to perform a function or produce a product.
  Example: Draft an email announcement for a new course module focused on digital ethics.
• Reflective prompts invite the AI to support deeper thinking about a topic or process.
  Example: Suggest three reflection questions students could use after completing a group presentation.

By interaction pattern

• Chain of thought prompting breaks a complex task into smaller parts. It guides the AI through a process one step at a time and improves clarity and refinement with each response.
  Example: Help me write a project proposal by outlining the reflection, solution, and conclusion sections individually.
• Socratic or reflective prompting uses open-ended questions to encourage reasoning and self-assessment. It turns the AI into a thought partner that helps you examine your ideas more critically.
  Example: What questions should I ask myself when designing an inclusive syllabus?
• Interactive or guided scenario prompting creates an ongoing dialogue, similar to a simulation or coaching conversation. The AI responds, waits for your feedback, and continues only when prompted.
  Example: You are my case study coach. Guide me through a neighborhood analysis step by step, waiting for my input before moving to the next stage.

Each of these types is useful in different situations. Match the purpose and the interaction pattern to what you need, then iterate to refine the result.

Prompting Styles

Just as instructors have different teaching styles, they also develop different prompting styles when working with AI. Some people prefer to provide all the information and context in one detailed prompt, while others build their request gradually, adding more details as the conversation develops. Both styles can be effective, and the right one depends on your workflow and comfort level.

• Formal prompting is a structured approach where all parts of the request are written together. This style usually includes the goal, audience, tone, and structure of the desired response. It works well when you already have a clear idea of what you want and need a complete draft or product. For example, you might ask, “Create a three-paragraph lesson overview for an introductory environmental science course that includes objectives, key topics, and a discussion prompt.”
• Conversational prompting is a more iterative approach. Instead of entering everything at once, you start with a simple request and add information or clarifications as the AI responds. This method can feel more natural for instructors who like to collaborate, experiment, and refine ideas step by step. For example, you might begin with “Help me draft a lesson overview for environmental science” and then add, “Include objectives that focus on sustainability and policy,” followed by, “Make it appropriate for a first-year course.”

There is no single best prompting style. Some instructors find that formal prompting saves time, while others prefer the flexibility and creativity of a conversational exchange. The key is to choose the approach that feels most natural for your workflow and allows you to stay in control of the direction of the response.

Why Good Prompting Matters

The quality of an AI response depends on the clarity and purpose of the prompt it receives. A thoughtful prompt leads to more accurate, creative, and relevant results, while a vague or incomplete one often produces generic or confusing information. For instructors, effective prompting is not about writing perfectly but about being intentional with context, purpose, and audience.

Good prompting allows instructors to guide the AI rather than react to it. When you specify what you want, such as the level of detail, the tone of the response, or the intended use, the AI becomes a more dependable support tool. Clear prompts can help generate examples that connect to learning objectives, improve assessment ideas, and create materials that are accessible for students.

Practicing good prompting also models critical thinking for students. It shows them how to ask meaningful questions, refine their ideas, and evaluate AI-generated content with a critical eye. When instructors prompt intentionally, they demonstrate how AI can be used thoughtfully and ethically as part of the learning process.

Elements of a Great Prompt

A strong prompt gives the AI enough context to understand your goal, your audience, and the format you expect. Clear structure helps the AI produce results that are useful, accurate, and aligned with your teaching values. One effective method for organizing prompts is the PARTS Framework, originally developed by Google for Education for use with Gemini.

• Persona – Tell the AI who it should be. Defining a role sets the tone and perspective of the response.
  Example: You are an instructional designer who specializes in active learning strategies for college courses.
• Act – State what you want the AI to do. Use clear verbs such as create, summarize, rewrite, or analyze.
  Example: Create a short lesson plan introducing students to Bloom’s Taxonomy.
• Recipient – Identify who the response is for. The AI will adjust tone and complexity based on your audience.
  Example: Write this lesson for first-year undergraduate students.
• Theme – Include the topic or concept to keep the AI focused on the right content area.
  Example: Focus on how Bloom’s Taxonomy supports deeper learning and reflection.
• Structure – Specify the format or model you want.
  Example: Present the response as a 5E lesson plan with an introduction, exploration, and reflection activity.

Using the PARTS Framework helps instructors communicate expectations clearly and reduces the need for extensive editing. It also encourages purposeful and transparent AI use, ensuring that outputs serve learning goals rather than replace human judgment.

Tips for Effective Prompting

Good prompting is less about writing perfectly and more about being intentional. Once you understand the elements of a great prompt, you can apply a few simple strategies to get clearer, more accurate, and more creative results.

Start with clear context.
Give the AI enough background to understand what you need. Include the course topic, the student level, and the goal of the task. The more context you provide, the more relevant the response will be.

Be specific about format and expectations.
Tell the AI what kind of output you want, such as a summary, lesson outline, quiz, or discussion question. Specific instructions help the AI produce a usable result rather than a general overview.

Use iteration to refine.
Treat prompting as a conversation. If the first response misses the mark, follow up with clarifying questions or adjustments. Iterating helps fine-tune the AI’s understanding of your needs.

Ask for reasoning or reflection.
Prompts that include “Explain your reasoning” or “Describe how you reached this conclusion” often produce more thoughtful, transparent responses. This approach also helps model critical thinking for students.

Review and edit all outputs.
Always check AI-generated material for accuracy, tone, and alignment with your teaching values. Revising the output ensures that it reflects your professional judgment and instructional goals.

Save strong prompts for reuse.
Keep a record of prompts that worked well so you can reuse or adapt them later. Over time, you will build a personal library of effective prompts that match your teaching style.

From Prompting to Fluency

Prompting is the foundation of effective AI use, but fluency comes from knowing how and when to apply it thoughtfully. Once instructors are comfortable writing clear and purposeful prompts, the next step is to use those skills to support learning goals, disciplinary values, and responsible practices.

Developing AI fluency means viewing AI as a collaborator in the teaching process rather than a shortcut. It involves planning prompts that align with course objectives, evaluating AI-generated results for accuracy and bias, and guiding students to think critically about how AI shapes their understanding. Over time, this approach strengthens both instructional design and digital literacy.

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The AI Fluency Article Series: Your Next Read

AI Fluency: Course Design

This article explores how AI can support the early stages of course planning, beginning with well-written learning objectives. It shows instructors how to identify disciplinary boundaries, consider the role of AI in real-world professional practice, and design courses that use AI responsibly and transparently. Practical examples and a hands-on scenario help instructors experiment with AI as a collaborative design partner.

Other Articles in This Series

• AI Fluency: 101 for Instructors
• AI Fluency: Prompting Basics – Current Read
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• AI Fluency: Designing Assessments and Summarizing Student Work
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References

Phan, N. (2025, September 17). A 6-Part Framework for Flawless AI Prompting Every Time. AI Fire. https://www.aifire.co/p/a-6-part-framework-for-flawless-ai-prompting-every-time