Selecting objective-aligned, high-quality course content is crucial for guiding students toward their learning goals and ensuring a meaningful educational experience. By leveraging AI tools, you can streamline content creation while maintaining alignment with course objectives.
The 3Ps framework—prep, purpose, and parameters—offers a structured approach to ensuring that AI-generated content is both effective and targeted. This resource introduces specific AI prompting techniques to help you optimize course content, ensuring quality, relevance, and alignment with learning goals.
Persona prompting
Persona prompting instructs the AI to take on a specific role or identity (e.g., instructional designer, expert in assessment), often with defined knowledge, tone, and values. This approach shapes responses to align with the goals and nuances associated with that persona’s expertise.
Start by setting a persona that reflects both subject matter expertise and pedagogical knowledge. This will guide the AI to produce content that resonates with your course’s goals, as well as incorporate tone and appropriate language.
Example Prompt
“You are an expert curriculum developer and subject matter expert in business writing. You develop quality objectives, topics, and content for courses and your content always aligns with learning goals.”
Constraint-based prompting
Constraint-based prompting allows you to set boundaries for length, complexity, or focus, ensuring that the content remains concise and relevant. Limit the scope of the content by specifying word count, the complexity of examples, or the number of key points to be covered.
Example Prompt
“Generate a 500-word summary of the main theories of motivation in psychology, focusing on self-determination theory and its application in workplace settings.”
Role-specific prompting
Role-specific prompting instructs the AI to adopt the viewpoint of a particular user or learner profile, ensuring the output is accessible and inclusive for diverse perspectives. When you check, ask the AI to evaluate the content from the perspective of various learners (e.g., non-native speakers, learners with disabilities) to ensure that it’s accessible.
Example Prompt
“Review this content on climate change from the perspective of an international student. How can the language be simplified or made more inclusive?”
One- or two-shot prompting
One-shot and two-shot prompting help clarify the AI’s understanding by providing one or two examples to illustrate the expected format, tone, or level of detail. One-shot prompting provides a single example to guide the AI, while two-shot prompting offers two contrasting examples to clarify nuanced differences. Provide examples that match the format and tone you want the content to reflect.
Example Prompt
“Here’s an example of a lesson on branding strategy that includes key terms, definitions, and case studies. Use this format to create a lesson on brand management.”
Template-based prompting
Template-based prompting involves guiding the AI to create a reusable framework or template for responses based on specified variables. This technique supports faculty by generating consistent feedback templates that can be customized for different types of content.
Ask the AI to generate a template that includes essential components, such as key concepts, learning activities, and assessment methods.
Example Prompt
“Create a lesson plan template for teaching environmental sustainability, ensuring it includes an introduction, key concepts, a case study, and assessment strategies.”
Self-interrogative prompting
Self-interrogative prompting encourages the AI to reframe the task or ask questions, ensuring it’s on the right track and fully aligned with course goals. Ask the AI to clarify its understanding of the content’s purpose and confirm its alignment with course objectives.
Example Prompt
“How do you interpret the task of creating content for a module on leadership? Are there any gaps in the course objectives that I need to address?”
Summarization-based prompting
Summarization-based prompting allows the AI to provide an overview of key topics before diving into detailed content. This helps to clarify the course’s scope and objectives from the outset. Request an initial summary of content topics to visualize and organize your course structure.
Example Prompt
“Provide a summary of the key topics that should be covered in an introductory course on business ethics, including key ethical theories and contemporary issues.”
Positive reinforcement prompting
Positive reinforcement emphasizes the value of high-quality content and motivates the AI to produce work that contributes to student success. Remind the AI of the positive impact its content can have on student learning and encourage it to focus on clarity, engagement, and accuracy.
Example Prompt
“Your attention to detail in this task impacts students’ success in the course.”
Chain-of-thought prompting
Chain-of-thought prompting helps break down complex tasks into smaller, logical steps. This technique helps the AI build coherent and detailed content that progresses logically from one idea to the next.
Example Prompt
“Complete this in order. First, create a series of lessons on time management that starts with understanding priorities, second follow up with practical techniques, and third, add a student project to apply these concepts. Provide a step-by-step simple list of your considerations as you crafted this content.”
Iterative prompting
Iterative prompting refines content through repeated rounds of feedback and adjustments. This technique ensures that the content is continuously improved to meet learning goals. After receiving initial content, refine it by prompting the AI to clarify or expand on certain sections to improve clarity and alignment.
Example Prompt
“Refine this lesson on decision-making by adding more real-world examples and clarifying the differences between types of decision-making models.”
Self-prompting
Self-prompting involves asking the AI to create its own prompt based on its understanding of the task, ensuring that its approach aligns with the course’s goals. Ask the AI to generate a prompt for the content creation task and ensure it matches the desired learning outcomes. Add in other strategies to the prompt request.
Example Prompt
“Act as an expert prompt generator. You engineer prompts that would yield the best response. Here is the desired prompt…”
By utilizing these prompt engineering strategies along with the components of the 3Ps framework—prep, purpose, and parameters—you can leverage AI to develop high-quality, aligned course content. Experimenting with these techniques allows for continuous improvement and supports the creation of engaging, relevant content in online education.
