GitHub Copilot Prompt for Creating a Course Plan
GitHub Copilot, the coding assistant powered by GitHub and OpenAI's artificial intelligence, is not limited to code generation. Thanks to its natural language understanding capabilities, it can be a valuable ally for teachers, trainers, and instructional designers who want to quickly structure a complete course plan. By formulating a well-constructed prompt directly in your code editor or via Copilot Chat, you can obtain a coherent pedagogical architecture including learning objectives, thematic progression, practical activities, and assessment methods. Copilot's advantage lies in its ability to adapt to the technical context: if you are creating a programming course, it can simultaneously suggest relevant code exercises. This guide offers an optimized prompt for generating a structured course plan, adaptable to any discipline, with variations according to your level of pedagogical requirement. Whether you are preparing professional training, a university module, or a practical workshop, these prompts will save you considerable time in the design phase.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Act as an experienced instructional designer. Create a comprehensive and structured course plan on the following topic: [COURSE_TOPIC]. The course is intended for an audience of [LEVEL: beginner/intermediate/advanced] and should run for [DURATION: e.g., 12 weeks, 3 days]. For each module, include: the module title, learning objectives formulated using Bloom's taxonomy (measurable action verbs), detailed content to be covered, associated practical activities or exercises, recommended resources, and evaluation criteria. Also add a general introduction to the course with necessary prerequisites, a logical pedagogical progression from simple to complex, and a final synthesis section with a capstone project. Format the result in Markdown with a clear hierarchy of headings.
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Answer 3 questions and Léa tailors the prompt to your situation.
Why this prompt works
This prompt is effective because it assigns a specific expert role to Copilot (instructional designer), which guides the quality and register of the response. The use of variables in square brackets allows immediate customization while maintaining a rigorous structure. The explicit reference to Bloom's taxonomy and evaluation criteria forces the model to produce pedagogically standard content rather than a simple list of topics.
Use Cases
Variants
Expected Output
You will obtain a complete course plan in Markdown, organized into progressive modules with measurable learning objectives, concrete practical activities, and evaluation methods for each section. The document will include an introduction with prerequisites, a coherent progression, and a final capstone project, ready to be adapted and used directly in your teaching environment.
Frequently Asked Questions
Can GitHub Copilot create a syllabus for non-technical subjects?
Yes, GitHub Copilot is capable of generating syllabi on virtually any topic, including humanities, marketing, languages, or management. Although the tool was originally designed for code, its underlying language model has extensive knowledge. For optimal results on a non-technical subject, be highly specific in describing the target audience and intended objectives in your prompt.
How do I adapt the generated syllabus to a specific format (Moodle, Google Classroom, etc.)?
Simply add a specific formatting instruction at the end of the prompt, for example: "Format the output for import into Moodle with appropriate section tags" or "Structure each module as a Google Classroom unit with separate assignments and resources." Copilot will adapt the output structure accordingly. You can also request an export in SCORM or xAPI format for compatible LMS platforms.
Does the generated syllabus meet professional educational standards?
The generated syllabus provides an excellent working foundation, but it requires review by an education professional to validate pedagogical alignment, assessment relevance, and suitability for the actual audience. Using the advanced variant that incorporates Bloom's taxonomy and the ADDIE model will yield a result more aligned with professional standards. However, fine-grained contextualization (local regulations, specific competency frameworks) will always need to be verified manually.
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