5 AI Hacks to Create Effective MCQs
In this post, I’ll walk you through 5 AI-powered strategies to streamline your assessment creation—saving you time while enhancing student learning.
😟 Writing Good MCQs Is Hard
Multiple choice questions (MCQs) are one of the most common forms of assessment.1 MCQs can be used to test knowledge and comprehension and higher-order cognitive skills such as application and analysis. 1 However, writing good multiple-choice questions is challenging. Faculty Focus has an article dedicated to the Seven Mistakes to Avoid When Writing Multiple-Choice Questions
Some of our common pain points when sitting down to write MCQs are:
Struggling to write plausible distractors (wrong answers that aren't obviously wrong)
Ensuring alignment with course learning objectives
Avoiding questions that test memorization instead of thinking
Lack of time to write, review, and revise MCQs for clarity and depth
But…why suffer when LLMs can generate MCQs in a fraction of the time it would take you?
5 Tips to Using LLMs to write MCQs
✅ 1. Start with a Goal
In my previous post, I discussed the 7 elements of effective prompting. When designing MCQs, my recommendation is to begin by providing context, audience, guidelines/framework, and examples.
Context & Audience: If you are designing MCQs for a course, consider uploading your course syllabus to the LLM. This will provide the context and audience. If you have MCQs you have generated in the past, upload a few of those as well. These will act as examples and guide the LLM to approximate your tone. For additional context, upload the textbook or readings that are being tested.
Framework: Depending on your purpose for the MCQs (knowledge check or application test), ask the LLM to focus on the specific levels of your framework (such as Bloom’s taxonomy, Miller’s pyramid).
I am uploading my course syllabus, readings, and examples of MCQs I have created in the past. I want you to write 5 MCQs for the first module of the course. 2 questions should be at the lower levels of Bloom’s taxonomy and 3 should assess application-level understanding.⚡ 2. Prompt for Distractors
Most LLMs default to easy distractors. When you specify "plausible but incorrect" options, you'll get items that challenge students' misconceptions rather than just their memory.
Generate 4 options: 1 correct and 3 distractors that reflect common student errors about osmosis. Avoid common errors associated with writing MCQs.🔎 3. Ask for Rationales to Speed Up Review
There are 2 benefits to doing this:
With built-in rationales, you can instantly evaluate content alignment and make edits without guesswork.
It will save you the stress of having to come up with feedback for each question.
Explain why each option is right or wrong in one sentence.👩🏫 4. Use Roleplay to Refine
Providing a role gives more context to the LLM. LLMs can critique their own output when prompted to do so. Pairing these two features of roleplay and self-critique helps you to iteratively polish your MCQs.
You are an expert nursing faculty member experienced in creating NCLEX-style MCQs. Review these questions and provide feedback on their quality. Then generate improved versions of the questions.🔄 5. Review, review, review
While LLMs are definitely getting better in their capabilities, they are still prone to hallucinations. You MUST review all the output to ensure accuracy.
Remember, you are still responsible for the quiz.
Other Pro Tips:
If you are designing questions to mimic a standardized test like the NCLEX or USMLE Step 1, add that to the context. Give me 5 Step 1 style exam questions.
Include case scenarios or case vignettes. Generate 5 MCQs using a case vignette style.
Use Feedback Loops: After students take the quiz, analyze their responses to identify areas for improvement in your questions.
Where can you use MCQs
If you have recorded lectures, use the transcript as context and generate MCQs for a quick knowledge check.
MCQs can also be used for specific readings or content you want to ensure your learners have reviewed. Upload the readings and let the LLM do the rest.
Key Takeaways
Context is king. The more detailed your input, the better the output.
Refinement is essential. Always review and revise AI-generated content.
Feedback loops improve quality. Use student data to continuously enhance your assessments.
Don't be afraid to experiment. There's no one-size-fits-all approach.
Here’s a handy one-page reference guide you can download and keep by your side.
References
1. Abdulghani, H. M., Ahmad, F., Irshad, M., Khalil, M. S., Al-Shaikh, G. K., Syed, S., ... & Haque, S. (2015). Faculty development programs improve the quality of Multiple Choice Questions items' writing. Scientific reports, 5(1), 9556.