"Any questions?" is the worst discussion prompt in online education. It puts the burden on students to figure out what they don't know, and the result is almost always silence. I've seen this pattern across thousands of courses on Ruzuku: the courses with active discussions don't leave prompts to chance. They plan them lesson by lesson, tied to what students just learned. ChatGPT can help you build those prompts in minutes instead of hours.
What you’ll walk away with:
- Discussion prompts generating real conversation, not one-word responses
- A prompt library organized by module and learning objective
- A framework for creating prompts connecting content to student experience
Why generic prompts don't work
Our platform data tells a clear story: courses with active community discussion average 65.5% completion versus 42.6% without — a 54% improvement. But "active" is the operative word. A discussion that opens with "What did you think of this lesson?" doesn't generate the kind of engagement that drives completion. It generates one-sentence responses and crickets.
The prompts that work are specific to the lesson content, ask students to connect the material to their own experience, and give them something concrete to respond to. One of our customers ran 21-day and 30-day challenges where she pre-built discussion prompts for every day. The engagement was strong — but she discovered that drip-scheduled prompts auto-notified all students at once, regardless of where they were in the course. The lesson: discussion timing needs to align with your curriculum structure, not just your calendar.
On Ruzuku, discussions are built into each lesson, so prompts appear right where the learning happens. Students see the prompt when they reach that lesson, not when a calendar says it's time. That's the architecture that makes per-lesson AI prompts actually useful.
The Bloom's Taxonomy approach
The educational AI tool Harmonize uses Bloom's Taxonomy to categorize discussion prompts by cognitive level, and it's a framework worth borrowing. Bloom's hierarchy — remember, understand, apply, analyze, evaluate, create — gives you a vocabulary for what you're actually asking students to do.
Here's why this matters practically: if every prompt is at the "remember" level ("What are the three types of..."), students feel like they're being quizzed. If every prompt is at the "evaluate" level ("Do you agree with the author's argument?"), students who haven't fully grasped the material feel lost. The sweet spot is varying the level to match where students are in your course.
Early modules: lean toward recall and comprehension. You're building shared vocabulary. Mid-course: application and analysis. Students connect the material to their own work. Late modules: evaluation and creation. Students synthesize what they've learned.
Prompts to try
Lesson-specific at varied cognitive levels
"Here is the content of my lesson on [paste lesson text or key points]. Generate 3 discussion prompts at different cognitive levels: (1) A recall/comprehension question that checks understanding of the core concept, (2) An application question that asks students to connect the concept to their own work or experience, and (3) An analysis question that asks students to compare, contrast, or evaluate. Format each with a label (Recall, Application, Analysis) so I can choose the right one for my students."
Week-long discussion arc
"I'm running a cohort course where students complete one module per week. This week's module covers [topic]. Create a discussion arc: a warm-up prompt for Monday that's low-stakes (share an example from your own experience), a mid-week prompt that digs deeper (analyze or apply the concept), and a Friday reflection prompt (what surprised you, what will you do differently). Keep each prompt under 40 words."
Peer learning prompts
"I teach [subject] to [audience]. This lesson covers [topic]. Write 2 discussion prompts that encourage peer learning — where students teach each other or build on each other's responses. Avoid yes/no questions. Each prompt should require students to share something specific from their own practice. Tone: warm, inviting, not academic."
The human layer
AI-generated prompts are a starting point, not a finish line. I've seen this repeatedly across 32,000+ courses on Ruzuku: the instructor's presence in the discussion matters more than the quality of the prompt. A mediocre prompt with an instructor who responds to every student outperforms a brilliant prompt that sits unanswered.
Here's what AI can't do: it can't read the room. If your students are struggling with a concept, you need a simpler prompt that meets them where they are. If they're racing ahead, you need a challenge question that stretches them. AI generates the default; you adjust based on what you're actually seeing.
My recommendation: generate prompts for your entire course in one sitting, then review them week by week as your cohort progresses. You'll often swap out the AI-generated prompt for something that responds to what came up in the previous week's discussion. That responsiveness is what makes students feel like they're in a real learning community, not a content delivery system.
What it gets wrong
- Too academic. ChatGPT defaults to a seminar-style tone — "critically evaluate the implications of..." Your students probably aren't in graduate school. Edit for warmth. "What surprised you about..." works better than "Analyze the significance of..."
- Too broad. AI loves sweeping questions: "How does this apply to your life?" That's too big. Narrower is better: "Think about the last student you worked with who struggled. How would this approach have changed what you did?"
- No awareness of group dynamics. AI doesn't know that your cohort has three people who dominate discussions and twelve who stay quiet. You might need prompts specifically designed to draw out quieter voices — "Share one thing you haven't mentioned yet" or pair-based exercises that don't require posting to the whole group.
- Can't replace sub-group conversations. Some courses need small-group discussions, not whole-cohort threads. One of our customers found that her students preferred breaking into WhatsApp groups for more intimate conversations. AI prompts work for the main discussion thread, but consider whether your students also need smaller spaces.
Related guides
- Creating Discussion Boards with Padlet — alternative discussion formats outside your course platform
- Using AI to Create Student Feedback Forms — collect structured input on your discussions
- Using AI to Identify At-Risk Students — spot the students who aren't participating
- Building Course Surveys with Google Forms — gather feedback on what's working
Build discussions into the learning
The best discussion prompts aren't bolted on after the lesson — they're woven into the curriculum from the start. Use AI to draft them, then refine based on what your students actually need. Start free on Ruzuku and build courses where discussions happen right inside each lesson, exactly where the learning is.