LiveSynthesis by LLMGrader
Pose a question, collect live responses from every participant, and use AI to create a fast first synthesis. See common ground, distinct perspectives, possible misconceptions, and ideas that might otherwise be missed.
A simple live cycle
LiveSynthesis turns group input into a visible, repeatable conversation loop. The synthesis can be prepared by the facilitator, assisted by an AI model, or refined using both.
See how the cycle supports different kinds of group work · Learn about the role of AI
Features and highlights
Everyone gets a structured opportunity to respond before the conversation is shaped by the first people who speak. Participants can revise their responses and ask questions privately.
Result: broader participation and more perspectives in view.
The synthesis identifies themes and ideas shared across the group, giving the facilitator a useful starting point for the next question or decision.
Result: shared ideas become visible sooner.
The response view and synthesis can surface minority views, edge cases, unresolved questions, and unusual ideas that might otherwise get lost in a fast-moving discussion.
Result: more nuance than a simple vote or attendance count.
A facilitator reviews and edits the fast first synthesis, then shares what is useful for a follow-up question, clarification, or decision. The human remains in control.
Result: technology that supports judgment instead of replacing it.
Ways to use it
A planning group responds to a proposed goal, identifies constraints, and names the work that matters most. The synthesis organizes priorities, dependencies, concerns, and ideas that deserve a closer look.
Result: a shared starting point for the next decision.
A facilitator asks for ideas about improving a process, designing a project, or solving a practical problem. Participants contribute simultaneously. The synthesis groups related ideas while preserving unusual or original proposals that could disappear in a fast-moving verbal discussion.
Result: common themes plus distinctive ideas, ready for the group to examine.
A class, team, or working group considers a problem with several plausible approaches. Participants explain what they would try and why. The synthesis organizes shared assumptions, competing tactics, risks, and tradeoffs without declaring a winner.
Result: a structured comparison that makes the discussion more focused.
Participants explain a concept, reading, proposal, or current situation in their own words. The facilitator sees where ideas converge, where they diverge, and which questions should shape the next conversation.
Result: timely evidence for a better next question—not an automatic grade.
After a reading, presentation, or meeting, the moderator asks what participants consider most important. The LLM identifies points supported across many responses and separates genuine disagreement from differences in emphasis. The moderator reviews and edits the result before sharing it.
Result: a participant-ready account of common ground and open questions.
Participants describe what is working, what remains confusing, or what would improve an activity. Rather than waiting for a later survey, the moderator can summarize the feedback during the session, ask for clarification, and make an immediate adjustment.
Result: feedback becomes part of the live process rather than an afterthought.
The moderator can preserve questions, responses, edited LLM syntheses, and private notes in local session history. A finished session can be revisited or exported for further analysis without making the application server a permanent repository of classroom work.
Result: useful local history with short-lived encrypted server recovery.
The role of AI
Reading and comparing dozens of changing responses is humanly difficult while a live discussion is moving forward. LiveSynthesis uses AI to produce a fast first synthesis that the human facilitator reviews, edits, and uses to guide the next part of the conversation.
The AI is not the participant, grader, or decision-maker. Questions, responses, messaging, attendance, and session history work without an LLM.
You can also copy a reviewed, anonymized session history into ChatGPT, Gemini, Claude, or an institution-approved external AI tool without configuring Ollama, an API key, or any LLM provider in LiveSynthesis. This is practical for shorter sessions and lets instructors follow required institutional workflows. For large groups or long responses, the built-in workflow is designed to handle the larger volume. The moderator reviews the text first and remains responsible for what is shared.
Explore privacy, model choices, costs, and deployment options →
User guides