Collect context
Receive the classroom messages and relevant lesson topic with appropriate permissions.
PRODUCT / LIVE CLASS INTELLIGENCEABHYASDHARA INTELLIGENCE LAB / PRODUCT NOTES
Hundreds of messages.
The questions that matter.
Busy live classes bury important doubts between greetings, repeated messages and irrelevant comments. The educator view aims to surface useful learning questions without handing moderation entirely to AI.
LIVE CLASS / QUESTION SIGNAL
SAMPLE QUEUEQuestion queue
A
Student A
QuestionSir, why do we divide by the original value in percentage change?
B
Student B
OtherGood morning everyone 👋
C
Student C
QuestionCan you explain the second step with another example?
D
Student D
ReviewJoin my channel!!!
⇡
Suggested teacher action
Address percentage change first. Review context-dependent messages before responding or moderating.
Static example; classification can make errors. No live chat or AI backend is connected to this website.
Visual illustration, not production application data.
01 / THE PROBLEMCLASSROOM SIGNAL
The comment feed should not decide the lesson.
In a fast-moving class, a teacher cannot read every comment. A useful assistant distinguishes likely academic doubts, clusters repeated questions and lets educators review uncertain messages.
WORKFLOW
From chat stream to teaching decisions.
A practical flow for teachers who need help during live instruction.
Classify carefully
Mark likely questions, greetings, off-topic comments and requests needing more context.
Group repeated doubts
Combine similar questions and show topic demand rather than raw message volume alone.
Teacher decides
Review, answer, postpone or approve suggested explanations. Avoid irreversible automatic penalties.
NEXT STEP
Make every live class more teachable
Explore how live-class intelligence can fit a coaching institute or education network.
PRACTICAL GUIDE / LIVE CLASS AI
Why question detection matters in a busy live classroom
Relevant considerations for applying learning intelligence with evidence, oversight and clear next steps.
Find academic intent within chat noise
A message stream often contains greetings, repeated messages, fragmentary questions and genuine doubts. The goal is not simply to block off-topic comments; it is to surface questions that the educator can meaningfully address while teaching.
Group repeated doubts
Several learners may ask about the same calculation in different words. Clustering those messages allows the teacher to answer once clearly, then see which follow-up questions remain unresolved. Ambiguous messages should be reviewed rather than silently treated as spam.
Keep the educator in control
Automated classification can be incorrect. The interface should preserve context, allow manual correction and require appropriate confirmation before a student is muted or a response is sent. Example messages on this website are scripted.