Many participants may not actively engage. Instead, simply ask them to leave feedback in the course chat. AI will then analyze and classify it, providing you with the key insights you need.
Many participants may not actively engage. Instead, simply ask them to leave feedback in the course chat. AI will then analyze and classify it, providing you with the key insights you need.
This prompt is effective because it ensures structured, actionable insights from participant feedback, rather than just raw reactions. Here’s why it works:
Comprehensive Categorization: It covers multiple dimensions of course evaluation (material, instructors, pace, engagement, etc.), making it easy to pinpoint strengths and weaknesses.
Clear, Objective Criteria: Instead of vague sentiment analysis, it anchors responses to specific aspects of the course, helping instructors make targeted improvements.
Handles Subjectivity with Structured Data: Participants’ comments can be emotional, ambiguous, or informal. This framework organizes their responses into meaningful categories without losing nuance.
To ensure high-quality feedback during and after the course, instructors should follow these best practices:
Observe Reactions & Adjust on the Spot:
Use Polls & Quick Check-Ins: Ask participants in the chat:
Encourage Open-Ended Questions:
Monitor Participation & Engagement Signals:
Use a Short Survey (5-7 Questions Max). Here are some example questions:
Encourage Honest Feedback (Psychological Safety)
Offer a Follow-up Space for Additional Questions. A post-session Q&A thread or office hours can help participants clarify doubts they may not have shared in real-time.
Either create a transcript of the sections or download the chat after every course to feed AI together with the prompt above
Based on the attached files, which are saved chats and transcripts of the course, classify participant feedback from training sessions based on key course evaluation criteria. The classification should help assess overall satisfaction, effectiveness, and areas for improvement. Classification Categories:
Quality of Material
How well-structured, relevant, and useful the course content was.
Includes comments about the clarity, depth, and applicability of the material.
Quality of Instructors
Feedback on the instructors' teaching style, clarity of explanations, responsiveness, and engagement with participants.
Pace of the Course
Was the course too fast, too slow, or just right?
Includes mentions of overwhelming content, rushed delivery, or need for more time.
Overall Impression
General sentiment about the course, whether it was engaging, informative, enjoyable, or lacking in some aspects.
Engagement & Interaction
Feedback on how interactive the course was, including discussions, Q&A sessions, and participant involvement.
Usefulness & Practicality
Comments about how applicable the learnings are to real-world tasks, daily work, or professional development.
Amount of Content
Feedback on whether the course had too much, too little, or the right amount of content.
Includes mentions of needing time for review or extra resources.
Follow-up & Additional Resources
Requests for further documentation, recordings, additional sessions, or clarification on specific topics.
Technical & Logistical Issues
Mentions of scheduling problems, connectivity issues, or difficulty accessing materials.
Example Classification:
Feedback: "Great training! Lots of content… need to review it to understand even more."
Quality of Material: Positive
Amount of Content: High
Overall Impression: Positive
Feedback: "Can we have a step-by-step document for the examples shown?"
Follow-up & Additional Resources: Requested
Feedback: "I feel overwhelmed, so much information at once!"
Pace of the Course: Too fast
Amount of Content: High
Instructions:
Analyze each participant's feedback and classify it into the above categories.
Assign a sentiment score (Positive, Neutral, or Negative) where applicable.
Identify key trends, such as whether the majority found the pace appropriate or the material useful.
Summarize the findings to help improve future sessions.
