Completed Education & Skills Arts, Culture & Design

Real-Time Feedback Collection Platform

In plain English

AI plain-English summary

Training facilitators can now collect real-time feedback from every participant in a room using nothing more than a QR code and a smartphone. Traditional feedback methods—paper forms, post-event surveys, show-of-hands polls—capture only what people remember to say later, or what they are willing to say aloud. This platform captures responses during the event itself, when impressions are freshest and most candid. Participants submit thoughts spontaneously, and facilitators can push questions to the whole room instantly. No downloads, no accounts, no friction. If adopted widely, this could transform how training providers, workshop leaders, and public engagement teams gather evidence of impact. Funders increasingly demand proof that events change attitudes or build skills. Currently, much of that evidence is lost—conversations that happen in coffee breaks, reactions that fade before a survey arrives. This platform turns those fleeting moments into structured data, ready for reporting and funding applications. The project is applied, not fundamental science. Its value lies in making an existing process—feedback collection—faster, more complete, and more honest. If it works, the main change will be invisible to most people: better evidence behind the training and events they attend.

View original technical description
The platform captures real-time feedback during live events through participants' smartphones via QR code access, with no downloads or accounts required. Facilitators can create and push questions instantly to all participants during workshops, training sessions or events, and participants can also submit spontaneous reflections. Responses are automatically structured for analysis, reporting and funding applications, transforming traditionally lost insights into valuable evidence.

View the original record at the funder ↗

Researchers

Laura Gates (Principal Investigator)

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Original classification

Research Grant

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