Trainbot: A Conversational Interface to Train Crowd Workers for Delivering On-Demand Therapy
Tahir Abbas (Eindhoven University of Technology)
Vassilis-Javed Khan (Eindhoven University of Technology)
Ujwal Gadiraju (TU Delft - Web Information Systems)
Panos Markopoulos (Eindhoven University of Technology)
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Abstract
On-demand emotional support is an expensive and elu- sive societal need that is exacerbated in difficult times – as witnessed during the COVID-19 pandemic. Prior work in affective crowdsourcing has examined ways to overcome technical challenges for providing on-demand emotional support to end users. This can be achieved by training crowd workers to provide thought- ful and engaging on-demand emotional support. Inspired by recent advances in conversational user interface research, we investigate the efficacy of a conversational user interface for training workers to deliver psychological support to users in need. To this end, we conducted a between-subjects experimental study on Prolific, wherein a group of workers (N=200) received training on motivational interviewing via either a conversational interface or a conventional web interface. Our results indicate that training workers in a conversational interface yields both better worker performance and improves their user experience in on-demand stress management tasks.