Trainbot: A Conversational Interface to Train Crowd Workers for Delivering On-Demand Therapy

Tahir Abbas, Javed Khan, Ujwal Gadiraju, Panos Markopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

12 Citations (Scopus)

Abstract

On-demand emotional support is an expensive and elusive 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 thoughtful 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.

Original languageEnglish
Title of host publicationHCOMP 2020 - Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing
EditorsLora Aroyo, Elena Simperl
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Pages3-12
Number of pages10
ISBN (Print)9781577358480
DOIs
Publication statusPublished - 1 Oct 2020

Fingerprint

Dive into the research topics of 'Trainbot: A Conversational Interface to Train Crowd Workers for Delivering On-Demand Therapy'. Together they form a unique fingerprint.

Cite this