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

Tahir Abbas, Vassilis-Javed Khan, Ujwal Gadiraju, Panos Markopoulos

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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.
Original languageEnglish
Title of host publicationProceedings of the Eighth AAAI Conference on Human Computation and Crowdsourcing
EditorsLora Aroyo, Elena Simperl
Pages3-12
ISBN (Electronic)978-1-57735-848-0
Publication statusPublished - 2020
Event8th AAAI Conference on Human Computation and Crowdsourcing - Virtual/online event due to COVID-19
Duration: 25 Oct 202029 Oct 2020
Conference number: 8

Conference

Conference8th AAAI Conference on Human Computation and Crowdsourcing
Abbreviated titleHCOMP 2020
Period25/10/2029/10/20

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