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

Author:

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

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.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. DECI: The 2nd Tutorial on Designing Effective Conversational Interfaces;Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-27

2. "Are we all in the same boat?" Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd Work;Proceedings of the CHI Conference on Human Factors in Computing Systems;2024-05-11

3. ContextBot;Proceedings of the 34th ACM Conference on Hypertext and Social Media;2023-09-04

4. DECI: A Tutorial on Designing Effective Conversational Interfaces;28th International Conference on Intelligent User Interfaces;2023-03-27

5. Understanding User Perceptions of Response Delays in Crowd-Powered Conversational Systems;Proceedings of the ACM on Human-Computer Interaction;2022-11-07

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