Ted Grover
Kael Rowan
Mary Czerwinski
ABSTRACT
Current research on building intelligent agents for aiding with productivity and focus in the workplace is quite limited, despite the ubiquity of information workers across the globe. In our work, we present a productivity agent which helps users schedule and block out time on their calendar to focus on important tasks, monitor and intervene with distractions, and reflect on their daily mood and goals in a single, standalone application. We created two different prototype versions of our agent: a text-based (TB) agent with a similar UI to a standard chatbot, and a more emotionally expressive virtual agent (VA) that employs a video avatar and the ability to detect and respond appropriately to users' emotions. We evaluated these two agent prototypes against an existing product (control) condition through a three-week, within subjects study design with 40 participants, across different work roles in a large organization. We found that participants scheduled 134% more time with the TB prototype, and 110% more time with the VA prototype for focused tasks compared to the control condition. Users reported that they felt more satisfied and productive with the VA agent. However, The perception of anthropomorphism in the VA was polarized, with several participants suggesting that the human appearance was unnecessary. We discuss important insights from our work for the future design of conversational agents for productivity, wellbeing, and focus in the workplace.
- Fatema Akbar, Ted Grover, Gloria Mark, and Michelle X Zhou. 2018. The Effects of Virtual Agents' Characteristics on User Impressions and Language Use. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion. ACM, 56.Google Scholar
Digital Library
- Anja Baethge and Thomas Rigotti. 2013. Interruptions to workflow: Their relationship with irritation and satisfaction with performance, and the mediating roles of time pressure and mental demands. Work & Stress 27, 1 (2013), 43--63.Google ScholarCross Ref
- Emad Barsoum, Cha Zhang, Cristian Canton Ferrer, and Zhengyou Zhang. 2016. Training deep networks for facial expression recognition with crowd-sourced label distribution. In Proceedings of the 18th ACM International Conference on Multimodal Interaction. ACM, 279--283.Google ScholarDigital Library
- Fabrica Binaria. 2019. TrackTime. https://www.fabbricabinaria.it/solutions/tracktimeGoogle Scholar
- Jelmer P Borst, Niels A Taatgen, and Hedderik van Rijn. 2015. What makes interruptions disruptive?: A process-model account of the effects of the problem state bottleneck on task interruption and resumption. In Proceedings of the 33rd annual ACM conference on human factors in computing systems. ACM, 2971--2980.Google ScholarDigital Library
- Stoney Brooks. 2015. Does personal social media usage affect efficiency and well-being? Computers in Human Behavior 46 (2015), 26--37.Google ScholarDigital Library
- Scott A Cambo, Daniel Avrahami, and Matthew L Lee. 2017. BreakSense: Combining physiological and location sensing to promote mobility during work-breaks. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 3595--3607.Google ScholarDigital Library
- Justin Cranshaw, Emad Elwany, Todd Newman, Rafal Kocielnik, Bowen Yu, Sandeep Soni, Jaime Teevan, and Andrés Monroy-Hernández. 2017. Calendar. help: Designing a workflow-based scheduling agent with humans in the loop. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2382--2393.Google ScholarDigital Library
- Mary Czerwinski, Eric Horvitz, and Susan Wilhite. 2004. A diary study of task switching and interruptions. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 175--182.Google ScholarDigital Library
- Andrew Faulring, Brad Myers, Ken Mohnkern, Bradley Schmerl, Aaron Steinfeld, John Zimmerman, Asim Smailagic, Jeffery Hansen, and Daniel Siewiorek. 2010. Agent-assisted task management that reduces email overload. In Proceedings of the 15th international conference on Intelligent user interfaces. ACM, 61--70.Google ScholarDigital Library
- Asma Ghandeharioun, Daniel McDuff, Mary Czerwinski, and Kael Rowan. 2018. EMMA: An Emotionally Intelligent Personal Assistant for Improving Wellbeing. arXiv preprint arXiv:1812.11423 (2018).Google Scholar
- Jacek Gwizdka. 2000. Timely reminders: a case study of temporal guidance in PIM and email tools usage. In Chi'00 extended abstracts on human factors in computing systems. ACM, 163--164.Google Scholar
- Annabell Ho, Jeff Hancock, and Adam S Miner. 2018. Psychological, relational, and emotional effects of self-disclosure after conversations with a chatbot. Journal of Communication 68, 4 (2018), 712--733.Google ScholarCross Ref
- Shamsi T Iqbal and Eric Horvitz. 2007. Conversations amidst computing: A study of interruptions and recovery of task activity. In International Conference on User Modeling. Springer, 350--354.Google ScholarDigital Library
- William Jones, Harry Bruce, and Susan Dumais. 2001. Keeping found things found on the web. In Proceedings of the tenth international conference on Information and knowledge management. ACM, 119--126.Google ScholarDigital Library
- Everlyne Kimani, Kael Rowan, Daniel McDuff, Mary Czerwinski, and Gloria Mark. 2019. A Conversational Agent in Support of Productivity and Wellbeing at Work. In Proceedings of the 7th Intenrational Conference on Affective Computing and Intelligent Interaction. IEEE.Google ScholarCross Ref
- Jonathan Klein, Youngme Moon, and Rosalind W Picard. 2002. This computer responds to user frustration: Theory, design, and results. Interacting with computers 14, 2 (2002), 119--140.Google ScholarCross Ref
- Rafal Kocielnik, Daniel Avrahami, Jennifer Marlow, Di Lu, and Gary Hsieh. 2018. Designing for workplace reflection: a chat and voice-based conversational agent. In Proceedings of the 2018 Designing Interactive Systems Conference. ACM, 881--894.Google ScholarDigital Library
- Gloria Mark, Mary Czerwinski, and Shamsi T Iqbal. 2018. Effects of Individual Differences in Blocking Workplace Distractions. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 92.Google ScholarDigital Library
- Gloria Mark, Daniela Gudith, and Ulrich Klocke. 2008. The cost of interrupted work: more speed and stress. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM, 107--110.Google ScholarDigital Library
- Gloria Mark, Shamsi Iqbal, and Mary Czerwinski. 2017. How blocking distractions affects workplace focus and productivity. In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers. ACM, 928--934.Google ScholarDigital Library
- Gloria Mark, Shamsi Iqbal, Mary Czerwinski, and Paul Johns. 2015. Focused, aroused, but so distractible: Temporal perspectives on multitasking and communications. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing. ACM, 903--916.Google ScholarDigital Library
- Gloria Mark, Shamsi T Iqbal, Mary Czerwinski, Paul Johns, and Akane Sano. 2016. Neurotics can't focus: An in situ study of online multitasking in the workplace. In Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, 1739--1744.Google ScholarDigital Library
- Daniel McDuff, Kael Rowan, Piali Choudhury, Jessica Wolk, ThuVan Pham, and Mary Czerwinski. 2019. A Multimodal Emotion Sensing Platform for Building Emotion-Aware Applications. arXiv preprint arXiv:1903.12133 (2019).Google Scholar
- Daniel C McFarlane and Kara A Latorella. 2002. The scope and importance of human interruption in human-computer interaction design. Human-Computer Interaction 17, 1 (2002), 1--61.Google ScholarDigital Library
- Microsoft. 2017. Bot Framework. https://dev.botframework.com/Google Scholar
- Microsoft. 2018. Language Instruction (LUIS). https://www.luis.ai/homeGoogle Scholar
- Microsoft. 2018. Project Conversation Learner. https://labs.cognitive.microsoft.com/en-us/project-conversation-learnerGoogle Scholar
- Microsoft. 2018. Turn Focus assist on or off in Windows 10. https://support.microsoft.com/en-us/help/4026996/windows-10-turn-focus-assist-on-or-offGoogle Scholar
- Christopher A Monk, J Gregory Trafton, and Deborah A Boehm-Davis. 2008. The effect of interruption duration and demand on resuming suspended goals. Journal of Experimental Psychology: Applied 14, 4 (2008), 299.Google ScholarCross Ref
- Tim Rietz, Ivo Benke, and Alexander Maedche. 2019. The Impact of Anthropomorphic and Functional Chatbot Design Features in Enterprise Collaboration Systems on User Acceptance. (2019).Google Scholar
- Monica A Riordan. 2017. Emojis as tools for emotion work: Communicating affect in text messages. Journal of Language and Social Psychology 36, 5 (2017), 549--567.Google ScholarCross Ref
- Brandon W Smit. 2016. Successfully leaving work at work: The self-regulatory underpinnings of psychological detachment. Journal of Occupational and Organizational Psychology 89, 3 (2016), 493--514.Google ScholarCross Ref
- Carlos Toxtli, Andrés Monroy-Hernández, and Justin Cranshaw. 2018. Understanding chatbot-mediated task management. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 58.Google ScholarDigital Library
- Steve Whittaker, Vaiva Kalnikaite, Victoria Hollis, and Andrew Guydish. 2016. `Don't Waste My Time': Use of Time Information Improves Focus. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 1729--1738.Google ScholarDigital Library
- Yang Yang, Xiaojuan Ma, and Pascale Fung. 2017. Perceived emotional intelligence in virtual agents. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. ACM, 2255--2262.Google ScholarDigital Library
- Michelle X Zhou, Gloria Mark, Jingyi Li, and Huahai Yang. 2019. Trusting Virtual Agents: The Effect of Personality. ACM Transactions on Interactive Intelligent Systems (TiiS) 9, 2--3 (2019), 10.Google ScholarDigital Library
- Manuela Züger, Sebastian C Müller, André N Meyer, and Thomas Fritz. 2018. Sensing interruptibility in the office: A field study on the use of biometric and computer interaction sensors. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 591.Google ScholarDigital Library
Index Terms
- Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work
Recommendations
How blocking distractions affects workplace focus and productivity
UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable ComputersInformation workers are faced with ever-increasing online distractions in the workplace. Website blockers are one solution toward preventing unwanted distractions. We conducted an in situ field study with 32 information workers in their workplace to ...
Affect-Aware Conversational Agent for Intelligent Tutoring of Students in Nursing Subjects
Intelligent Tutoring SystemsAbstractIn many social professions employees require skills in affect- and situation-aware social interaction. One option for teaching and training such social interaction skills by computer-based training methodology is the use of dialogue simulations. ...
Emotion AI at Work: Implications for Workplace Surveillance, Emotional Labor, and Emotional Privacy
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing SystemsWorkplaces are increasingly adopting emotion AI, promising benefits to organizations. However, little is known about the perceptions and experiences of workers subject to emotion AI in the workplace. Our interview study with (n=15) US adult workers ...
Comments