Papers
Topics
Authors
Recent
2000 character limit reached

AI-Powered Reminders for Collaborative Tasks: Experiences and Futures (2403.01365v1)

Published 3 Mar 2024 in cs.HC

Abstract: Email continues to serve as a central medium for managing collaborations. While unstructured email messaging is lightweight and conducive to coordination, it is easy to overlook commitments and requests for collaborations that are embedded in the text of free-flowing communications. Twenty-one years ago, Bellotti et al. proposed TaskMaster with the goal of redesigning the email interface to have explicit task management capabilities. Recently, AI-based task recognition and reminder services have been introduced in major email systems as one approach to managing asynchronous collaborations. While these services have been provided to millions of people around the world, there is little understanding of how people interact with and benefit from them. We explore knowledge workers' experiences with Microsoft's Viva Daily Briefing Email to better understand how AI-powered reminders can support asynchronous collaborations. Through semi-structured interviews and surveys, we shed light on how AI-powered reminders are incorporated into workflows to support asynchronous collaborations. We identify what knowledge workers prefer AI-powered reminders to remind them about and how they would like to interact with these reminders. Using mixed methods and a self-assessment methodology, we investigate the relationship between information workers' work styles and the perceived value of the Viva Daily Briefing Email to identify users who are more likely to benefit from AI-powered reminders for asynchronous collaborations. We conclude by discussing the experiences and futures of AI-powered reminders for collaborative tasks and asynchronous collaborations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. 2018. Gmail will now remind you to respond. http://workspaceupdates.googleblog.com/2018/05/gmail-remind-respond.html
  2. The interplay of intention maintenance and cue monitoring in younger and older adults’ prospective memory. Memory & cognition 45 (06 2017). https://doi.org/10.3758/s13421-017-0720-5
  3. Time-of-day effects on prospective memory. Behavioural Brain Research 376 (2019). https://doi.org/10.1016/j.bbr.2019.112179
  4. What a To-Do: Studies of Task Management towards the Design of a Personal Task List Manager. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vienna, Austria) (CHI ’04). Association for Computing Machinery, New York, NY, USA, 735–742. https://doi.org/10.1145/985692.985785
  5. Taking Email to Task: The Design and Evaluation of a Task Management Centered Email Tool. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Ft. Lauderdale, Florida, USA) (CHI ’03). Association for Computing Machinery, New York, NY, USA, 345–352. https://doi.org/10.1145/642611.642672
  6. Quality versus quantity: E-mail-centric task management and its relation with overload. Human–Computer Interaction 20, 1-2 (2005), 89–138. https://doi.org/10.1207/s15327051hci2001&2_4
  7. How to remember what to remember: exploring possibilities for digital reminder systems. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies 1, 3 (2017), 1–20. https://doi.org/10.1145/3130903
  8. An effect of chronic stress on prospective memory via alteration of resting-state hippocampal subregion functional connectivity. Scientific Reports 9, 1 (2019), 1–9. https://doi.org/10.1038/s41598-019-56111-9
  9. Task-focused summarization of email. In Text Summarization Branches Out. 43–50. https://aclanthology.org/W04-1008.pdf
  10. Laura A Dabbish and Robert E Kraut. 2006. Email overload at work: An analysis of factors associated with email strain. In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work. 431–440. https://doi.org/10.1145/1180875.1180941
  11. The impact of COVID-19 on digital communication patterns. Humanities and Social Sciences Communications 9, 1 (May 2022), 1–11. https://doi.org/10.1057/s41599-022-01190-9
  12. R Key Dismukes. 2012. Prospective memory in workplace and everyday situations. Current Directions in Psychological Science 21, 4 (2012), 215–220. https://doi.org/10.1177/0963721412447621
  13. Nicolas Ducheneaut and Victoria Bellotti. 2001. E-mail as habitat: an exploration of embedded personal information management. interactions 8, 5 (2001), 30–38. https://doi.org/10.1145/382899.383305
  14. Gilles O. Einstein and Mark A. McDaniel. 1990. Normal aging and prospective memory. Journal of experimental psychology. Learning, memory, and cognition 16 4 (1990), 717–26. https://doi.org/10.1037/0278-7393.16.4.717
  15. Aging and prospective memory: the influence of increased task demands at encoding and retrieval. Psychology and aging 12, 3 (1997), 479. https://doi.org/10.1037/0882-7974.12.3.479
  16. Memory problems at work: Their range, frequency and severity. Rank Xerox, EuroPARC (1992). https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/memory-problems-92.pdf
  17. Agent-assisted task management that reduces email overload. In Proceedings of the 15th international conference on Intelligent user interfaces. 61–70. https://doi.org/10.1145/1719970.1719980
  18. Sam J Gilbert. 2015. Strategic offloading of delayed intentions into the external environment. Quarterly journal of experimental psychology 68, 5 (2015), 971–992.
  19. Outsourcing Memory to External Tools: A Review of ’Intention Offloading’. Psychonomic bulletin & review (2022). https://doi.org/10.31234/osf.io/ahqtz
  20. Prospective memory errors in everyday life: Does instruction matter? Memory 28, 2 (2020), 196–203. https://doi.org/10.1080/09658211.2019.1707227
  21. Eric Horvitz. 2022. On the horizon: Interactive and compositional deepfakes. In Proceedings of the 2022 International Conference on Multimodal Interaction. 653–661. https://dl.acm.org/doi/abs/10.1145/3536221.3558175
  22. Learning predictive models of memory landmarks. In Proceedings of the Annual Meeting of the Cognitive Science Society, Vol. 26. https://doi.org/10.19066/cogsci.2009.20.4.006
  23. Attention-Sensitive Alerting. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (Stockholm, Sweden). 305–313.
  24. Edward Cutrell Mary Czerwinski Eric Horvitz. 2001. Notification, disruption, and memory: Effects of messaging interruptions on memory and performance. In Human-Computer Interaction: INTERACT, Vol. 1. 263. https://doi.org/ftp/Interact2001.pdf
  25. Age benefits in everyday prospective memory: The influence of personal task importance, use of reminders and everyday stress. Aging, Neuropsychology, and Cognition 19, 1-2 (2012), 84–101. https://doi.org/10.1080/13825585.2011.629288
  26. Shamsi T Iqbal and Eric Horvitz. 2007. Disruption and recovery of computing tasks: field study, analysis, and directions. In Proceedings of the SIGCHI conference on Human factors in computing systems. 677–686. https://doi.org/10.1145/1240624.1240730
  27. Shamsi T Iqbal and Eric Horvitz. 2010. Notifications and awareness: a field study of alert usage and preferences. In Proceedings of the 2010 ACM conference on Computer supported cooperative work. 27–30. https://doi.org/10.1145/1718918.1718926
  28. Imagining Future Digital Assistants at Work: A Study of Task Management Needs. International Journal of Human-Computer Studies (2022). https://doi.org/10.1016/j.ijhcs.2022.102905
  29. A conversational agent in support of productivity and wellbeing at work. In 2019 8th international conference on affective computing and intelligent interaction (ACII). IEEE, 1–7. https://doi.org/10.1109/acii.2019.8925488
  30. EmailValet: Managing email overload through private, accountable crowdsourcing. In Proceedings of the 2013 conference on Computer supported cooperative work. 1291–1300. https://doi.org/10.1145/2441776.2441922
  31. Jan-Peter Krämer. 2010. PIM-Mail: consolidating task and email management. In CHI’10 Extended Abstracts on Human Factors in Computing Systems. 4411–4416. https://doi.org/10.1145/1753846.1754162
  32. Wendy E Mackay. 1988. More than just a communication system: diversity in the use of electronic mail. In Proceedings of the 1988 ACM conference on Computer-supported cooperative work. 344–353. https://doi.org/10.1145/62266.62293
  33. Jasmin Manseau. 2020. AI in the workplace: A qualitative analysis of intelligent employee assistants. (2020). https://core.ac.uk/download/pdf/326836018.pdf
  34. Kim McMurtry. 2014. Managing email overload in the workplace. Performance Improvement 53, 7 (2014), 31–37. https://doi.org/10.1002/pfi.21424
  35. Individual differences in cognitive offloading: A comparison of intention offloading, pattern copy, and short-term memory capacity. Cognitive Research: Principles and Implications 6, 1 (2021), 34.
  36. Smart to-do: Automatic generation of to-do items from emails. arXiv preprint arXiv:2005.06282 (2020).
  37. Interruptive notifications in support of task management. International Journal of Human-Computer Studies 79 (2015), 20–34. https://doi.org/10.1016/j.ijhcs.2015.02.001
  38. What is the role of frontal lobe damage in memory disorders.
  39. Understanding Chatbot-Mediated Task Management. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (Montreal QC, Canada) (CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–6. https://doi.org/10.1145/3173574.3173632
  40. Learning about work tasks to inform intelligent assistant design. In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval. 5–14. https://doi.org/10.1145/3295750.3298934
  41. Stefan Walter and Beat Meier. 2014. How important is importance for prospective memory? A review. Frontiers in Psychology 5 (2014). https://doi.org/10.3389/fpsyg.2014.00657
  42. Steve Whittaker and Candace Sidner. 1996. Email overload: exploring personal information management of email. In Proceedings of the SIGCHI conference on Human factors in computing systems. 276–283. https://doi.org/10.4324/9781315806389-24
Citations (2)
View on Semantic Scholar

Summary

  • The paper finds that AI-powered reminders improve task management by helping users track overlooked, non-urgent commitments in email communications.
  • It employs a mixed-methods approach, combining quantitative analysis and user interviews to uncover varied engagement patterns in adopting reminder tools.
  • The study proposes future enhancements through adaptive, context-aware features tailored to individual workstyles, aiming to boost productivity and collaboration.

AI-Powered Reminders: Enhancing Asynchronous Collaborations in the Workplace

Introduction

Email remains a principal means for managing asynchronous collaborations in the modern workplace. However, managing commitments and collaboration requests embedded within unstructured email conversations can prove challenging, leading to overlooked commitments and the deterioration of collaborative efforts. AI-based task recognition and reminder services have been recently integrated into major email systems to address these challenges. Despite their widespread adoption, the manner in which knowledge workers interact with and derive benefits from AI-powered reminders, specifically in the context of Microsoft’s Viva Daily Briefing Email, has been scantily explored. This paper provides a comprehensive analysis of how AI-powered reminders are incorporated into workflows and their impact on asynchronous collaborations.

Experiences with AI-Powered Reminders

Our investigation into the use of Viva Daily Briefing Email by knowledge workers at Microsoft revealed varied integration into daily workflows. Some users actively incorporated AI-powered reminders into their work routines, adapting their communication strategies to enhance the reminders' utility. Others engaged with the tool more passively, perceiving it primarily as a fail-safe against missing non-critical tasks. The value identified by users was in being reminded of overlooked tasks, with a distinct appreciation for reminders concerning tasks that were not urgent yet important to address eventually.

The incorporation and perceived value of AI-powered reminders were influenced by the extent and nature of users’ asynchronous communications. For instance, reminders were more impactful for users with extensive commitments communicated via email. However, the efficiency of AI-powered reminders was partly hindered by instances where reminders surfaced tasks already tracked or completed by the user, underscoring an opportunity for enhancing the relevance and personalization of reminder systems.

Preferences and Workstyles

Knowledge workers expressed a desire for reminders about tasks that they are more likely to overlook – specifically, tasks of lower urgency or importance that may not make it onto their explicit to-do lists. These findings align with literature suggesting that people tend to prioritize and better remember tasks they perceive as high-priority.

Our quantitative analysis unveiled that users who found the daily briefing email valuable were often those who did not already have stringent systems for managing tasks or those less likely to delegate tasks. Interestingly, how individuals interacted with the AI-powered reminders—ranging from closely reading to actively using the embedded functionalities—strongly correlated with their perceived value of these reminders.

Future Directions for AI-Powered Reminders

Interview responses also pointed toward a demand for more nuanced interactions with AI-powered reminders beyond the existing capabilities of the Viva Daily Briefing Email. Users expressed desires for functionalities allowing for quicker response actions within the email environment, the ability to schedule tasks directly into calendars, and avenues for easily organizing and referring back to tasks without immediate action requirements. These insights indicate a need for task reminder systems that are more intricately tailored to individual work habits and styles, potentially through adaptive AI methods that learn from user interactions to offer personalized task management support.

Conclusion

Despite the promise shown by AI-powered reminders in enhancing task management and asynchronous collaboration, there is considerable room for improving how these systems understand and adapt to individual user preferences, workstyles, and needs. Future research should explore the development of more dynamic, context-aware reminder systems that can more effectively parse and prioritize tasks based on user-defined criteria of importance and urgency. Additionally, further studies could investigate the integration of AI-powered reminders across various platforms and communication channels to provide a more unified and comprehensive support system for managing work-related tasks and commitments. By advancing these areas, AI-powered reminders could significantly augment human productivity, collaboration, and overall work satisfaction in increasingly complex and distributed work environments.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Slide Deck Streamline Icon: https://streamlinehq.com

Whiteboard

Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

  1. Generalizability of email-based findings to non-email AI-powered reminder systems
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Sign up for free to view the 4 tweets with 190 likes about this paper.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up for Free