[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3627043.3659570acmconferencesArticle/Chapter ViewAbstractPublication PagesumapConference Proceedingsconference-collections
research-article
Open access

Pervasive Chatbots: Investigating Chatbot Interventions for Multi-Device Applications

Published: 22 June 2024 Publication History

Abstract

The inherent social characteristics of humans make them prone to adopting distributed and collaborative applications easily. Although fundamental methods and technologies have been defined and developed over the years to construct these applications, their adoption in practice is uncommon because end-users may be puzzled about how to use them without much hassle. Indeed, commonly, these applications require a certain level of technical expertise and awareness to use them correctly. Fortunately, AI-chatbot interventions are envisioned to assist and support various human tasks. In this paper, we contribute pervasive chatbots as a solution that fosters a more transparent and user-friendly interconnection of devices in distributed and collaborative environments. Through two rigorous user studies, firstly, we quantify the perception of users toward distributed and collaborative applications (N = 56 participants). Secondly, we analyze the benefits of adopting pervasive chatbots when compared with the chatbot reference model designed for assistance and recommendations (N = 24 participants). Our results suggest that pervasive chatbots can significantly enhance the practicability of distributed and collaborative applications, reducing the time and effort needed for collaboration with surrounding devices by 57%. With this information, we then provide design and development implications to integrate pervasive chatbot interventions in distributed and collaborative environments. Moreover, challenges and opportunities are also provided to highlight the remaining issues that need to be addressed to realize the full vision of pervasive chatbots for any multi-device application. Our work paves the way towards the proliferation of sophisticated and highly decentralized computing environments that are easily interconnected.

References

[1]
Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, and Claudio Pomo. 2022. Top-n recommendation algorithms: A quest for the state-of-the-art. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. 121–131.
[2]
Evan Asfoura, Gamal Kassem, Belal Alhuthaifi, and Fozi Belhaj. 2023. Developing Chatbot Conversational Systems & the Future Generation Enterprise Systems.International Journal of Interactive Mobile Technologies 17, 10 (2023).
[3]
Ivo Benke 2020. Chatbot-based emotion management for distributed teams: A participatory design study. ACM on HCI 4, CSCW2 (2020), 1–30.
[4]
Agon Bexheti, Marc Langheinrich, Ivan Elhart, and Nigel Davies. 2019. Securely Storing and Sharing Memory Cues in Memory Augmentation Systems: A Practical Approach. In 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom. IEEE, 1–10.
[5]
Simone Borsci 2022. The Chatbot Usability Scale: the design and pilot of a usability scale for interaction with AI-based conversational agents. Personal and Ubiquitous Computing 26 (2022), 95–119.
[6]
Petter Bae Brandtzaeg and Asbjørn Følstad. 2017. Why people use chatbots. In INSCI 2017, Proceedings 4. Springer, 377–392.
[7]
Virginia Braun 2021. The online survey as a qualitative research tool. International journal of social research methodology 24, 6 (2021), 641–654.
[8]
John Brooke. 2013. SUS: a retrospective. Journal of usability studies 8, 2 (2013), 29–40.
[9]
John Canny. 2002. Collaborative filtering with privacy via factor analysis. In ACM SIGIR - Research and development in information retrieval. 238–245.
[10]
I-Chiu Chang 2022. Why would you use medical chatbots? interview and survey. International Journal of Medical Informatics 165 (2022).
[11]
Mohamed Amine Chatti 2022. Is more always better? The effects of personal characteristics and level of detail on the perception of explanations in a recommender system. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. 254–264.
[12]
Ana Paula Chaves and Marco Aurelio Gerosa. 2021. How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. International Journal of Human–Computer Interaction 37, 8 (2021), 729–758.
[13]
Rui Chen 2018. A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access 6 (2018), 64301–64320.
[14]
Shuang Chen 2020. Distributed task offloading game in multiserver mobile edge computing networks. Complexity 2020 (2020), 1–14.
[15]
Siqi Chen 2022. An intelligent chatbot for negotiation dialogues. In 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles. IEEE, 1172–1177.
[16]
Farooq Dar, Mohan Liyanage, Marko Radeta, Zhigang Yin, Agustin Zuniga, Sokol Kosta, Sasu Tarkoma, Petteri Nurmi, and Huber Flores. 2023. Upscaling fog computing in oceans for underwater pervasive data science using low-cost micro-clouds. ACM Transactions on Internet of Things 4, 2 (2023), 1–29.
[17]
Anind K Dey and Jonna Häkkilä. 2008. Context-awareness and mobile devices. In Handbook of research on user interface design and evaluation for mobile technology. IGI Global, 205–217.
[18]
Peter Diamond and Hannu Vartiainen. 2012. Behavioral economics and its applications. Princeton University Press.
[19]
Yushuang Dong 2016. Design of multi-terminal Mobile learning platform. In IEEE Educational Innovation through Technology. 37–41.
[20]
Juan Fang, Mengyuan Zhang, Zhiyuan Ye, Jiamei Shi, and Jianhua Wei. 2021. Smart collaborative optimizations strategy for mobile edge computing based on deep reinforcement learning. Computers & Electrical Engineering 96 (2021), 107539.
[21]
Ernst Fehr 2002. Strong reciprocity, human cooperation, and the enforcement of social norms. Human nature 13 (2002), 1–25.
[22]
Carlos Bermejo Fernandez 2022. Implementing GDPR for mobile and ubiquitous computing. In International Workshop on Mobile Computing Systems and Applications. 88–94.
[23]
Huber Flores. 2024. AI Sensors and Dashboards. IEEE Computer Magazine (2024).
[24]
Huber Flores 2020. COSINE: Collaborator selector for cooperative multi-device sensing and computing. In IEEE PerCom. IEEE, 1–10.
[25]
Huber Flores 2022. Collaboration stability: Quantifying the success and failure of opportunistic collaboration. Computer 55, 8 (2022), 70–81.
[26]
Asbjørn Følstad 2021. Future directions for chatbot research: an interdisciplinary research agenda. Computing 103, 12 (2021), 2915–2942.
[27]
Giuseppe Ghiani, Jussi Polet, Ville Antila, and Jani Mäntyjärvi. 2015. Evaluating context-aware user interface migration in multi-device environments. Journal of Ambient Intelligence and Humanized Computing 6 (2015), 259–277.
[28]
Jiaqi Gong, Yu Huang, Philip I Chow, Karl Fua, Matthew S Gerber, Bethany A Teachman, and Laura E Barnes. 2019. Understanding behavioral dynamics of social anxiety among college students through smartphone sensors. Information Fusion 49 (2019), 57–68.
[29]
Sandra G Hart. 2006. NASA-task load index (NASA-TLX); 20 years later. In Proceedings of the human factors and ergonomics society annual meeting, Vol. 50. Sage publications Sage CA: Los Angeles, CA, 904–908.
[30]
Simo Hosio 2016. Monetary assessment of battery life on smartphones. In CHI Conference on Human Factors in Computing Systems. 1869–1880.
[31]
Weijiao Huang, Khe Foon Hew, and Luke K Fryer. 2022. Chatbots for language learning—Are they really useful? A systematic review of chatbot-supported language learning. Journal of Computer Assisted Learning 38, 1 (2022), 237–257.
[32]
Yu-Shan Huang and Wei-Kang Kao. 2021. Chatbot service usage during a pandemic: fear and social distancing. The Service Industries Journal 41, 13-14 (2021), 964–984.
[33]
Srini Janarthanam. 2017. Hands-on chatbots and conversational UI development: build chatbots and voice user interfaces with Chatfuel, Dialogflow, Microsoft Bot Framework, Twilio, and Alexa Skills. Packt Publishing Ltd.
[34]
Vijay Kumari, Abhishek Ashwanikumar Sharma, Yashvardhan Sharma, and Lavika Goel. 2023. Scalability and Sustainability in Chatbot and Mobile Application Development. In 2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 397–403.
[35]
John D Lee and Katrina A See. 2004. Trust in automation: Designing for appropriate reliance. Human factors 46, 1 (2004), 50–80.
[36]
Youngki Lee 2012. Comon: Cooperative ambience monitoring platform with continuity and benefit awareness. In Proceedings of the 10th international conference on Mobile systems, applications, and services. 43–56.
[37]
Michal Levin. 2014. Designing multi-device experiences: An ecosystem approach to user experiences across devices. " O’Reilly Media, Inc.".
[38]
Kathryn J Lively 2010. Equity, emotion, and household division of labor response. Social psychology quarterly 73, 4 (2010), 358–379.
[39]
Khalid Mahmood Malik, Ali Javed, Hafiz Malik, and Aun Irtaza. 2020. A light-weight replay detection framework for voice controlled IoT devices. IEEE Journal of Selected Topics in Signal Processing 14, 5 (2020), 982–996.
[40]
Chulhong Min, Akhil Mathur, Alessandro Montanari, and Fahim Kawsar. 2022. SensiX: A system for best-effort inference of machine learning models in multi-device environments. IEEE Transactions on Mobile Computing (2022).
[41]
Fan Mo, Shamsabadi, 2020. Darknetz: towards model privacy at the edge using trusted execution environments. In Proceedings of ACM MobiSys 2020. 161–174.
[42]
Abderrahmen Mtibaa, Martin May, and Mostafa Ammar. 2011. Social forwarding in mobile opportunistic networks: A case of peoplerank. In Handbook of Optimization in Complex Networks: Communication and Social Networks. Springer, 387–425.
[43]
Grazia Murtarelli, Anne Gregory, and Stefania Romenti. 2021. A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots. Journal of Business Research 129 (2021), 927–935.
[44]
Obert Muzurura 2023. Application of Artificial Intelligence for virtual teaching assistance (Case study: Introduction to Information Technology). International Research Journal of Engineering and Technology (IRJET) (2023).
[45]
Alexios Mylonas 2013. Smartphone sensor data as digital evidence. Computers & Security 38 (2013), 51–75.
[46]
Tom Nadarzynski, Oliver Miles, Aimee Cowie, and Damien Ridge. 2019. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: A mixed-methods study. Digital health 5 (2019), 2055207619871808.
[47]
Thomas Neumayr, Hans-Christian Jetter, Mirjam Augstein, Judith Friedl, and Thomas Luger. 2018. Domino: A descriptive framework for hybrid collaboration and coupling styles in partially distributed teams. Proceedings of the ACM on Human-Computer Interaction 2, CSCW (2018), 1–24.
[48]
Ngoc Thi Nguyen 2021. Intelligent shifting cues: Increasing the awareness of multi-device interaction opportunities. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. 213–223.
[49]
Ngoc Thi Nguyen 2023. Is the Price Right? The Economic Value of Sharing Sensors. IEEE Transactions on Computational Social Systems (2023).
[50]
Abdul-Rasheed Ottun 2022. Social-aware federated learning: Challenges and opportunities in collaborative data training. IEEE Internet Computing (2022).
[51]
Robert A Peterson. 2000. Constructing effective questionnaires. SAGE Publications, Inc.
[52]
Ahmad Rahmati and Lin Zhong. 2009. Human–battery interaction on mobile phones. Pervasive and Mobile Computing 5, 5 (2009), 465–477.
[53]
Tareq Rasul 2023. The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching 6, 1 (2023).
[54]
Minjin Rheu 2021. Systematic review: Trust-building factors and implications for conversational agent design. Int. Journal of HCI 37, 1 (2021).
[55]
Ahmed Seffah 2004. Multi-devices “Multiple” user interfaces: development models and research opportunities. Journal of Systems and Software 73, 2 (2004), 287–300.
[56]
Sean Sirur, Jason RC Nurse, and Helena Webb. 2018. Are we there yet? Understanding the challenges faced in complying with the General Data Protection Regulation (GDPR). In Proceedings of the 2nd International Workshop on Multimedia Privacy and Security. 88–95.
[57]
Xiaoyuan Su and Taghi M Khoshgoftaar. 2009. A survey of collaborative filtering techniques. Advances in artificial intelligence 2009 (2009).
[58]
Guoming Tang, Kui Wu, Yangjing Wu, Huan Wang, and Guangwu Qian. 2021. Modeling and Alleviating Low-Battery Anxiety for Mobile Users in Video Streaming Services. IEEE Internet of Things Journal 9, 7 (2021), 5065–5079.
[59]
Suzanne Tolmeijer, Ujwal Gadiraju, Ramya Ghantasala, Akshit Gupta, and Abraham Bernstein. 2021. Second chance for a first impression? Trust development in intelligent system interaction. In Proceedings of the 29th ACM Conference on user modeling, adaptation and personalization. 77–87.
[60]
Sacha Trifunovic, Sylvia T Kouyoumdjieva, Bernhard Distl, Ljubica Pajevic, Gunnar Karlsson, and Bernhard Plattner. 2017. A decade of research in opportunistic networks: challenges, relevance, and future directions. IEEE Communications Magazine 55, 1 (2017), 168–173.
[61]
Rebecca Wald, Evelien Heijselaar, and Tibor Bosse. 2021. Make your own: The potential of chatbot customization for the development of user trust. In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. 382–387.
[62]
Tin-Yu Wu 2014. Incentive mechanism for P2P file sharing based on social network and game theory. Journal of Network and Computer Applications 41 (2014), 47–55.
[63]
Zhe Yang 2016. A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access 4 (2016).
[64]
Yong Yuan and Fei-Yue Wang. 2018. Blockchain and cryptocurrencies: Model, techniques, and applications. IEEE Transactions on Systems, Man, and Cybernetics: Systems 48, 9 (2018), 1421–1428.
[65]
Xianfeng Zhang and Qin Zhang. 2005. Online trust forming mechanism: approaches and an integrated model. In ACM ICEC’05. 201–209.
[66]
Vincent Zheng 2010. Collaborative filtering meets mobile recommendation: A user-centered approach. In AAAI, Vol. 24. 236–241.
[67]
Zhenyun Zhuang, Kyu-Han Kim, and Jatinder Pal Singh. 2010. Improving energy efficiency of location sensing on smartphones. In Proceedings of the 8th international conference on Mobile systems, applications, and services. 315–330.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
UMAP '24: Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization
June 2024
338 pages
ISBN:9798400704338
DOI:10.1145/3627043
This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2024

Check for updates

Author Tags

  1. Decentralized infrastructures
  2. collaborative computing
  3. distributed computing
  4. opportunistic networks

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Funding Sources

Conference

UMAP '24
Sponsor:

Acceptance Rates

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 226
    Total Downloads
  • Downloads (Last 12 months)226
  • Downloads (Last 6 weeks)37
Reflects downloads up to 03 Jan 2025

Other Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media