Skip to main content
SpringerLink
Log in

Differences in Knowledge Adoption Among Task Types in Human-AI Collaboration Under the Chronic Disease Prevention Scenario

  • Conference paper
  • First Online:
Wisdom, Well-Being, Win-Win (iConference 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14598))

Included in the following conference series:

  • 80 Accesses

Abstract

Chronic disease prevention is crucial for maintaining national health and reducing medical burden. Transmission of disease prevention knowledge to people through human-AI collaboration is an emerging disruptive and revolutionary approach. Nonetheless, little research has been aimed at the knowledge adoption in different tasks under this scenario. This study explored the differences in knowledge adoption among task types in human-AI collaboration under the chronic disease prevention scenario. Twelve participants were recruited to complete the factual, interpretive, and exploratory tasks in human-AI collaboration. The subjective efficiency and effectiveness of knowledge adoption were obtained by questionnaires. The objective efficiency, including search time, switch frequency, and number of queries, was counted by Screen Recorder, while experts scored the objective effectiveness. Furthermore, non-parametric tests were used to compare the differences. The results showed that objective efficiency varied among different task types. Participants spent more time in the interpretive task and switched more pages in the exploratory task. Then, perceived effectiveness was the worst in the interpretive task. Finally, the participants got lower scores in the factual task and higher scores in the interpretive task. Therefore, suggestions for the means of human-AI collaboration have been proposed under the chronic disease scenario, including identifying scenarios to enhance user adaptation and immersion in completing different health tasks, enhancing the transparency and explainability of AI, especially in interpretive tasks, and adding references in the process of acquiring and understanding knowledge.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dona, S.W.A., Angeles, M.R., Hall, N., Watts, J.J., Peeters, A., Hensher, M.: Impacts of chronic disease prevention programs implemented by private health insurers: a systematic review. BMC Health Serv. Res. 21(1), 1222 (2021)

    Article  Google Scholar 

  2. Bunnell, R., et al.: Fifty Communities putting prevention to work: accelerating chronic disease prevention through policy, systems and environmental change. J. Community Health 37(5), 1081–1090 (2012)

    Article  Google Scholar 

  3. Chiyaka, E.T., et al.: Influence of interaction between community health workers and adults with chronic diseases on risk mitigation through care coordination. Inter. J. Care Coordin. 25(2–3), 57–65 (2022)

    Article  Google Scholar 

  4. Rodriguez, B., et al.: Community health workers during COVID-19 supporting their role in current and future public health responses. J. Ambul. Care Manage. 46(3), 203–209 (2023)

    Article  Google Scholar 

  5. Sranacharoenpong, K., Hanning, R.M.: Diabetes prevention education program for community health care workers in Thailand. J. Community Health 37(3), 610–618 (2012)

    Article  Google Scholar 

  6. Carvajal, S.C., et al.: Evidence for long-term impact of pasos adelante: using a community-wide survey to evaluate chronic disease risk modification in prior program participants. Int. J. Environ. Res. Public Health 10(10), 4701–4717 (2013)

    Article  Google Scholar 

  7. Tsai, J.H.C., Petrescu-Prahova, M.: Community interagency connections for immigrant worker health interventions, King County, Washington State, 2012–2013. prev. chronic dis. 13, e73 (2017)

    Google Scholar 

  8. Loske, D., Klumpp, M.: Human-AI collaboration in route planning: an empirical efficiency-based analysis in retail logistics. Int. J. Prod. Econ. 241, 108236 (2021)

    Article  Google Scholar 

  9. Jiang, N., Liu, X.H., Liu, H.F., Lim, E.T.K., Tan, C.W., Gu, J.B.: Beyond AI-powered context-aware services: the role of human-AI collaboration. Industrial Manag. Data Syst, ahead-of-print (2022)

    Google Scholar 

  10. Sqalli, M.T. and Al-Thani, D.: AI-supported Health coaching model for patients with chronic diseases. In: 16th International Symposium on Wireless Communication Systems (ISWCS), pp. 452–456. IEEE, New York (2020)

    Google Scholar 

  11. Patel, K., et al.: A survey on artificial intelligence techniques for chronic diseases: open issues and challenges. Artif. Intell. Rev. 55(5), 3747–3800 (2021)

    Article  Google Scholar 

  12. Xie, I., Joo, S.: Factors affecting the selection of search tactics: Tasks, knowledge, process, and systems. Inf. Process. Manage. 48(2), 254–270 (2012)

    Article  Google Scholar 

  13. Li, Y.: Exploring the relationships between work and search tasks in information search. J. Am. Soc. Inform. Sci. Technol. 60(2), 275–291 (2009)

    Article  Google Scholar 

  14. Xie, I.: Dimensions of tasks: Influences on information-seeking and retrieving process. J. Documentation 65(3), 339–366 (2009)

    Article  Google Scholar 

  15. Tushman, M.L.: Technical communication in R&D laboratories: the impact of project work characteristics. Acad. Manag. J. 21(4), 624–645 (1978)

    Article  Google Scholar 

  16. Liu, J.J., Kim, C.S., Creel, C.: Exploring search task difficulty reasons in different task types and user knowledge groups. Inf. Process. Manage. 51(3), 273–285 (2015)

    Article  Google Scholar 

  17. Kim, J.: Task difficulty as a predictor and indicator of web searching interaction. In: Conference on Human Factors in Computing Systems, pp. 959–964. Assoc Computing Machinery, New York (2006)

    Google Scholar 

  18. Li, Y.L., Belkin, N.L.: A faceted approach to conceptualizing tasks in information seeking. Inf. Process. Manage. 44(6), 1822–1837 (2008)

    Article  Google Scholar 

  19. MacMullin, S.D., Taylor, R.S.: Problem dimensions and information traits. Inf. Soc. 3, 91–111 (1984)

    Article  Google Scholar 

  20. Campbell, D.J.: Task complexity: A review and analysis. Acad. Manag. Rev. 13(1), 40–52 (1988)

    Article  Google Scholar 

  21. Xie, I.: Planned and situated aspects in interactive ir: patterns of user interactions and information seeking strategies. Proc. ASIS Annual Meeting 34, 101–110 (1997)

    Google Scholar 

  22. Algon, J.: Classifications of tasks, steps, and information-related behaviors of individuals on project teams. In: Vakkari, P., Savolainen, R., Dervin, B. (eds.) International Conference on Research in Information Needs, Seeking and Use in Different Contents, pp. 205–221. Taylor Graham, London (1997)

    Google Scholar 

  23. Cartright, M.A., White, R.W., Horvitz, E.: Intentions and attention in exploratory health search. In: 34th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 65–74. Assoc Computing Machinery, New York (2011)

    Google Scholar 

  24. Chi, Y., He, D.Q., Jeng, W.: Laypeople’s source selection in online health infor-mation-seeking process. J. Am. Soc. Inf. Sci. 71(12), 1484–1499 (2020)

    Google Scholar 

  25. Kim, K.S., Allen, B.: Cognitive and task influences on web searching behavior. J. Am. Soc. Inform. Sci. Technol. 53(2), 109–119 (2002)

    Article  Google Scholar 

  26. Ke, Q., Du, J.T., Geng, Y.X., Xie, Y.S.: Studying health anxiety related attentional bi-as during online health information seeking: impacts of stages and task types. Inf. Process. Manage. 60(5), 103453 (2023)

    Article  Google Scholar 

  27. Cichocki, A., Kuleshov, A.P.: Future trends for human-AI collaboration: a comprehensive taxonomy of AI/AGI using multiple intelligences and learning styles. Comput. Intell. Neurosci. 2021, 8893795 (2021)

    Article  Google Scholar 

  28. Guimaraes, D., Paulino, D., Correia, A., Trigo, L., Brazdil, P., Paredes, H.: Towards a human-AI hybrid framework for inter-researcher similarity detection. In: Nurnberger, A., et al. (eds.) 2nd IEEE International Conference on Human-Machine Systems (ICHMS), pp. 123–126. IEEE, New York (2021)

    Google Scholar 

  29. Kim, E., Hong, J., Lee, H., Ko, M.: Colorbo: envisioned mandala coloring through human-AI collaboration. In: 27th Annual International Conference on Intelligent User Interfaces (ACM IUI), pp. 15–26. Assoc Computing Machinery, New York (2022)

    Google Scholar 

  30. Zhao, Z.J., Ma, X.J.: A compensation method of two-stage image generation for human-ai collaborated in-situ fashion design in augmented reality environment. In: 1st IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), pp. 76–83. IEEE, New York (2019)

    Google Scholar 

  31. Okamura, K., Yamada, S.: Adaptive trust calibration for human-AI collaboration. PLoS ONE 15(2), e0229132 (2020)

    Article  Google Scholar 

  32. Zhang, H.Y., et al.: PathNarratives: Data annotation for pathological human-AI collaborative diagnosis. Front. Med. 9, 1070072 (2023)

    Article  Google Scholar 

  33. Sharma, A., Lin, I.W., Miner, A.S., Atkins, D.C., Althoff, T.: Human-AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support. Nat. Mach. Intell. 5(1), 46–57 (2023)

    Article  Google Scholar 

  34. Gu, H.Y., et al.: Improving workflow integration with xpath: design and evaluation of a human-AI diagnosis system in pathology. ACM Trans. Comput.-Human Interact. 30(2), 28 (2023)

    Article  Google Scholar 

  35. Kocaballi, A.B., et al.: Envisioning an artificial intelligence documentation assistant for future primary care consultations: a co-design study with general practitioners. J. Am. Med. Inform. Assoc. 27(11), 1695–1704 (2020)

    Article  Google Scholar 

  36. Cabitza, F., Campagner, A., Sconfienza, L.M.: Studying human-AI collaboration protocols: the case of the Kasparov’s law in radiological double reading. Health Inform. Sci. Syst. 9(1), 8 (2021)

    Article  Google Scholar 

  37. Wang, F., Fan, H., Liu, G.: Big data knowledge service framework based on knowledge fusion. In: Fred, A., Dietz, J., Aveiro, D., Liu, K., Bernardino, J., Filipe, J. (eds.) 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR), pp. 116–123. Scitepress, Portugal (2016)

    Google Scholar 

  38. Berger, A., Tymula, A.: Controlling ambiguity: The illusion of control in choice under risk and ambiguity. J. Risk Uncertain. 65(3), 261–284 (2022)

    Article  Google Scholar 

  39. Paliwoda-Pekosz, G., Dymek, D.m Grabowski, M.: Adoption of emerging information technologies through the lenses of knowledge acquisition. In: 27th Annual Americas Conference on Information Systems (AMCIS). Assoc Information Systems, Atlanta (2021)

    Google Scholar 

  40. Makkonen, H.: Information processing perspective on organisational innovation adoption process. Technol. Anal. Strategic Manag. 33(6), 612–624 (2020)

    Article  Google Scholar 

  41. Li, Y.L., Belkin, N.J.: An exploration of the relationships between work task and interactive information search behavior. J. Am. Soc. Inform. Sci. Technol. 61(9), 1771–1789 (2010)

    Article  Google Scholar 

  42. Li, Y.L., Yuan, X.J., Che, R.Q.: An investigation of task characteristics and users’ evaluation of interaction design in different online health information systems. Inf. Process. Manage. 58(3), 102476 (2021)

    Article  Google Scholar 

  43. He, X., Zhang, H.S., Bian, J.: User-centered design of a web-based crowdsourcing-integrated semantic text annotation tool for building a mental health knowledge base. J. Biomed. Inform. 110, 103571 (2020)

    Article  Google Scholar 

  44. Gong, Y., Zhang, J.J.: Toward a human-centered hyperlipidemia management system: the interaction between internal and external information on relational data search. J. Med. Syst. 35(2), 169–177 (2011)

    Article  Google Scholar 

  45. King, K., et al.: The impact of a location-sensing electronic health record on clinician efficiency and accuracy: a pilot simulation study. Appl. Clin. Inform. 9(4), 841–848 (2018)

    Article  Google Scholar 

  46. Hilliard, R.W., Haskell, J., Gardner, R.L.: Are specific elements of electronic health record use associated with clinician burnout more than others? J. Am. Med. Inform. Assoc. 27(9), 1401–1410 (2020)

    Article  Google Scholar 

  47. Del Fiol, G., et al.: Formative evaluation of a patient-specific clinical knowledge summarization tool. Int. J. Med. Informatics 86, 126–134 (2016)

    Article  Google Scholar 

  48. Wasmann, J.W., Pragt, L., Eikelboom, R., Swanepoel, D.: Digital Approaches to automated and machine learning assessments of hearing: scoping review. J. Med. Internet Res. 24(2), e32581 (2022)

    Article  Google Scholar 

  49. Wang, N.: Knowledge adoption: a new perspective and the influence of knowledge characteristics. In: 52nd Annual Hawaii International Conference on System Sciences, pp. 5548–5557 (2019)

    Google Scholar 

  50. Lawson, S.: Examining the relationship between organizational culture and knowledge management. Nova Southeastern University (2003)

    Google Scholar 

  51. Max, W.S.: Trust in AI: interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient. In: FAccT 2022: ACM Conference on Fairness, Accountability, and Transparency. ACM, New York (2022)

    Google Scholar 

  52. Devine, D.J., Kozlowski, S.W.J.: Domain-specific knowledge and task characteristics in decision making. Organ. Behav. Hum. Decis. Process. 64(3), 294–306 (1995)

    Article  Google Scholar 

  53. Zhu, Y.J., Takama, Y., Kato, Y., Kori, S., Ishikawa, H., Yamaguchi, K.: Introduction of Search engine focusing on trend-related queries to market of data. In: Zhou, Z.H., et al. (eds.) 14th IEEE International Conference on Data Mining (IEEE ICDM), pp. 511–516. IEEE, New York (2014)

    Google Scholar 

  54. Van den Berg, H.A.: Three shapes of organizational knowledge. J. Knowl. Manag. 17(2), 159–174 (2013)

    Article  Google Scholar 

  55. Ángel, A.C., Adam, P. and Jason, I.H.: Improving Human-AI Collaboration With Descriptions of AI Behavior. In ACM Human-Computer Interaction, vol. 7. ACM, New York (2023)

    Google Scholar 

  56. Roberto V.Z., et al.: On assessing trustworthy AI in healthcare. machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Fronti. Hum. Dynam. 3, 673104 (2021)

    Google Scholar 

  57. Inthiran, A., Alhashmi, S.M., Ahmed, P.K.: A preliminary study on the usage of search assisting features when searching for a personal health task. Aslib J. Inf. Manag.  67(2), 159–181 (2015)

    Article  Google Scholar 

  58. Tusche, A., Bockler, A., Kanske, P., Trautwein, F.M., Singer, T.: Decoding the chari-table brain: empathy, perspective taking, and attention shifts differentially predict altruistic giving. J. Neurosci. 36(17), 4719–4732 (2016)

    Article  Google Scholar 

  59. Sicilia M.A.: Traceability for trustworthy AI: a review of models and tools. Big Data a Cognitive Comput. 5(2) (2021)

    Google Scholar 

Download references

Acknowledgments

This study was supported by the National Social Science Fund of China (No: 20ATQ008).

Data Statements

The data in this study and the published paper (DOI: https://doi.org/10.16353/j.cnki.1000-7490.2023.12.014.) are from the same experiment. This study supplemented the analysis of significant differences in tasks based on existing experiments.

Author information

Authors and Affiliations

  1. Center for Studies of Information Resources, Wuhan University, Wuhan, 430072, China

    Quan Lu

  2. School of Information Management, Wuhan University, Wuhan, 430072, China

    Quan Lu & Xueying Peng

Authors
  1. Quan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xueying Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xueying Peng .

Editor information

Editors and Affiliations

  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Kista, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lu, Q., Peng, X. (2024). Differences in Knowledge Adoption Among Task Types in Human-AI Collaboration Under the Chronic Disease Prevention Scenario. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14598. Springer, Cham. https://doi.org/10.1007/978-3-031-57867-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-57867-0_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57866-3

  • Online ISBN: 978-3-031-57867-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation