Skip to main content
SpringerLink
Log in

Leveraging Cognitive Science and Artificial Intelligence to Save Lives

  • Conference paper
  • First Online:
Artificial Intelligence in Education (AIED 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11626))

Included in the following conference series:

Abstract

Medical error is the third-leading cause of death in the United States, just behind heart disease and cancer. We describe a software platform used to train healthcare workers to prevent their errors. The platform (Amplifire) harnesses artificial intelligence and principles of cognitive psychology. Amplifire’s AI continuously decides whether and when to require additional learning events, provide corrective and metacognitive feedback, and/or deliver self-regulatory guidance for the learner (e.g., “slow down”). Amplifire was deployed to several thousand nurses at a large healthcare system in attempts to reduce the rate of two types of hospital-acquired infections. The result was a 48% reduction in central-line-associated bloodstream infections (CLABSI) and a 32% reduction in catheter-associated urinary-tract infections (CAUTI). These findings demonstrate the effectiveness of using cognitive science along with AI in an e-learning platform.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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. Kapros, E., Koutsombogera, M. (eds.): Designing for the User Experience in Learning Systems. HCIS, pp. 1–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94794-5

    Book  Google Scholar 

  2. Gertner, A., Conati, C., VanLehn, K.: Procedural help in Andes: generating hints using a Bayesian network student model. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, AAAI, vol. 98, pp. 106–111. The MIT Press, Cambridge (1998)

    Google Scholar 

  3. Falmagne, J.-C., Cosyn, E., Doignon, J.-P., Thiéry, N.: The assessment of knowledge, in theory and in practice. In: Missaoui, R., Schmidt, J. (eds.) ICFCA 2006. LNCS (LNAI), vol. 3874, pp. 61–79. Springer, Heidelberg (2006). https://doi.org/10.1007/11671404_4

    Chapter  Google Scholar 

  4. Olney, A.M., et al.: Guru: a computer tutor that models expert human tutors. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds.) ITS 2012. LNCS, vol. 7315, pp. 256–261. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30950-2_32

    Chapter  Google Scholar 

  5. Bjork, R.: Memory and metamemory considerations in the training of human beings. In: Metcalfe, J., Shimamura, A. (eds.) Metacognition: Knowing About Knowing, pp. 185–205. MIT Press, Cambridge (1994)

    Google Scholar 

  6. Roediger, H., Karpicke, J.: Test-enhanced learning: taking memory tests improves long-term retention. Psychol. Sci. 17, 249–255 (2006)

    Article  Google Scholar 

  7. Bhatnagar, S., Lasry, N., Desmarais, M., Charles, E.: DALITE: asynchronous peer instruction for MOOCs. In: Verbert, K., Sharples, M., Klobučar, T. (eds.) EC-TEL 2016. LNCS, vol. 9891, pp. 505–508. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45153-4_50

    Chapter  Google Scholar 

  8. Bransford, J., Schwartz, D.: Rethinking transfer: a simple proposal with multiple implications. In: Iran-Nejad, A., Pearson, P. (eds.) Review of Research in Education, vol. 24, pp. 61–100. American Educational Research Association, Washington, DC (1999)

    Google Scholar 

  9. Hays, M., Kornell, N., Bjork, R.: When and why a failed test potentiates the effectiveness of subsequent study. J. Exp. Psychol. Learn. Mem. Cogn. 39, 290–296 (2013)

    Article  Google Scholar 

  10. Pashler, H., Rohrer, D., Cepeda, N., Carpenter, S.: Enhancing learning and retarding forgetting: choices and consequences. Psychon. Bull. Rev. 14, 187–193 (2007)

    Article  Google Scholar 

  11. Bruno, J.: Using MCW-APM test scoring to evaluate economics curricula. J. Econ. Educ. 20(1), 5–22 (1989)

    MathSciNet  Google Scholar 

  12. Soderstrom, N., Clark, C., Halamish, V., Bjork, E.: Judgments of learning as memory modifiers. J. Exp. Psychol. Learn. Mem. Cogn. 41, 553–558 (2015)

    Article  Google Scholar 

  13. Sparck, E., Bjork, E., Bjork, R.: On the learning benefits of confidence-weighted testing. In: Cognitive Research: Principles and Implications, vol. 1 (2016)

    Google Scholar 

  14. Azevedo, R.: Computer environments as metacognitive tools for enhancing learning. Educ. Psychol. 40, 193–197 (2010)

    Article  Google Scholar 

  15. Butler, A., Karpicke, J., Roediger, H.: The effect of type and timing of feedback on learning from multiple-choice tests. J. Exp. Psychol. Appl. 13, 273–281 (2007)

    Article  Google Scholar 

  16. Shute, V., Hansen, E., Almond, R.: An Assessment for Learning System Called ACED: Designing for Learning Effectiveness and Accessibility. ETS Research Report Series, pp. 1–45 (2007)

    Google Scholar 

  17. McLaren, B.M., van Gog, T., Ganoe, C., Yaron, D., Karabinos, M.: Worked examples are more efficient for learning than high-assistance instructional software. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M.F. (eds.) AIED 2015. LNCS (LNAI), vol. 9112, pp. 710–713. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19773-9_98

    Chapter  Google Scholar 

  18. Pashler, H., Cepeda, N., Wixted, J., Rohrer, D.: When does feedback facilitate learning of words? J. Exp. Psychol. Learn. Mem. Cogn. 31, 3–8 (2005)

    Article  Google Scholar 

  19. Hays, M., Kornell, N., Bjork, R.: The costs and benefits of providing feedback during learning. Psychon. Bull. Rev. 17, 797–801 (2010)

    Article  Google Scholar 

  20. Butler, A., Karpicke, J., Roediger, H.: Corrective a metacognitive error: feedback increases retention of low-confidence correct responses. J. Exp. Psychol. Learn. Mem. Cogn. 34, 918–928 (2008)

    Article  Google Scholar 

  21. Butterfield, B., Metcalfe, J.: Errors committed with high confidence are hyper-corrected. J. Exp. Psychol. Learn. Mem. Cogn. 27, 1491–1494 (2001)

    Article  Google Scholar 

  22. Karpicke, J., Roediger, H.: Repeated retrieval during learning is the key to long-term retention. J. Mem. Lang. 57, 151–162 (2007)

    Article  Google Scholar 

  23. Cepeda, N., Pashler, H., Vul, E., Wixted, J., Rohrer, D.: Distributed practice in verbal recall tasks: a review and quantitative synthesis. Psychol. Bull. 132, 354–380 (2006)

    Article  Google Scholar 

  24. Landauer, T., Bjork, R.: Optimal rehearsal patterns and name learning. In: Gruneberg, M., Morris, P., Sykes, R. (eds.) Practical Aspects of Memory, pp. 625–632. Academic Press, London (1978)

    Google Scholar 

  25. Hays, M., Darrell, J., Smith, C.: The forgetting curve(s) of 710,870 real-world learners. Poster Presented at the American Psychological Association 124th Annual Convention, Denver, CO, USA (2016)

    Google Scholar 

  26. Makary, M., Daniel, M.: Medical error—the third leading cause of death in the US. BMJ 353, 2139 (2016)

    Article  Google Scholar 

  27. Joint Commission: Patient safety. Joint Commission Online (2015). https://www.jointcommission.org/assets/1/23/jconline_April_29_15.pdf. Accessed 8 Feb 2019

  28. CDC: Vital Signs: Central Line–Associated Blood Stream Infections — United States, 2001, 2008, and 2009. Morbidity and Mortality Weekly Report (MMWR), vol. 60, pp. 1–6 (2011)

    Google Scholar 

  29. Mayer, R.: Using multimedia for e-learning. J. Comput. Assist. Learn. 33, 403–423 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Amplifire, Boulder, CO, 80301, USA

    Matthew Jensen Hays & Aaron Richard Glick

  2. University of Illinois Urbana-Champaign, Champaign, IL, 61801, USA

    H. Chad Lane

Authors
  1. Matthew Jensen Hays
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aaron Richard Glick
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Chad Lane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew Jensen Hays .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hays, M.J., Glick, A.R., Lane, H.C. (2019). Leveraging Cognitive Science and Artificial Intelligence to Save Lives. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23207-8_71

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23206-1

  • Online ISBN: 978-3-030-23207-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation