Skip to main content

Teaching End-User Development in the Time of IoT and AI

  • Conference paper
  • First Online:
Sense, Feel, Design (INTERACT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13198))

Included in the following conference series:

Abstract

The combination of the Internet of Things (IoT) and Artificial Intelligence (AI) has made it possible to introduce numerous automations in our daily environments. Many new interesting possibilities and opportunities have been enabled, but there are also risks and problems. Often these problems originated from approaches that have not been able to consider the users’ viewpoint sufficiently. We need to empower people in order to actually understand the automations in their surroundings environments, modify them, and create new ones, even if they have no programming knowledge. It is thus important that the curricula of programs in several disciplines (artificial intelligence, computer science, human-computer interaction, psychology, design, …) discuss these problems and some possible solutions able to provide people with the possibility to control and create their daily automations. In this paper I propose a possible way to organise and structure teaching of the concepts, methods and tools for this purpose, and which can be adopted in the relevant curricula.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Notes

  1. 1.

    https://www.statista.com/statistics/1101442/iot-number-of-connected-devices-worldwide/.

  2. 2.

    https://nodered.org/.

  3. 3.

    https://ifttt.com/create.

  4. 4.

    https://tare.isti.cnr.it/RuleEditor/login.

  5. 5.

    http://scratch.mit.edu.

  6. 6.

    http://appinventor.mit.edu/explore/.

  7. 7.

    https://giove.isti.cnr.it/demo/pat/.

  8. 8.

    https://africa.isti.cnr.it/.

  9. 9.

    https://github.com/andrematt/trigger_action_rules.

References

  1. Brackenbury, W., et al.: How users interpret bugs in trigger-action programming. In: CHI, p. 552 (2019)

    Google Scholar 

  2. Corcella, L., Manca, M., Paternò, F.: Personalizing a student home behaviour. In: Barbosa, S., Markopoulos, P., Paternò, F., Stumpf, S., Valtolina, S. (eds.) IS-EUD 2017. LNCS, vol. 10303, pp. 18–33. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58735-6_2

    Chapter  Google Scholar 

  3. Corno, F., De Russis, L., Monge Roffarello, A.: Empowering end users in debugging trigger-action rules. In: CHI 388 (2019)

    Google Scholar 

  4. Corno, F., De Russis, L., Monge Roffarello, A.: Devices, information, and people: abstracting the internet of things for end-user personalization. In: Fogli, D., Tetteroo, D., Barricelli, B.R., Borsci, S., Markopoulos, P., Papadopoulos, G.A. (eds.) IS-EUD 2021. LNCS, vol. 12724, pp. 71–86. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79840-6_5

    Chapter  Google Scholar 

  5. Elsden, C., Feltwell, T., Lawson, S., Vines, J.: Vines: recipes for programmable money. In: CHI, p. 251 (2019)

    Google Scholar 

  6. Gallo, S., Manca, M., Mattioli, A., Paternò, F., Santoro, C.: Comparative analysis of composition paradigms for personalization rules in iot settings. In: Fogli, D., Tetteroo, D., Barricelli, B.R., Borsci, S., Markopoulos, P., Papadopoulos, G.A. (eds.) IS-EUD 2021. LNCS, vol. 12724, pp. 53–70. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-79840-6_4

    Chapter  Google Scholar 

  7. Ghiani, G., Manca, M., Paternò, F., Santoro, C.: Personalization of context-dependent applications through trigger-action rules. ACM Trans. Comput. Hum. Interact. 24(2), 1–33 (2017)

    Article  Google Scholar 

  8. Huang, H., Cakmak, M.: Supporting mental model accuracy in trigger-action programming. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2015), pp. 215–225 (2015)

    Google Scholar 

  9. Leonardi, N., Manca, M., Paternò, F., Santoro, C.: Trigger-action programming for personalising humanoid robot behaviour. In: ACM Conference on Human Factors in Computing Systems (CHI 2019), Glasgow, p. 445 (2019)

    Google Scholar 

  10. Liao, Q.V., Gruen, D., Miller, S.: Questioning the AI: informing design practices for explainable AI user experiences. In: CHI (2020)

    Google Scholar 

  11. Manca, M., Paternò, F., Santoro, C.: Remote monitoring of end-user created automations in field trials. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03239-0

  12. Manca, M., Paternò, F., Santoro, C., Corcella, L.: Supporting end-user debugging of trigger-action rules for IoT applications. Int. J. Hum. Comput. Stud. 123, 56–69 (2019)

    Article  Google Scholar 

  13. Markopoulos, P., Nichols, J., Paternò, F., Pipek, V.: End-user development for the internet of things. ACM Trans. Comput. Hum. Interact. (TOCHI) 24(2), 1–3 (2017)

    Article  Google Scholar 

  14. Mattioli, A., Paternò, F.: A visual environment for end-user creation of IoT customization rules with recommendation support. In: International Conference on Advanced Visual Interfaces (AVI 2020) (2020). https://doi.org/10.1145/3399715.3399833

  15. Salovaara, A., Bellucci, A., Vianello, A., Jacucci, G.: Programmable smart home toolkits should better address households’ social needs. In: CHI Conference on Human Factors in Computing Systems (CHI 2021), May 8–13, Yokohama, Japan, p. 14. ACM, New York, NY, USA (2021)

    Google Scholar 

  16. Srinivasan, V., Koehler, C., Jin, H.: Ruleselector: selecting conditional action rules from user behavior patterns. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2(1), 1–34 (2018)

    Article  Google Scholar 

  17. Ur, B., McManus, E., Ho, M.P.Y., Littman, M.L.: Practical trigger-action programming in the smart home. CHI 2014, 803–812 (2014)

    Google Scholar 

  18. Yang, R., Newman, M.W.: Learning from a learning thermostat: lessons for intelligent systems for the home. In: 2013 ACM international joint conference on Pervasive and ubiquitous computing, pp. 93–102 (2013)

    Google Scholar 

  19. Zhang, L., et al.: Trace2TAP: synthesizing trigger-action programs from traces of behavior. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4(3), 1–26 (2020). https://doi.org/10.1145/3411838

    Article  Google Scholar 

Download references

Acknowledgments

This work is partially supported by the Italian Ministry of University and Research (MUR) under grant PRIN 2017 “EMPATHY: EMpowering People in deAling with internet of THings ecosYstems”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabio Paternò .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paternò, F. (2022). Teaching End-User Development in the Time of IoT and AI. In: Ardito, C., et al. Sense, Feel, Design. INTERACT 2021. Lecture Notes in Computer Science, vol 13198. Springer, Cham. https://doi.org/10.1007/978-3-030-98388-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98388-8_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98387-1

  • Online ISBN: 978-3-030-98388-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics