Skip to main content

A Survey of the General Public’s Views on the Ethics of Using AI in Education

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11625)

Abstract

Recent scandals arising from the use of algorithms for user profiling to further political and marketing gain have popularized the debate over the ethical and legal implications of using such ‘artificial intelligence’ in social media. The need for a legal framework to protect the general public’s data is not new, yet it is not clear whether recent changes in data protection law in Europe, with the introduction of the GDPR, have highlighted the importance of privacy and led to a healthy concern from the general public over online user tracking and use of data. Like search engines, social media and online shopping platforms, intelligent tutoring systems aim to personalize learning and thus also rely on algorithms that automatically profile individual learner traits. A number of studies have been published on user perceptions of trust in robots and computer agents. Unsurprisingly, studies of AI in education have focused on efficacy, so the extent of learner awareness, and acceptance, of tracking and profiling algorithms remains unexplored. This paper discusses the ethical and legal considerations for, and presents a case study examining the general public’s views of, AI in education. A survey was recently taken of attendees at a national science festival event highlighting state-of-the-art AI technologies in education. Whilst most participants (77%) were worried about the use of their data, in learning systems fewer than 8% of adults were ‘not happy’ being tracked, as opposed to nearly two-thirds (63%) of children surveyed.

Keywords

  • Ethics
  • Trust
  • GDPR

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-23204-7_17
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-23204-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Fig. 1.

Notes

  1. 1.

    Video demonstrations of Oscar CITS and Hendrix CITS intelligent techniques can be found at www.AnnabelLatham.co.uk.

References

  1. Pariser, E.: The Filter Bubble: What The Internet Is Hiding From You. Penguin, London (2011)

    Google Scholar 

  2. Zuboff, S.: Big other: surveillance capitalism and the prospects of an information civilization. J. Inf. Technol. 30, 75–89 (2015)

    CrossRef  Google Scholar 

  3. Hoofnagle, C.J., King, J., Li, S., Turow, J.: How different are young adults from older adults when it comes to information privacy attitudes and policies? SSRN Electron. J. (2010). http://www.ssrn.com/abstract=1589864. Accessed 02 Feb 2019

  4. The Cambridge Analytica Files | The Guardian. https://www.theguardian.com/news/series/cambridge-analytica-files. Accessed 02 Feb 2019

  5. Burns, H., Luckhardt, C.A., Parlett, J.W., Redfield, C.L.: Intelligent Tutoring Systems: Evolutions in Design. Psychology Press, London (1991)

    Google Scholar 

  6. Van Lehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46(4), 197–221 (2011)

    CrossRef  Google Scholar 

  7. Fallout from Facebook data scandal may hit health research | New Scientist. https://www.newscientist.com/article/2164521-fallout-from-facebook-data-scandal-may-hit-health-research/. Accessed 02 Feb 2019

  8. Lin, H.C.K., Wu, C.H., Hsueh, Y.P.: The influence of using affective tutoring system in accounting remedial instruction on learning performance and usability. Comput. Hum. Behav. 41, 514–522 (2014)

    CrossRef  Google Scholar 

  9. Ammar, M.B., Neji, M., Alimi, A.M., Gouardères, G.: The affective tutoring system. Expert Syst. Appl. 37(4), 3013–3023 (2010)

    CrossRef  Google Scholar 

  10. Latham, A., Crockett, K., McLean, D., Edmonds, B.: A conversational intelligent tutoring system to automatically predict learning styles. Comput. Educ. 59(1), 95–109 (2012)

    CrossRef  Google Scholar 

  11. Holmes, M., Latham, A., Crockett, K., O’Shea, J.D.: Near real-time comprehension classification with artificial neural networks: decoding e-learner non-verbal behavior. IEEE Trans. Learn. Technol. 11(1), 5–12 (2018)

    CrossRef  Google Scholar 

  12. Hengstler, M., Enkel, E., Duelli, S.: Applied artificial intelligence and trust—the case of autonomous vehicles and medical assistance devices. Technol. Forecast. Soc. Chang. 105, 105–120 (2016)

    CrossRef  Google Scholar 

  13. König, M., Neumayr, L.: Users’ resistance towards radical innovations: the case of the self-driving car. Transp. Res. Part F: Traffic psychol. Behav. 44, 42–52 (2017)

    CrossRef  Google Scholar 

  14. Aiken, R.M., Epstein, R.G.: Ethical guidelines for AI in education: starting a conversation. Int. J. Artif. Intell. Educ. 11, 163–176 (2000)

    Google Scholar 

  15. Hines, A.: Jobs and infotech: work in the information society. Futurist 28(1), 9–11 (1994)

    Google Scholar 

  16. Shneiderman, B.: Human values and the future of technology: a declaration of responsibility. ACM SIGCAS Comput. Soc. 29(3), 5–9 (1999)

    CrossRef  Google Scholar 

  17. Mumford, L.: Technics and Civilization. Harcourt Brace and World, Inc., New York (1934)

    Google Scholar 

  18. Nichols, M., Holmes, W.: Don’t do evil: implementing artificial intelligence in universities. towards personalized guidance and support for learning. In: Proceedings of the 10th European Distance and E-Learning Network Research Workshop, Barcelona, p. 109 (2018)

    Google Scholar 

  19. Gitelman, L., Jackson, V.: Introduction. In: Gitelman, L. (ed.) “Raw data” is an oxymoron, pp. 1–14. The MIT Press, Cambridge (2013)

    CrossRef  Google Scholar 

  20. Artificial Intelligence’s white guy problem: The New York Times. https://www.nytimes.com/2016/06/26/opinion/sunday/artificial-intelligences-white-guy-problem.html. Accessed 06 Aug 2018

  21. UK wants to lead the world in tech ethics…but what does that mean? | Ada Lovelace Institute. https://www.adalovelaceinstitute.org/uk-wants-to-lead-the-world-in-tech-ethicsbut-what-does-that-mean/. Accessed 10 Aug 2018

  22. Prinsloo, P., Slade, S.: Student vulnerability, agency and learning analytics: an exploration. J. Learn. Anal. 3(1), 159–182 (2016)

    CrossRef  Google Scholar 

  23. Boddington, P.: Towards a code of ethics for artificial intelligence. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-60648-4

    CrossRef  Google Scholar 

  24. Trust and transparency. http://lcfi.ac.uk/projects/ai-trust-and-society/trust-and-transparency/. Accessed 08/13 Aug 2018

  25. Social well-being and data ethics | Ada Lovelace Institute. https://www.adalovelaceinstitute.org/socialwell-being-and-data-ethics-tim-gardams-speech-to-techuk-digital-ethics-summit/. Accessed 10 Aug 2018

  26. DeepMind announces ethics group to focus on problems of AI. Technology | The Guardian. https://www.theguardian.com/technology/2017/oct/04/google-deepmind-ai-artificialintelligence-ethics-group-problemsAccessed 10 Aug 2018

  27. About OpenAI. https://openai.com/about/. Accessed 13 Aug 2018

  28. Making artificial intelligence socially just: why the current focus on ethics is not enough | British Politics and Policy at LSE. https://blogs.lse.ac.uk/politicsandpolicy/artificial-intelligence-and-society-ethics/. Accessed 06 July 2018

  29. Bolton College used IBM Watson to build a virtual assistant that enhances teaching, learning and information access – Watson. https://www.ibm.com/blogs/watson/2017/08/bolton-college-uses-ibm-watson-ai-to-build-virtual-assistant-that-enhances-teaching-learning-and-assessment/. Accessed 17 Aug 2018

  30. What happened when a professor built a chatbot to be his teaching assistant - The Washington Post. https://www.washingtonpost.com/news/innovations/wp/2016/05/11/this-professor-stunned-his-students-when-he-revealed-the-secret-identity-of-his-teaching-assistant/?noredirect=on&utm_term=.87781a0e81de. Accessed 13 Aug 2018

  31. The ethics of Artificial Intelligence in education - University Business. https://universitybusiness.co.uk/Article/the-ethics-ofartificial-intelligence-in-education-who-care/. Accessed 10 Aug 2018

  32. Key Changes with the General Data Protection Regulation – EUGDPR. https://eugdpr.org/the-regulation/. Accessed 08 Feb 2019

  33. Tsang, L., Mulryne, J., Strom, L.: The impact of artificial intelligence on medical innovation in the European Union and United States. Intellect. Prop. Technol. Law J. 7, 2018 (2017)

    Google Scholar 

  34. Wachter, S., Mittelstadt, B., Floridi, L.: Why a right to explanation of automated decision-making does not exist in the general data protection regulation. Int. Data Priv. Law 7(2), 76–99 (2017)

    CrossRef  Google Scholar 

  35. Europeans Express Positive Views on AI and Robotics: Report on Preliminary Results from Public Consultations | McCarthy Tetrault. https://www.mccarthy.ca/en/insights/blogs/cyberlex/europeans-express-positive-views-ai-and-robotics-report-preliminary-results-public-consultations. Accessed 01 Feb 2019

  36. Draft Ethics Guidelines for Trustworthy AI | Digital Single Market. https://ec.europa.eu/digital-single-market/en/news/draft-ethics-guidelines-trustworthy-ai. Accessed 30 Jan 2019

  37. AI Principles – Future of Life Institute. https://futureoflife.org/ai-principles/?cn-reloaded=1. Accessed 05 Feb 2019

  38. Ethically Aligned Design, Version 2 (EADv2) | IEEE Standards Association. https://ethicsinaction.ieee.org/. Accessed 29 Dec 2018

  39. Papanikolaou, K.A., Grigoriadou, M., Kornilakis, H., Magoulas, G.D.: Personalizing the Interaction in a web-based educational hypermedia system: the case of INSPIRE. User Model. User-Adap. Inter. 13(3), 213–267 (2003)

    CrossRef  Google Scholar 

  40. Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. Int. J. Artif. Intell. Educ. 13, 156–169 (2003)

    Google Scholar 

  41. Sidney, K.D., Craig, S.D., Gholson, B., Franklin, S., Picard, R., Graesser, A.C.: Integrating affect sensors in an intelligent tutoring system. In Affective Interactions: The Computer in the Affective Loop Workshop, pp. 7–13 (2005)

    Google Scholar 

  42. Arroyo, I., Cooper, D.G., Burleson, W., Woolf, B.P., Muldner, K., Christopherson, R.: Emotion sensors go to school. In: AIED, vol. 200, pp. 17–24 (2009)

    Google Scholar 

  43. Crockett, K., Latham, A., Whitton, N.: On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees. Int. J. Hum. Comput. Stud. 97, 98–115 (2017)

    CrossRef  Google Scholar 

  44. Latham, A., Crockett, K., McLean, D.: An adaptation algorithm for an intelligent natural language tutoring system. Comput. Educ. 71, 97–110 (2014)

    CrossRef  Google Scholar 

  45. The Flesch Reading Ease and Flesch-Kincaid Grade Level – readable.io. https://readable.io/blog/the-flesch-reading-ease-and-flesch-kincaid-grade-level/. Accessed 05 June 2018

Download references

Acknowledgements

The study described in this paper was supported by Manchester Metropolitan University, IEEE Women in Engineering United Kingdom and Ireland, IEEE Women in Computational Intelligence and Manchester Science Museum.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Annabel Latham .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Latham, A., Goltz, S. (2019). A Survey of the General Public’s Views on the Ethics of Using AI in Education. 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 11625. Springer, Cham. https://doi.org/10.1007/978-3-030-23204-7_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23204-7_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23203-0

  • Online ISBN: 978-3-030-23204-7

  • eBook Packages: Computer ScienceComputer Science (R0)