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2019 | OriginalPaper | Chapter

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

Authors : Annabel Latham, Sean Goltz

Published in: Artificial Intelligence in Education

Publisher: Springer International Publishing

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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.

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Footnotes
1
Video demonstrations of Oscar CITS and Hendrix CITS intelligent techniques can be found at www.​AnnabelLatham.​co.​uk.
 
Literature
1.
go back to reference Pariser, E.: The Filter Bubble: What The Internet Is Hiding From You. Penguin, London (2011) Pariser, E.: The Filter Bubble: What The Internet Is Hiding From You. Penguin, London (2011)
2.
go back to reference Zuboff, S.: Big other: surveillance capitalism and the prospects of an information civilization. J. Inf. Technol. 30, 75–89 (2015)CrossRef Zuboff, S.: Big other: surveillance capitalism and the prospects of an information civilization. J. Inf. Technol. 30, 75–89 (2015)CrossRef
5.
go back to reference Burns, H., Luckhardt, C.A., Parlett, J.W., Redfield, C.L.: Intelligent Tutoring Systems: Evolutions in Design. Psychology Press, London (1991) Burns, H., Luckhardt, C.A., Parlett, J.W., Redfield, C.L.: Intelligent Tutoring Systems: Evolutions in Design. Psychology Press, London (1991)
6.
go back to reference Van Lehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46(4), 197–221 (2011)CrossRef Van Lehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46(4), 197–221 (2011)CrossRef
8.
go back to reference 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 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
9.
go back to reference Ammar, M.B., Neji, M., Alimi, A.M., Gouardères, G.: The affective tutoring system. Expert Syst. Appl. 37(4), 3013–3023 (2010)CrossRef Ammar, M.B., Neji, M., Alimi, A.M., Gouardères, G.: The affective tutoring system. Expert Syst. Appl. 37(4), 3013–3023 (2010)CrossRef
10.
go back to reference 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 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
11.
go back to reference 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 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
12.
go back to reference 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 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
13.
go back to reference 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 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
14.
go back to reference Aiken, R.M., Epstein, R.G.: Ethical guidelines for AI in education: starting a conversation. Int. J. Artif. Intell. Educ. 11, 163–176 (2000) Aiken, R.M., Epstein, R.G.: Ethical guidelines for AI in education: starting a conversation. Int. J. Artif. Intell. Educ. 11, 163–176 (2000)
15.
go back to reference Hines, A.: Jobs and infotech: work in the information society. Futurist 28(1), 9–11 (1994) Hines, A.: Jobs and infotech: work in the information society. Futurist 28(1), 9–11 (1994)
16.
go back to reference Shneiderman, B.: Human values and the future of technology: a declaration of responsibility. ACM SIGCAS Comput. Soc. 29(3), 5–9 (1999)CrossRef Shneiderman, B.: Human values and the future of technology: a declaration of responsibility. ACM SIGCAS Comput. Soc. 29(3), 5–9 (1999)CrossRef
17.
go back to reference Mumford, L.: Technics and Civilization. Harcourt Brace and World, Inc., New York (1934) Mumford, L.: Technics and Civilization. Harcourt Brace and World, Inc., New York (1934)
18.
go back to reference 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) 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)
19.
go back to reference Gitelman, L., Jackson, V.: Introduction. In: Gitelman, L. (ed.) “Raw data” is an oxymoron, pp. 1–14. The MIT Press, Cambridge (2013)CrossRef Gitelman, L., Jackson, V.: Introduction. In: Gitelman, L. (ed.) “Raw data” is an oxymoron, pp. 1–14. The MIT Press, Cambridge (2013)CrossRef
22.
go back to reference Prinsloo, P., Slade, S.: Student vulnerability, agency and learning analytics: an exploration. J. Learn. Anal. 3(1), 159–182 (2016)CrossRef Prinsloo, P., Slade, S.: Student vulnerability, agency and learning analytics: an exploration. J. Learn. Anal. 3(1), 159–182 (2016)CrossRef
33.
go back to reference 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) 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)
34.
go back to reference 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 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
39.
go back to reference 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 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
40.
go back to reference Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. Int. J. Artif. Intell. Educ. 13, 156–169 (2003) Brusilovsky, P., Peylo, C.: Adaptive and intelligent web-based educational systems. Int. J. Artif. Intell. Educ. 13, 156–169 (2003)
41.
go back to reference 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) 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)
42.
go back to reference 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) 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)
43.
go back to reference 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 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
44.
go back to reference Latham, A., Crockett, K., McLean, D.: An adaptation algorithm for an intelligent natural language tutoring system. Comput. Educ. 71, 97–110 (2014)CrossRef Latham, A., Crockett, K., McLean, D.: An adaptation algorithm for an intelligent natural language tutoring system. Comput. Educ. 71, 97–110 (2014)CrossRef
Metadata
Title
A Survey of the General Public’s Views on the Ethics of Using AI in Education
Authors
Annabel Latham
Sean Goltz
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-23204-7_17

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