%0 Book Section %1 duBoulay2022 %A du Boulay, Benedict %B Handbook of Open, Distance and Digital Education %C Singapore %D 2022 %I Springer Singapore %K administration artificialintelligence bias dashboard e-learning e-teaching education ethics learninganalytics %P 1--16 %R 10.1007/978-981-19-0351-9_6-1 %T Artificial Intelligence in Education and Ethics %U https://doi.org/10.1007/978-981-19-0351-9_6-1 %X This chapter traces the ethical issues around applying artificial intelligence (AI) in education from the early days of artificial intelligence in education in the 1970s to the current state of this field, including the increasing sophistication of the system interfaces and the rise in data use and misuse. While in the early days most tools were largely learner-facing, now there are tools that are teacher-facing, supporting their management of the classroom, and administrator-facing, assisting in their management of cohorts of students. Learner-facing tools now take into account the affective and motivational aspects of learning as well as the cognitive. The rise of data collection and its associated analytic tools has enabled the development of dashboards for the dynamic management and reflective understanding of learners, teachers, and administrators. Ethical issues hardly figured in the early days of the field but now they loom large. This is because of the legitimate fears that learners' and teachers' autonomy will be compromised, that learner data will be collected and potentially misappropriated for other purposes, and that AI will introduce extra biases into educational decisions and increase existing inequity and also because of the scary reputation that AI has in general. %@ 978-981-19-0351-9

@inbook{duBoulay2022, abstract = {This chapter traces the ethical issues around applying artificial intelligence (AI) in education from the early days of artificial intelligence in education in the 1970s to the current state of this field, including the increasing sophistication of the system interfaces and the rise in data use and misuse. While in the early days most tools were largely learner-facing, now there are tools that are teacher-facing, supporting their management of the classroom, and administrator-facing, assisting in their management of cohorts of students. Learner-facing tools now take into account the affective and motivational aspects of learning as well as the cognitive. The rise of data collection and its associated analytic tools has enabled the development of dashboards for the dynamic management and reflective understanding of learners, teachers, and administrators. Ethical issues hardly figured in the early days of the field but now they loom large. This is because of the legitimate fears that learners' and teachers' autonomy will be compromised, that learner data will be collected and potentially misappropriated for other purposes, and that AI will introduce extra biases into educational decisions and increase existing inequity and also because of the scary reputation that AI has in general.}, added-at = {2022-04-03T13:11:35.000+0200}, address = {Singapore}, author = {du Boulay, Benedict}, biburl = {https://www.bibsonomy.org/bibtex/24c5d6bf7bb7a90b5f5c194d696251481/ereidt}, booktitle = {Handbook of Open, Distance and Digital Education}, doi = {10.1007/978-981-19-0351-9_6-1}, interhash = {ec256140da229d3f3b99d6fee6e18608}, intrahash = {4c5d6bf7bb7a90b5f5c194d696251481}, isbn = {978-981-19-0351-9}, keywords = {administration artificialintelligence bias dashboard e-learning e-teaching education ethics learninganalytics}, pages = {1--16}, publisher = {Springer Singapore}, timestamp = {2022-04-03T13:11:35.000+0200}, title = {Artificial Intelligence in Education and Ethics}, url = {https://doi.org/10.1007/978-981-19-0351-9_6-1}, year = 2022 }