Ethics of AI in Education: Towards a Community-Wide Framework

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountabi...

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Vydáno v:	International journal of artificial intelligence in education Ročník 32; číslo 3; s. 504 - 526
Hlavní autoři:	Holmes, Wayne, Porayska-Pomsta, Kaska, Holstein, Ken, Sutherland, Emma, Baker, Toby, Shum, Simon Buckingham, Santos, Olga C., Rodrigo, Mercedes T., Cukurova, Mutlu, Bittencourt, Ig Ibert, Koedinger, Kenneth R.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer New York 01.09.2022 Springer Springer Nature B.V
Témata:	Artificial Intelligence Check Lists Community Relations Computer Science Computers and Education Consent Education Educational objectives Educational Research Educational Technology Ethics Guidelines Informed Consent Learning analytics Legal Responsibility Privacy Questions Researchers Stakeholders Sustainable Development Teachers Teaching Methods Technology Uses in Education The FATE of AIED User Interfaces and Human Computer Interaction United Kingdom > UK Australia United States > US Spain Ethics Human cognition Agency Pedagogy Artificial intelligence in education Fairness
ISSN:	1560-4292, 1560-4306
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Abstract	While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically , to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.
AbstractList	While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between "doing ethical things" and "doing things ethically," to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community's leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context. While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically , to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far from trivial. As a first step towards addressing this critical gap, we invited 60 of the AIED community’s leading researchers to respond to a survey of questions about ethics and the application of AI in educational contexts. In this paper, we first introduce issues around the ethics of AI in education. Next, we summarise the contributions of the 17 respondents, and discuss the complex issues that they raised. Specific outcomes include the recognition that most AIED researchers are not trained to tackle the emerging ethical questions. A well-designed framework for engaging with ethics of AIED that combined a multidisciplinary approach and a set of robust guidelines seems vital in this context.
Author	Porayska-Pomsta, Kaska Santos, Olga C. Holmes, Wayne Cukurova, Mutlu Sutherland, Emma Koedinger, Kenneth R. Shum, Simon Buckingham Holstein, Ken Rodrigo, Mercedes T. Bittencourt, Ig Ibert Baker, Toby
Author_xml	– sequence: 1 givenname: Wayne orcidid: 0000-0002-8352-1594 surname: Holmes fullname: Holmes, Wayne email: wayne.holmes@ucl.ac.uk organization: UCL Knowledge Lab, UCL – sequence: 2 givenname: Kaska orcidid: 0000-0002-9433-4022 surname: Porayska-Pomsta fullname: Porayska-Pomsta, Kaska organization: UCL Knowledge Lab, UCL – sequence: 3 givenname: Ken surname: Holstein fullname: Holstein, Ken organization: Carnegie Mellon University – sequence: 4 givenname: Emma surname: Sutherland fullname: Sutherland, Emma organization: Nesta – sequence: 5 givenname: Toby surname: Baker fullname: Baker, Toby organization: Nesta – sequence: 6 givenname: Simon Buckingham orcidid: 0000-0002-6334-7429 surname: Shum fullname: Shum, Simon Buckingham organization: University of Technology Sydney – sequence: 7 givenname: Olga C. orcidid: 0000-0002-9281-4209 surname: Santos fullname: Santos, Olga C. organization: UNED – sequence: 8 givenname: Mercedes T. orcidid: 0000-0001-7881-7756 surname: Rodrigo fullname: Rodrigo, Mercedes T. organization: Ateneo de Manila University – sequence: 9 givenname: Mutlu orcidid: 0000-0001-5843-4854 surname: Cukurova fullname: Cukurova, Mutlu organization: UCL Knowledge Lab, UCL – sequence: 10 givenname: Ig Ibert orcidid: 0000-0001-5676-2280 surname: Bittencourt fullname: Bittencourt, Ig Ibert organization: Federal University of Alagoas – sequence: 11 givenname: Kenneth R. surname: Koedinger fullname: Koedinger, Kenneth R. organization: Carnegie Mellon University
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References	World Economic Forum. (2019). Generation AI. May 2019 Workshop Report.http://www3.weforum.org/docs/WEF_Generation_AI_%20May_2019_Workshop_Report.pdf AI Ethics Initiative. (2017). https://aiethicsinitiative.org FriedmanBKahnPHJrHuman agency and responsible computing: Implications for computer system designJournal of Systems and Software199217171410.1016/0164-1212(92)90075-U Potgieter, I. (2020). Privacy concerns in educational data mining and learning analytics. The International Review of Information Ethics, 28. https://informationethics.ca/index.php/irie/article/view/384 MacfarlaneBTeaching with integrity: The Ethics of higher education practice2003AbingdonRoutledge Sacharidis, D., Mukamakuza, C. P., & Werthner, H. (2020). Fairness and diversity in social-based recommender systems. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 83–88. https://doi.org/10.1145/3386392.3397603. AndersonBSimpsonMEthical issues in online educationOpen Learning: The Journal of Open, Distance and e-Learning200722212913810.1080/02680510701306673 AI Now Institute. (2017). https://ainowinstitute.org UNESCOArtificial intelligence in education: Challenges and opportunities for sustainable development2019 Murtarelli, G., Gregory, A., & Romenti, S. (2020). A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots. Journal of Business Research, S0148296320305944. https://doi.org/10.1016/j.jbusres.2020.09.018. GearhartDLack of Ethics for eLearning: Two sides of the ethical coinInternational Journal of Technoethics201234334010.4018/jte.2012100103 Zapata-RiveraJ-DGreerJEInteracting with inspectable bayesian student modelsInternational Journal of Artificial Intelligence in Education2004142127163 Bietti, E. (2020). From Ethics Washing to Ethics Bashing: A View on Tech Ethics from within Moral Philosophy. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ’20. Association for Computing Machinery (pp. 210–219).New York. https://doi.org/10.1145/3351095.3372860. TarranBWhat can we learn from the Facebook—Cambridge Analytica scandal?Significance20181534510.1111/j.1740-9713.2018.01139.x WhittakerMCrawfordKDobbeRFriedGKaziunasEMathurVMyersWestSRichardsonRSchultzJSchwartzOAI now report 20182018AI Now InstituteNew York University WEF. (2020). Chatbots RESET. A Framework for Governing Responsible Use of Conversational AI in Healthcare. World Economic Forum. http://www3.weforum.org/docs/WEF_Governance_of_Chatbots_in_Healthcare_2020.pdf RichardsDDignumVSupporting and challenging learners through pedagogical agents: Addressing ethical issues through designing for valueBritish Journal of Educational Technology20195062885290110.1111/bjet.12863 Ada Lovelace Institute. (2019). https://www.adalovelaceinstitute.org Porayska-PomstaKRajendranGKnoxJWangYGallagherMAccountability in human and artificial intelligence decision-making as the basis for diversity and educational inclusionArtificial Intelligence and Inclusive Education: Speculative Futures and Emerging Practices2019SingaporeSpringer395910.1007/978-981-13-8161-4_3 Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G Norman, D. (2019). Why might human-centered design be harmful? Jnd.Org. https://jnd.org/human-centered_design_considered_harmful AikenRMEpsteinRGEthical guidelines for AI in education: Starting a conversationInternational Journal of Artificial Intelligence in Education200011163176 FloridiLTranslating principles into practices of digital Ethics: Five risks of being unethicalPhilosophy & Technology201932218519310.1007/s13347-019-00354-x House of Lords. (2018). Ai in the Uk: Ready, Willing and Able. https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf Schelenz, L., Segal, A., & Gal, K. (2020). Best practices for transparency in machine generated personalization. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 23–28. https://doi.org/10.1145/3386392.3397593. The Institute for Ethical AI & Machine Learning. (2018). https://ethical.institute Buckingham ShumSFergusonRMartinez-MaldonadoRHuman-Centred learning analyticsJournal of Learning Analytics2019621910.18608/jla.2019.62.1 Drachsler, H., & Greller, W. (2016). Privacy and analytics: It’s a DELICATE issue a checklist for trusted learning analytics. Proceedings of the sixth international conference on Learning Analytics & Knowledge, 89–98. http://dl.acm.org/citation.cfm?id=2883893 Université de Montréal. (2018). Declaration of Montréal for a responsible development of AI. Université de Montréal. https://www.montrealdeclaration-responsibleai.com Holmes, W., Bektik, D., Di Gennaro, M., Woolf, B. P., & Luckin, R. (2019a). Ethics in AIED: Who cares? In S. Isotani, E. Millán, A. Ogan, P. Hastings, & R. Luckin (Eds.), Artificial Intelligence in Education (Vol. 11625, pp. 424–425). Cham, Switzerland: Springer International Publishing AG. https://doi.org/10.1007/978-3-030-23204-7. Mobasher, B., Kleanthous, S., Ekstrand, M., Berendt, B., Otterbacher, J., & Shulner Tal, A. (2020). FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 404–405. https://doi.org/10.1145/3340631.3398671. SladeSPrinslooPLearning analytics ethical issues and dilemmasAmerican Behavioral Scientist201357101510152910.1177/0002764213479366 Labarthe, H., Luengo, V., & Bouchet, F. (2018). Analyzing the relationships between learning analytics, educational data mining and AI for education. In Guin, N. & Kumar, A. (Eds.), ITS 2018 Workshop Proceedings (pp. 63–71). BullSKayJSMILI☺: A framework for interfaces to learning data in open learner models, learning analytics and related fieldsInternational Journal of Artificial Intelligence in Education201626129333110.1007/s40593-015-0090-8 JobinAIencaMVayenaEArtificial intelligence: The global landscape of ethics guidelinesNature Machine Intelligence20191938939910.1038/s42256-019-0088-2 PetersRSEthics and education1970LondonAllen & Unwin Holmes, W., Bialik, M., & Fadel, C. (2019b). Artificial intelligence in education. Promises and Implications for Teaching and Learning. Center for Curriculum Redesign. Reich, J., & Ito, M. (2017). From good intentions to real outcomes: Equity by design in learning technologies. Digital Media and Learning Research Hub. Conati, C., Porayska-Pomsta, K., & Mavrikis, M. (2018). AI in education needs interpretable machine learning: Lessons from open learner Modelling. ArXiv:1807.00154 [Cs]. http://arxiv.org/abs/1807.00154 KobsaABrusilovskyPKobsaANejdlWPrivacy-enhanced web personalizationThe Adaptive Web: Methods and Strategies of Web Personalization2007BerlinSpringer13615410.1007/978-3-540-72079-9_4Generic User Modeling Systems European Union. (2019). Ethics guidelines for trustworthy AI. Independent High-Level Expert Group on Artificial Intelligence. European Union. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai Holmes, W., Bektik, D., Whitelock, D., & Woolf, B. P. (2018). Ethics in AIED: Who Cares? In C. P. Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Eds.), Artificial Intelligence in Education (Vol. 10948, pp. 551–553). Cham, Switzerland: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-93846-2. SchreckJSecurity and Privacy in User Modeling2003NetherlandsSpringer10.1007/978-94-017-0377-21034.68033 AnwarMGreerJFacilitating trust in privacy-preserving E-learning environmentsIEEE Transactions on Learning Technologies201251627310.1109/TLT.2011.23 Aristotle. (2009). The Nicomachean Ethics n/e. Brown, L. (ed.) Ross, D. (Trans.) (Revised edition). OUP Oxford. DeepMind Ethics & Society. (2017). https://deepmind.com/about/ethics-and-society Lewthwaite, S. (2014). Web accessibility standards and disability: Developing critical perspectives on accessibility. Disability and Rehabilitation, 36(16), 1375–1383. https://doi.org/10.3109/09638288.2014.938178 WinfieldAFTJirotkaMEthical governance is essential to building trust in robotics and artificial intelligence systemsPhilosophical Transactions of the Royal Society A201837621332018008510.1098/rsta.2018.0085 Deshpande, K. V., Pan, S., & Foulds, J. R. (2020). Mitigating demographic Bias in AI-based resume filtering. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 268–275. https://doi.org/10.1145/3386392.3399569. SharkeyAJShould we welcome robot teachers?Ethics and Information Technology201641828329710.1007/s10676-016-9387-z Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Vuorikari, R. (2016). Research evidence on the use of learning analytics: Implications for education policy. http://oro.open.ac.uk/48173 Holstein, K., McLaren, B. M., & Aleven, V. (2019). Designing for complementarity: Teacher and student needs for orchestration support in AI-enhanced classrooms. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education (Vol. 11625, pp. 157–171). Cham, Switzerland: Springer International Publishing AG. https://doi.org/10.1007/978-3-030-23204-7. Future of Life Institute. (2013). Future of Life Institute. https://futureoflife.org/ai-safety-research BoddingtonPTowards a code of ethics for artificial intelligence research2017BerlinSpringer10.1007/978-3-319-60648-4 B Macfarlane (239_CR32) 2003 239_CR42 239_CR41 RM Aiken (239_CR4) 2000; 11 239_CR49 239_CR47 A Kobsa (239_CR29) 2007 RS Peters (239_CR36) 1970 239_CR34 239_CR33 239_CR31 239_CR30 J Schreck (239_CR43) 2003 239_CR39 239_CR38 239_CR35 239_CR2 239_CR3 239_CR1 B Tarran (239_CR46) 2018; 15 AJ Sharkey (239_CR44) 2016; 4 A Jobin (239_CR28) 2019; 1 239_CR23 J-D Zapata-Rivera (239_CR54) 2004; 14 AFT Winfield (239_CR52) 2018; 376 239_CR21 239_CR8 S Bull (239_CR11) 2016; 26 239_CR27 239_CR26 239_CR25 239_CR7 239_CR24 K Porayska-Pomsta (239_CR37) 2019 UNESCO (239_CR48) 2019 P Boddington (239_CR9) 2017 D Richards (239_CR40) 2019; 50 B Friedman (239_CR20) 1992; 17 D Gearhart (239_CR22) 2012; 3 M Anwar (239_CR6) 2012; 5 239_CR12 239_CR53 239_CR50 M Whittaker (239_CR51) 2018 239_CR18 239_CR17 239_CR16 239_CR15 239_CR14 239_CR13 S Slade (239_CR45) 2013; 57 B Anderson (239_CR5) 2007; 22 L Floridi (239_CR19) 2019; 32 S Buckingham Shum (239_CR10) 2019; 6
References_xml	– reference: DeepMind Ethics & Society. (2017). https://deepmind.com/about/ethics-and-society – reference: Aristotle. (2009). The Nicomachean Ethics n/e. Brown, L. (ed.) Ross, D. (Trans.) (Revised edition). OUP Oxford. – reference: GearhartDLack of Ethics for eLearning: Two sides of the ethical coinInternational Journal of Technoethics201234334010.4018/jte.2012100103 – reference: JobinAIencaMVayenaEArtificial intelligence: The global landscape of ethics guidelinesNature Machine Intelligence20191938939910.1038/s42256-019-0088-2 – reference: Zapata-RiveraJ-DGreerJEInteracting with inspectable bayesian student modelsInternational Journal of Artificial Intelligence in Education2004142127163 – reference: Buckingham ShumSFergusonRMartinez-MaldonadoRHuman-Centred learning analyticsJournal of Learning Analytics2019621910.18608/jla.2019.62.1 – reference: Deshpande, K. V., Pan, S., & Foulds, J. R. (2020). Mitigating demographic Bias in AI-based resume filtering. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 268–275. https://doi.org/10.1145/3386392.3399569. – reference: SchreckJSecurity and Privacy in User Modeling2003NetherlandsSpringer10.1007/978-94-017-0377-21034.68033 – reference: The Institute for Ethical AI & Machine Learning. (2018). https://ethical.institute – reference: AnwarMGreerJFacilitating trust in privacy-preserving E-learning environmentsIEEE Transactions on Learning Technologies201251627310.1109/TLT.2011.23 – reference: Conati, C., Porayska-Pomsta, K., & Mavrikis, M. (2018). AI in education needs interpretable machine learning: Lessons from open learner Modelling. ArXiv:1807.00154 [Cs]. http://arxiv.org/abs/1807.00154 – reference: AikenRMEpsteinRGEthical guidelines for AI in education: Starting a conversationInternational Journal of Artificial Intelligence in Education200011163176 – reference: Holmes, W., Bektik, D., Whitelock, D., & Woolf, B. P. (2018). Ethics in AIED: Who Cares? In C. P. Rosé, R. Martínez-Maldonado, H. U. Hoppe, R. Luckin, M. Mavrikis, K. Porayska-Pomsta, B. McLaren, & B. du Boulay (Eds.), Artificial Intelligence in Education (Vol. 10948, pp. 551–553). Cham, Switzerland: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-93846-2. – reference: Reich, J., & Ito, M. (2017). From good intentions to real outcomes: Equity by design in learning technologies. Digital Media and Learning Research Hub. – reference: Université de Montréal. (2018). Declaration of Montréal for a responsible development of AI. Université de Montréal. https://www.montrealdeclaration-responsibleai.com – reference: MacfarlaneBTeaching with integrity: The Ethics of higher education practice2003AbingdonRoutledge – reference: UNESCOArtificial intelligence in education: Challenges and opportunities for sustainable development2019 – reference: WinfieldAFTJirotkaMEthical governance is essential to building trust in robotics and artificial intelligence systemsPhilosophical Transactions of the Royal Society A201837621332018008510.1098/rsta.2018.0085 – reference: SharkeyAJShould we welcome robot teachers?Ethics and Information Technology201641828329710.1007/s10676-016-9387-z – reference: Mobasher, B., Kleanthous, S., Ekstrand, M., Berendt, B., Otterbacher, J., & Shulner Tal, A. (2020). FairUMAP 2020: The 3rd Workshop on Fairness in User Modeling, Adaptation and Personalization. Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 404–405. https://doi.org/10.1145/3340631.3398671. – reference: Holmes, W., Bektik, D., Di Gennaro, M., Woolf, B. P., & Luckin, R. (2019a). Ethics in AIED: Who cares? In S. Isotani, E. Millán, A. Ogan, P. Hastings, & R. Luckin (Eds.), Artificial Intelligence in Education (Vol. 11625, pp. 424–425). Cham, Switzerland: Springer International Publishing AG. https://doi.org/10.1007/978-3-030-23204-7. – reference: Schelenz, L., Segal, A., & Gal, K. (2020). Best practices for transparency in machine generated personalization. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 23–28. https://doi.org/10.1145/3386392.3397593. – reference: BullSKayJSMILI☺: A framework for interfaces to learning data in open learner models, learning analytics and related fieldsInternational Journal of Artificial Intelligence in Education201626129333110.1007/s40593-015-0090-8 – reference: Potgieter, I. (2020). Privacy concerns in educational data mining and learning analytics. The International Review of Information Ethics, 28. https://informationethics.ca/index.php/irie/article/view/384 – reference: World Economic Forum. (2019). Generation AI. May 2019 Workshop Report.http://www3.weforum.org/docs/WEF_Generation_AI_%20May_2019_Workshop_Report.pdf – reference: Labarthe, H., Luengo, V., & Bouchet, F. (2018). Analyzing the relationships between learning analytics, educational data mining and AI for education. In Guin, N. & Kumar, A. (Eds.), ITS 2018 Workshop Proceedings (pp. 63–71). – reference: AndersonBSimpsonMEthical issues in online educationOpen Learning: The Journal of Open, Distance and e-Learning200722212913810.1080/02680510701306673 – reference: Sacharidis, D., Mukamakuza, C. P., & Werthner, H. (2020). Fairness and diversity in social-based recommender systems. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, 83–88. https://doi.org/10.1145/3386392.3397603. – reference: SladeSPrinslooPLearning analytics ethical issues and dilemmasAmerican Behavioral Scientist201357101510152910.1177/0002764213479366 – reference: TarranBWhat can we learn from the Facebook—Cambridge Analytica scandal?Significance20181534510.1111/j.1740-9713.2018.01139.x – reference: Ada Lovelace Institute. (2019). https://www.adalovelaceinstitute.org – reference: Holmes, W., Bialik, M., & Fadel, C. (2019b). Artificial intelligence in education. Promises and Implications for Teaching and Learning. Center for Curriculum Redesign. – reference: Lewthwaite, S. (2014). Web accessibility standards and disability: Developing critical perspectives on accessibility. Disability and Rehabilitation, 36(16), 1375–1383. https://doi.org/10.3109/09638288.2014.938178 – reference: WEF. (2020). Chatbots RESET. A Framework for Governing Responsible Use of Conversational AI in Healthcare. World Economic Forum. http://www3.weforum.org/docs/WEF_Governance_of_Chatbots_in_Healthcare_2020.pdf – reference: Ferguson, R., Brasher, A., Clow, D., Cooper, A., Hillaire, G., Mittelmeier, J., Rienties, B., Ullmann, T., & Vuorikari, R. (2016). Research evidence on the use of learning analytics: Implications for education policy. http://oro.open.ac.uk/48173/ – reference: Murtarelli, G., Gregory, A., & Romenti, S. (2020). A conversation-based perspective for shaping ethical human–machine interactions: The particular challenge of chatbots. Journal of Business Research, S0148296320305944. https://doi.org/10.1016/j.jbusres.2020.09.018. – reference: Porayska-PomstaKRajendranGKnoxJWangYGallagherMAccountability in human and artificial intelligence decision-making as the basis for diversity and educational inclusionArtificial Intelligence and Inclusive Education: Speculative Futures and Emerging Practices2019SingaporeSpringer395910.1007/978-981-13-8161-4_3 – reference: BoddingtonPTowards a code of ethics for artificial intelligence research2017BerlinSpringer10.1007/978-3-319-60648-4 – reference: AI Now Institute. (2017). https://ainowinstitute.org – reference: WhittakerMCrawfordKDobbeRFriedGKaziunasEMathurVMyersWestSRichardsonRSchultzJSchwartzOAI now report 20182018AI Now InstituteNew York University – reference: European Union. (2019). Ethics guidelines for trustworthy AI. Independent High-Level Expert Group on Artificial Intelligence. European Union. https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai – reference: FloridiLTranslating principles into practices of digital Ethics: Five risks of being unethicalPhilosophy & Technology201932218519310.1007/s13347-019-00354-x – reference: Future of Life Institute. (2013). Future of Life Institute. https://futureoflife.org/ai-safety-research – reference: RichardsDDignumVSupporting and challenging learners through pedagogical agents: Addressing ethical issues through designing for valueBritish Journal of Educational Technology20195062885290110.1111/bjet.12863 – reference: Bietti, E. (2020). From Ethics Washing to Ethics Bashing: A View on Tech Ethics from within Moral Philosophy. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, FAT* ’20. Association for Computing Machinery (pp. 210–219).New York. https://doi.org/10.1145/3351095.3372860. – reference: Drachsler, H., & Greller, W. (2016). 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Jnd.Org. https://jnd.org/human-centered_design_considered_harmful – reference: PetersRSEthics and education1970LondonAllen & Unwin – reference: KobsaABrusilovskyPKobsaANejdlWPrivacy-enhanced web personalizationThe Adaptive Web: Methods and Strategies of Web Personalization2007BerlinSpringer13615410.1007/978-3-540-72079-9_4Generic User Modeling Systems – reference: House of Lords. (2018). Ai in the Uk: Ready, Willing and Able. https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/100.pdf – reference: FriedmanBKahnPHJrHuman agency and responsible computing: Implications for computer system designJournal of Systems and Software199217171410.1016/0164-1212(92)90075-U – reference: Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. 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SubjectTerms	Artificial Intelligence Check Lists Community Relations Computer Science Computers and Education Consent Education Educational objectives Educational Research Educational Technology Ethics Guidelines Informed Consent Learning analytics Legal Responsibility Privacy Questions Researchers Stakeholders Sustainable Development Teachers Teaching Methods Technology Uses in Education The FATE of AIED User Interfaces and Human Computer Interaction
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