Ethics of AI in Education: Towards a Community-Wide Framework - 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. (International journal of artificial intelligence in education, 2022)
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.