Introduction

While the range of AI techniques and technologies that are researched in classrooms and discussed at conferences are extensive and growing, the ethical consequences are rarely fully considered (at least, there is very little published work considering the ethics). In short, as a field (while we apply our university research regulations), we are working without any fully-worked out moral groundings specific to the field of AIED.

In fact, AIED techniques raise an indeterminate number of self-evident but as yet unanswered ethical questions. To begin with, concerns exist about the large volumes of data collected to support AIED (such as the recording of student competencies, emotions, strategies, and misconceptions). Who owns and who is able to access this data, what are the privacy concerns, how should the data be analysed, interpreted and shared, and who should be considered responsible if something goes wrong?

However, while data raises major ethical concerns for the field of AIED, AIED ethics cannot be reduced to questions about data. Other major ethical concerns include the potential for bias (conscious or unconscious) incorporated into AIED algorithms and impacting negatively on the civil rights of individual students (in terms of gender, age, race, social status, income inequality…). But these particular AIED ethical concerns, centred on data and bias, are the ‘known unknowns’. What about the ‘unknown unknowns’, the ethical issues raised by the field of AIED that have yet to be even identified?

AIED ethical questions include:

  • What are the criteria for ethically acceptable AIED?
  • How does the transient nature of student goals, interests and emotions impact on the ethics of AIED?
  • What are the AIED ethical obligations of private organisations (developers of AIED products) and public authorities (schools and universities involved in AIED research)?
  • How might schools, students and teachers opt out from, or challenge, how they are represented in large datasets?
  • What are the ethical implications of not being able to easily interrogate how AIED deep decisions (using multi-level neural networks) are made?

Strategies are also needed for risk amelioration since AI algorithms are vulnerable to hacking and manipulation. Where AIED interventions target behavioural change (such as by ‘nudging’ individuals towards a particular course of action), the entire sequence of AIED enhanced pedagogical activity also needs to be ethically warranted. And finally, it is important to recognise another perspective on AIED ethical questions: in each instance, the ethical cost of inaction and failure to innovate must be balanced against the potential for AIED innovation to result in real benefits for learners, educators and educational institutions.

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