Publication
Privacy and Ethics Reference Framework for education data
Education data offers increasing opportunities to make higher education better, more effective and more efficient. In order to benefit, students
11 May 2022
The promise of AI is now being heard everywhere. While Deep Blue’s victory over Kasparov in the mid-1990s may have seemed like an incident (a combination of player fatigue and brute force computer power), winning a game of chess from a computer has now become impossible. Not because of the increase in computing power, but mainly because of its intelligent use. Even the much more complex game Go has been sacrificed to artificial intelligence. When and how will AI also claim its role in education?
More and more often, voices are heard about the promise of AI in education – that AI will help make our education better or more efficient. Suppliers of student monitoring systems and digital learning environments build AI tools into their software. The European Union calls together experts in education to join forces. And also initiatives (hackathons, working groups, coalitions) to take up the subject are taking place more often.
And yet, there are also doubts. The promise that AI can help education is still difficult to substantiate with appealing examples of (broadly) applicable solutions. Until then, many prefer to wait and see. Perhaps an even more important cause of doubt is the – justified – concern about what can go wrong. Examples such as Google’s algorithms recognising black people as ‘gorillas’ and the escalating effect of recommendations on social platforms, feed those doubts. AI as a wolf in sheep’s clothing.
The beautiful examples are just a matter of time. That AI will help us in education is beyond doubt. There will be applications that will help us. Not immediately as grand and compelling as some think or want, but with small, thoughtful steps. As we always do in education. Perhaps first with smart formative tests, and later with finding ‘soft spots’ in a curriculum, the optimal use of space and time for teaching staff, the emergence of data-assisted tutoring and the forecasting of flexible student flows.
But this cannot be done without addressing concerns about unintended but harmful effects. These effects have the potential to not only break the promise of AI, but can also damage what education stands for as a public organisation: open, inclusive education with an emancipating function. As a sector, it is important that we clearly state why we use education data and AI, where we draw the line (up to what point do we want to use AI, where not), how we do that and how we organise it. The national reference framework for privacy and ethics in education data, which the zone Secure and reliable use of education datais working on together with institutions, experts and student unions, is a way to do this. This framework helps to realise the promise of AI, so that it can really contribute to the quality and efficiency of our education.
June has been declared the month of Artificial Intelligence in education – a cooperation between Special Interest Group AI in education, SURF, the Dutch AI coalition working group education and the Acceleration Plan. Dive into the world of AI with various webinars, hackatons and lectures. Also read the other blogs of our members of the Versnellingsplan or participate in the activities that are organised.
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