A Manifesto for a Pro-Actively Responsible AI in Education

The field of AIED, as defined by the work conducted under the auspices of the International Society of Artificial Intelligence in Education, has been built on big and well-intentioned ambitions to understand, devise and scale-up best learning and teaching practices to as many students as possible. This ambition has been bolstered most notably by the Bloom (1984) studies, which are still routinely cited throughout the AIED literature as a key justification and motivation for the field. This ambition had bootstrapped much of the work within the field and it has spurred in-depth research examining how specific populations of students learn, what are the prerequisites (cognitive, affective, and pedagogic) for successful learning, and how AIED technologies might be designed to help develop and capitalise on such learning prerequisites. Personalisation through adaptivity of assessment and feedback (for the purpose of this article used in the broad sense of pedagogical support) remains at the heart of the work conducted by AIED researchers, regardless of their specific areas of specialisation, or their philosophical or epistemological perspectives. This is why, to date, the AIED community repeatedly voted to retain its long-debated connection with the wider field of AI - a domain like AIED insofar as its central paradigm of adaptive agent technologies, but unlike AIED as far as its aim to emulate human capacities only to the extent that it is useful to a given application's success in achieving its specific goals.