These three themes translate into challenges for AIED research that need to be addressed in order to successfully investigate and develop AIED technology for informal and situated professional learning. From an ethics perspective, this is particularly problematic as much data that would be relevant for use within learning technologies hasn’t been collected for the purposes of learning. Thirdly, learning is not necessarily a shared goal in informal professional learning settings.
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Secondly, manual tracking comes with high learner control over data. Such data may be private, proprietary, or both. Instead, due to the situatedness of learning, relevant data is also about others (colleagues, customers, clients) and other objects from the learner’s context. In our interpretation, we have developed three themes: Firstly, in informal and situated professional learning, relevant data about professional learning – to be used as a basis for learning analytics and reflection or as a basis for adaptive systems - is not only about learners. We describe the encountered data-related ethics issues. The foundation of our vision is a secondary analysis of five research cases that offer insights related to data-driven adaptive technologies for informal professional learning. In this paper we formulate a vision how AIED research could address data-related ethics issues in informal and situated professional learning. In both cases, data are the foundation of AIED research and data-related ethics are thus central to AIED research. The hallmark of this community’s work has been i) data-driven design of learning technology and ii) machine-learning enabled personalized interventions. AI-enabled scaffolding and just-in-time and situated learning in the workplace offer a new frontier for future impact of AIED. This is true both for re-skilling from disappearing jobs, as well as for staying current within a professional domain.
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