Collaborative Creation and Training of Social Bots in Learning Communities
Alexander Tobias Neumann, Peter de Lange, Ralf Klamma · 2019
The interaction between instructors and students is one of the key concepts to improve the student's learning process. To personalize learning on a massive scale, social bots can be used as supporting technology. However, their development for virtual learning environments currently requires deep technical knowledge. This leaves learner communities relying on highly-skilled developers to generate and tailor these social bots. Participatory design, end-user development and model-driven principles bear the potential to close this technical gap. In this paper, we propose a model-driven approach for creating social bots. Using our framework, learners can create, train and utilize these for self-hosted virtual learning environments relying on OpenAPI specifications offered, e.g. by Blackboard. We support both retrieval-based bots that react to certain events in predefined ways, as well as generative bots by utilizing open source deep learning technologies. Our first evaluation shows the usefulness of model-driven generation and utilization of social bots. We see the potential of this approach to move the development closer to the actual learner.