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F. Ansari, W Mayrhofer:
"Reciprocal Learning Assistance Systems in Smart Manufacturing: Transformation from Unidirectional to Bidirectional Learning Technology in Manufacturing Enterprises";
in: "Online Teaching and Learning in Higher Education", P. Isaias et al. (ed.); Springer, 2020, (invited), ISBN: 978-3-030-48190-2, 16 pages.

Collaboration of human and intelligent machines may establish physical or cognitive reciprocal dependencies, especially through sharing workplace and sharing knowledge. Human learning has been considered as a subject in the field of education, pedagogy, and cognitive psychology describing and modeling human learning processes. The ultimate goal is to better understand how humans acquire, store, and demonstrate knowledge, skill, ability and competence, and thus how they continuously support and improve the learning process, and pertained learning outcomes. What can we still learn from existing theories and natural phenomena to promote learning in smart factories? This book chapter provides an overview of technology-assisted learning and deepens insights into "human-machine reciprocal learning." This novel approach is an enabler to generate and foster collective human-machine learning across a smart factory. The interlinking of digital profiles of humans and machines permits the identification and measurement of learning outcomes through sharing of workplace and performing of (shared) tasks. To achieve this goal and subsequently to transform today´s smart factory into a self-learning factory, the concept model of AUTODIDACT focusing on the envisaged use-cases at TU Wien Pilot Factory Industry 4.0 is presented. Finally, underlying objectives and research questions related to reciprocal learning and the implementation of such a reciprocal learning assistance system are outlined.

Human-machine interaction; Reciprocal learning; Collective intelligence; Industry 4.0; Smart factory

http://dx.doi.org/10.1007/978-3-030-48190-2_10