ML-Based Teaching Systems: A Conceptual Framework
Philipp Spitzer Niklas Kühl Daniel Heinz Gerhard Satzger
As the shortage of skilled workers continues to be a pressing issue,
exacerbated by demographic change, it is becoming a critical challenge for
organizations to preserve the knowledge of retiring experts and to pass it on
to novices. While this knowledge transfer has traditionally taken place through
personal interaction, it lacks scalability and requires significant resources
and time. IT-based teaching systems have addressed this scalability issue, but
their development is still tedious and time-consuming. In this work, we
investigate the potential of machine learning (ML) models to facilitate
knowledge transfer in an organizational context, leading to more cost-effective
IT-based teaching systems. Through a systematic literature review, we examine
key concepts, themes, and dimensions to better understand and design ML-based
teaching systems. To do so, we capture and consolidate the capabilities of ML
models in IT-based teaching systems, inductively analyze relevant concepts in
this context, and determine their interrelationships. We present our findings
in the form of a review of the key concepts, themes, and dimensions to
understand and inform on ML-based teaching systems. Building on these results,
our work contributes to research on computer-supported cooperative work by
conceptualizing how ML-based teaching systems can preserve expert knowledge and
facilitate its transfer from SMEs to human novices. In this way, we shed light
on this emerging subfield of human-computer interaction and serve to build an
interdisciplinary research agenda.