Hemmler, Y., & Ifenthaler, D. (2022). Four perspectives on personalized and adaptive learning environments for workplace learning. In D. Ifenthaler & S. Seufert (Eds.), Artificial intelligence education in the context of work (pp. 27–39). Springer. https://doi.org/10.1007/978-3-031-14489-9_2
A major challenge in designing training programs for workplace learning and professional development is to design programs that are appropriate for learners from various backgrounds and with different abilities and preferences. Current research focusing on artificial intelligence in education suggests that these challenges may be overcome through the support of personalized and adaptive learning. The aim of this chapter is to evaluate the relevance and feasibility of different indicators for personalized and adaptive learning environments for workplace learning from four perspectives: First, we discuss which indicators are relevant from a pedagogical perspective. Second, from an ethical perspective, we outline how privacy issues might limit the collection and analysis of indicators. Third, from a data analytics perspective, we outline challenges associated with data quality and validity. Fourth, from an information systems perspective, we discuss which indicators are feasible for implementation into digital learning systems. It is concluded that advances on adoption models as well as on policy recommendations are required to move artificial intelligence for workplace learning forward.