AI-Enabled Self-Regulated Learning : A Multi-Layer Taxonomy Development

Titelangaben

Grüneke, Timo ; Guggenberger, Tobias ; Hofmeister, Sofie ; Stoetzer, Jens-Christian:
AI-Enabled Self-Regulated Learning : A Multi-Layer Taxonomy Development.
In: Proceedings of the 32nd European Conference on Information Systems (ECIS). - Paphos, Cyprus , 2024
ISBN 978-1-958200-10-0

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Abstract

In light of the widespread adoption of Artificial Intelligence (AI), educators are increasingly exploring
innovative applications of this technology within their domain of expertise. Notably, research indicates
the capability of AI to facilitate proactive control over the learning process by students, fostering what
is commonly referred to as self-regulated learning (SRL). In this vein, our research undertook the
development of a taxonomy, thereby contributing to theory and practice by furnishing a comprehensive
overview elucidating pertinent dimensions and characteristics intrinsic to AI-based learning systems
and their impact on SRL. By incorporating a Technological Mediation Learning perspective and the
socio-technical system framework, our taxonomy contributes to a nuanced understanding of AI-based
learning systems within the realm of SRL. Consequently, our research establishes a foundational
framework for delving into the potentialities of AI-based learning systems, thereby enhancing
educational practices and assisting learners in navigating their cognitive processes.