Paper Number
1057
Paper Type
Complete Research Paper
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.
Recommended Citation
Grueneke, Timo; Guggenberger, Tobias; Hofmeister, Sofie; and Stoetzer, Jens-Christian, "AI-Enabled Self-Regulated Learning: A Multi-Layer Taxonomy Development" (2024). ECIS 2024 Proceedings. 4.
https://aisel.aisnet.org/ecis2024/track13_learning_teach/track13_learning_teach/4
AI-Enabled Self-Regulated Learning: A Multi-Layer Taxonomy Development
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.
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