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Evaluation Methods for an AI-Supported Learning Management System: Quantifying and Qualifying Added Values for Teaching and Learning

 
: Rerhaye, Lisa; Altun, Daniela; Krauss, Christopher; Müller, Christoph

:

Sottilare, Robert (Ed.); Schwarz, Jessica (Ed.):
Adaptive Instructional Systems. Design and Evaluation. Third International Conference, AIS 2021. Proceedings. Pt.I : Held as Part of the 23rd HCI International Conference, HCII 2021, Virtual Event, July 24-29, 2021
Cham: Springer Nature, 2021 (Lecture Notes in Computer Science 12792)
ISBN: 978-3-030-77856-9 (Print)
ISBN: 978-3-030-77857-6 (Online)
ISBN: 978-3-030-77858-3
pp.394-411
International Conference on Adaptive Instructional Systems (AIS) <3, 2021, Online>
International Conference on Human-Computer Interaction (HCI International) <23, 2021, Online>
English
Conference Paper
Fraunhofer FKIE ()
Fraunhofer FOKUS ()
artificial intelligence; Learning Management Systems (LMS); evaluation

Abstract
Artificial intelligence offers great opportunities for the future, including for teaching and learning. Applications such as personalized recommendations and learning paths based on learning analytics [i.e. 1], the integration of serious games in intelligent tutoring systems [2], intelligent agents in the form of chatbots [3], and other emerging applications promise great benefits for individualized digital learning. However, what value do these applications really add and how can these benefits be measured?
With this article, we would like to give a brief overview of AI-supported functionalities for learning management system as well as their possible benefits for future learning environments. Furthermore, we outline methods for a comprehensive evaluation that meets the users’ needs and concretizes the actual benefit of an AI-supported LMS.

: http://publica.fraunhofer.de/documents/N-640172.html