JOURNAL ARTICLE
Using AI Techniques for Building Personalized LMS Learning Modules.
Published In: International Journal of Semantic Computing, 2026, v. 20, n. 1. P. 71 1 of 3
Database: Applied Science & Technology Source Ultimate 2 of 3
Authored By: Schimanke, Florian; Ruf, Boris 3 of 3
Abstract
With the release of ChatGPT, Pandora's box of generative AI has been opened, and the new technology is here to stay. This also impacts academic education, where it bears certain challenges while also providing new opportunities. Students and educators alike will have to adapt to the newly available technology and find ways to use it in a meaningful and profitable way. One way to make good use of generative AI in the classroom is to build personalized learning environments for students that adapt to their individual progress and enhance the learning process with technologies that are tailored to the modern students. In the presented work, two of these technologies and the concept of spaced repetition are used to build a personalized learning module within the learning management system (LMS) "ILIAS". One of the tools used is an AI chatbot based on the ChatGPT API that is trained on the lecturer's actual course materials and enhanced with answers from ChatGPT itself. The other technology improves educational videos by providing learners with a button to pause the video at any time and receive additional explanations about the content currently discussed, while considering the context of the entire video so far. These two AI tools are then provided in combination with a spaced repetition algorithm, which creates a personal learning environment that is highly tailored to the individual learners' needs. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Semantic Computing. 2026/03, Vol. 20, Issue 1, p71
- Document Type:Article
- Subject Area:Education
- Publication Date:2026
- ISSN:1793351X
- DOI:10.1142/S1793351X26410047
- Accession Number:193121375
- Copyright Statement:Copyright of International Journal of Semantic Computing is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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