Auto-generated language learning online courses using generative AI models like ChatGPT
dc.contributor.author | Rüdian, Sylvio | |
dc.contributor.author | Pinkwart, Niels | |
dc.contributor.editor | Röpke, René | |
dc.contributor.editor | Schroeder, Ulrik | |
dc.date.accessioned | 2023-08-30T09:09:41Z | |
dc.date.available | 2023-08-30T09:09:41Z | |
dc.date.issued | 2023 | |
dc.description.abstract | Generating online courses is always a trade-off between possibilities, technical limitations, and quality. State-of-the-art generative models can assist teachers in the creation process. However, generating learning materials is highly complex. Hence, teachers mainly create them manually. In this paper, learning content for a concrete micro-learning template is generated focusing on the field of language teaching. It intends that learners can find correct responses by logical thinking. Teachers provide a topic as input. Then, the approach asks for the required information using GPT3.5 with instructional prompts and combines responses to form a language learning unit. The quality of the resulting learning content, focusing on correctness, and appropriateness, is evaluated and discussed to examine the practicability of the tool, and alternatives are given. | en |
dc.identifier.doi | 10.18420/delfi2023-14 | |
dc.identifier.isbn | 978-3-88579-732-6 | |
dc.identifier.pissn | 1617-5468 | |
dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/42239 | |
dc.language.iso | en | |
dc.publisher | Gesellschaft für Informatik e.V. | |
dc.relation.ispartof | 21. Fachtagung Bildungstechnologien (DELFI) | |
dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-322 | |
dc.subject | Language learning | |
dc.subject | generative AI models | |
dc.subject | auto-generated course units | |
dc.title | Auto-generated language learning online courses using generative AI models like ChatGPT | en |
dc.type | Text/Conference Paper | |
gi.citation.endPage | 76 | |
gi.citation.publisherPlace | Bonn | |
gi.citation.startPage | 65 | |
gi.conference.date | 11.-13. September 2023 | |
gi.conference.location | Aachen | |
gi.conference.review | full | |
gi.conference.sessiontitle | Best-Paper-Kandidaten |
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