Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-28645
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMahlow, Cerstin-
dc.date.accessioned2023-09-08T13:53:34Z-
dc.date.available2023-09-08T13:53:34Z-
dc.date.issued2023-08-
dc.identifier.issn1617-2639de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/28645-
dc.descriptionSpecial Issue: Inklusiver Schrift(sprach)erwerb Deutsch, https://doi.org/10.17192/obst.2023.101, Editors: Manuela Böhm, Christiane Hohensteinde_CH
dc.description.abstractRapid advances in artificial intelligence (AI), specifically large language models (LLMs), have recently generated significant debate. This article explores the impact of these developments on learning to write in a first or second/foreign language, specifically German. We examine the technology behind AI-based tools and the natural language processing (NLP) tasks for which they were originally designed. This will help us identify the possibilities and limitations of their use in the context of language learning. We then examine how this technology can be used effectively in language teaching and learning. In conclusion: the availability of these tools will allow language teaching to focus on the non-mechanical aspects of writing; automatically generated personalized teaching and learning materials will make room for and support human-human interaction.de_CH
dc.language.isoende_CH
dc.publisherUniversitätsbibliothek Marburgde_CH
dc.relation.ispartofOsnabrücker Beiträge zur Sprachtheoriede_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectArtificial intelligencede_CH
dc.subjectLarge language modelde_CH
dc.subjectNatural language processingde_CH
dc.subjectWritten language acquisitionde_CH
dc.subject.ddc410.285: Computerlinguistikde_CH
dc.subject.ddc808: Rhetorik und Schreibende_CH
dc.titleLarge language models and artificial intelligence as tools for teaching and learning writingde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementAngewandte Linguistikde_CH
zhaw.organisationalunitInstitute of Language Competence (ILC)de_CH
dc.identifier.doi10.17192/obst.2023.101.8607de_CH
dc.identifier.doi10.21256/zhaw-28645-
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end196de_CH
zhaw.pages.start175de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume101de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedDigital Linguisticsde_CH
zhaw.webfeedZHAW digitalde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
Appears in collections:Publikationen Angewandte Linguistik

Files in This Item:
File Description SizeFormat 
2023_Mahlow_LLM-and-AI-for-teaching-and-learning-writing_OBST.pdf182.99 kBAdobe PDFThumbnail
View/Open
Show simple item record
Mahlow, C. (2023). Large language models and artificial intelligence as tools for teaching and learning writing. Osnabrücker Beiträge Zur Sprachtheorie, 101, 175–196. https://doi.org/10.17192/obst.2023.101.8607
Mahlow, C. (2023) ‘Large language models and artificial intelligence as tools for teaching and learning writing’, Osnabrücker Beiträge zur Sprachtheorie, 101, pp. 175–196. Available at: https://doi.org/10.17192/obst.2023.101.8607.
C. Mahlow, “Large language models and artificial intelligence as tools for teaching and learning writing,” Osnabrücker Beiträge zur Sprachtheorie, vol. 101, pp. 175–196, Aug. 2023, doi: 10.17192/obst.2023.101.8607.
MAHLOW, Cerstin, 2023. Large language models and artificial intelligence as tools for teaching and learning writing. Osnabrücker Beiträge zur Sprachtheorie. August 2023. Bd. 101, S. 175–196. DOI 10.17192/obst.2023.101.8607
Mahlow, Cerstin. 2023. “Large Language Models and Artificial Intelligence as Tools for Teaching and Learning Writing.” Osnabrücker Beiträge Zur Sprachtheorie 101 (August): 175–96. https://doi.org/10.17192/obst.2023.101.8607.
Mahlow, Cerstin. “Large Language Models and Artificial Intelligence as Tools for Teaching and Learning Writing.” Osnabrücker Beiträge Zur Sprachtheorie, vol. 101, Aug. 2023, pp. 175–96, https://doi.org/10.17192/obst.2023.101.8607.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.