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Pack, Austin; Maloney, Jeffrey – Teaching English with Technology, 2023
With recent public access to large language models via chatbots, the field of language education is seeing unprecedented levels of interest in how AI will affect language learning and teaching. As attention is primarily focused on student misuse of the technology, the potential affordances of generative AI tools may often be overlooked. In this…
Descriptors: Artificial Intelligence, Natural Language Processing, Man Machine Systems, Language Acquisition
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Lin, Phoebe M. S. – Applied Linguistics, 2012
With the ever increasing number of studies on formulaic language, we are beginning to learn more about the processing of formulaic language (e.g. Ellis et al. 2008; Siyanova et al. 2011), its use in speech (e.g. Aijmer 1996; Wood 2012) and writing (e.g. Hyland 2008a, 2008b) and its application in natural language processing (e.g. Tschichold 2000).…
Descriptors: Evidence, Language Research, Applied Linguistics, Memory
Kapa, Leah Lynn – ProQuest LLC, 2013
Prior research has established an executive function advantage among bilinguals as compared to monolingual peers. These non-linguistic cognitive advantages are largely assumed to result from the experience of managing two linguistic systems. However, the possibility remains that the relationship between bilingualism and executive function is…
Descriptors: Artificial Languages, Executive Function, Adults, Bilingualism
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Hudson Kam, Carla L.; Newport, Elissa L. – Cognitive Psychology, 2009
When natural language input contains grammatical forms that are used probabilistically and inconsistently, learners will sometimes reproduce the inconsistencies; but sometimes they will instead regularize the use of these forms, introducing consistency in the language that was not present in the input. In this paper we ask what produces such…
Descriptors: Form Classes (Languages), Artificial Languages, Adult Learning, Linguistic Input