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Samar Ibrahim; Ghazala Bilquise – Education and Information Technologies, 2025
Language is an essential component of human communication and interaction. Advances in Artificial Intelligence (AI) technology, specifically in Natural Language Processing (NLP) and speech-recognition, have made is possible for conversational agents, also known as chatbots, to converse with language learners in a way that mimics human speech.…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Benchmarking
Song Yang; Ying Dong; Zhong Gen Yu – International Journal of Information and Communication Technology Education, 2024
AI chatbots, e.g. ChatGPT, are becoming increasingly popular in education as a means to enhance student learning experiences and improve teaching efficiency. This study utilizes NVivo 12 Plus to examine the role of AI chatbots in education, ethical considerations, and sentimental analysis regarding the utilization of ChatGPT in education. ChatGPT…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Ethics
Jessie S. Barrot – Technology, Knowledge and Learning, 2024
This emerging technology report delves into the role of ChatGPT, an OpenAI conversational AI, in language learning. The initial section introduces ChatGPT's nature and highlights its features, including accessibility, personalization, immersive learning, and instant feedback, which render it a valuable asset for language learners and educators…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Language Acquisition
Antonie Alm; Yuki Watanabe – Iranian Journal of Language Teaching Research, 2023
This paper explores the implications of ChatGPT for language teaching through the lens of Paulo Freire's critical pedagogy. A review of recent research on ChatGPT reveals promising opportunities for personalised and interactive learning, but also risks of propagating cultural bias, plagiarism and passive learning. Freire's concepts of 'banking'…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Language Acquisition
Alex Warstadt – ProQuest LLC, 2022
Data-driven learning uncontroversially plays a role in human language acquisition--how large a role is a matter of much debate. The success of artificial neural networks in NLP in recent years calls for a re-evaluation of our understanding of the possibilities for learning grammar from data alone. This dissertation argues the case for using…
Descriptors: Language Acquisition, Artificial Intelligence, Computational Linguistics, Ethics
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
Fu, Shixuan; Gu, Huimin; Yang, Bo – British Journal of Educational Technology, 2020
Traditional educational giants and natural language processing companies have launched several artificial intelligence (AI)-enabled digital learning applications to facilitate language learning. One typical application of AI in digital language education is the automatic scoring application that provides feedback on pronunciation repeat outcomes.…
Descriptors: Affordances, Artificial Intelligence, Computer Assisted Testing, Scoring
Mahowald, Kyle; Kachergis, George; Frank, Michael C. – First Language, 2020
Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec -- which have been extremely successful in modern natural language processing (NLP) applications -- count? Although these models often have ample parametric complexity to store exemplars from their training data, they also…
Descriptors: Models, Language Processing, Computational Linguistics, Language Acquisition
Schuler, Kathryn D.; Kodner, Jordan; Caplan, Spencer – First Language, 2020
In 'Against Stored Abstractions,' Ambridge uses neural and computational evidence to make his case against abstract representations. He argues that storing only exemplars is more parsimonious -- why bother with abstraction when exemplar models with on-the-fly calculation can do everything abstracting models can and more -- and implies that his…
Descriptors: Language Processing, Language Acquisition, Computational Linguistics, Linguistic Theory
Kolodny, Oren; Lotem, Arnon; Edelman, Shimon – Cognitive Science, 2015
We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural-language corpora. Given…
Descriptors: Grammar, Natural Language Processing, Computer Mediated Communication, Graphs
Madden, Carol; Hoen, Michel; Dominey, Peter Ford – Brain and Language, 2010
This article addresses issues in embodied sentence processing from a "cognitive neural systems" approach that combines analysis of the behavior in question, analysis of the known neurophysiological bases of this behavior, and the synthesis of a neuro-computational model of embodied sentence processing that can be applied to and tested in the…
Descriptors: Sentences, Simulation, Interaction, Language Processing