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Matthew Landers – Higher Education for the Future, 2025
This article presents a brief overview of the state-of-the-art in large language models (LLMs) like ChatGPT and discusses the difficulties that these technologies create for educators with regard to assessment. Making use of the 'arms race' metaphor, this article argues that there are no simple solutions to the 'AI problem'. Rather, this author…
Descriptors: Ethics, Cheating, Plagiarism, Artificial Intelligence
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Debby R. E. Cotton; Peter A. Cotton; J. Reuben Shipway – Innovations in Education and Teaching International, 2024
The use of artificial intelligence in academia is a hot topic in the education field. ChatGPT is an AI tool that offers a range of benefits, including increased student engagement, collaboration, and accessibility. However, is also raises concerns regarding academic honesty and plagiarism. This paper examines the opportunities and challenges of…
Descriptors: Integrity, Cheating, Artificial Intelligence, Man Machine Systems
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Ardeshir Geranpayeh – Language Teaching Research Quarterly, 2023
The recent surge in the popularity of Large Language Models (LLM) for language assessment underscores the growing significance of cost-effective language evaluation in our increasingly digitalized society. This paper posits that the application of computational psychometrics can enable the incorporation of technology into language assessment,…
Descriptors: Computational Linguistics, Psychometrics, Second Language Learning, Second Language Instruction
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Tahereh Firoozi; Okan Bulut; Mark J. Gierl – International Journal of Assessment Tools in Education, 2023
The proliferation of large language models represents a paradigm shift in the landscape of automated essay scoring (AES) systems, fundamentally elevating their accuracy and efficacy. This study presents an extensive examination of large language models, with a particular emphasis on the transformative influence of transformer-based models, such as…
Descriptors: Turkish, Writing Evaluation, Essays, Accuracy
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Jae Q. J. Liu; Kelvin T. K. Hui; Fadi Al Zoubi; Zing Z. X. Zhou; Dino Samartzis; Curtis C. H. Yu; Jeremy R. Chang; Arnold Y. L. Wong – International Journal for Educational Integrity, 2024
The application of artificial intelligence (AI) in academic writing has raised concerns regarding accuracy, ethics, and scientific rigour. Some AI content detectors may not accurately identify AI-generated texts, especially those that have undergone paraphrasing. Therefore, there is a pressing need for efficacious approaches or guidelines to…
Descriptors: Artificial Intelligence, Investigations, Identification, Human Factors Engineering
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Hsu, Anne S.; Chater, Nick; Vitanyi, Paul M. B. – Cognition, 2011
There is much debate over the degree to which language learning is governed by innate language-specific biases, or acquired through cognition-general principles. Here we examine the probabilistic language acquisition hypothesis on three levels: We outline a novel theoretical result showing that it is possible to learn the exact "generative model"…
Descriptors: Linguistics, Prediction, Natural Language Processing, Language Acquisition
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Jordan, Sally; Mitchell, Tom – British Journal of Educational Technology, 2009
A natural language based system has been used to author and mark short-answer free-text assessment tasks. Students attempt the questions online and are given tailored and relatively detailed feedback on incorrect and incomplete responses, and have the opportunity to repeat the task immediately so as to learn from the feedback provided. The answer…
Descriptors: Feedback (Response), Test Items, Natural Language Processing, Teaching Methods
Kotani, Katsunori; Yoshimi, Takehiko; Isahara, Hitoshi – Online Submission, 2010
In textbooks, foreign (second) language reading proficiency is often evaluated through comprehension questions. In case, authentic texts are used as reading material, such questions should be prepared by teachers. However, preparing appropriate questions may be a very demanding task for teachers. This paper introduces a method for automatically…
Descriptors: Foreign Countries, Reading Comprehension, Reading Materials, Predictive Measurement
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Kreymer, Oleg – Online Information Review, 2002
Evaluates the current state of natural language processing information retrieval systems from the user's point of view, focusing on the structure and components of the systems' help mechanisms. Topics include user/system interaction; semantic parsing; syntactic parsing; semantic mapping; and concept matching. (Author/LRW)
Descriptors: Evaluation Methods, Information Retrieval, Man Machine Systems, Natural Language Processing