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Hatice Yildiz Durak; Figen Egin; Aytug Onan – European Journal of Education, 2025
Generative artificial intelligence (GenAI) models, such as ChatGPT, Gemini, and BingAI, have become integral to educational sciences, bringing about significant transformations in the education system and the processes of knowledge production. These advancements have facilitated new methods of teaching, learning, and information dissemination.…
Descriptors: Writing (Composition), Discussion, Artificial Intelligence, Natural Language Processing
Ramotowska, Sonia; Steinert-Threlkeld, Shane; Maanen, Leendert; Szymanik, Jakub – Cognitive Science, 2023
According to logical theories of meaning, a meaning of an expression can be formalized and encoded in truth conditions. Vagueness of the language and individual differences between people are a challenge to incorporate into the meaning representations. In this paper, we propose a new approach to study truth-conditional representations of vague…
Descriptors: Computation, Models, Semantics, Decision Making
Funda Nayir; Tamer Sari; Aras Bozkurt – Journal of Educational Technology and Online Learning, 2024
From personalized advertising to economic forecasting, artificial intelligence (AI) is becoming an increasingly important element of our daily lives. These advancements raise concerns regarding the transhumanist perspective and associated discussions in the context of technology-human interaction, as well as the influence of artificial…
Descriptors: Artificial Intelligence, Technology Uses in Education, Humanism, Capacity Building
Wulff, Peter – Education and Information Technologies, 2023
Scientists use specific terms to denote concepts, objects, phenomena, etc. The terms are then connected with each other in sentences that are used in science-specific language. Representing these connections through term networks can yield valuable insights into central terms and properties of the interconnections between them. Furthermore,…
Descriptors: Network Analysis, Natural Sciences, Encyclopedias, Electronic Publishing
LiCausi, Taylor J.; McFarland, Daniel A. – Higher Education: The International Journal of Higher Education Research, 2022
The rise of computational methods and rich textual data has spawned a series of studies that map the contours of academic knowledge produced in various fields. However, while many fields span academic cultures, studies have neglected disciplinary dynamics that may be especially useful for understanding knowledge production in fields with subject…
Descriptors: Linguistics, Language Research, Doctoral Dissertations, Natural Language Processing
Mei-Rong Alice Chen – Educational Technology & Society, 2024
The increase in popularity of Generative Artificial Intelligence Chatbots, or GACs, has created a potentially fruitful opportunity to enhance teaching English as a Foreign Language (EFL). This study investigated the possibility of using GACs to give EFL students metalinguistic guidance (MG) in linguistics courses. Language competency gaps, a lack…
Descriptors: Metacognition, Transformative Learning, English (Second Language), Artificial Intelligence
Taylor, Joshua; Kochem, Timothy – Diaspora, Indigenous, and Minority Education, 2022
Technology has paved the way for new modalities in language learning; however, indigenous populations often lack access to these tools. This critical literature review examines how researchers have tried to overcome this hurdle by reaching out to marginalized indigenous populations and providing access to emerging technologies. We used 63 texts…
Descriptors: Second Language Learning, Second Language Instruction, Language Maintenance, Indigenous Populations
Alexopoulou, Theodora; Michel, Marije; Murakami, Akira; Meurers, Detmar – Language Learning, 2017
Large-scale learner corpora collected from online language learning platforms, such as the EF-Cambridge Open Language Database (EFCAMDAT), provide opportunities to analyze learner data at an unprecedented scale. However, interpreting the learner language in such corpora requires a precise understanding of tasks: How does the prompt and input of a…
Descriptors: Linguistics, Accuracy, Natural Language Processing, Linguistic Performance
MacArthur, Charles A.; Jennings, Amanda; Philippakos, Zoi A. – Grantee Submission, 2018
The study developed a model of linguistic constructs to predict writing quality for college basic writers and analyzed how those constructs changed following instruction. Analysis used a corpus of argumentative essays from a quasi-experimental, instructional study with 252 students (MacArthur, Philippakos, & Ianetta, 2015) that found large…
Descriptors: College Students, Writing Skills, Writing Evaluation, Writing Achievement
Pareja-Lora, Antonio – Research-publishing.net, 2016
For the new approaches to language e-learning (e.g. language blended learning, language autonomous learning or mobile-assisted language learning) to succeed, some automatic functions for error correction (for instance, in exercises) will have to be included in the long run in the corresponding environments and/or applications. A possible way to…
Descriptors: Electronic Learning, Automation, Error Correction, Natural Language Processing
Skalicky, Stephen; Crossley, Scott A.; McNamara, Danielle S.; Muldner, Kasia – Creativity Research Journal, 2017
Creativity is commonly assessed using divergent thinking tasks, which measure the fluency, flexibility, originality, and elaboration of participant output on a variety of different tasks. This study assesses the degree to which creativity can be identified based on linguistic features of participants' language while completing collaborative…
Descriptors: Creativity, Creative Thinking, Problem Solving, Linguistics
Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Grantee Submission, 2015
This study builds upon previous work aimed at developing a student model of reading comprehension ability within the intelligent tutoring system, iSTART. Currently, the system evaluates students' self-explanation performance using a local, sentence-level algorithm and does not adapt content based on reading ability. The current study leverages…
Descriptors: Reading Comprehension, Reading Skills, Natural Language Processing, Intelligent Tutoring Systems
Allen, Laura K.; Snow, Erica L.; McNamara, Danielle S. – Grantee Submission, 2014
In the current study, we utilize natural language processing techniques to examine relations between the linguistic properties of students' self-explanations and their reading comprehension skills. Linguistic features of students' aggregated self-explanations were analyzed using the Linguistic Inquiry and Word Count (LIWC) software. Results…
Descriptors: Natural Language Processing, Reading Comprehension, Linguistics, Predictor Variables
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
Rajkumar, Rajakrishnan – ProQuest LLC, 2012
Natural Language Generation (NLG) is the process of generating natural language text from an input, which is a communicative goal and a database or knowledge base. Informally, the architecture of a standard NLG system consists of the following modules (Reiter and Dale, 2000): content determination, sentence planning (or microplanning) and surface…
Descriptors: Natural Language Processing, Linguistics, Language Processing, Models
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