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Showing 46 to 60 of 930 results Save | Export
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Muhammad Bilal Saqib; Saba Zia – Journal of Applied Research in Higher Education, 2025
Purpose: The notion of using a generative artificial intelligence (AI) engine for text composition has gained excessive popularity among students, educators and researchers, following the introduction of ChatGPT. However, this has added another dimension to the daunting task of verifying originality in academic writing. Consequently, the market…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Evaluation
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Abdullah Al-Abri – Education and Information Technologies, 2025
This study explores the impact of ChatGPT, an advanced Large Language Model (LLM), as a virtual tutor in online education across five key dimensions: answering questions, writing assistance, study resources, exam preparation, and availability. Utilizing an experimental design, 68 undergraduate students from a public university interacted with…
Descriptors: Artificial Intelligence, Natural Language Processing, Man Machine Systems, Intelligent Tutoring Systems
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Oscar Stuhler; Cat Dang Ton; Etienne Ollion – Sociological Methods & Research, 2025
Generative AI (GenAI) is quickly becoming a valuable tool for sociological research. Already, sociologists employ GenAI for tasks like classifying text and simulating human agents. We point to another major use case: the extraction of structured information from unstructured text. Information Extraction (IE) is an established branch of Natural…
Descriptors: Artificial Intelligence, Sociology, Social Science Research, Natural Language Processing
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Chelsea Chandler; Rohit Raju; Jason G. Reitman; William R. Penuel; Monica Ko; Jeffrey B. Bush; Quentin Biddy; Sidney K. D’Mello – International Educational Data Mining Society, 2025
We investigated methods to enhance the generalizability of large language models (LLMs) designed to classify dimensions of collaborative discourse during small group work. Our research utilized five diverse datasets that spanned various grade levels, demographic groups, collaboration settings, and curriculum units. We explored different model…
Descriptors: Artificial Intelligence, Models, Natural Language Processing, Discourse Analysis
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Seyed Parsa Neshaei; Richard Lee Davis; Paola Mejia-Domenzain; Tanya Nazaretsky; Tanja Käser – International Educational Data Mining Society, 2025
Deep learning models for text classification have been increasingly used in intelligent tutoring systems and educational writing assistants. However, the scarcity of data in many educational settings, as well as certain imbalances in counts among the annotated labels of educational datasets, limits the generalizability and expressiveness of…
Descriptors: Artificial Intelligence, Classification, Natural Language Processing, Technology Uses in Education
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Afza Diyana Abdullah; Xiaoting Qiu; Huan Li; Muhammad Kamarul Kabilan – Reading Research Quarterly, 2025
Academic reading, a cornerstone of postgraduate education, often presents challenges, particularly for non-native English speakers. These include complex texts, extensive vocabulary, and integrating diverse sources. This study investigates the potential of ChatGPT as an academic reading tool for postgraduate students, emphasizing its usability,…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Graduate Students
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Valentine Joseph Owan; Ibrahim Abba Mohammed; Ahmed Bello; Tajudeen Ahmed Shittu – Contemporary Educational Technology, 2025
Despite the increasing interest in artificial intelligence technologies in education, there is a gap in understanding the factors influencing the adoption of ChatGPT among Nigerian higher education students. Research has not comprehensively explored these factors in the Nigerian context, leaving a significant gap in understanding technology…
Descriptors: Student Behavior, Foreign Countries, Artificial Intelligence, Natural Language Processing
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Elisa Martinez Marroquin; Bouchra Senadji – International Journal of Information and Learning Technology, 2025
Purpose: Technology, such as artificial intelligence (AI), is transforming the way we work; however, it is yet to systemically transform learning at the workplace beyond augmentation of formal education's learning processes. This paper derives functional requirements for technologies that support workplace learning and assesses the suitability and…
Descriptors: Workplace Learning, Artificial Intelligence, Educational Change, Technology Integration
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Mike Perkins; Jasper Roe; Darius Postma; James McGaughran; Don Hickerson – Journal of Academic Ethics, 2024
This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI's ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors…
Descriptors: Artificial Intelligence, Student Evaluation, Identification, Natural Language Processing
Mengjiao Zhang – ProQuest LLC, 2024
The rise of Artificial Intelligence technology has raised concerns about the potential compromise of privacy due to the handling of personal data. Private AI prevents cybercrimes and falsehoods and protects human freedom and trust. While Federated Learning offers a solution by model training across decentralized devices or servers, thereby…
Descriptors: Privacy, Cooperative Learning, Natural Language Processing, Learning Processes
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Da-Wei Zhang; Melissa Boey; Yan Yu Tan; Alexis Hoh Sheng Jia – npj Science of Learning, 2024
This study evaluates the ability of large language models (LLMs) to deliver criterion-based grading and examines the impact of prompt engineering with detailed criteria on grading. Using well-established human benchmarks and quantitative analyses, we found that even free LLMs achieve criterion-based grading with a detailed understanding of the…
Descriptors: Artificial Intelligence, Natural Language Processing, Criterion Referenced Tests, Grading
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Li Chen; Gen Li; Boxuan Ma; Cheng Tang; Masanori Yamada – International Association for Development of the Information Society, 2024
This paper proposes a three-step approach to develop knowledge graphs that integrate textbook-based target knowledge graph with student dialogue-based knowledge graphs. The study was conducted in seventh-grade STEM classes, following a collaborative problem solving process. First, the proposed approach generates a comprehensive target knowledge…
Descriptors: Concept Mapping, Graphs, Cooperative Learning, Problem Solving
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Beatriz Carbajal-Carrera – Australian Review of Applied Linguistics, 2024
The growing implementation of Generative AI (GenAI) in education has implications on the representation of knowledge and identity across languages. In a context where content biases have been reported in AI-generated content, it becomes relevant to interrogate the ways in which AI technologies represent different linguistic identities. This…
Descriptors: Artificial Intelligence, Sociolinguistics, Language Usage, Bias
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Benny G. Johnson; Jeffrey S. Dittel; Rachel Van Campenhout – International Educational Data Mining Society, 2024
Combining formative practice with the primary expository content in a learning by doing method is a proven approach to increase student learning. Artificial intelligence has led the way for automatic question generation (AQG) systems that can generate volumes of formative practice otherwise prohibitive with human effort. One such AQG system was…
Descriptors: Artificial Intelligence, Automation, Textbooks, Questioning Techniques
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Debora Weber-Wulff; Alla Anohina-Naumeca; Sonja Bjelobaba; Tomáš Foltýnek; Jean Guerrero-Dib; Olumide Popoola; Petr Šigut; Lorna Waddington – International Journal for Educational Integrity, 2023
Recent advances in generative pre-trained transformer large language models have emphasised the potential risks of unfair use of artificial intelligence (AI) generated content in an academic environment and intensified efforts in searching for solutions to detect such content. The paper examines the general functionality of detection tools for…
Descriptors: Artificial Intelligence, Identification, Man Machine Systems, Accuracy
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