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Higher Education Students' ChatGPT Use Behavior: Structural Equation Modelling of Contributing Factors through a Modified UTAUT Model
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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
Phonemic Fluency and Rhyme Production in People with Aphasic Word Retrieval Disorders: Sub-Lexical Written Language Skills Matter
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Anja Wunderlich – International Journal of Language & Communication Disorders, 2025
Background: In everyday communication, word retrieval is semantically driven. A similar processing mechanism can be assumed for category fluency tasks. In contrast, in phonemic fluency tasks or rhyme production, the retrieval process must be based on the word form. In phonemic fluency, executive and language functions have been discussed as…
Descriptors: Aphasia, Written Language, Language Skills, Language Processing
Viability Study of Machine Learning Models to Identify Talented Students at the Early Stage of Their College Study
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Qixuan Wu; Hyung Jae Chang; Long Ma – Journal of Advanced Academics, 2025
It is very important to identify talented students as soon as they are admitted to college so that appropriate resources are provided and allocated to them to optimize and excel in their education. Currently, this process is labor-intensive and time-consuming, as it involves manual reviews of each student's academic record. This raises the…
Descriptors: Electronic Learning, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
Applications of Machine Learning for At-Risk Student Prediction in Online Education: A 10-Year Systematic Review of Literature
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Hui Shi; Nuodi Zhang; Secil Caskurlu; Hunhui Na – Journal of Computer Assisted Learning, 2025
Background: The growth of online education has provided flexibility and access to a wide range of courses. However, the self-paced and often isolated nature of these courses has been associated with increased dropout and failure rates. Researchers employed machine learning approaches to identify at-risk students, but multiple issues have not been…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, At Risk Students
Boosting Student Engagement in STEM: Integrating Large Language Model-Based Virtual Agents into Alternate Reality Games
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Minkai Wang; Jingdong Zhu; Gwo-Jen Hwang; Shao-Chen Chang; Qi-Fan Yang; Di Zhang – Journal of Computer Assisted Learning, 2025
Background: STEM education aims to develop innovation and problem-solving skills through interdisciplinary learning, yet struggles to foster student engagement and interdisciplinary thinking. Whilst alternate reality games (ARGs) can boost motivation via game-based problem-solving, integrating large language models (LLMs) remains underexplored.…
Descriptors: Learner Engagement, STEM Education, Natural Language Processing, Artificial Intelligence
Does Reading Performance Vary According to Available Working Memory Capacity and Syntactic Parsing Skills?
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Amir Mahshanian; Mohammadtaghi Shahnazari; Ahmad Moinzadeh – TESL-EJ, 2025
This study investigates the relationships among working memory (WM), syntactic parsing ability (SP), and L2 reading performance across varying proficiency levels. A cohort of 120 L1-Persian EFL learners was categorized into beginner, intermediate, and advanced proficiency groups based on their IELTS scores. Participants completed a reading span…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Reading Achievement