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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
Jasmine Spencer; Hasibe Kahraman; Elisabeth Beyersmann – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2024
Reading morphologically complex words requires analysis of their morphemic subunits (e.g., play + er); however, the positional constraints of morphemic processing are still little understood. The current study involved three unprimed lexical decision experiments to directly compare the positional encoding of stems and affixes during reading and to…
Descriptors: Morphemes, Suffixes, Word Recognition, College Students
Abdulrahman M. Al-Zahrani – SAGE Open, 2025
This study examines the impact of Artificial Intelligence (AI) chatbots on the loss of human connection and emotional support among higher education students. To do so, a quantitative research design is employed. An online survey questionnaire is distributed to a sample of 819 higher education students, assessing concerns about human connection,…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, College Students
Qinghao Guan; Yangxi Han – Innovations in Education and Teaching International, 2025
As generative AI (GenAI) continues to permeate academia, distinguishing between student-authored essays and those by Large Language Models (LLMs) becomes crucial for maintaining academic integrity. This study conducted a survey on the ethical awareness of using generative AI tools among a group of STEM students (n=156). Also, we empirically…
Descriptors: Foreign Countries, College Students, Artificial Intelligence, Intelligent Tutoring Systems
Stefan Wöhner; Andreas Mädebach; Herbert Schriefers; Jörg D. Jescheniak – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2024
This study traced different types of distractor effects in the picture-word interference (PWI) task across repeated naming. Starting point was a PWI study by Kurtz et al. (2018). It reported that naming a picture (e.g., of a duck) was slowed down by a distractor word phonologically related to an alternative picture name from a different taxonomic…
Descriptors: Naming, Interference (Learning), Foreign Countries, College Students
Yuan Chih Fu; Jin Hua Chen; Kai Chieh Cheng; Xuan Fen Yuan – Higher Education: The International Journal of Higher Education Research, 2024
Using data from approximately 342,000 course-taking records collected from 4406 college students enrolled at Taipei Tech during the 2009-2012 academic years, we examine the impact of multidisciplinarity on students' academic performance. Our study contributes to the literature in three ways. First, by applying natural language processing (NLP), we…
Descriptors: College Students, Interdisciplinary Approach, Academic Achievement, Natural Language Processing
Kazuya Saito; Adam Tierney – Language Learning, 2025
This study expands on the practical application of the critical role of auditory processing in the rate of naturalistic L2 speech acquisition. In Study 1, the prosodic production of English by 46 Chinese college students was tracked over a five-month study abroad program in the UK. Learners with extensive L2 input opportunities demonstrated…
Descriptors: Second Language Learning, English (Second Language), College Students, Foreign Students
Emanuel Bylund; Steven Samuel; Panos Athanasopoulos – Language Learning, 2024
Research has shown that speakers of different languages may differ in their cognitive and perceptual processing of reality. A common denominator of this line of investigation has been its reliance on the sensory domain of vision. The aim of our study was to extend the scope to a new sense-taste. Using as a starting point crosslinguistic…
Descriptors: Foreign Countries, Language Usage, Classification, Language Processing
Nisar Ahmed Dahri; Noraffandy Yahaya; Waleed Mugahed Al-Rahmi – Education and Information Technologies, 2025
Enhancing student academic success and career readiness is important in the rapidly evolving educational field. This study investigates the influence of ChatGPT, an AI tool, on these outcomes using the Stimulus-Organism-Response (SOR) theory and constructs from the Technology Acceptance Model (TAM). The aim is to explore how ChatGPT impacts…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Career Readiness
Muhammad Farrukh Shahzad; Shuo Xu; Hira Zahid – Education and Information Technologies, 2025
Artificial Intelligence (AI) technologies have rapidly transformed the education sector and affect student learning performance, particularly in China, a burgeoning educational landscape. The development of generative artificial intelligence (AI) based technologies, such as chatbots and large language models (LLMs) like ChatGPT, has completely…
Descriptors: Artificial Intelligence, Technology Uses in Education, Academic Achievement, Self Efficacy
Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
Maria Eleftheriou; Muhammad Ahmer; Daniel Fredrick – Contemporary Educational Technology, 2025
Like many student writing centers, the American University of Sharjah Writing Center is seeing a rise in student reliance upon generative AI (GenAI) tools, which are artificial intelligence systems capable of generating human-like text. Peer tutors frequently seek guidance on how to approach student papers involving GenAI tools such as ChatGPT,…
Descriptors: Laboratories, Writing (Composition), Artificial Intelligence, Man Machine Systems
Ahmet Yusuf Cevher; Serkan Yildirim – Turkish Online Journal of Distance Education, 2025
This study investigates the impact and role of an instructional chatbot, ARUChatbot, in a distance education setting. Using a sequential explanatory mixed-methods design, the research involved 130 students from Ardahan University's Basic Information Technologies course. Participants were selected through purposive sampling. Quantitative data were…
Descriptors: Distance Education, Artificial Intelligence, Man Machine Systems, Natural Language Processing
Moabu Jimmy Chandafa; Fang Huang – International Journal of Technology in Education, 2025
Artificial Intelligence (AI) has the potential to revolutionize education as it develops, offering dynamic, individualized, and effective learning experiences that might change teaching practices. However, there is still inconsistency and limitations in the integration and use of AI in Tanzanian universities. Therefore, the study delt to…
Descriptors: Artificial Intelligence, Technology Uses in Education, Student Attitudes, Teacher Attitudes
Ambre Denis-Noël; Pascale Colé; Deirdre Bolger; Chotiga Pattamadilok – Scientific Studies of Reading, 2024
Purpose: In adults with dyslexia (DYS), the persistent influence of phonological deficits on spoken language processing has mainly been examined in either perceptual tasks or those tapping complex cognitive operations. Much less attention is devoted to spoken word recognition per se. Our study aimed to fill this gap. Method: Adults with and…
Descriptors: Foreign Countries, College Students, Dyslexia, Language Processing

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