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Brian W. Stone – Teaching of Psychology, 2025
Background: Students in higher education are using generative artificial intelligence (AI) despite mixed messages and contradictory policies. Objective: This study helps answer outstanding questions about many aspects of AI in higher education: familiarity, usage, perceptions of peers, ethical/social views, and AI grading. Method: I surveyed 733…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Ayse Merzifonluoglu; Habibe Gunes – European Journal of Education, 2025
Artificial intelligence (AI) is significantly shaping education and currently influencing pre-service teachers' academic and professional journeys. To explore this influence, the present study examines 389 Generation Z pre-service teachers' attitudes towards AI and its impact on educational decision-making at two state universities, using an…
Descriptors: Decision Making, Artificial Intelligence, Teacher Attitudes, Age Groups
Pulido, Manuel F.; López-Beltrán, Priscila – Cognitive Science, 2023
Previous work on individual differences has revealed limitations in the ability of existing measures (e.g., working memory) to predict language processing. Recent evidence suggests that an individual's sensitivity to detect the statistical regularities present in language (i.e., "chunk sensitivity") may significantly modulate online…
Descriptors: Phrase Structure, Native Speakers, Gender Differences, Cues
Dania Bilal; Li-Min Cassandra Huang – Information and Learning Sciences, 2025
Purpose: This paper aims to investigate user voice-switching behavior in voice assistants (VAs), embodiments and perceived trust in information accuracy, usefulness and intelligence. The authors addressed four research questions: RQ1. What is the nature of users' voice-switching behavior in VAs? RQ2: What are user preferences for embodied voice…
Descriptors: Undergraduate Students, Artificial Intelligence, Natural Language Processing, Information Retrieval
Usani Joseph Ofem; Valentine Joseph Owan; Mary Arikpo Iyam; Maryrose Ify Udeh; Pauline Mbua Anake; Sylvia Victor Ovat – Education and Information Technologies, 2025
While previous studies have explored students' use of different AI tools for academic purposes, studies that have specifically investigated students' use of ChatGPT for dishonest academic purposes in Nigeria are lacking. The consequence of this contextual and knowledge gap is a lack of specific understanding regarding students' engagement with…
Descriptors: Student Attitudes, Usability, Artificial Intelligence, Technology Uses in Education
Ibrahim Abba Mohammed; Ahmed Bello; Bala Ayuba – Education and Information Technologies, 2025
In spite of the emergence of studies seeking to integrate chatbot into education, there is a wide literature gap in the Nigerian contexts. While most studies focus on the design and development of chatbots, there exists a very scarce literature on the effect of ChatGPT chatbot on students' achievement. To address this gap, this study checked the…
Descriptors: Natural Language Processing, Artificial Intelligence, Academic Achievement, Computer Science Education
Junghwan Kim; Michelle Klopfer; Jacob R. Grohs; Hoda Eldardiry; James Weichert; Larry A. Cox II; Dale Pike – Innovative Higher Education, 2025
As generative artificial intelligence (GenAI) tools such as ChatGPT become more capable and accessible, their use in educational settings is likely to grow. However, the academic community lacks a comprehensive understanding of the perceptions and attitudes of students and instructors toward these new tools. In the Fall 2023 semester, we surveyed…
Descriptors: College Faculty, Teacher Attitudes, College Students, Student Attitudes
Pauline Frizelle; Ana Oliveira-Buckley; Tricia Biancone; Jorge Oliveira; Paul Fletcher; Dorothy V. M. Bishop; Cristina McKean – International Journal of Language & Communication Disorders, 2025
Introduction: The present study investigated English-speaking 5-9 year olds' (n = 600, normative sample) comprehension of relative, adverbial and complement clauses using the Test of Complex Syntax-Electronic (TECS-E), an online interactive assessment. with strong test-retest reliability, concurrent validity and internal consistency. Method: Using…
Descriptors: Syntax, Child Language, Young Children, Language Tests
Ntabo, Victor Ondara; Onyango, James Ogola; Ndiritu, Nelson Ng'arua – Advances in Language and Literary Studies, 2022
Food is useful in the transference of semantic aspects that are vital in the construction of masculinity in society. Consequently, foodsemic metaphors that aid in the conceptualization of "omosacha" (a man) are pervasive in Ekegusii. Metaphor use may, however, present difficulties in comprehension due to the various interpretations that…
Descriptors: Food, Semantics, Psycholinguistics, Masculinity
Adnane Ez-zizi; Dagmar Divjak; Petar Milin – Language Learning, 2024
Since its first adoption as a computational model for language learning, evidence has accumulated that Rescorla-Wagner error-correction learning (Rescorla & Wagner, 1972) captures several aspects of language processing. Whereas previous studies have provided general support for the Rescorla-Wagner rule by using it to explain the behavior of…
Descriptors: Error Correction, Second Language Learning, Second Language Instruction, Gender Differences
Brendan Bartanen; Andrew Kwok; Andrew Avitabile; Brian Heseung Kim – Educational Researcher, 2025
Heightened concerns about the health of the teaching profession highlight the importance of studying the early teacher pipeline. This exploratory, descriptive article examines preservice teachers' expressed motivation for pursuing a teaching career. Using data from a large teacher education program in Texas, we use a natural language processing…
Descriptors: Career Choice, Teaching (Occupation), Preservice Teachers, Student Attitudes
Irene Picton; Christina Clark – National Literacy Trust, 2024
Recent developments in technology have accelerated the influence of artificial intelligence (AI) on our lives. The National Literacy Trust is interested in exploring how such platforms might influence, and potentially redefine, what it means to be literate in the digital age. Based on data from more than 50,000 children and young people taking…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
Ghadeer Sawalha; Imran Taj; Abdulhadi Shoufan – Cogent Education, 2024
Large language models present new opportunities for teaching and learning. The response accuracy of these models, however, is believed to depend on the prompt quality which can be a challenge for students. In this study, we aimed to explore how undergraduate students use ChatGPT for problem-solving, what prompting strategies they develop, the link…
Descriptors: Cues, Artificial Intelligence, Natural Language Processing, Technology Uses in Education
Lago, Sol; Stone, Kate; Oltrogge, Elise; Veríssimo, João – Language Learning, 2023
Second language (L2) learners make gender errors with possessive pronouns. In production, these errors are modulated by the gender match between the possessor and possessee noun. We examined whether this so-called match effect extends to L2 comprehension by attempting to replicate a recent study on gender predictions in first language (L1) German…
Descriptors: Language Processing, Native Language, German, Second Language Learning
Bower, Corinne A.; Foster, Lindsey; Zimmermann, Laura; Verdine, Brian N.; Marzouk, Maya; Islam, Siffat; Golinkoff, Roberta Michnick; Hirsh-Pasek, Kathy – Developmental Psychology, 2020
Early spatial skills predict the development of later spatial and mathematical skills. Yet, it is unclear how comprehension of the words that capture spatial relations, words like behind and under, might be associated with children's early spatial and mathematics skills. The current study addressed this question by conducting a moderated mediation…
Descriptors: Preschool Children, Gender Differences, Socioeconomic Status, Mathematics Skills

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