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Peter Wulff; Lukas Mientus; Anna Nowak; Andreas Borowski – International Journal of Technology in Education and Science, 2025
Important prerequisites for effective teaching in science, technology, engineering, and mathematics (STEM) are outlined in the refined consensus model of pedagogical content knowledge: Teachers need to become able to apply their pedagogical content knowledge in practice, called enacted pedagogical content knowledge. To support pre-service STEM…
Descriptors: Artificial Intelligence, STEM Education, Preservice Teacher Education, Pedagogical Content Knowledge
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Olivia Metzner; Yindong Wang; Wendy Symes; Yizhen Huang; Lena Keller; Gerard de Melo; Rebecca Lazarides – British Journal of Educational Psychology, 2025
Background: Recent studies have examined the relation between teacher motivation, motivational messages and student learning but are limited to an achievement-related context, primarily using survey data. Moreover, our understanding of the relation between various teacher characteristics, such as teacher self-efficacy (TSE), and their motivational…
Descriptors: Teacher Motivation, Motivation Techniques, Academic Achievement, Teacher Characteristics
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Jiuzhou Hao; Vasiliki Chondrogianni; Patrick Sturt – Journal of Child Language, 2025
The present study investigated whether children's difficulty with non-canonical structures is due to their non-adult-like use of linguistic cues or their inability to revise misinterpretations using late-arriving cues. We adopted a priming production task and a self-paced listening task with picture verification, and included three Mandarin…
Descriptors: Child Language, Sentences, Sentence Structure, Mandarin Chinese
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Vasiliki Paltsoglou; Kostas Zafiropoulos – Open Education Studies, 2025
The usage of artificial intelligence (AI) in education is quickly growing, with chatbots gaining popularity as potential tools for supporting teaching and learning. This study looks into the elements that influence teachers' willingness to use chatbots in their teaching techniques. Drawing on the Unified Theory of Acceptance and Use of Technology,…
Descriptors: Foreign Countries, Elementary School Teachers, Artificial Intelligence, Natural Language Processing
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Fiachra Long – Studies in Philosophy and Education, 2025
Conversation of a particular sort holds the key to learning. I argue here that peer to peer conversation promotes two features that are essential to progressive learning, namely 'contestation' and 'communication.' Traditional learning is principally concerned with whether students have reached a standard of knowledge and skill prescribed by some…
Descriptors: Artificial Intelligence, Natural Language Processing, Intelligent Tutoring Systems, Peer Relationship
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Aditi Jhaveri – Journal of the Scholarship of Teaching and Learning, 2025
This essay examines the potential impact of paid-for or premium language models, where some students may be able to afford advanced models generating superior outputs while others could face inequities due to financial constraints. It explores how this dynamic can exacerbate the digital divide, challenge traditional as well as more recent…
Descriptors: Natural Language Processing, Artificial Intelligence, Technology Uses in Education, Equal Education
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Stephanie Fuchs; Alexandra Werth; Cristóbal Méndez; Jonathan Butcher – Journal of Engineering Education, 2025
Background: High-quality feedback is crucial for academic success, driving student motivation and engagement while research explores effective delivery and student interactions. Advances in artificial intelligence (AI), particularly natural language processing (NLP), offer innovative methods for analyzing complex qualitative data such as feedback…
Descriptors: Artificial Intelligence, Training, Data Analysis, Natural Language Processing
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Duncan Gillard; Sarah Cassidy; Ben Anderson – Educational Psychology in Practice, 2025
B. F. Skinner's work in the field of verbal behaviour represented a movement of global significance. However, in today's age, even those who appreciate its profound importance in the archives of psychology accept that it did not sufficiently account for complex human language. Recent advances in psychological science have led to the emergence of a…
Descriptors: Educational Psychology, Behavior Theories, Mental Health, Models
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Natalie V. Covington; Olivia Vruwink – International Journal of Artificial Intelligence in Education, 2025
ChatGPT and other large language models (LLMs) have the potential to significantly disrupt common educational practices and assessments, given their capability to quickly generate human-like text in response to user prompts. LLMs GPT-3.5 and GPT-4 have been tested against many standardized and high-stakes assessment materials (e.g. SAT, Uniform…
Descriptors: Artificial Intelligence, Technology Uses in Education, Undergraduate Study, Introductory Courses
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Jionghao Lin; Zifei Han; Danielle R. Thomas; Ashish Gurung; Shivang Gupta; Vincent Aleven; Kenneth R. Koedinger – International Journal of Artificial Intelligence in Education, 2025
One-on-one tutoring is widely acknowledged as an effective instructional method, conditioned on qualified tutors. However, the high demand for qualified tutors remains a challenge, often necessitating the training of novice tutors (i.e., trainees) to ensure effective tutoring. Research suggests that providing timely explanatory feedback can…
Descriptors: Artificial Intelligence, Technology Uses in Education, Tutor Training, Trainees
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Qian Liu; Anjin Hu; Tehmina Gladman; Steve Gallagher – Innovative Higher Education, 2025
ChatGPT has sparked heated discussion in higher education since its public release and increasing empirical studies have been made available examining its application to higher education teaching and learning. To capture and synthesize the initial scholarly developments in this topic, we undertook a scoping review of empirical research into the…
Descriptors: Artificial Intelligence, Natural Language Processing, Higher Education, Student Evaluation
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Serrien, Deborah J.; O'Regan, Louise – Cognitive Research: Principles and Implications, 2022
Hemispheric lateralisation is a fundamental principle of functional brain organisation. We studied two core cognitive functions--language and visuospatial attention--that typically lateralise in opposite cerebral hemispheres. In this work, we tested both left- and right-handed participants on lexical decision-making as well as on symmetry…
Descriptors: Brain Hemisphere Functions, Language, Attention, Spatial Ability
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Joo, Sehrang; Yousif, Sami R.; Keil, Frank C. – Cognitive Science, 2022
Adults and children 'promiscuously' endorse teleological answers to 'why' questions--a tendency linked to arguments that humans are intuitively theistic and naturally unscientific. But how do people arrive at an endorsement of a teleological answer? Here, we show that the endorsement of teleological answers need not imply unscientific reasoning (n…
Descriptors: Questioning Techniques, Intuition, Preferences, Adults
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Abu-Zhaya, Rana; Arnon, Inbal; Borovsky, Arielle – Cognitive Science, 2022
Meaning in language emerges from multiple words, and children are sensitive to multi-word frequency from infancy. While children successfully use cues from single words to generate linguistic predictions, it is less clear whether and how they use multi-word sequences to guide real-time language processing and whether they form predictions on the…
Descriptors: Sentences, Language Processing, Semantics, Prediction
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Tessler, Michael Henry; Goodman, Noah D. – Cognitive Science, 2022
The meanings of natural language utterances depend heavily on context. Yet, what counts as context is often only implicit in conversation. The utterance "it's warm outside" signals that the temperature outside is relatively high, but the temperature could be high relative to a number of different "comparison classes": other…
Descriptors: Language Processing, Speech, Context Effect, Form Classes (Languages)
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