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Na Gao; Peng Zhou; Stephen Crain – Language Acquisition: A Journal of Developmental Linguistics, 2025
This study investigates how speakers of Mandarin interpret negative sentences with the conjunction ("he" 'and'). Our experiments test three predictions that follow from the proposal that the Mandarin conjunction is a positive polarity item (PPI) for both children and adults. On this account, the Mandarin conjunction should be interpreted…
Descriptors: Mandarin Chinese, Prediction, Form Classes (Languages), Phrase Structure
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Andrew Potter; Mitchell Shortt; Maria Goldshtein; Rod D. Roscoe – Grantee Submission, 2025
Broadly defined, academic language (AL) is a set of lexical-grammatical norms and registers commonly used in educational and academic discourse. Mastery of academic language in writing is an important aspect of writing instruction and assessment. The purpose of this study was to use Natural Language Processing (NLP) tools to examine the extent to…
Descriptors: Academic Language, Natural Language Processing, Grammar, Vocabulary Skills
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Dan Song; Alexander F. Tang – Language Learning & Technology, 2025
While many studies have addressed the benefits of technology-assisted L2 writing, limited research has delved into how generative artificial intelligence (GAI) supports students in completing their writing tasks in Mandarin Chinese. In this study, 26 university-level Mandarin Chinese foreign language students completed two writing tasks on two…
Descriptors: Artificial Intelligence, Second Language Learning, Standardized Tests, Writing Tests
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Hui-Chun Chu; Yi-Chun Lu; Yun-Fang Tu – Educational Technology & Society, 2025
This study guided 97 undergraduates using generative artificial intelligence (GenAI) to conduct multimodal digital storytelling (M-DST) learning activities. Furthermore, the study examined the differences in M-DST ability and critical thinking awareness among undergraduates with different levels of learning motivation and their perceptions of this…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Undergraduate Students
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Jiarui Hou; James F. Lee; Stephen Doherty – Journal of Computer Assisted Learning, 2025
Background: Recent research has demonstrated the potential of mobile-assisted learning to enhance learners' learning outcomes. In contrast, the learning processes in this regard are much less explored using eye tracking technology. Objective: This systematic review study aims to synthesise the relevant work to reflect the current state of eye…
Descriptors: State of the Art Reviews, Eye Movements, Electronic Learning, Handheld Devices
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YiHsuan Wood; Jeffrey J. Green; Ellen Knell; Yu Liu – Language Awareness, 2025
This study used eye-tracking to investigate the real-time processing of phonetic and semantic radicals (components of Chinese characters that give clues to their pronunciation and meaning) by intermediate-level university Chinese foreign language (CFL) learners. Additionally, the study examined how knowledge and awareness of radicals affect…
Descriptors: Eye Movements, Chinese, Second Language Learning, Second Language Instruction
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Xiaohong Liu; Baoxin Guo; Wei He; Xiaoyong Hu – Journal of Educational Computing Research, 2025
Generative artificial intelligence (GenAI) has significant potential for educational innovation, although its impact on students' learning outcomes remains controversial. This study aimed to examine the impact of GenAI on the learning outcomes of K-12 and higher education students, and explore the moderating factors influencing this impact. A…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Technology Uses in Education
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Feiwen Xiao; Ellen Wenting Zou; Jiaju Lin; Zhaohui Li; Dandan Yang – British Journal of Educational Technology, 2025
Large language model (LLM)-based conversational agents (CAs), with their advanced generative capabilities and human-like conversational interfaces, can serve as reading partners for children during dialogic reading and have shown promise in enhancing children's comprehension and conversational skills. However, there is limited research on the…
Descriptors: Childrens Literature, Electronic Books, Artificial Intelligence, Natural Language Processing
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Tobias Wyrwich; Marcus Kubsch; Hendrik Drachsler; Knut Neumann – Physical Review Physics Education Research, 2025
Students struggle to acquire the needed energy understanding to meaningfully participate in the energy discourse about socially relevant topics, such as energy transformation or climate change. Identifying students on differing learning trajectories, as well as differences in knowledge used, is essential to help students achieve the needed energy…
Descriptors: Learning Processes, Physics, Energy, Science Instruction
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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
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Jongwon Lee; Tereza Cimová; Ellen J. Foster; Derek France; Lenka Krajnáková; Lynn Moorman; Sonja Rewhorn; Jiaqi Zhang – International Research in Geographical and Environmental Education, 2025
Generative artificial intelligence (GenAI) represents a major leap forward in AI technology, offering the potential to reshape education in various aspects. This paper explores the transformative potential of GenAI in geography education, focusing on its impacts across curriculum, pedagogy, assessment, and fieldwork, through the lens of the…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Geography
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Philip Slobodsky; Mariana Durcheva – International Journal of Mathematical Education in Science and Technology, 2025
AI-based bots (ChatGPT) are capable of solving mathematics problems, and students often use them for homework preparation, self-learning, etc. This raises a number of didactical and technical questions: How can students submit assignments containing complex mathematical expressions using only a keyboard? How should mathematics errors in ChatGPT's…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Mathematics Instruction
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Zijing Hu – Educational Process: International Journal, 2025
Background/purpose: The need for effective, accessible, and culturally sensitive training models has become increasingly important, particularly in cross-cultural and resource-limited contexts, such as Africa. Traditional Chinese Medicine has gained global recognition for its holistic and preventive approach to health. This study explores the…
Descriptors: Asian Culture, Medicine, Health Education, Artificial Intelligence
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Benjamin Brummernhenrich; Christian L. Paulus; Regina Jucks – British Journal of Educational Technology, 2025
Generative AI systems like chatbots are increasingly being introduced into learning, teaching and assessment scenarios at universities. While previous research suggests that users treat chatbots like humans, computer systems are still often perceived as less trustworthy, potentially impairing their usefulness in learning contexts. How are…
Descriptors: Higher Education, Artificial Intelligence, College Students, Feedback (Response)
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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
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