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Showing 436 to 450 of 1,765 results Save | Export
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Alexandra S. Dylman; Marie-France Champoux-Larsson; Candice Frances – Educational Psychology, 2025
We report four experiments investigating the effect of prosody on listening comprehension in 11-13-year-old children. Across all experiments, participants listened to short object descriptions and answered content-based questions about said objects. In Experiments 1-3, the descriptions were read in an emotionally positive or neutral tone of voice.…
Descriptors: Intonation, Middle School Students, Foreign Countries, Listening Comprehension
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Mehedi Hasan Anik; Shahriar Nafees Chowdhury Raaz; Nushat Khan – International Journal of Artificial Intelligence in Education, 2025
Artificial intelligence (AI) technologies, especially language models like ChatGPT, are revolutionizing academic writing by generating human-like text and supporting thesis development. While some research explores ChatGPT in academic writing, none compares the experiences of young researchers using AI for the first time versus non-users in their…
Descriptors: Artificial Intelligence, Theses, Research, Science Education
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Anjee Gorkhali; Asim Shrestha – International Journal of Information and Learning Technology, 2025
Purpose: Educators are raising ethical concerns over the use of ChatGPT in schools. They have implemented various strategies to minimize its use, particularly by labeling ChatGPT-produced work as plagiarism. However, the use of ChatGPT among students is still on the rise. Our study aims to find the behavioral motivation behind students' increased…
Descriptors: Self Concept, Prevention, Student Behavior, Artificial Intelligence
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Kadir Karakaya – Asian Journal of Distance Education, 2025
This article explores human-AI interaction with large language models or conversational agents in complex information tasks with a focus on prompt engineering strategies. The paper reviews the current literature on the use of artificial intelligence (AI) for complex information tasks that are often nonlinear and entail interpretation,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Man Machine Systems, Interaction
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Brian Heseung Kim; Julie J. Park; Pearl Lo; Dominique Baker; Nancy Wong; Stephanie Breen; Huong Truong; Jia Zheng; Kelly Rosinger; OiYan A. Poon – Research in Higher Education, 2025
Letters of recommendation from school counselors are required to apply to most selective universities. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted through the Common Application. We examine how the length…
Descriptors: Letters (Correspondence), Advocacy, School Counselors, High Schools
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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
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Arzu Atasoy; Saieed Moslemi Nezhad Arani – Education and Information Technologies, 2025
There is growing interest in the potential of Artificial Intelligence (AI) to assist in various educational tasks, including writing assessment. However, the comparative efficacy of human and AI-powered systems in this domain remains a subject of ongoing exploration. This study aimed to compare the accuracy of human raters (teachers and…
Descriptors: Writing (Composition), Writing Evaluation, Student Evaluation, Artificial Intelligence
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Karen Singer-Freeman; Kristi Verbeke; Betsy Barre – Higher Learning Research Communications, 2025
Objectives: The present study investigated the extent to which AI use and opinions about its use will vary by academic stage and academic task in ways that align with Budwig's (2013) developmental stages. Methods: We surveyed 1,259 students from different academic stages (first-year students, sophomores, juniors, seniors, and graduate students) at…
Descriptors: Undergraduate Students, Graduate Students, Educational Technology, Educational Policy
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Lisa Marie Ripoll Y Schmitz; Philipp Sonnleitner – Large-scale Assessments in Education, 2025
Background: The increasing capabilities of generative artificial intelligence (AI), exemplified by OpenAI's transformer-based language model GPT-4 (ChatGPT), have drawn attention to its application in educational contexts. This study evaluates the potential of such models in generating German reading comprehension texts for educational large-scale…
Descriptors: Artificial Intelligence, Technology Uses in Education, Man Machine Systems, Written Language
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Amanda Light Dunbar; Sandra Chang-Kredl – Changing English: Studies in Culture and Education, 2025
Long before ChatGPT, it was an open secret that students did not always read the books they were assigned in their English Language Arts (ELA) classes, relying instead on online study guides like SparkNotes. Via a retrospective survey, our exploratory study examined (1) the rate of SparkNotes use among high-school ELA students; (2) why students…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Language Arts
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Lei Cao; Kien Tsong Chau; Wan Ahmad Jaafar Wan Yahaya – International Journal of Game-Based Learning, 2025
In cultural relic restoration learning, developing both knowledge proficiency and self-efficacy is essential for academic success and professional competency. However, conventional learning methods often lack interactive elements that support cognitive engagement and skill acquisition. To address this limitation, this study introduced a…
Descriptors: Game Based Learning, Artificial Intelligence, Acoustics, Technology Uses in Education
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Wesley Morris; Scott Crossley; Langdon Holmes; Chaohua Ou; Mihai Dascalu; Danielle McNamara – International Journal of Artificial Intelligence in Education, 2025
As intelligent textbooks become more ubiquitous in classrooms and educational settings, the need to make them more interactive arises. An alternative is to ask students to generate knowledge in response to textbook content and provide feedback about the produced knowledge. This study develops Natural Language Processing models to automatically…
Descriptors: Formative Evaluation, Feedback (Response), Textbooks, Artificial Intelligence
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Wongvorachan, Tarid; Lai, Ka Wing; Bulut, Okan; Tsai, Yi-Shan; Chen, Guanliang – Journal of Applied Testing Technology, 2022
Feedback is a crucial component of student learning. As advancements in technology have enabled the adoption of digital learning environments with assessment capabilities, the frequency, delivery format, and timeliness of feedback derived from educational assessments have also changed progressively. Advanced technologies powered by Artificial…
Descriptors: Artificial Intelligence, Feedback (Response), Learning Analytics, Natural Language Processing
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Matsuda, Noboru; Wood, Jesse; Shrivastava, Raj; Shimmei, Machi; Bier, Norman – Journal of Educational Data Mining, 2022
A model that maps the requisite skills, or knowledge components, to the contents of an online course is necessary to implement many adaptive learning technologies. However, developing a skill model and tagging courseware contents with individual skills can be expensive and error prone. We propose a technology to automatically identify latent…
Descriptors: Skills, Models, Identification, Courseware
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Ariely, Moriah; Nazaretsky, Tanya; Alexandron, Giora – International Journal of Artificial Intelligence in Education, 2023
Machine learning algorithms that automatically score scientific explanations can be used to measure students' conceptual understanding, identify gaps in their reasoning, and provide them with timely and individualized feedback. This paper presents the results of a study that uses Hebrew NLP to automatically score student explanations in Biology…
Descriptors: Artificial Intelligence, Algorithms, Natural Language Processing, Hebrew
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