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Liu Yifu; Ma Yan; Huang Libing; Xiao Chunling; Ding Tao – Education and Information Technologies, 2025
Interventions utilizing human-computer interaction (HCI) in autism rehabilitation have gained popularity, yet their efficacy has not been fully elucidated. This meta-analysis assessed 30 international studies from the past decade that employed HCIs for autism intervention. The findings revealed a moderate positive effect (g = 0.484) on improving…
Descriptors: Man Machine Systems, Interaction, Intervention, Autism Spectrum Disorders
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Shugang Li; Kexin Peng; Boyi Zhu; Fang Liu – Education and Information Technologies, 2025
In the digital age, online learning platforms utilizing advanced technologies such as artificial intelligence can provide more diverse and abundant educational resources compared to offline learning environments. However, online learners still face emotional expression and inertia trap problems. Knowledge co-creation virtual teachers (KCVTs) can…
Descriptors: Technology Uses in Education, Artificial Intelligence, Electronic Learning, Web Based Instruction
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Pablo Flores Romero; Kin Nok Nicholas Fung; Guang Rong; Benjamin Ultan Cowley – npj Science of Learning, 2025
Large Language Models (LLMs) present a radically new paradigm for the study of "information foraging behavior." We study how LLM technology is used for pedagogical content creation by a sample of 25 participants in a doctoral-level Artificial Intelligence (AI) in Education course, and the role of computational-thinking skills in shaping…
Descriptors: Man Machine Systems, Artificial Intelligence, Natural Language Processing, Interaction
Paul T. von Hippel; Brendan A. Schuetze – Annenberg Institute for School Reform at Brown University, 2025
Researchers across many fields have called for greater attention to heterogeneity of treatment effects--shifting focus from the average effect to variation in effects between different treatments, studies, or subgroups. True heterogeneity is important, but many reports of heterogeneity have proved to be false, non-replicable, or exaggerated. In…
Descriptors: Educational Research, Replication (Evaluation), Generalizability Theory, Inferences
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Linnea Waade Biermann; Anne Sofie Borsch; Nina Langer Primdahl; Signe Smith Jervelund; An Verelst; Ilse Derluyn; Morten Skovdal – Social Psychology of Education: An International Journal, 2025
Learning a new language is a challenge facing most young immigrants and refugees arriving in a new resettlement country. Yet, learning the resettlement country language is critical for the young immigrants and refugees' life chances, in terms of future education, social integration, and participation in the labour market. While literature…
Descriptors: Foreign Countries, Second Language Learning, Adolescents, Immigrants
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Nurhan Aktas – Reading & Writing Quarterly, 2025
This 9-week mixed-methods study aimed to examine the effect of online reciprocal teaching on fourth grade primary school students' (N = 30) reading comprehension skills and reading motivation. As data collection tools, the "Reading Comprehension Test" was used to determine students' reading comprehension levels, while the…
Descriptors: Electronic Learning, Grade 4, Elementary School Students, Reading Comprehension
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Ha Nguyen; Victoria Nguyen; Sara Ludovise; Rossella Santagata – British Journal of Educational Technology, 2025
While offering the potential to support learning interactions, emerging AI applications like Large Language Models (LLMs) come with ethical concerns. Grounding technology design in human values can address AI ethics and ensure adoption. To this end, we apply Value-Sensitive Design--involving empirical, conceptual and technical investigations--to…
Descriptors: Artificial Intelligence, Educational Technology, Technology Uses in Education, High Schools
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Melody Edwards; Marty Ludlum; Therese L. Williams; Edward Walker; Kristen Gregory – Research in Higher Education Journal, 2025
In the spring of 2020, students and educators faced an abrupt change. Most classes went to online learning, while others transitioned to a synchronous remote structure. In the hasty switch, there was little regard for the best practices for online learning. Students were forced into a learning environment they had rarely experienced, and, in some…
Descriptors: COVID-19, Pandemics, Online Courses, Technology Uses in Education
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Chee-Kit Looi; Fenglin Jia – Education and Information Technologies, 2025
Since the advent of chatbots enabled by Generative AI such as ChatGPT, their application in the domain of education has been linked to promises of personalizing learning (PL). Through a study of conversational interactions of graduate students with such chatbots, this paper provides an empirical study of how current ChatGPT technologies can enable…
Descriptors: Individualized Instruction, Artificial Intelligence, Technology Uses in Education, Educational Technology
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Belle Dang; Luna Huynh; Faaiz Gul; Carolyn Rosé; Sanna Järvelä; Andy Nguyen – British Journal of Educational Technology, 2025
The rise of generative artificial intelligence (GAI), especially with multimodal large language models like GPT-4o, sparked transformative potential and challenges for learning and teaching. With potential as a cognitive offloading tool, GAI can enable learners to focus on higher-order thinking and creativity. Yet, this also raises questions about…
Descriptors: Man Machine Systems, Artificial Intelligence, Technology Uses in Education, Cooperative Learning
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Frank Wehrmann; Raphael Zender – Journal of Computer Assisted Learning, 2025
Background: The role of virtual reality (VR) in education is increasing, which raises questions about VR learning in multi-user settings. While collaborative VR learning, characterised by shared goals and low division of labour, is well-researched, cooperative VR learning, which emphasises role differentiation and task interdependence, remains…
Descriptors: Cooperative Learning, Computer Simulation, Learning Processes, Individualized Instruction
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Apdelmi; Leo Agung Sutimin; Djono – Educational Process: International Journal, 2025
Background/purpose. The lack of culturally contextualised teaching materials in history education has led to limited understanding of local traditions among senior high school students. This study aimed to develop interactive history teaching materials based on Kenduri Sko, a significant cultural practice in Kerinci, to enhance students' cultural…
Descriptors: Culturally Relevant Education, History Instruction, High School Students, Indigenous Knowledge
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Kevser Hava; Aslihan Aslan; Ziya Aslan – Malaysian Online Journal of Educational Technology, 2025
This study investigates the metaphors used by primary school students (aged 7-8) to describe Artificial Intelligence (AI), aiming to clarify their conceptual understanding of the subject. The metaphor analysis method was employed to explore how students conceptualize AI and to uncover the underlying cognitive structures influencing their…
Descriptors: Elementary School Students, Student Attitudes, Artificial Intelligence, Knowledge Level
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Rachel Bleiman; Hwanhee Park; Aunshul Rege – Journal of Cybersecurity Education, Research and Practice, 2025
The online dating industry generated 2.98 billion USD in 2023 and is estimated to reach 3.6 billion USD by 2025. Not surprisingly, online dating platforms are rife with romance scams that cause financial damages, with estimated losses of 1.3 billion USD in 2022 alone. Additionally, victims suffer emotional and psychological harms. This paper…
Descriptors: Dating (Social), Computer Mediated Communication, Deception, Victims
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E. Zhang; Wafaa Alduraidi; Makenna Snyder; Ezra Kaiser; Spencer Hunley; Ann Davis; Eve-Lynn Nelson; Nancy Cheak-Zamora – Autism: The International Journal of Research and Practice, 2025
Autistic adolescents and young adults face barriers to accessing health care, particularly during the pediatric-to-adult care transition. This study compared health care access, utilization, and provider interactions between rural and urban autistic adolescents and young adults. Autistic adolescents and young adults (N = 180) aged 14-25 were…
Descriptors: Autism Spectrum Disorders, Adolescents, Rural Urban Differences, Access to Health Care
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