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Shoba S. Meera; Divya Swaminathan; Sri Ranjani Venkata Murali; Reny Raju; Malavi Srikar; Sahana Shyam Sundar; Senthil Amudhan; Alejandrina Cristia; Rahul Pawar; Achuth Rao; Prathyusha P. Vasuki; Shree Volme; Ashok Mysore – Journal of Speech, Language, and Hearing Research, 2025
Purpose: The Language ENvironment Analysis (LENA) technology uses automated speech processing (ASP) algorithms to estimate counts such as total adult words and child vocalizations, which helps understand children's early language environment. This ASP has been validated in North American English and other languages in predominantly monolingual…
Descriptors: Foreign Countries, Multilingualism, Adults, Speech Communication
Antonios Kafa – International Journal of Educational Management, 2025
Purpose: The rapid digitalization and emergence of AI tools are transforming school organizations. However, limited research exists on how school leaders integrate these technologies into their leadership practices. This study focuses on the experiences of school leaders in Cyprus, exploring the benefits and challenges of adopting digital and AI…
Descriptors: Artificial Intelligence, Computer Uses in Education, Elementary Schools, Secondary Schools
Jiawei Xiong; George Engelhard; Allan S. Cohen – Measurement: Interdisciplinary Research and Perspectives, 2025
It is common to find mixed-format data results from the use of both multiple-choice (MC) and constructed-response (CR) questions on assessments. Dealing with these mixed response types involves understanding what the assessment is measuring, and the use of suitable measurement models to estimate latent abilities. Past research in educational…
Descriptors: Responses, Test Items, Test Format, Grade 8
Xueqiao Zhang; Chao Zhang; Jianwen Sun; Jun Xiao; Yi Yang; Yawei Luo – IEEE Transactions on Learning Technologies, 2025
Large language models (LLMs) have significantly advanced smart education in the artificial general intelligence era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: 1) customized generation: generating niche-targeted teaching content based on…
Descriptors: Artificial Intelligence, Instructional Design, Technology Uses in Education, Cognitive Ability
Jose Berengueres – Discover Education, 2025
GPT-based models have enabled the creation of natural language chatbots that support both Inquiry-Based and Structured Learning approaches. This study offers a direct comparison of these two paradigms within a UNIX Shell scripting course by means of two chatbots: a Lesson Plan-Driven chatbot that ensures all students cover the same topics…
Descriptors: Lesson Plans, Artificial Intelligence, Technology Uses in Education, Natural Language Processing
John Mark R. Asio; Dante P. Sardina – Journal of Pedagogical Research, 2025
Artificial Intelligence (AI) is taking the educational system by storm due to its various implications and endless possibilities. Nevertheless, the teachers, the schools, and most importantly, the students have different perspectives on using AI in their learning experience, especially when gender is involved. In this study, the proponents delve…
Descriptors: Gender Differences, Artificial Intelligence, Technology Uses in Education, Anxiety
Chat or Cheat? Academic Dishonesty, Risk Perceptions, and ChatGPT Usage in Higher Education Students
Silvia Ortiz-Bonnin; Joanna Blahopoulou – Social Psychology of Education: An International Journal, 2025
Academic dishonesty remains a persistent concern for educational institutions, threatening the reputation of universities. The emergence of Artificial Intelligence (AI) tools exacerbates this challenge as they can be used for chatting but also for cheating. Several scientific papers have analyzed the advantages and risks of using AI tools like…
Descriptors: Artificial Intelligence, Technology Uses in Education, Cheating, Risk
Jeremie Bouchard – Education and Information Technologies, 2025
ChatGPT is now widely understood in academia and the media as a 'game changer' in education. Detractors see it as fostering ethically problematic educational practices and a threat to the development of critical thinking skills, while fans see it as improving education by, in part, creating a more personalized educational experience. Meanwhile,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Practices, Ethics
Sarah Burriss; Blaine Smith; Amanda Yoshiko Shimizu; Melanie Hundley; Emily Pendergrass; Ole Molvig – Contemporary Issues in Technology and Teacher Education (CITE Journal), 2025
Generative artificial intelligence (AI) models are increasingly able to produce and combine sophisticated text, image, and audio. These advancements are challenging composers and teachers, as they work to reimagine and resist ways that composition and creative work are changing. This paper reports on one analysis in a larger study on multimodal…
Descriptors: Ethics, Artificial Intelligence, Writing (Composition), Computer Uses in Education
Nina Masjedi; Elaine B. Clarke; Catherine Lord – Journal of Autism and Developmental Disorders, 2025
This study examined trajectories of repetitive sensorimotor (RSM), insistence on sameness (IS), and verbal RRBs from ages 2-19 in a well-characterized longitudinal cohort. We also tested the factor structure of the ADI-R restricted and repetitive behavior (RRB) domain at age 19 and the inclusion of a verbal RRBs factor, in addition to previously…
Descriptors: Autism Spectrum Disorders, Symptoms (Individual Disorders), Behavior, Children
Marianne Miserandino – Teaching of Psychology, 2025
Introduction: Artificial intelligence (AI) presents challenges and opportunities for higher education. The challenge is to incorporate the benefits of AI while minimizing its potential for misuse and undermining of learning. The opportunity is that AI allows instructors to assess learning authentically by fostering creative, engaging, realistic,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Evaluation Methods, Undergraduate Study
Zifeng Liu; Wanli Xing; Xinyue Jiao; Chenglu Li; Wangda Zhu – Education and Information Technologies, 2025
The ability of large language models (LLMs) to generate code has raised concerns in computer science education, as students may use tools like ChatGPT for programming assignments. While much research has focused on higher education, especially for languages like Java and Python, little attention has been given to K-12 settings, particularly for…
Descriptors: High School Students, Coding, Artificial Intelligence, Electronic Learning
Evaluation of AI Content Generation Tools for Verification of Academic Integrity in Higher Education
Muhammad Bilal Saqib; Saba Zia – Journal of Applied Research in Higher Education, 2025
Purpose: The notion of using a generative artificial intelligence (AI) engine for text composition has gained excessive popularity among students, educators and researchers, following the introduction of ChatGPT. However, this has added another dimension to the daunting task of verifying originality in academic writing. Consequently, the market…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Evaluation
Weikang Lu; Chenghua Lin – Education and Information Technologies, 2025
Artificial intelligence is increasingly integrated into daily life, and modern educated individuals should have the ability to use AI tools correctly to improve work, study, and life efficiency. In this context, artificial intelligence literacy has been proposed. Due to the lack of consensus on the constructs of artificial intelligence literacy,…
Descriptors: Artificial Intelligence, Digital Literacy, Student Attitudes, College Students
Abdullah Al-Abri – Education and Information Technologies, 2025
This study explores the impact of ChatGPT, an advanced Large Language Model (LLM), as a virtual tutor in online education across five key dimensions: answering questions, writing assistance, study resources, exam preparation, and availability. Utilizing an experimental design, 68 undergraduate students from a public university interacted with…
Descriptors: Artificial Intelligence, Natural Language Processing, Man Machine Systems, Intelligent Tutoring Systems

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