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Bin Tan; Nour Armoush; Elisabetta Mazzullo; Okan Bulut; Mark J. Gierl – International Journal of Assessment Tools in Education, 2025
This study reviews existing research on the use of large language models (LLMs) for automatic item generation (AIG). We performed a comprehensive literature search across seven research databases, selected studies based on predefined criteria, and summarized 60 relevant studies that employed LLMs in the AIG process. We identified the most commonly…
Descriptors: Artificial Intelligence, Test Items, Automation, Test Format
Usani Joseph Ofem; Valentine Joseph Owan; Cletus Ibout; Sylvai Victor Ovat – Pedagogical Research, 2025
This study employed repeated measures ANOVA to assess the reliability of an instrument designed to measure utilization, awareness, and perception of AI in research among 150 undergraduate students. Validated instruments with robust psychometric properties were used for the study. Data collection occurred in three phases spaced two weeks apart,…
Descriptors: Statistical Analysis, Test Reliability, Undergraduate Students, Attitude Measures
Deniz Ünal; Zeynep Çavus Erdem; Zühal Gün Sahin – Education and Information Technologies, 2025
ChatGPT, an artificial intelligence powered chat tool that accomplishes essential work with its language understanding and text generation capabilities, has started to benefit education and many other areas with new updates. This study predicted the ability to create a number sense achievement test with ChatGPT version 3.5. It showed that the test…
Descriptors: Artificial Intelligence, Technology Uses in Education, Achievement Tests, Test Construction
Derek C. Briggs – Journal of Educational and Behavioral Statistics, 2024
I consider recent attempts to establish standards, principles, and goals for artificial intelligence (AI) through the lens of educational measurement. Distinctions are made between generative AI and AI-adjacent methods and applications of AI in formative versus summative assessment contexts. While expressing optimism about its possibilities, I…
Descriptors: Artificial Intelligence, Standard Setting, Standards, Measurement
Nan Xie; Zhengxu Li; Haipeng Lu; Wei Pang; Jiayin Song; Beier Lu – IEEE Transactions on Learning Technologies, 2025
Classroom engagement is a critical factor for evaluating students' learning outcomes and teachers' instructional strategies. Traditional methods for detecting classroom engagement, such as coding and questionnaires, are often limited by delays, subjectivity, and external interference. While some neural network models have been proposed to detect…
Descriptors: Learner Engagement, Artificial Intelligence, Technology Uses in Education, Educational Technology
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
Félix González-Carrasco; Felipe Espinosa Parra; Izaskun Álvarez-Aguado; Sebastián Ponce Olguín; Vanessa Vega Córdova; Miguel Roselló-Peñaloza – British Journal of Learning Disabilities, 2025
Background: The study focuses on the need to optimise assessment scales for support needs in individuals with intellectual and developmental disabilities. Current scales are often lengthy and redundant, leading to exhaustion and response burden. The goal is to use machine learning techniques, specifically item-reduction methods and selection…
Descriptors: Artificial Intelligence, Intellectual Disability, Developmental Disabilities, Individual Needs
Arzu Deveci Topal; Asiye Toker Gökçe; Canan Dilek Eren; Aynur Kolburan Geçer – Journal of Learning and Teaching in Digital Age, 2025
This study aims to adapt to Turkish the "Scale for the assessment of non-experts: AI literacy" developed by Laupichler et al. (2023a). The scale consists of 31 items with three sub-dimensions: technical understanding, critical thinking, and practical applications. The data required for the validity and reliability study of the scale were…
Descriptors: Artificial Intelligence, Technological Literacy, Measures (Individuals), Foreign Countries
Kimin Chung; Soohwan Kim; Yeonju Jang; Seongyune Choi; Hyeoncheol Kim – Education and Information Technologies, 2025
As artificial intelligence(AI) is utilised throughout society, the need to improve AI literacy as an essential competency, not only for specific experts but also for general citizens, is increasing. Therefore, several studies are being conducted on AI education, and attempts are being made to introduce it into the regular education curriculum.…
Descriptors: Artificial Intelligence, Technological Literacy, Diagnostic Tests, Elementary School Students
Fumiko Yoshida; Gary J. Conti; Toyoaki Yamauchi; Misa Kawanishi – Journal of Education and Learning, 2025
This paper presents the development and validation of the Learning & Educator Nurturing Style (LENS), a new inventory in Japanese designed to identify and assess educational philosophy. Based on the Philosophies Held by Instructors of Lifelong-learners (PHIL) framework, LENS was created through a rigorous back-translation process to ensure…
Descriptors: Foreign Countries, Educational Philosophy, Test Construction, Test Validity
Sahin Gokcearslan; Hatice Yildiz Durak; Mustafa Serkan Gunbatar; Nilufer Atman Uslu; Aysun Nuket Elci – International Journal of Technology in Education, 2025
GenAI's advanced natural language processing capabilities will revolutionize numerous areas, ranging from a paradigm shift in education to the economy. Along with the positive aspects of GenAI, ethical and social risks are also one of the negative aspects that attract attention in the literature. The purpose of this study is to test the role of…
Descriptors: Artificial Intelligence, College Students, Knowledge Level, Student Attitudes
Helen Zhang; Anthony Perry; Irene Lee – International Journal of Artificial Intelligence in Education, 2025
The rapid expansion of Artificial Intelligence (AI) in our society makes it urgent and necessary to develop young students' AI literacy so that they can become informed citizens and critical consumers of AI technology. Over the past decade many efforts have focused on developing curricular materials that make AI concepts accessible and engaging to…
Descriptors: Test Construction, Test Validity, Measures (Individuals), Artificial Intelligence
Liangliang Xia; Kexin Shen; Herui Sun; Xin An; Yan Dong – Education and Information Technologies, 2025
While generative artificial intelligence (AI) empowers students in their learning, it may also have the potential to undermine their learning agency. Understanding student learning agency in the generative AI-supported contexts (SLA-GAI) has become critical for educators. However, student learning agency is a domain-specific construct, current…
Descriptors: Artificial Intelligence, Technology Uses in Education, Test Construction, Test Validity
Understanding AI Guilt: The Development, Pilot-Testing, and Validation of an Instrument for Students
Cecilia Ka Yuk Chan – Education and Information Technologies, 2025
This study explores the concept of AI guilt, a psychological phenomenon where individuals feel guilt or moral discomfort when using generative AI tools, fearing negative perceptions from others or feeling disingenuous (Chan, 2024). The phenomenon has become increasingly relevant as AI tools gain prominence in educational contexts. This paper…
Descriptors: Artificial Intelligence, Anxiety, Measures (Individuals), Psychological Patterns
Feifei Wang; Alan C. K. Cheung; Ching Sing Chai; Jin Liu – Education and Information Technologies, 2025
As learners are able to perceive interactivity when interacting with instructors or peer learners in traditional learning environments, learners are similarly able to perceive interactivity when interacting with artificial intelligence (AI) in AI-supported learning environments. Advancements in AI, such as generative AI including ChatGPT and…
Descriptors: Test Construction, Test Validity, Interaction, Artificial Intelligence

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