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Unggi Lee; Yeil Jeong; Junbo Koh; Gyuri Byun; Yunseo Lee; Hyunwoong Lee; Seunmin Eun; Jewoong Moon; Cheolil Lim; Hyeoncheol Kim – Smart Learning Environments, 2024
This preliminary study explores how GPT-4 Vision (GPT-4V) technology can be integrated into teacher analytics through observational assessment, aiming to improve reflective teaching practice. Our study develops a Video-based Automatic Assessment System (VidAAS) powered by GPT-4V. This approach uses Generative Artificial Intelligence (GenAI) to…
Descriptors: Observation, Teaching Methods, Artificial Intelligence, Behavior
T. Revell; W. Yeadon; G. Cahilly-Bretzin; I. Clarke; G. Manning; J. Jones; C. Mulley; R. J. Pascual; N. Bradley; D. Thomas; F. Leneghan – International Journal for Educational Integrity, 2024
Generative AI has prompted educators to reevaluate traditional teaching and assessment methods. This study examines AI's ability to write essays analysing Old English poetry; human markers assessed and attempted to distinguish them from authentic analyses of poetry by first-year undergraduate students in English at the University of Oxford. Using…
Descriptors: Artificial Intelligence, Authors, Integrity, Essays
Dazhen Tong; Yang Tao; Kangkang Zhang; Xinxin Dong; Yangyang Hu; Sudong Pan; Qiaoyi Liu – Asia Pacific Education Review, 2024
Artificial intelligence (AI) technologies have been consistently influencing the progress of education for an extended period, with its impact becoming more significant especially after the launch of ChatGPT-3.5 at the end of November 2022. In the field of physics education, recent research regarding the performance of ChatGPT-3.5 in solving…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Performance
Bev Lyseng – ProQuest LLC, 2024
This research draws upon a qualitative intrinsic case study methodology to gather perceptions of 15 school leaders in the province of Alberta, Canada, around social and emotional competencies in relation to their own leadership, school culture, and student learning. The premise behind this study is that school leaders who are socially and…
Descriptors: Foreign Countries, Leadership, School Culture, Interpersonal Competence
Deborah Snedden – ProQuest LLC, 2024
This action research addressed three primary questions: 1) Does developing the EI of school leaders improve staff job satisfaction? 2) Does developing the EI of school leaders enhance the school climate? 3) Does developing the EI of school leaders lead to increased student achievement? Pre- and post-intervention surveys were conducted using the…
Descriptors: Action Research, Emotional Intelligence, Instructional Leadership, Teacher Administrator Relationship
Eric Yang; Cheryl Beil – New Directions for Higher Education, 2024
Artificial intelligence (AI) and machine learning (ML) have transformed the landscape of data management in higher education institutions, necessitating a critical evaluation of existing data privacy policies and practices. This research delves into the inadequacies of current frameworks in adapting to the swift evolution of Big Data. Student,…
Descriptors: Artificial Intelligence, Teacher Attitudes, Student Attitudes, College Students
Jennifer Gunn – English in Texas, 2024
This paper considers the impact of technological processes on human thought, specifically the implications of artificial intelligence (AI) on writing instruction. The main purpose of this paper is to present instructional considerations that will elevate human voice and reduce student temptations to turn to AI unreasonably to produce a piece of…
Descriptors: Artificial Intelligence, Technology Uses in Education, Writing Instruction, Developmental Studies Programs
Jiangyue Liu; Siran Li – Journal of Educational Computing Research, 2024
Pair Programming is considered an effective approach to programming education, but the synchronous collaboration of two programmers involves complex coordination, making this method difficult to be widely adopted in educational settings. Artificial Intelligence (AI) code-generation tools have outstanding capabilities in program generation and…
Descriptors: Artificial Intelligence, Programming, Technology Uses in Education, Coding
David Ernesto Salinas-Navarro; Eliseo Vilalta-Perdomo; Rosario Michel-Villarreal; Luis Montesinos – Interactive Technology and Smart Education, 2024
Purpose: This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The…
Descriptors: Experiential Learning, Learning Activities, Performance Based Assessment, Artificial Intelligence
Steven A. Stolz; Ali Lucas Winterburn; Edward Palmer – Educational Philosophy and Theory, 2024
The recent proliferation of Large Language Models (LLMs) raises questions as to the role of such tools both within an educational learning environment and their epistemic capacity. If, as Alfred North Whitehead remarked, western philosophy indeed 'consists of a series of footnotes to Plato', it would be of doubtless importance to evaluate the…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Philosophy
Melanie Palmer; Zhaonan Fang; Matthew J. Hollocks; Tony Charman; Andrew Pickles; Gillian Baird; Emily Simonoff – Journal of Autism and Developmental Disorders, 2024
Objective: Attention Deficit Hyperactivity Disorder (ADHD) is a common co-occurring condition in autistic individuals. ADHD is sometimes first recognised in young adulthood because ADHD symptoms may be misattributed to autism due to superficial overlap in presentation and diagnostic overshadowing. It should be investigated whether ADHD…
Descriptors: Screening Tests, Attention Deficit Hyperactivity Disorder, Young Adults, Autism Spectrum Disorders
Adrian Kirwan – Irish Educational Studies, 2024
Since its arrival in late 2022, ChatGPT has occupied the minds of academics, administrators and students. Reactions to the emergence of Large Language Models (LLMs) have varied but significant anxieties about their impact on assessment have arisen. To address these concerns, this article serves three purposes; firstly, it seeks to gauge the…
Descriptors: Integrity, Computational Linguistics, Artificial Intelligence, Technology Uses in Education
Jimmy Tobin; Phillip Nelson; Bob MacDonald; Rus Heywood; Richard Cave; Katie Seaver; Antoine Desjardins; Pan-Pan Jiang; Jordan R. Green – Journal of Speech, Language, and Hearing Research, 2024
Purpose: This study examines the effectiveness of automatic speech recognition (ASR) for individuals with speech disorders, addressing the gap in performance between read and conversational ASR. We analyze the factors influencing this disparity and the effect of speech mode--specific training on ASR accuracy. Method: Recordings of read and…
Descriptors: Foreign Countries, Speech Impairments, Computational Linguistics, Artificial Intelligence
Yinying Wang – Discover Education, 2024
In this perspective article, I explore the implications of artificial intelligence (AI)-enabled algorithmic decisions on education governance. Three main questions are explored: (1) Are algorithmic decisions de facto policy decisions? (2) What distinct features of algorithmic decisions necessitate a re-evaluation of education governance? (3) How…
Descriptors: Algorithms, Decision Making, Artificial Intelligence, Educational Administration
Gulnur Tyulepberdinova; Madina Mansurova; Talshyn Sarsembayeva; Sulu Issabayeva; Darazha Issabayeva – Journal of Computer Assisted Learning, 2024
Background: This study aims to assess how well several machine learning (ML) algorithms predict the physical, social, and mental health condition of university students. Objectives: The physical health measurements used in the study include BMI (Body Mass Index), %BF (percentage of Body Fat), BSC (Blood Serum Cholesterol), SBP (Systolic Blood…
Descriptors: Artificial Intelligence, Algorithms, Predictor Variables, Physical Health

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