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Peer reviewed
Direct linkR. Thapa; A. Garikipati; M. Ciobanu; N.P. Singh; E. Browning; J. DeCurzio; G. Barnes; F.A. Dinenno; Q. Mao; R. Das – Journal of Autism and Developmental Disorders, 2024
Purpose: Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum…
Descriptors: Autism Spectrum Disorders, Symptoms (Individual Disorders), Clinical Diagnosis, Artificial Intelligence
Giulio F. Marchena Sekli; Amy Godo; José Carlos Véliz – Journal of Information Technology Education: Research, 2024
Aim/Purpose: This paper aims to address the gap in comprehensive, real-world applications of Generative Artificial Intelligence (GenAI) in education, particularly in higher education settings. Despite the evident potential of GenAI in transforming educational practices, there is a lack of consolidated knowledge about its practical effectiveness…
Descriptors: Artificial Intelligence, Technology Uses in Education, Higher Education, Educational Research
Xiang Feng; Keyi Yuan; Xiu Guan; Longhui Qiu – Interactive Learning Environments, 2024
Datasets are critical for emotion analysis in the machine learning field. This study aims to explore emotion analysis datasets and related benchmarks in online learning, since, currently, there are very few studies that explore the same. We have scientifically labeled the topic and nine-category emotion of 4715 comment texts in online learning…
Descriptors: MOOCs, Psychological Patterns, Artificial Intelligence, Prediction
Shang Shanshan; Geng Sen – Journal of Computer Assisted Learning, 2024
Background: Artificial intelligence-generated content (AIGC) has stepped into the spotlight with the emergence of ChatGPT, making effective use of AIGC for education a hot topic. Objectives: This study seeks to explore the effectiveness of integrating AIGC into programming learning through debugging. First, the study presents three levels of AIGC…
Descriptors: Artificial Intelligence, Educational Technology, Technology Integration, Programming
Jinsook Lee; Yann Hicke; Renzhe Yu; Christopher Brooks; René F. Kizilcec – British Journal of Educational Technology, 2024
Large language models (LLMs) are increasingly adopted in educational contexts to provide personalized support to students and teachers. The unprecedented capacity of LLM-based applications to understand and generate natural language can potentially improve instructional effectiveness and learning outcomes, but the integration of LLMs in education…
Descriptors: Artificial Intelligence, Technology Uses in Education, Equal Education, Algorithms
Kalervo N. Gulson; Sam Sellar – Journal of Education Policy, 2024
The growing use of artificial intelligence in education extends and intensifies technologies of governing, including datafication, performativity and accountability. In this article, we outline how the use of AI and data science has the disruptive potential to create new norms in education policy and governance. We report on an ethnographic…
Descriptors: Artificial Intelligence, Educational Policy, Governance, Evidence
Karlis Kanders; Louis Stupple-Harris; Laurie Smith; Jenny Louise Gibson – Infant and Child Development, 2024
Generative artificial intelligence (GAI) is rapidly becoming ubiquitous in many contexts. There is limited scholarship, however, in the fields of Developmental Psychology and Early Childhood Education exploring the implications of generative AI for babies and young children. In this Perspectives piece, we discuss potential use cases,…
Descriptors: Artificial Intelligence, Early Childhood Education, Child Development, Infants
Mary Kalantzis; Bill Cope – Reading Research Quarterly, 2025
The latest mutation of Artificial Intelligence, Generative AI, is more than anything a technology of writing. It is a machine that can write. In a world-historical frame, the significance of this cannot be understated. This is a technology in which the unnatural language of code tangles with the natural language of everyday life. Its form of…
Descriptors: Artificial Intelligence, Natural Language Processing, Literacy Education, Technology Uses in Education
Ercikan, Kadriye; McCaffrey, Daniel F. – Journal of Educational Measurement, 2022
Artificial-intelligence-based automated scoring is often an afterthought and is considered after assessments have been developed, resulting in nonoptimal possibility of implementing automated scoring solutions. In this article, we provide a review of Artificial intelligence (AI)-based methodologies for scoring in educational assessments. We then…
Descriptors: Artificial Intelligence, Automation, Scores, Educational Assessment
Monsalve-Pulido, Julian; Aguilar, Jose; Montoya, Edwin – Education and Information Technologies, 2023
The adaptation of traditional systems to service-oriented architectures is very frequent, due to the increase in technologies for this type of architecture. This has led to the construction of frameworks or methodologies for adapting computational projects to service-oriented architecture (SOA) technology. In this work, a framework for adaptation…
Descriptors: Artificial Intelligence, Information Technology, Design, Governance
Kebede, Mihiretu M.; Le Cornet, Charlotte; Fortner, Renée Turzanski – Research Synthesis Methods, 2023
We aimed to evaluate the performance of supervised machine learning algorithms in predicting articles relevant for full-text review in a systematic review. Overall, 16,430 manually screened titles/abstracts, including 861 references identified relevant for full-text review were used for the analysis. Of these, 40% (n = 6573) were sub-divided for…
Descriptors: Automation, Literature Reviews, Artificial Intelligence, Algorithms
Kataoka, Yuki; Taito, Shunsuke; Yamamoto, Norio; So, Ryuhei; Tsutsumi, Yusuke; Anan, Keisuke; Banno, Masahiro; Tsujimoto, Yasushi; Wada, Yoshitaka; Sagami, Shintaro; Tsujimoto, Hiraku; Nihashi, Takashi; Takeuchi, Motoki; Terasawa, Teruhiko; Iguchi, Masahiro; Kumasawa, Junji; Ichikawa, Takumi; Furukawa, Ryuki; Yamabe, Jun; Furukawa, Toshi A. – Research Synthesis Methods, 2023
There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts…
Descriptors: Competition, Classification, Diagnostic Tests, Accuracy
Elkhatat, Ahmed M.; Elsaid, Khaled; Almeer, Saeed – International Journal for Educational Integrity, 2023
The proliferation of artificial intelligence (AI)-generated content, particularly from models like ChatGPT, presents potential challenges to academic integrity and raises concerns about plagiarism. This study investigates the capabilities of various AI content detection tools in discerning human and AI-authored content. Fifteen paragraphs each…
Descriptors: Artificial Intelligence, Integrity, Plagiarism, Educational Technology
Download full textBaena-Rojas, Jose Jaime; Castillo-Martínez, Isolda Margarita; Méndez-Garduño, Juana Isabel; Suárez-Brito, Paloma; López-Caudana, Edgar Omar – Journal of Social Studies Education Research, 2023
Various technological devices, especially information communications technologies (ICTs), have become increasingly remarkable in higher education to help develop students' skills and qualifications. Considering this trend, supported by several academic theories, this paper proposes a breakthrough guidebook for universities and other scholastic…
Descriptors: Information Technology, Artificial Intelligence, Robotics, Higher Education
Zhao, Li; Zheng, Yi; Zhao, Junbang; Li, Guoqiang; Compton, Brian J.; Zhang, Rui; Fang, Fang; Heyman, Gail D.; Lee, Kang – Child Development, 2023
Academic cheating is common, but little is known about its early emergence. It was examined among Chinese second to sixth graders (N = 2094; 53% boys, collected between 2018 and 2019) using a machine learning approach. Overall, 25.74% reported having cheated, which was predicted by the best machine learning algorithm (Random Forest) at a mean…
Descriptors: Cheating, Elementary School Students, Artificial Intelligence, Foreign Countries
